Prompt:
Implement the Python class `QuerySchedulers` described below.

Class description:
测试查询schedulers接口 /api/schedulers/query

Method signatures and docstrings:
- def test_case01(self): 根据scheduler name模糊查询
- def test_case02(self): 根据flowtype-dataflow查询
- def test_case03(self): 根据flowtype-workflow查询
- def test_case04(self): 根据flowtype-streamflow查询
- def test_case05(self): flowtype+name组合查询scheduler
- def test_case06(self): query:根据上次修改时间查询全部的scheduler

Implement the Python class `QuerySchedulers` described below.

Class description:
测试查询schedulers接口 /api/schedulers/query

Method signatures and docstrings:
- def test_case01(self): 根据scheduler name模糊查询
- def test_case02(self): 根据flowtype-dataflow查询
- def test_case03(self): 根据flowtype-workflow查询
- def test_case04(self): 根据flowtype-streamflow查询
- def test_case05(self): flowtype+name组合查询scheduler
- def test_case06(self): query:根据上次修改时间查询全部的scheduler

<|skeleton|>
class QuerySchedulers:
    """测试查询schedulers接口 /api/schedulers/query"""

    def test_case01(self):
        """根据scheduler name模糊查询"""
        <|body_0|>

    def test_case02(self):
        """根据flowtype-dataflow查询"""
        <|body_1|>

    def test_case03(self):
        """根据flowtype-workflow查询"""
        <|body_2|>

    def test_case04(self):
        """根据flowtype-streamflow查询"""
        <|body_3|>

    def test_case05(self):
        """flowtype+name组合查询scheduler"""
        <|body_4|>

    def test_case06(self):
        """query:根据上次修改时间查询全部的scheduler"""
        <|body_5|>

<|end_skeleton|>

<|body_start_0|>
        keyword = '%student%'
        data = {'fieldList': [{'fieldName': 'name', 'fieldValue': keyword, 'comparatorOperator': 'LIKE'}], 'sortObject': {'field': 'lastModifiedTime', 'orderDirection': 'DESC'}, 'offset': 0, 'limit': 8}
        fieldValue = data['fieldList'][0]['fieldValue'][1:-1]
        res = requests.post(url=self.query_scheduler_url, headers=get_headers(HOST_189), data=json.dumps(data))
        query_results = dict_res(res.text)
        self.assertEqual(res.status_code, 200, '查询失败')
        query_results = dict_res(query_results['content'][0])
        query_result_name = query_results['name']
        self.assertIn(fieldValue, query_result_name, '查询结果中scheduler的name和查询关键词name不一致')
<|end_body_0|>

<|body_start_1|>
        data = {'fieldList': [{'fieldName': 'flowType', 'fieldValue': 'dataflow', 'comparatorOperator': 'LIKE'}], 'sortObject': {'field': 'lastModifiedTime', 'orderDirection': 'DESC'}, 'offset': 0, 'limit': 8}
        fieldValue = data['fieldList'][0]['fieldValue']
        res = requests.post(url=self.query_scheduler_url, headers=get_headers(HOST_189), data=json.dumps(data))
        query_results = dict_res(res.text)
        query_results = dict_res(query_results['content'][0])
        query_result_flowType = query_results['flowType']
        self.assertEqual(res.status_code, 200, '查询失败')
        self.assertEqual(fieldValue, query_result_flowType, '查询结果中scheduler关联flowtype和查询关键词flowType不一致')
<|end_body_1|>

<|body_start_2|>
        data = {'fieldList': [{'fieldName': 'flowType', 'fieldValue': 'workflow', 'comparatorOperator': 'LIKE'}], 'sortObject': {'field': 'lastModifiedTime', 'orderDirection': 'DESC'}, 'offset': 0, 'limit': 8}
        fieldValue = data['fieldList'][0]['fieldValue']
        res = requests.post(url=self.query_scheduler_url, headers=get_headers(HOST_189), data=json.dumps(data))
        self.assertEqual(res.status_code, 200, '查询失败')
<|end_body_2|>

<|body_start_3|>
        data = {'fieldList': [{'fieldName': 'flowType', 'fieldValue': 'streamflow', 'comparatorOperator': 'LIKE'}], 'sortObject': {'field': 'lastModifiedTime', 'orderDirection': 'DESC'}, 'offset': 0, 'limit': 8}
        fieldValue = data['fieldList'][0]['fieldValue']
        res = requests.post(url=self.query_scheduler_url, headers=get_headers(HOST_189), data=json.dumps(data))
        query_results = dict_res(res.text)
        self.assertEqual(res.status_code, 200, '查询失败')
<|end_body_3|>

<|body_start_4|>
        data = {'fieldList': [{'fieldName': 'name', 'fieldValue': '%gbj%', 'comparatorOperator': 'LIKE'}, {'fieldName': 'flowType', 'fieldValue': 'workflow', 'comparatorOperator': 'EQUAL'}], 'sortObject': {'field': 'lastModifiedTime', 'orderDirection': 'DESC'}, 'offset': 0, 'limit': 8}
        data_name = data['fieldList'][0]['fieldValue'][1:-1]
        data_flowType = data['fieldList'][1]['fieldValue']
        res = requests.post(url=self.query_scheduler_url, headers=get_headers(HOST_189), data=json.dumps(data))
        self.assertEqual(200, res.status_code, 'flowtype+name组合查询scheduler失败:%s' % res.text)
<|end_body_4|>

<|body_start_5|>
        end_time = get_time()
        start_time = get_time() - 10 * 24 * 3600 * 1000
        data = {'fieldList': [{'fieldName': 'lastModifiedTime', 'fieldValue': start_time, 'comparatorOperator': 'GREATER_THAN'}, {'fieldName': 'lastModifiedTime', 'fieldValue': end_time, 'comparatorOperator': 'LESS_THAN'}], 'sortObject': {'field': 'lastModifiedTime', 'orderDirection': 'DESC'}, 'offset': 0, 'limit': 8}
        res = requests.post(url=self.query_scheduler_url, headers=get_headers(HOST_189), data=json.dumps(data))
        query_results = dict_res(res.text)
        first_Time = query_results['content'][0]['lastModifiedTime']
        self.assertEqual(end_time > first_Time > start_time, True, '查询结果的lastModifiedTime不包含在起始时间内，查询结果不正确')
<|end_body_5|>

Solution:
class QuerySchedulers:
    """测试查询schedulers接口 /api/schedulers/query"""

    def test_case01(self):
        """根据scheduler name模糊查询"""
        keyword = '%student%'
        data = {'fieldList': [{'fieldName': 'name', 'fieldValue': keyword, 'comparatorOperator': 'LIKE'}], 'sortObject': {'field': 'lastModifiedTime', 'orderDirection': 'DESC'}, 'offset': 0, 'limit': 8}
        fieldValue = data['fieldList'][0]['fieldValue'][1:-1]
        res = requests.post(url=self.query_scheduler_url, headers=get_headers(HOST_189), data=json.dumps(data))
        query_results = dict_res(res.text)
        self.assertEqual(res.status_code, 200, '查询失败')
        query_results = dict_res(query_results['content'][0])
        query_result_name = query_results['name']
        self.assertIn(fieldValue, query_result_name, '查询结果中scheduler的name和查询关键词name不一致')

    def test_case02(self):
        """根据flowtype-dataflow查询"""
        data = {'fieldList': [{'fieldName': 'flowType', 'fieldValue': 'dataflow', 'comparatorOperator': 'LIKE'}], 'sortObject': {'field': 'lastModifiedTime', 'orderDirection': 'DESC'}, 'offset': 0, 'limit': 8}
        fieldValue = data['fieldList'][0]['fieldValue']
        res = requests.post(url=self.query_scheduler_url, headers=get_headers(HOST_189), data=json.dumps(data))
        query_results = dict_res(res.text)
        query_results = dict_res(query_results['content'][0])
        query_result_flowType = query_results['flowType']
        self.assertEqual(res.status_code, 200, '查询失败')
        self.assertEqual(fieldValue, query_result_flowType, '查询结果中scheduler关联flowtype和查询关键词flowType不一致')

    def test_case03(self):
        """根据flowtype-workflow查询"""
        data = {'fieldList': [{'fieldName': 'flowType', 'fieldValue': 'workflow', 'comparatorOperator': 'LIKE'}], 'sortObject': {'field': 'lastModifiedTime', 'orderDirection': 'DESC'}, 'offset': 0, 'limit': 8}
        fieldValue = data['fieldList'][0]['fieldValue']
        res = requests.post(url=self.query_scheduler_url, headers=get_headers(HOST_189), data=json.dumps(data))
        self.assertEqual(res.status_code, 200, '查询失败')

    def test_case04(self):
        """根据flowtype-streamflow查询"""
        data = {'fieldList': [{'fieldName': 'flowType', 'fieldValue': 'streamflow', 'comparatorOperator': 'LIKE'}], 'sortObject': {'field': 'lastModifiedTime', 'orderDirection': 'DESC'}, 'offset': 0, 'limit': 8}
        fieldValue = data['fieldList'][0]['fieldValue']
        res = requests.post(url=self.query_scheduler_url, headers=get_headers(HOST_189), data=json.dumps(data))
        query_results = dict_res(res.text)
        self.assertEqual(res.status_code, 200, '查询失败')

    def test_case05(self):
        """flowtype+name组合查询scheduler"""
        data = {'fieldList': [{'fieldName': 'name', 'fieldValue': '%gbj%', 'comparatorOperator': 'LIKE'}, {'fieldName': 'flowType', 'fieldValue': 'workflow', 'comparatorOperator': 'EQUAL'}], 'sortObject': {'field': 'lastModifiedTime', 'orderDirection': 'DESC'}, 'offset': 0, 'limit': 8}
        data_name = data['fieldList'][0]['fieldValue'][1:-1]
        data_flowType = data['fieldList'][1]['fieldValue']
        res = requests.post(url=self.query_scheduler_url, headers=get_headers(HOST_189), data=json.dumps(data))
        self.assertEqual(200, res.status_code, 'flowtype+name组合查询scheduler失败:%s' % res.text)

    def test_case06(self):
        """query:根据上次修改时间查询全部的scheduler"""
        end_time = get_time()
        start_time = get_time() - 10 * 24 * 3600 * 1000
        data = {'fieldList': [{'fieldName': 'lastModifiedTime', 'fieldValue': start_time, 'comparatorOperator': 'GREATER_THAN'}, {'fieldName': 'lastModifiedTime', 'fieldValue': end_time, 'comparatorOperator': 'LESS_THAN'}], 'sortObject': {'field': 'lastModifiedTime', 'orderDirection': 'DESC'}, 'offset': 0, 'limit': 8}
        res = requests.post(url=self.query_scheduler_url, headers=get_headers(HOST_189), data=json.dumps(data))
        query_results = dict_res(res.text)
        first_Time = query_results['content'][0]['lastModifiedTime']
        self.assertEqual(end_time > first_Time > start_time, True, '查询结果的lastModifiedTime不包含在起始时间内，查询结果不正确')


Prompt:
Implement the Python class `ProjectGateway` described below.

Class description:
Gateway for project database operations.

Method signatures and docstrings:
- def get_project(self) -> Project: Get project metadata.
- def update_project(self, project: Project): Update project metadata.

Implement the Python class `ProjectGateway` described below.

Class description:
Gateway for project database operations.

Method signatures and docstrings:
- def get_project(self) -> Project: Get project metadata.
- def update_project(self, project: Project): Update project metadata.

<|skeleton|>
class ProjectGateway:
    """Gateway for project database operations."""

    def get_project(self) -> Project:
        """Get project metadata."""
        <|body_0|>

    def update_project(self, project: Project):
        """Update project metadata."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        try:
            return self.database_dispatcher.current_database['project']
        except KeyError as e:
            raise ValueError() from e
<|end_body_0|>

<|body_start_1|>
        from renku import __version__
        database = self.database_dispatcher.current_database
        try:
            if database['project']:
                database.remove_root_object('project')
        except KeyError:
            pass
        project.agent_version = __version__
        database.add_root_object('project', project)
<|end_body_1|>

Solution:
class ProjectGateway:
    """Gateway for project database operations."""

    def get_project(self) -> Project:
        """Get project metadata."""
        try:
            return self.database_dispatcher.current_database['project']
        except KeyError as e:
            raise ValueError() from e

    def update_project(self, project: Project):
        """Update project metadata."""
        from renku import __version__
        database = self.database_dispatcher.current_database
        try:
            if database['project']:
                database.remove_root_object('project')
        except KeyError:
            pass
        project.agent_version = __version__
        database.add_root_object('project', project)


Prompt:
Implement the Python class `CommentManager` described below.

Class description:
ye custom filter sakhtim ke to view ya (model property) rahat tar betunim content_object vase comment haro bargardoonim.

Method signatures and docstrings:
- def filter_by_model(self, instance): to khat e zir ma az instance.__class__ estefade kardim ke modelio migire ke to view ya to (model propert) behesh pas midim.
- def form_create_by_model(self, instance, object_id, user, content, parent_obj=None): tu inja darim modeli ke dare request midea ro migirim(filter_by_model) baad bayad check konim bebinim model.exists ya na age bud daghighan mesle form to view ammal mikonim (bedoone commit=False). vase API content o bayad shabihe request begirm(validated_data.get('content')).

Implement the Python class `CommentManager` described below.

Class description:
ye custom filter sakhtim ke to view ya (model property) rahat tar betunim content_object vase comment haro bargardoonim.

Method signatures and docstrings:
- def filter_by_model(self, instance): to khat e zir ma az instance.__class__ estefade kardim ke modelio migire ke to view ya to (model propert) behesh pas midim.
- def form_create_by_model(self, instance, object_id, user, content, parent_obj=None): tu inja darim modeli ke dare request midea ro migirim(filter_by_model) baad bayad check konim bebinim model.exists ya na age bud daghighan mesle form to view ammal mikonim (bedoone commit=False). vase API content o bayad shabihe request begirm(validated_data.get('content')).

<|skeleton|>
class CommentManager:
    """ye custom filter sakhtim ke to view ya (model property) rahat tar betunim content_object vase comment haro bargardoonim."""

    def filter_by_model(self, instance):
        """to khat e zir ma az instance.__class__ estefade kardim ke modelio migire ke to view ya to (model propert) behesh pas midim."""
        <|body_0|>

    def form_create_by_model(self, instance, object_id, user, content, parent_obj=None):
        """tu inja darim modeli ke dare request midea ro migirim(filter_by_model) baad bayad check konim bebinim model.exists ya na age bud daghighan mesle form to view ammal mikonim (bedoone commit=False). vase API content o bayad shabihe request begirm(validated_data.get('content'))."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        content_type = ContentType.objects.get_for_model(instance.__class__)
        obj_id = instance.id
        queryset = super(CommentManager, self).filter(content_type=content_type, object_id=obj_id).filter(parent=None)
        return queryset
<|end_body_0|>

<|body_start_1|>
        content_type = ContentType.objects.get(model=instance)
        print(content_type)
        uuid = uuid_generator()
        if parent_obj:
            parent_obj = parent_obj
        comment_form = super(CommentManager, self).create(user=user, content=content, content_type=content_type, object_id=object_id, parent=parent_obj, uuid=uuid)
        return comment_form
<|end_body_1|>

Solution:
class CommentManager:
    """ye custom filter sakhtim ke to view ya (model property) rahat tar betunim content_object vase comment haro bargardoonim."""

    def filter_by_model(self, instance):
        """to khat e zir ma az instance.__class__ estefade kardim ke modelio migire ke to view ya to (model propert) behesh pas midim."""
        content_type = ContentType.objects.get_for_model(instance.__class__)
        obj_id = instance.id
        queryset = super(CommentManager, self).filter(content_type=content_type, object_id=obj_id).filter(parent=None)
        return queryset

    def form_create_by_model(self, instance, object_id, user, content, parent_obj=None):
        """tu inja darim modeli ke dare request midea ro migirim(filter_by_model) baad bayad check konim bebinim model.exists ya na age bud daghighan mesle form to view ammal mikonim (bedoone commit=False). vase API content o bayad shabihe request begirm(validated_data.get('content'))."""
        content_type = ContentType.objects.get(model=instance)
        print(content_type)
        uuid = uuid_generator()
        if parent_obj:
            parent_obj = parent_obj
        comment_form = super(CommentManager, self).create(user=user, content=content, content_type=content_type, object_id=object_id, parent=parent_obj, uuid=uuid)
        return comment_form


Prompt:
Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def reverseVowels(self, s): :type s: str :rtype: str
- def reverseVowels1(self, s): :type s: str :rtype: str

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def reverseVowels(self, s): :type s: str :rtype: str
- def reverseVowels1(self, s): :type s: str :rtype: str

<|skeleton|>
class Solution:

    def reverseVowels(self, s):
        """:type s: str :rtype: str"""
        <|body_0|>

    def reverseVowels1(self, s):
        """:type s: str :rtype: str"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        vowels = ['a', 'e', 'i', 'o', 'u']
        i = 0
        j = len(s) - 1
        s = list(s)
        while i < j:
            if s[i].lower() in vowels and s[j].lower() in vowels:
                s[i], s[j] = (s[j], s[i])
                i += 1
                j -= 1
            elif s[i].lower() not in vowels and s[j].lower() not in vowels:
                i += 1
                j -= 1
            elif s[i].lower() in vowels and s[j].lower() not in vowels:
                j -= 1
            else:
                i += 1
        return ''.join(s)
<|end_body_0|>

<|body_start_1|>
        vowels = ['a', 'e', 'i', 'o', 'u']
        i = 0
        j = len(s) - 1
        s = list(s)
        while i < j:
            if s[i].lower() in vowels:
                if s[j].lower() in vowels:
                    s[i], s[j] = (s[j], s[i])
                    i += 1
                    j -= 1
                else:
                    j -= 1
            elif s[j].lower() not in vowels:
                i += 1
                j -= 1
            else:
                i += 1
        return ''.join(s)
<|end_body_1|>

Solution:
class Solution:
    def reverseVowels(self, s):
        """:type s: str :rtype: str"""
        vowels = ['a', 'e', 'i', 'o', 'u']
        i = 0
        j = len(s) - 1
        s = list(s)
        while i < j:
            if s[i].lower() in vowels and s[j].lower() in vowels:
                s[i], s[j] = (s[j], s[i])
                i += 1
                j -= 1
            elif s[i].lower() not in vowels and s[j].lower() not in vowels:
                i += 1
                j -= 1
            elif s[i].lower() in vowels and s[j].lower() not in vowels:
                j -= 1
            else:
                i += 1
        return ''.join(s)

    def reverseVowels1(self, s):
        """:type s: str :rtype: str"""
        vowels = ['a', 'e', 'i', 'o', 'u']
        i = 0
        j = len(s) - 1
        s = list(s)
        while i < j:
            if s[i].lower() in vowels:
                if s[j].lower() in vowels:
                    s[i], s[j] = (s[j], s[i])
                    i += 1
                    j -= 1
                else:
                    j -= 1
            elif s[j].lower() not in vowels:
                i += 1
                j -= 1
            else:
                i += 1
        return ''.join(s)


add

Prompt:
Implement the Python class `FirstUnique` described below.

Class description:
Implement the FirstUnique class.

Method signatures and docstrings:
- def __init__(self, nums: List[int]): count = Counter(nums) self.unique = {} self.other = {} for item in nums: if count[item]==1: self.unique[item] = True else: if item not in self.other: self.other[item] = True
- def showFirstUnique(self) -> int: if not self.unique: return -1 for item in self.unique: return item
- def add(self, value: int) -> None: if value not in self.other and value not in self.unique: self.unique[value] = True return if value in self.unique: self.unique.pop(value) self.other[value] = True

Implement the Python class `FirstUnique` described below.

Class description:
Implement the FirstUnique class.

Method signatures and docstrings:
- def __init__(self, nums: List[int]): count = Counter(nums) self.unique = {} self.other = {} for item in nums: if count[item]==1: self.unique[item] = True else: if item not in self.other: self.other[item] = True
- def showFirstUnique(self) -> int: if not self.unique: return -1 for item in self.unique: return item
- def add(self, value: int) -> None: if value not in self.other and value not in self.unique: self.unique[value] = True return if value in self.unique: self.unique.pop(value) self.other[value] = True

<|skeleton|>
class FirstUnique:

    def __init__(self, nums: List[int]):
        """count = Counter(nums) self.unique = {} self.other = {} for item in nums: if count[item]==1: self.unique[item] = True else: if item not in self.other: self.other[item] = True"""
        <|body_0|>

    def showFirstUnique(self) -> int:
        """if not self.unique: return -1 for item in self.unique: return item"""
        <|body_1|>

    def add(self, value: int) -> None:
        """if value not in self.other and value not in self.unique: self.unique[value] = True return if value in self.unique: self.unique.pop(value) self.other[value] = True"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self.queue = deque([])
        count = Counter(nums)
        self.judge = {}
        for num in nums:
            self.queue.append(num)
            if count[num] == 1:
                self.judge[num] = True
            else:
                self.judge[num] = False
<|end_body_0|>

<|body_start_1|>
        while self.queue and (not self.judge[self.queue[0]]):
            self.queue.popleft()
        if self.queue:
            return self.queue[0]
        return -1
<|end_body_1|>

<|body_start_2|>
        if value not in self.judge:
            self.judge[value] = True
        else:
            self.judge[value] = False
        self.queue.append(value)
<|end_body_2|>

Solution:
class FirstUnique:
    def __init__(self, nums: List[int]):
        """count = Counter(nums) self.unique = {} self.other = {} for item in nums: if count[item]==1: self.unique[item] = True else: if item not in self.other: self.other[item] = True"""
        self.queue = deque([])
        count = Counter(nums)
        self.judge = {}
        for num in nums:
            self.queue.append(num)
            if count[num] == 1:
                self.judge[num] = True
            else:
                self.judge[num] = False

    def showFirstUnique(self) -> int:
        """if not self.unique: return -1 for item in self.unique: return item"""
        while self.queue and (not self.judge[self.queue[0]]):
            self.queue.popleft()
        if self.queue:
            return self.queue[0]
        return -1

    def add(self, value: int) -> None:
        """if value not in self.other and value not in self.unique: self.unique[value] = True return if value in self.unique: self.unique.pop(value) self.other[value] = True"""
        if value not in self.judge:
            self.judge[value] = True
        else:
            self.judge[value] = False
        self.queue.append(value)


generateRealizationsAntitheticVariables

Prompt:
Implement the Python class `GenerateBlackScholes` described below.

Class description:
In this class we generate N realizations of a log-normal process dX_t = r X_t dt + sigma X_t dW_t at time T>0. We do it by writing X_T = X_0 exp((r- 0.5 sigma^2) T + sigma T^0.5 Z), where Z is a standard normal random variable. We proceed in two different ways: - we generate the N realizations of Z directly; - we first generate N/2 realizations of Z, and then set Z(j+N/2) = - Z(j). The second way accounts for the Antithetic Variables variance reduction method. Attributes ---------- numberOfSimulations : int the number of simulated values of the process at maturity T : float the maturity of the option initialValue : float the initial value of the process sigma : float the standard deviation r

Method signatures and docstrings:
- def __init__(self, numberOfSimulations, T, initialValue, sigma, r=0, seed=None): Parameters ---------- numberOfSimulations : int the number of simulated values of the process at maturity T : float the maturity of the option initialValue : float the initial value of the process sigma : float the standard deviation r : float the interest rate. Default = 0 seed : int the seed to generate the sequence both with standard Monte Carlo and Antithetic variables. Default = None
- def generateRealizations(self): It generates a number N = self.numberOfSimulations of realizations of the log-normal process at time self.T. In particular, it does it by generating N values of standard normal random variables Z(j), j = 1, ..., N, and computing for every j X_T(j) = X_0 exp((r- 0.5 sigma^2) T + sigma T^0.5 Z(j)) Returns ------- blackScholesRealizations : list a vector representing the realizations of the process
- def generateRealizationsAntitheticVariables(self): It generates a number N = self.numberOfSimulations of realizations of the log-normal process at time self.T. In particular, it does it by first generating N values of standard normal random variables Z(j), j = 1, ..., N/2, Z(n/2+j)=-Z(j), j = 1, ..., N/2 and computing for every j X_T(j) = X_0 exp((r- 0.5 sigma^2) T + sigma T^0.5 Z(j)). If N is odd, N/2 is defined as the smallest integer >= N/2 Returns ------- blackScholesRealizations : list a vector representing the realizations of the process

Implement the Python class `GenerateBlackScholes` described below.

Class description:
In this class we generate N realizations of a log-normal process dX_t = r X_t dt + sigma X_t dW_t at time T>0. We do it by writing X_T = X_0 exp((r- 0.5 sigma^2) T + sigma T^0.5 Z), where Z is a standard normal random variable. We proceed in two different ways: - we generate the N realizations of Z directly; - we first generate N/2 realizations of Z, and then set Z(j+N/2) = - Z(j). The second way accounts for the Antithetic Variables variance reduction method. Attributes ---------- numberOfSimulations : int the number of simulated values of the process at maturity T : float the maturity of the option initialValue : float the initial value of the process sigma : float the standard deviation r

Method signatures and docstrings:
- def __init__(self, numberOfSimulations, T, initialValue, sigma, r=0, seed=None): Parameters ---------- numberOfSimulations : int the number of simulated values of the process at maturity T : float the maturity of the option initialValue : float the initial value of the process sigma : float the standard deviation r : float the interest rate. Default = 0 seed : int the seed to generate the sequence both with standard Monte Carlo and Antithetic variables. Default = None
- def generateRealizations(self): It generates a number N = self.numberOfSimulations of realizations of the log-normal process at time self.T. In particular, it does it by generating N values of standard normal random variables Z(j), j = 1, ..., N, and computing for every j X_T(j) = X_0 exp((r- 0.5 sigma^2) T + sigma T^0.5 Z(j)) Returns ------- blackScholesRealizations : list a vector representing the realizations of the process
- def generateRealizationsAntitheticVariables(self): It generates a number N = self.numberOfSimulations of realizations of the log-normal process at time self.T. In particular, it does it by first generating N values of standard normal random variables Z(j), j = 1, ..., N/2, Z(n/2+j)=-Z(j), j = 1, ..., N/2 and computing for every j X_T(j) = X_0 exp((r- 0.5 sigma^2) T + sigma T^0.5 Z(j)). If N is odd, N/2 is defined as the smallest integer >= N/2 Returns ------- blackScholesRealizations : list a vector representing the realizations of the process

<|skeleton|>
class GenerateBlackScholes:
    """In this class we generate N realizations of a log-normal process dX_t = r X_t dt + sigma X_t dW_t at time T>0. We do it by writing X_T = X_0 exp((r- 0.5 sigma^2) T + sigma T^0.5 Z), where Z is a standard normal random variable. We proceed in two different ways: - we generate the N realizations of Z directly; - we first generate N/2 realizations of Z, and then set Z(j+N/2) = - Z(j). The second way accounts for the Antithetic Variables variance reduction method. Attributes ---------- numberOfSimulations : int the number of simulated values of the process at maturity T : float the maturity of the option initialValue : float the initial value of the process sigma : float the standard deviation r"""

    def __init__(self, numberOfSimulations, T, initialValue, sigma, r=0, seed=None):
        """Parameters ---------- numberOfSimulations : int the number of simulated values of the process at maturity T : float the maturity of the option initialValue : float the initial value of the process sigma : float the standard deviation r : float the interest rate. Default = 0 seed : int the seed to generate the sequence both with standard Monte Carlo and Antithetic variables. Default = None"""
        <|body_0|>

    def generateRealizations(self):
        """It generates a number N = self.numberOfSimulations of realizations of the log-normal process at time self.T. In particular, it does it by generating N values of standard normal random variables Z(j), j = 1, ..., N, and computing for every j X_T(j) = X_0 exp((r- 0.5 sigma^2) T + sigma T^0.5 Z(j)) Returns ------- blackScholesRealizations : list a vector representing the realizations of the process"""
        <|body_1|>

    def generateRealizationsAntitheticVariables(self):
        """It generates a number N = self.numberOfSimulations of realizations of the log-normal process at time self.T. In particular, it does it by first generating N values of standard normal random variables Z(j), j = 1, ..., N/2, Z(n/2+j)=-Z(j), j = 1, ..., N/2 and computing for every j X_T(j) = X_0 exp((r- 0.5 sigma^2) T + sigma T^0.5 Z(j)). If N is odd, N/2 is defined as the smallest integer >= N/2 Returns ------- blackScholesRealizations : list a vector representing the realizations of the process"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self.numberOfSimulations = numberOfSimulations
        self.T = T
        self.initialValue = initialValue
        self.sigma = sigma
        self.r = r
        np.random.seed(seed)
<|end_body_0|>

<|body_start_1|>
        standardNormalRealizations = np.random.standard_normal(self.numberOfSimulations)
        firstPart = self.initialValue * math.exp((self.r - 0.5 * self.sigma ** 2) * self.T)

        def BSFunction(x):
            return firstPart * math.exp(self.sigma * math.sqrt(self.T) * x)
        blackScholesRealizations = [BSFunction(x) for x in standardNormalRealizations]
        return blackScholesRealizations
<|end_body_1|>

<|body_start_2|>
        standardNormalRealizations = np.random.standard_normal(math.ceil(self.numberOfSimulations / 2))
        firstPart = self.initialValue * math.exp((self.r - 0.5 * self.sigma ** 2) * self.T)
        blackScholesRealizations = [firstPart * math.exp(self.sigma * math.sqrt(self.T) * x) for x in standardNormalRealizations] + [firstPart * math.exp(self.sigma * math.sqrt(self.T) * -x) for x in standardNormalRealizations]
        return blackScholesRealizations
<|end_body_2|>

Solution:
class GenerateBlackScholes:
    """In this class we generate N realizations of a log-normal process dX_t = r X_t dt + sigma X_t dW_t at time T>0. We do it by writing X_T = X_0 exp((r- 0.5 sigma^2) T + sigma T^0.5 Z), where Z is a standard normal random variable. We proceed in two different ways: - we generate the N realizations of Z directly; - we first generate N/2 realizations of Z, and then set Z(j+N/2) = - Z(j). The second way accounts for the Antithetic Variables variance reduction method. Attributes ---------- numberOfSimulations : int the number of simulated values of the process at maturity T : float the maturity of the option initialValue : float the initial value of the process sigma : float the standard deviation r"""

    def __init__(self, numberOfSimulations, T, initialValue, sigma, r=0, seed=None):
        """Parameters ---------- numberOfSimulations : int the number of simulated values of the process at maturity T : float the maturity of the option initialValue : float the initial value of the process sigma : float the standard deviation r : float the interest rate. Default = 0 seed : int the seed to generate the sequence both with standard Monte Carlo and Antithetic variables. Default = None"""
        self.numberOfSimulations = numberOfSimulations
        self.T = T
        self.initialValue = initialValue
        self.sigma = sigma
        self.r = r
        np.random.seed(seed)

    def generateRealizations(self):
        """It generates a number N = self.numberOfSimulations of realizations of the log-normal process at time self.T. In particular, it does it by generating N values of standard normal random variables Z(j), j = 1, ..., N, and computing for every j X_T(j) = X_0 exp((r- 0.5 sigma^2) T + sigma T^0.5 Z(j)) Returns ------- blackScholesRealizations : list a vector representing the realizations of the process"""
        standardNormalRealizations = np.random.standard_normal(self.numberOfSimulations)
        firstPart = self.initialValue * math.exp((self.r - 0.5 * self.sigma ** 2) * self.T)

        def BSFunction(x):
            return firstPart * math.exp(self.sigma * math.sqrt(self.T) * x)
        blackScholesRealizations = [BSFunction(x) for x in standardNormalRealizations]
        return blackScholesRealizations

    def generateRealizationsAntitheticVariables(self):
        """It generates a number N = self.numberOfSimulations of realizations of the log-normal process at time self.T. In particular, it does it by first generating N values of standard normal random variables Z(j), j = 1, ..., N/2, Z(n/2+j)=-Z(j), j = 1, ..., N/2 and computing for every j X_T(j) = X_0 exp((r- 0.5 sigma^2) T + sigma T^0.5 Z(j)). If N is odd, N/2 is defined as the smallest integer >= N/2 Returns ------- blackScholesRealizations : list a vector representing the realizations of the process"""
        standardNormalRealizations = np.random.standard_normal(math.ceil(self.numberOfSimulations / 2))
        firstPart = self.initialValue * math.exp((self.r - 0.5 * self.sigma ** 2) * self.T)
        blackScholesRealizations = [firstPart * math.exp(self.sigma * math.sqrt(self.T) * x) for x in standardNormalRealizations] + [firstPart * math.exp(self.sigma * math.sqrt(self.T) * -x) for x in standardNormalRealizations]
        return blackScholesRealizations


Prompt:
Implement the Python class `ExternalStatePolicy` described below.

Class description:
Represents how to handle external state during serialization. See the `tf.data.Options.experimental_external_state_policy` documentation for more information.

Method signatures and docstrings:
- def _to_proto(cls, obj): Convert enum to proto.
- def _from_proto(cls, pb): Convert proto to enum.

Implement the Python class `ExternalStatePolicy` described below.

Class description:
Represents how to handle external state during serialization. See the `tf.data.Options.experimental_external_state_policy` documentation for more information.

Method signatures and docstrings:
- def _to_proto(cls, obj): Convert enum to proto.
- def _from_proto(cls, pb): Convert proto to enum.

<|skeleton|>
class ExternalStatePolicy:
    """Represents how to handle external state during serialization. See the `tf.data.Options.experimental_external_state_policy` documentation for more information."""

    def _to_proto(cls, obj):
        """Convert enum to proto."""
        <|body_0|>

    def _from_proto(cls, pb):
        """Convert proto to enum."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if obj == cls.IGNORE:
            return dataset_options_pb2.ExternalStatePolicy.POLICY_IGNORE
        if obj == cls.FAIL:
            return dataset_options_pb2.ExternalStatePolicy.POLICY_FAIL
        if obj == cls.WARN:
            return dataset_options_pb2.ExternalStatePolicy.POLICY_WARN
        raise ValueError('%s._to_proto() is called with undefined enum %s.' % (cls.__name__, obj.name))
<|end_body_0|>

<|body_start_1|>
        if pb == dataset_options_pb2.ExternalStatePolicy.POLICY_IGNORE:
            return cls.IGNORE
        if pb == dataset_options_pb2.ExternalStatePolicy.POLICY_FAIL:
            return cls.FAIL
        if pb == dataset_options_pb2.ExternalStatePolicy.POLICY_WARN:
            return cls.WARN
        raise ValueError('%s._from_proto() is called with undefined enum %s.' % (cls.__name__, pb))
<|end_body_1|>

Solution:
class ExternalStatePolicy:
    """Represents how to handle external state during serialization. See the `tf.data.Options.experimental_external_state_policy` documentation for more information."""

    def _to_proto(cls, obj):
        """Convert enum to proto."""
        if obj == cls.IGNORE:
            return dataset_options_pb2.ExternalStatePolicy.POLICY_IGNORE
        if obj == cls.FAIL:
            return dataset_options_pb2.ExternalStatePolicy.POLICY_FAIL
        if obj == cls.WARN:
            return dataset_options_pb2.ExternalStatePolicy.POLICY_WARN
        raise ValueError('%s._to_proto() is called with undefined enum %s.' % (cls.__name__, obj.name))

    def _from_proto(cls, pb):
        """Convert proto to enum."""
        if pb == dataset_options_pb2.ExternalStatePolicy.POLICY_IGNORE:
            return cls.IGNORE
        if pb == dataset_options_pb2.ExternalStatePolicy.POLICY_FAIL:
            return cls.FAIL
        if pb == dataset_options_pb2.ExternalStatePolicy.POLICY_WARN:
            return cls.WARN
        raise ValueError('%s._from_proto() is called with undefined enum %s.' % (cls.__name__, pb))


Prompt:
Implement the Python class `TestChocolateMachine` described below.

Class description:
Test class to test chocolate_machine module

Method signatures and docstrings:
- def setUp(self): setUp class
- def test_stats(self): test stats functionality
- def test_has_raw_materials(self): test has_raw_materials functionality
- def test_has_raw_materials_handles_insufficient_raw_materials(self): test has_raw_materials handles insufficient raw materials functionality
- def test_bake_chocolate_bar(self): test bake_chocolate_bar functionality

Implement the Python class `TestChocolateMachine` described below.

Class description:
Test class to test chocolate_machine module

Method signatures and docstrings:
- def setUp(self): setUp class
- def test_stats(self): test stats functionality
- def test_has_raw_materials(self): test has_raw_materials functionality
- def test_has_raw_materials_handles_insufficient_raw_materials(self): test has_raw_materials handles insufficient raw materials functionality
- def test_bake_chocolate_bar(self): test bake_chocolate_bar functionality

<|skeleton|>
class TestChocolateMachine:
    """Test class to test chocolate_machine module"""

    def setUp(self):
        """setUp class"""
        <|body_0|>

    def test_stats(self):
        """test stats functionality"""
        <|body_1|>

    def test_has_raw_materials(self):
        """test has_raw_materials functionality"""
        <|body_2|>

    def test_has_raw_materials_handles_insufficient_raw_materials(self):
        """test has_raw_materials handles insufficient raw materials functionality"""
        <|body_3|>

    def test_bake_chocolate_bar(self):
        """test bake_chocolate_bar functionality"""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        CHOICES = ('dark', 'caramel', 'mint', 'surprise', 'stats', 'shutdown')
        choice = 'dark'
        self.chocolate_machine = ChocolateMachine(CHOICES)
        self.selection = CHOCOLATE_CHOICES[choice]
<|end_body_0|>

<|body_start_1|>
        d_raw_materials = {'sugar': 2, 'butter': 2, 'caramel': 15, 'dark chocolate': 30, 'mint chocolate': 30, 'milk chocolate': 30, 'light corn syrup': 2, 'sweetened condensed milk': 2, 'vanilla extract': 2, "Reese's Pieces": 15}
        return_1 = "sugar 2 tablespoons remaining\nbutter 2 teaspoons remaining\ndark chocolate 30 tablespoons remaining\nmint chocolate 30 tablespoons remaining\nmilk chocolate 30 tablespoons remaining\nlight corn syrup 2 teaspoons remaining\nsweetened condensed milk 2 teaspoons remaining\nvanilla extract 2 teaspoons remaining\nReese's Pieces 15 tablespoons remaining\nTotal Money Collected: $0.00\n"
        string_1 = self.chocolate_machine.stats(d_raw_materials)
        self.assertEqual(string_1, return_1)
<|end_body_1|>

<|body_start_2|>
        m_raw_materials = self.selection['ingredients']
        d_raw_materials = {'sugar': 2, 'butter': 2, 'caramel': 15, 'dark chocolate': 30, 'mint chocolate': 30, 'milk chocolate': 30, 'light corn syrup': 2, 'sweetened condensed milk': 2, 'vanilla extract': 2, "Reese's Pieces": 15}
        return_1 = True
        bool_1 = self.chocolate_machine.has_raw_materials(m_raw_materials, d_raw_materials)
        self.assertEqual(bool_1, return_1)
<|end_body_2|>

<|body_start_3|>
        m_raw_materials = self.selection['ingredients']
        d_raw_materials = {'sugar': 0, 'butter': 0, 'caramel': 15, 'dark chocolate': 0, 'mint chocolate': 30, 'milk chocolate': 30, 'light corn syrup': 0, 'sweetened condensed milk': 0, 'vanilla extract': 0, "Reese's Pieces": 15}
        return_1 = 'Machine Needs Additional: sugar\nMachine Needs Additional: butter\nMachine Needs Additional: dark chocolate\nMachine Needs Additional: light corn syrup\nMachine Needs Additional: sweetened condensed milk\nMachine Needs Additional: vanilla extract\n'
        bool_1 = self.chocolate_machine.has_raw_materials(m_raw_materials, d_raw_materials)
        self.assertEqual(bool_1, return_1)
<|end_body_3|>

<|body_start_4|>
        chocolate_choice = 'dark'
        m_raw_materials = self.selection['ingredients']
        d_raw_materials = {'sugar': 2, 'butter': 2, 'caramel': 15, 'dark chocolate': 30, 'mint chocolate': 30, 'milk chocolate': 30, 'light corn syrup': 2, 'sweetened condensed milk': 2, 'vanilla extract': 2, "Reese's Pieces": 15}
        return_1 = 'A dark chocolate bar dispensed!'
        string_1 = self.chocolate_machine.bake_chocolate_bar(chocolate_choice, m_raw_materials, d_raw_materials)
        self.assertEqual(string_1, return_1)
<|end_body_4|>

Solution:
class TestChocolateMachine:
    """Test class to test chocolate_machine module"""

    def setUp(self):
        """setUp class"""
        CHOICES = ('dark', 'caramel', 'mint', 'surprise', 'stats', 'shutdown')
        choice = 'dark'
        self.chocolate_machine = ChocolateMachine(CHOICES)
        self.selection = CHOCOLATE_CHOICES[choice]

    def test_stats(self):
        """test stats functionality"""
        d_raw_materials = {'sugar': 2, 'butter': 2, 'caramel': 15, 'dark chocolate': 30, 'mint chocolate': 30, 'milk chocolate': 30, 'light corn syrup': 2, 'sweetened condensed milk': 2, 'vanilla extract': 2, "Reese's Pieces": 15}
        return_1 = "sugar 2 tablespoons remaining\nbutter 2 teaspoons remaining\ndark chocolate 30 tablespoons remaining\nmint chocolate 30 tablespoons remaining\nmilk chocolate 30 tablespoons remaining\nlight corn syrup 2 teaspoons remaining\nsweetened condensed milk 2 teaspoons remaining\nvanilla extract 2 teaspoons remaining\nReese's Pieces 15 tablespoons remaining\nTotal Money Collected: $0.00\n"
        string_1 = self.chocolate_machine.stats(d_raw_materials)
        self.assertEqual(string_1, return_1)

    def test_has_raw_materials(self):
        """test has_raw_materials functionality"""
        m_raw_materials = self.selection['ingredients']
        d_raw_materials = {'sugar': 2, 'butter': 2, 'caramel': 15, 'dark chocolate': 30, 'mint chocolate': 30, 'milk chocolate': 30, 'light corn syrup': 2, 'sweetened condensed milk': 2, 'vanilla extract': 2, "Reese's Pieces": 15}
        return_1 = True
        bool_1 = self.chocolate_machine.has_raw_materials(m_raw_materials, d_raw_materials)
        self.assertEqual(bool_1, return_1)

    def test_has_raw_materials_handles_insufficient_raw_materials(self):
        """test has_raw_materials handles insufficient raw materials functionality"""
        m_raw_materials = self.selection['ingredients']
        d_raw_materials = {'sugar': 0, 'butter': 0, 'caramel': 15, 'dark chocolate': 0, 'mint chocolate': 30, 'milk chocolate': 30, 'light corn syrup': 0, 'sweetened condensed milk': 0, 'vanilla extract': 0, "Reese's Pieces": 15}
        return_1 = 'Machine Needs Additional: sugar\nMachine Needs Additional: butter\nMachine Needs Additional: dark chocolate\nMachine Needs Additional: light corn syrup\nMachine Needs Additional: sweetened condensed milk\nMachine Needs Additional: vanilla extract\n'
        bool_1 = self.chocolate_machine.has_raw_materials(m_raw_materials, d_raw_materials)
        self.assertEqual(bool_1, return_1)

    def test_bake_chocolate_bar(self):
        """test bake_chocolate_bar functionality"""
        chocolate_choice = 'dark'
        m_raw_materials = self.selection['ingredients']
        d_raw_materials = {'sugar': 2, 'butter': 2, 'caramel': 15, 'dark chocolate': 30, 'mint chocolate': 30, 'milk chocolate': 30, 'light corn syrup': 2, 'sweetened condensed milk': 2, 'vanilla extract': 2, "Reese's Pieces": 15}
        return_1 = 'A dark chocolate bar dispensed!'
        string_1 = self.chocolate_machine.bake_chocolate_bar(chocolate_choice, m_raw_materials, d_raw_materials)
        self.assertEqual(string_1, return_1)


Prompt:
Implement the Python class `AggregatedSubtreeInfo` described below.

Class description:
Implementation of the 'AggregatedSubtreeInfo' model. Aggregated information about a node subtree. Attributes: environment (EnvironmentAggregatedSubtreeInfoEnum): Specifies the environment such as 'kSQL' or 'kVMware', where the Protection Source exists. Supported environment types such as 'kView', 'kSQL', 'kVMware', etc. NOTE: 'kPuppeteer' refers to Cohesity's Remote Adapter. 'kVMware' indicates the VMware Protection Source environment. 'kHyperV' indicates the HyperV Protection Source environment. 'kSQL' indicates the SQL Protection Source environment. 'kView' indicates the View Protection Source environment. 'kPuppeteer' indicates the Cohesity's Remote Adapter. 'kPhysical' indicates the phys

Method signatures and docstrings:
- def __init__(self, environment=None, leaves_count=None, total_logical_size=None): Constructor for the AggregatedSubtreeInfo class
- def from_dictionary(cls, dictionary): Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class.

Implement the Python class `AggregatedSubtreeInfo` described below.

Class description:
Implementation of the 'AggregatedSubtreeInfo' model. Aggregated information about a node subtree. Attributes: environment (EnvironmentAggregatedSubtreeInfoEnum): Specifies the environment such as 'kSQL' or 'kVMware', where the Protection Source exists. Supported environment types such as 'kView', 'kSQL', 'kVMware', etc. NOTE: 'kPuppeteer' refers to Cohesity's Remote Adapter. 'kVMware' indicates the VMware Protection Source environment. 'kHyperV' indicates the HyperV Protection Source environment. 'kSQL' indicates the SQL Protection Source environment. 'kView' indicates the View Protection Source environment. 'kPuppeteer' indicates the Cohesity's Remote Adapter. 'kPhysical' indicates the phys

Method signatures and docstrings:
- def __init__(self, environment=None, leaves_count=None, total_logical_size=None): Constructor for the AggregatedSubtreeInfo class
- def from_dictionary(cls, dictionary): Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class.

<|skeleton|>
class AggregatedSubtreeInfo:
    """Implementation of the 'AggregatedSubtreeInfo' model. Aggregated information about a node subtree. Attributes: environment (EnvironmentAggregatedSubtreeInfoEnum): Specifies the environment such as 'kSQL' or 'kVMware', where the Protection Source exists. Supported environment types such as 'kView', 'kSQL', 'kVMware', etc. NOTE: 'kPuppeteer' refers to Cohesity's Remote Adapter. 'kVMware' indicates the VMware Protection Source environment. 'kHyperV' indicates the HyperV Protection Source environment. 'kSQL' indicates the SQL Protection Source environment. 'kView' indicates the View Protection Source environment. 'kPuppeteer' indicates the Cohesity's Remote Adapter. 'kPhysical' indicates the phys"""

    def __init__(self, environment=None, leaves_count=None, total_logical_size=None):
        """Constructor for the AggregatedSubtreeInfo class"""
        <|body_0|>

    def from_dictionary(cls, dictionary):
        """Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.environment = environment
        self.leaves_count = leaves_count
        self.total_logical_size = total_logical_size
<|end_body_0|>

<|body_start_1|>
        if dictionary is None:
            return None
        environment = dictionary.get('environment')
        leaves_count = dictionary.get('leavesCount')
        total_logical_size = dictionary.get('totalLogicalSize')
        return cls(environment, leaves_count, total_logical_size)
<|end_body_1|>

Solution:
class AggregatedSubtreeInfo:
    """Implementation of the 'AggregatedSubtreeInfo' model. Aggregated information about a node subtree. Attributes: environment (EnvironmentAggregatedSubtreeInfoEnum): Specifies the environment such as 'kSQL' or 'kVMware', where the Protection Source exists. Supported environment types such as 'kView', 'kSQL', 'kVMware', etc. NOTE: 'kPuppeteer' refers to Cohesity's Remote Adapter. 'kVMware' indicates the VMware Protection Source environment. 'kHyperV' indicates the HyperV Protection Source environment. 'kSQL' indicates the SQL Protection Source environment. 'kView' indicates the View Protection Source environment. 'kPuppeteer' indicates the Cohesity's Remote Adapter. 'kPhysical' indicates the phys"""

    def __init__(self, environment=None, leaves_count=None, total_logical_size=None):
        """Constructor for the AggregatedSubtreeInfo class"""
        self.environment = environment
        self.leaves_count = leaves_count
        self.total_logical_size = total_logical_size

    def from_dictionary(cls, dictionary):
        """Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class."""
        if dictionary is None:
            return None
        environment = dictionary.get('environment')
        leaves_count = dictionary.get('leavesCount')
        total_logical_size = dictionary.get('totalLogicalSize')
        return cls(environment, leaves_count, total_logical_size)


forward

Prompt:
Implement the Python class `MarginLoss` described below.

Class description:
Implement the MarginLoss class.

Method signatures and docstrings:
- def __init__(self, margin=0.2, nu=0, beta=1.2, n_classes=100, beta_constant=False, sampling_method='distance'): Basic Margin Loss as proposed in 'Sampling Matters in Deep Embedding Learning'. Args: margin: float, fixed triplet margin (see also TripletLoss). nu: float, regularisation weight for beta. Zero by default (in literature as well). beta: float, initial value for trainable class margins. Set to default literature value. n_classes: int, number of target class. Required because it dictates the number of trainable class margins. beta_constant: bool, set to True if betas should not be trained. sampling_method: str, sampling method to use to generate training triplets. Returns: Nothing!
- def forward(self, batch, labels): Args: batch: torch.Tensor() [(BS x embed_dim)], batch of embeddings labels: np.ndarray [(BS x 1)], for each element of the batch assigns a class [0,...,C-1] Returns: margin loss (torch.Tensor(), batch-averaged)

Implement the Python class `MarginLoss` described below.

Class description:
Implement the MarginLoss class.

Method signatures and docstrings:
- def __init__(self, margin=0.2, nu=0, beta=1.2, n_classes=100, beta_constant=False, sampling_method='distance'): Basic Margin Loss as proposed in 'Sampling Matters in Deep Embedding Learning'. Args: margin: float, fixed triplet margin (see also TripletLoss). nu: float, regularisation weight for beta. Zero by default (in literature as well). beta: float, initial value for trainable class margins. Set to default literature value. n_classes: int, number of target class. Required because it dictates the number of trainable class margins. beta_constant: bool, set to True if betas should not be trained. sampling_method: str, sampling method to use to generate training triplets. Returns: Nothing!
- def forward(self, batch, labels): Args: batch: torch.Tensor() [(BS x embed_dim)], batch of embeddings labels: np.ndarray [(BS x 1)], for each element of the batch assigns a class [0,...,C-1] Returns: margin loss (torch.Tensor(), batch-averaged)

<|skeleton|>
class MarginLoss:

    def __init__(self, margin=0.2, nu=0, beta=1.2, n_classes=100, beta_constant=False, sampling_method='distance'):
        """Basic Margin Loss as proposed in 'Sampling Matters in Deep Embedding Learning'. Args: margin: float, fixed triplet margin (see also TripletLoss). nu: float, regularisation weight for beta. Zero by default (in literature as well). beta: float, initial value for trainable class margins. Set to default literature value. n_classes: int, number of target class. Required because it dictates the number of trainable class margins. beta_constant: bool, set to True if betas should not be trained. sampling_method: str, sampling method to use to generate training triplets. Returns: Nothing!"""
        <|body_0|>

    def forward(self, batch, labels):
        """Args: batch: torch.Tensor() [(BS x embed_dim)], batch of embeddings labels: np.ndarray [(BS x 1)], for each element of the batch assigns a class [0,...,C-1] Returns: margin loss (torch.Tensor(), batch-averaged)"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super(MarginLoss, self).__init__()
        self.margin = margin
        self.n_classes = n_classes
        self.beta_constant = beta_constant
        self.beta_val = beta
        self.beta = beta if beta_constant else torch.nn.Parameter(torch.ones(n_classes) * beta)
        self.nu = nu
        self.sampling_method = sampling_method
        self.sampler = TupleSampler(method=sampling_method)
<|end_body_0|>

<|body_start_1|>
        if isinstance(labels, torch.Tensor):
            labels = labels.detach().cpu().numpy()
        sampled_triplets = self.sampler.give(batch, labels)
        d_ap, d_an = ([], [])
        for triplet in sampled_triplets:
            train_triplet = {'Anchor': batch[triplet[0], :], 'Positive': batch[triplet[1], :], 'Negative': batch[triplet[2]]}
            pos_dist = ((train_triplet['Anchor'] - train_triplet['Positive']).pow(2).sum() + 1e-08).pow(1 / 2)
            neg_dist = ((train_triplet['Anchor'] - train_triplet['Negative']).pow(2).sum() + 1e-08).pow(1 / 2)
            d_ap.append(pos_dist)
            d_an.append(neg_dist)
        d_ap, d_an = (torch.stack(d_ap), torch.stack(d_an))
        if self.beta_constant:
            beta = self.beta
        else:
            beta = torch.stack([self.beta[labels[triplet[0]]] for triplet in sampled_triplets]).type(torch.FloatTensor)
        pos_loss = torch.nn.functional.relu(d_ap - beta + self.margin)
        neg_loss = torch.nn.functional.relu(beta - d_an + self.margin)
        pair_count = torch.sum((pos_loss > 0.0) + (neg_loss > 0.0)).type(torch.FloatTensor)
        loss = torch.sum(pos_loss + neg_loss) if pair_count == 0.0 else torch.sum(pos_loss + neg_loss) / pair_count
        if self.nu:
            loss = loss + beta_regularisation_loss.type(torch.FloatTensor)
        return loss
<|end_body_1|>

Solution:
class MarginLoss:
    def __init__(self, margin=0.2, nu=0, beta=1.2, n_classes=100, beta_constant=False, sampling_method='distance'):
        """Basic Margin Loss as proposed in 'Sampling Matters in Deep Embedding Learning'. Args: margin: float, fixed triplet margin (see also TripletLoss). nu: float, regularisation weight for beta. Zero by default (in literature as well). beta: float, initial value for trainable class margins. Set to default literature value. n_classes: int, number of target class. Required because it dictates the number of trainable class margins. beta_constant: bool, set to True if betas should not be trained. sampling_method: str, sampling method to use to generate training triplets. Returns: Nothing!"""
        super(MarginLoss, self).__init__()
        self.margin = margin
        self.n_classes = n_classes
        self.beta_constant = beta_constant
        self.beta_val = beta
        self.beta = beta if beta_constant else torch.nn.Parameter(torch.ones(n_classes) * beta)
        self.nu = nu
        self.sampling_method = sampling_method
        self.sampler = TupleSampler(method=sampling_method)

    def forward(self, batch, labels):
        """Args: batch: torch.Tensor() [(BS x embed_dim)], batch of embeddings labels: np.ndarray [(BS x 1)], for each element of the batch assigns a class [0,...,C-1] Returns: margin loss (torch.Tensor(), batch-averaged)"""
        if isinstance(labels, torch.Tensor):
            labels = labels.detach().cpu().numpy()
        sampled_triplets = self.sampler.give(batch, labels)
        d_ap, d_an = ([], [])
        for triplet in sampled_triplets:
            train_triplet = {'Anchor': batch[triplet[0], :], 'Positive': batch[triplet[1], :], 'Negative': batch[triplet[2]]}
            pos_dist = ((train_triplet['Anchor'] - train_triplet['Positive']).pow(2).sum() + 1e-08).pow(1 / 2)
            neg_dist = ((train_triplet['Anchor'] - train_triplet['Negative']).pow(2).sum() + 1e-08).pow(1 / 2)
            d_ap.append(pos_dist)
            d_an.append(neg_dist)
        d_ap, d_an = (torch.stack(d_ap), torch.stack(d_an))
        if self.beta_constant:
            beta = self.beta
        else:
            beta = torch.stack([self.beta[labels[triplet[0]]] for triplet in sampled_triplets]).type(torch.FloatTensor)
        pos_loss = torch.nn.functional.relu(d_ap - beta + self.margin)
        neg_loss = torch.nn.functional.relu(beta - d_an + self.margin)
        pair_count = torch.sum((pos_loss > 0.0) + (neg_loss > 0.0)).type(torch.FloatTensor)
        loss = torch.sum(pos_loss + neg_loss) if pair_count == 0.0 else torch.sum(pos_loss + neg_loss) / pair_count
        if self.nu:
            loss = loss + beta_regularisation_loss.type(torch.FloatTensor)
        return loss


Prompt:
Implement the Python class `ImmutableDictionary` described below.

Class description:
Implement the ImmutableDictionary class.

Method signatures and docstrings:
- def __init__(self, dictionary): Recursively turn dict to ImmutableDictionary
- def __getattr__(self, name): Access to self['attribute'] as self.attribute

Implement the Python class `ImmutableDictionary` described below.

Class description:
Implement the ImmutableDictionary class.

Method signatures and docstrings:
- def __init__(self, dictionary): Recursively turn dict to ImmutableDictionary
- def __getattr__(self, name): Access to self['attribute'] as self.attribute

<|skeleton|>
class ImmutableDictionary:

    def __init__(self, dictionary):
        """Recursively turn dict to ImmutableDictionary"""
        <|body_0|>

    def __getattr__(self, name):
        """Access to self['attribute'] as self.attribute"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if not isinstance(dictionary, ImmutableDictionary):
            for k, v in dictionary.iteritems():
                dictionary[k] = Utils.make_immutable(v)
        super(ImmutableDictionary, self).__init__(dictionary)
<|end_body_0|>

<|body_start_1|>
        if name in self:
            return self[name]
        try:
            return self[name]
        except KeyError:
            raise AttributeError("'%s' object has no attribute '%s'" % (self.__class__.__name__, name))
<|end_body_1|>

Solution:
class ImmutableDictionary:
    def __init__(self, dictionary):
        """Recursively turn dict to ImmutableDictionary"""
        if not isinstance(dictionary, ImmutableDictionary):
            for k, v in dictionary.iteritems():
                dictionary[k] = Utils.make_immutable(v)
        super(ImmutableDictionary, self).__init__(dictionary)

    def __getattr__(self, name):
        """Access to self['attribute'] as self.attribute"""
        if name in self:
            return self[name]
        try:
            return self[name]
        except KeyError:
            raise AttributeError("'%s' object has no attribute '%s'" % (self.__class__.__name__, name))


Prompt:
Implement the Python class `SSLTransport` described below.

Class description:
Transport that works over SSL

Method signatures and docstrings:
- def _setup_transport(self): Wrap the socket in an SSL object, either the new Python 2.6 version, or the older Python 2.5 and lower version.
- def _read(self, n): It seems that SSL Objects read() method may not supply as much as you're asking for, at least with extremely large messages. somewhere > 16K - found this in the test_channel.py test_large unittest.
- def _write(self, s): Write a string out to the SSL socket fully.

Implement the Python class `SSLTransport` described below.

Class description:
Transport that works over SSL

Method signatures and docstrings:
- def _setup_transport(self): Wrap the socket in an SSL object, either the new Python 2.6 version, or the older Python 2.5 and lower version.
- def _read(self, n): It seems that SSL Objects read() method may not supply as much as you're asking for, at least with extremely large messages. somewhere > 16K - found this in the test_channel.py test_large unittest.
- def _write(self, s): Write a string out to the SSL socket fully.

<|skeleton|>
class SSLTransport:
    """Transport that works over SSL"""

    def _setup_transport(self):
        """Wrap the socket in an SSL object, either the new Python 2.6 version, or the older Python 2.5 and lower version."""
        <|body_0|>

    def _read(self, n):
        """It seems that SSL Objects read() method may not supply as much as you're asking for, at least with extremely large messages. somewhere > 16K - found this in the test_channel.py test_large unittest."""
        <|body_1|>

    def _write(self, s):
        """Write a string out to the SSL socket fully."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        if HAVE_PY26_SSL:
            self.sslobj = ssl.wrap_socket(self.sock)
            self.sslobj.do_handshake()
        else:
            self.sslobj = socket.ssl(self.sock)
<|end_body_0|>

<|body_start_1|>
        result = self.sslobj.read(n)
        while len(result) < n:
            s = self.sslobj.read(n - len(result))
            if not s:
                raise IOError('Socket closed')
            result += s
        return result
<|end_body_1|>

<|body_start_2|>
        while s:
            n = self.sslobj.write(s)
            if not n:
                raise IOError('Socket closed')
            s = s[n:]
<|end_body_2|>

Solution:
class SSLTransport:
    """Transport that works over SSL"""

    def _setup_transport(self):
        """Wrap the socket in an SSL object, either the new Python 2.6 version, or the older Python 2.5 and lower version."""
        if HAVE_PY26_SSL:
            self.sslobj = ssl.wrap_socket(self.sock)
            self.sslobj.do_handshake()
        else:
            self.sslobj = socket.ssl(self.sock)

    def _read(self, n):
        """It seems that SSL Objects read() method may not supply as much as you're asking for, at least with extremely large messages. somewhere > 16K - found this in the test_channel.py test_large unittest."""
        result = self.sslobj.read(n)
        while len(result) < n:
            s = self.sslobj.read(n - len(result))
            if not s:
                raise IOError('Socket closed')
            result += s
        return result

    def _write(self, s):
        """Write a string out to the SSL socket fully."""
        while s:
            n = self.sslobj.write(s)
            if not n:
                raise IOError('Socket closed')
            s = s[n:]


minAddToMakeValid

Prompt:
Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def minAddToMakeValid(self, S): 解法1:利用）不能再匹配成功的原理，用bal计数balance当前的结果。 比较技巧性的解法，bal维持一个balance，一直在-1，0，n之间浮动 symobol = '('则+1，=')'则-1， bal=0，代表目前凑对成功， bal=-1代表目前剩余一个')'，此时就ans+=1，把这个多余的')'计数到最后结果，因为）不可能再匹配成功了，且bal+=1置为0 bal=n，代表剩余n个'('，还有希望匹配成功。
- def minAddToMakeValid1(self, S): 比较简单理解的方法

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def minAddToMakeValid(self, S): 解法1:利用）不能再匹配成功的原理，用bal计数balance当前的结果。 比较技巧性的解法，bal维持一个balance，一直在-1，0，n之间浮动 symobol = '('则+1，=')'则-1， bal=0，代表目前凑对成功， bal=-1代表目前剩余一个')'，此时就ans+=1，把这个多余的')'计数到最后结果，因为）不可能再匹配成功了，且bal+=1置为0 bal=n，代表剩余n个'('，还有希望匹配成功。
- def minAddToMakeValid1(self, S): 比较简单理解的方法

<|skeleton|>
class Solution:

    def minAddToMakeValid(self, S):
        """解法1:利用）不能再匹配成功的原理，用bal计数balance当前的结果。 比较技巧性的解法，bal维持一个balance，一直在-1，0，n之间浮动 symobol = '('则+1，=')'则-1， bal=0，代表目前凑对成功， bal=-1代表目前剩余一个')'，此时就ans+=1，把这个多余的')'计数到最后结果，因为）不可能再匹配成功了，且bal+=1置为0 bal=n，代表剩余n个'('，还有希望匹配成功。"""
        <|body_0|>

    def minAddToMakeValid1(self, S):
        """比较简单理解的方法"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        ans = bal = 0
        for symbol in S:
            bal += 1 if symbol == '(' else -1
            if bal == -1:
                ans += 1
                bal += 1
        return ans + bal
<|end_body_0|>

<|body_start_1|>
        while '()' in S:
            S = S.replace('()', '')
        return len(S)
<|end_body_1|>

Solution:
class Solution:
    def minAddToMakeValid(self, S):
        """解法1:利用）不能再匹配成功的原理，用bal计数balance当前的结果。 比较技巧性的解法，bal维持一个balance，一直在-1，0，n之间浮动 symobol = '('则+1，=')'则-1， bal=0，代表目前凑对成功， bal=-1代表目前剩余一个')'，此时就ans+=1，把这个多余的')'计数到最后结果，因为）不可能再匹配成功了，且bal+=1置为0 bal=n，代表剩余n个'('，还有希望匹配成功。"""
        ans = bal = 0
        for symbol in S:
            bal += 1 if symbol == '(' else -1
            if bal == -1:
                ans += 1
                bal += 1
        return ans + bal

    def minAddToMakeValid1(self, S):
        """比较简单理解的方法"""
        while '()' in S:
            S = S.replace('()', '')
        return len(S)