code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  # Copyright 2023 The HuggingFace Inc. team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  import ast
  import difflib
  from collections.abc import Mapping
  from typing import Any, Callable, Dict
  
  
  
  
  
  class 					_UpperCAmelCase							(		lowerCAmelCase_     ):
  		pass
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase=None      ,			lowercase=False			)      ->      List[Any]:
  			try:
  						__lowerCAmelCase      		=       ast.parse(lowercase			)
  			except SyntaxError as e:
  						print("""The code generated by the agent is not valid.\n"""      ,			lowercase			)
  						return
  			if state is None:
  						__lowerCAmelCase      		=       {}
  			__lowerCAmelCase      		=       None
  			for idx, node in enumerate(expression.body			):
  						try:
  									__lowerCAmelCase      		=       evaluate_ast(lowercase      ,			lowercase      ,			lowercase			)
  						except InterpretorError as e:
  									__lowerCAmelCase      		=       f'Evaluation of the code stopped at line {idx} before the end because of the following error'
  									if chat_mode:
  												msg += (
  												    f'. Copy paste the following error message and send it back to the agent:\nI get an error: \'{e}\''
  												)
  									else:
  												msg += f':\n{e}'
  									print(lowercase			)
  									break
  						if line_result is not None:
  									__lowerCAmelCase      		=       line_result
  
  			return result
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase			)      ->      str:
  			if isinstance(lowercase      ,			ast.Assign			):
  						# Assignement -> we evaluate the assignement which should update the state
  						# We return the variable assigned as it may be used to determine the final result.
  						return evaluate_assign(lowercase      ,			lowercase      ,			lowercase			)
  			elif isinstance(lowercase      ,			ast.Call			):
  						# Function call -> we return the value of the function call
  						return evaluate_call(lowercase      ,			lowercase      ,			lowercase			)
  			elif isinstance(lowercase      ,			ast.Constant			):
  						# Constant -> just return the value
  						return expression.value
  			elif isinstance(lowercase      ,			ast.Dict			):
  						# Dict -> evaluate all keys and values
  						__lowerCAmelCase      		=       [evaluate_ast(lowercase      ,			lowercase      ,			lowercase			) for k in expression.keys]
  						__lowerCAmelCase      		=       [evaluate_ast(lowercase      ,			lowercase      ,			lowercase			) for v in expression.values]
  						return dict(zip(lowercase      ,			lowercase			)			)
  			elif isinstance(lowercase      ,			ast.Expr			):
  						# Expression -> evaluate the content
  						return evaluate_ast(expression.value      ,			lowercase      ,			lowercase			)
  			elif isinstance(lowercase      ,			ast.For			):
  						# For loop -> execute the loop
  						return evaluate_for(lowercase      ,			lowercase      ,			lowercase			)
  			elif isinstance(lowercase      ,			ast.FormattedValue			):
  						# Formatted value (part of f-string) -> evaluate the content and return
  						return evaluate_ast(expression.value      ,			lowercase      ,			lowercase			)
  			elif isinstance(lowercase      ,			ast.If			):
  						# If -> execute the right branch
  						return evaluate_if(lowercase      ,			lowercase      ,			lowercase			)
  			elif hasattr(lowercase      ,			"""Index"""			) and isinstance(lowercase      ,			ast.Index			):
  						return evaluate_ast(expression.value      ,			lowercase      ,			lowercase			)
  			elif isinstance(lowercase      ,			ast.JoinedStr			):
  						return "".join([str(evaluate_ast(lowercase      ,			lowercase      ,			lowercase			)			) for v in expression.values]			)
  			elif isinstance(lowercase      ,			ast.List			):
  						# List -> evaluate all elements
  						return [evaluate_ast(lowercase      ,			lowercase      ,			lowercase			) for elt in expression.elts]
  			elif isinstance(lowercase      ,			ast.Name			):
  						# Name -> pick up the value in the state
  						return evaluate_name(lowercase      ,			lowercase      ,			lowercase			)
  			elif isinstance(lowercase      ,			ast.Subscript			):
  						# Subscript -> return the value of the indexing
  						return evaluate_subscript(lowercase      ,			lowercase      ,			lowercase			)
  			else:
  						# For now we refuse anything else. Let's add things as we need them.
  						raise InterpretorError(f'{expression.__class__.__name__} is not supported.'			)
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase			)      ->      Optional[Any]:
  			__lowerCAmelCase      		=       assign.targets
  			__lowerCAmelCase      		=       evaluate_ast(assign.value      ,			lowercase      ,			lowercase			)
  
  			if len(lowercase			) == 1:
  						__lowerCAmelCase      		=       result
  			else:
  						if len(lowercase			) != len(lowercase			):
  									raise InterpretorError(f'Expected {len(lowercase			)} values but got {len(lowercase			)}.'			)
  						for var_name, r in zip(lowercase      ,			lowercase			):
  									__lowerCAmelCase      		=       r
  			return result
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase			)      ->      List[str]:
  			if not isinstance(call.func      ,			ast.Name			):
  						raise InterpretorError(
  						    f'It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func} of '
  						    f'type {type(call.func			)}.'			)
  			__lowerCAmelCase      		=       call.func.id
  			if func_name not in tools:
  						raise InterpretorError(
  						    f'It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func.id}).'			)
  
  			__lowerCAmelCase      		=       tools[func_name]
  			# Todo deal with args
  			__lowerCAmelCase      		=       [evaluate_ast(lowercase      ,			lowercase      ,			lowercase			) for arg in call.args]
  			__lowerCAmelCase      		=       {keyword.arg: evaluate_ast(keyword.value      ,			lowercase      ,			lowercase			) for keyword in call.keywords}
  			return func(*lowercase      ,			**lowercase			)
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase			)      ->      Union[str, Any]:
  			__lowerCAmelCase      		=       evaluate_ast(subscript.slice      ,			lowercase      ,			lowercase			)
  			__lowerCAmelCase      		=       evaluate_ast(subscript.value      ,			lowercase      ,			lowercase			)
  			if isinstance(lowercase      ,			(list, tuple)			):
  						return value[int(lowercase			)]
  			if index in value:
  						return value[index]
  			if isinstance(lowercase      ,			lowercase			) and isinstance(lowercase      ,			lowercase			):
  						__lowerCAmelCase      		=       difflib.get_close_matches(lowercase      ,			list(value.keys()			)			)
  						if len(lowercase			) > 0:
  									return value[close_matches[0]]
  
  			raise InterpretorError(f'Could not index {value} with \'{index}\'.'			)
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase			)      ->      int:
  			if name.id in state:
  						return state[name.id]
  			__lowerCAmelCase      		=       difflib.get_close_matches(name.id      ,			list(state.keys()			)			)
  			if len(lowercase			) > 0:
  						return state[close_matches[0]]
  			raise InterpretorError(f'The variable `{name.id}` is not defined.'			)
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase			)      ->      Dict:
  			if len(condition.ops			) > 1:
  						raise InterpretorError("""Cannot evaluate conditions with multiple operators"""			)
  
  			__lowerCAmelCase      		=       evaluate_ast(condition.left      ,			lowercase      ,			lowercase			)
  			__lowerCAmelCase      		=       condition.ops[0]
  			__lowerCAmelCase      		=       evaluate_ast(condition.comparators[0]      ,			lowercase      ,			lowercase			)
  
  			if isinstance(lowercase      ,			ast.Eq			):
  						return left == right
  			elif isinstance(lowercase      ,			ast.NotEq			):
  						return left != right
  			elif isinstance(lowercase      ,			ast.Lt			):
  						return left < right
  			elif isinstance(lowercase      ,			ast.LtE			):
  						return left <= right
  			elif isinstance(lowercase      ,			ast.Gt			):
  						return left > right
  			elif isinstance(lowercase      ,			ast.GtE			):
  						return left >= right
  			elif isinstance(lowercase      ,			ast.Is			):
  						return left is right
  			elif isinstance(lowercase      ,			ast.IsNot			):
  						return left is not right
  			elif isinstance(lowercase      ,			ast.In			):
  						return left in right
  			elif isinstance(lowercase      ,			ast.NotIn			):
  						return left not in right
  			else:
  						raise InterpretorError(f'Operator not supported: {comparator}'			)
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase			)      ->      Union[str, Any]:
  			__lowerCAmelCase      		=       None
  			if evaluate_condition(if_statement.test      ,			lowercase      ,			lowercase			):
  						for line in if_statement.body:
  									__lowerCAmelCase      		=       evaluate_ast(lowercase      ,			lowercase      ,			lowercase			)
  									if line_result is not None:
  												__lowerCAmelCase      		=       line_result
  			else:
  						for line in if_statement.orelse:
  									__lowerCAmelCase      		=       evaluate_ast(lowercase      ,			lowercase      ,			lowercase			)
  									if line_result is not None:
  												__lowerCAmelCase      		=       line_result
  			return result
  
  
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase			)      ->      int:
  			__lowerCAmelCase      		=       None
  			__lowerCAmelCase      		=       evaluate_ast(for_loop.iter      ,			lowercase      ,			lowercase			)
  			for counter in iterator:
  						__lowerCAmelCase      		=       counter
  						for expression in for_loop.body:
  									__lowerCAmelCase      		=       evaluate_ast(lowercase      ,			lowercase      ,			lowercase			)
  									if line_result is not None:
  												__lowerCAmelCase      		=       line_result
  			return result
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  from sklearn.metrics import roc_auc_score
  
  import datasets
  
  
  _a      :				str											=					"""
  This metric computes the area under the curve (AUC) for the Receiver Operating Characteristic Curve (ROC). The return values represent how well the model used is predicting the correct classes, based on the input data. A score of `0.5` means that the model is predicting exactly at chance, i.e. the model's predictions are correct at the same rate as if the predictions were being decided by the flip of a fair coin or the roll of a fair die. A score above `0.5` indicates that the model is doing better than chance, while a score below `0.5` indicates that the model is doing worse than chance.
  
  This metric has three separate use cases:
      - binary: The case in which there are only two different label classes, and each example gets only one label. This is the default implementation.
      - multiclass: The case in which there can be more than two different label classes, but each example still gets only one label.
      - multilabel: The case in which there can be more than two different label classes, and each example can have more than one label.
  """
  
  _a      :				Optional[int]											=					"""
  Args:
  - references (array-like of shape (n_samples,) or (n_samples, n_classes)): Ground truth labels. Expects different input based on use case:
      - binary: expects an array-like of shape (n_samples,)
      - multiclass: expects an array-like of shape (n_samples,)
      - multilabel: expects an array-like of shape (n_samples, n_classes)
  - prediction_scores (array-like of shape (n_samples,) or (n_samples, n_classes)): Model predictions. Expects different inputs based on use case:
      - binary: expects an array-like of shape (n_samples,)
      - multiclass: expects an array-like of shape (n_samples, n_classes)
      - multilabel: expects an array-like of shape (n_samples, n_classes)
  - average (`str`): Type of average, and is ignored in the binary use case. Defaults to 'macro'. Options are:
      - `'micro'`: Calculates metrics globally by considering each element of the label indicator matrix as a label. Only works with the multilabel use case.
      - `'macro'`: Calculate metrics for each label, and find their unweighted mean.  This does not take label imbalance into account.
      - `'weighted'`: Calculate metrics for each label, and find their average, weighted by support (i.e. the number of true instances for each label).
      - `'samples'`: Calculate metrics for each instance, and find their average. Only works with the multilabel use case.
      - `None`:  No average is calculated, and scores for each class are returned. Only works with the multilabels use case.
  - sample_weight (array-like of shape (n_samples,)): Sample weights. Defaults to None.
  - max_fpr (`float`): If not None, the standardized partial AUC over the range [0, `max_fpr`] is returned. Must be greater than `0` and less than or equal to `1`. Defaults to `None`. Note: For the multiclass use case, `max_fpr` should be either `None` or `1.0` as ROC AUC partial computation is not currently supported for `multiclass`.
  - multi_class (`str`): Only used for multiclass targets, where it is required. Determines the type of configuration to use. Options are:
      - `'ovr'`: Stands for One-vs-rest. Computes the AUC of each class against the rest. This treats the multiclass case in the same way as the multilabel case. Sensitive to class imbalance even when `average == 'macro'`, because class imbalance affects the composition of each of the 'rest' groupings.
      - `'ovo'`: Stands for One-vs-one. Computes the average AUC of all possible pairwise combinations of classes. Insensitive to class imbalance when `average == 'macro'`.
  - labels (array-like of shape (n_classes,)): Only used for multiclass targets. List of labels that index the classes in
      `prediction_scores`. If `None`, the numerical or lexicographical order of the labels in
      `prediction_scores` is used. Defaults to `None`.
  Returns:
      roc_auc (`float` or array-like of shape (n_classes,)): Returns array if in multilabel use case and `average='None'`. Otherwise, returns `float`.
  Examples:
      Example 1:
          >>> roc_auc_score = datasets.load_metric(\"roc_auc\")
          >>> refs = [1, 0, 1, 1, 0, 0]
          >>> pred_scores = [0.5, 0.2, 0.99, 0.3, 0.1, 0.7]
          >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores)
          >>> print(round(results['roc_auc'], 2))
          0.78
  
      Example 2:
          >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multiclass\")
          >>> refs = [1, 0, 1, 2, 2, 0]
          >>> pred_scores = [[0.3, 0.5, 0.2],
          ...                 [0.7, 0.2, 0.1],
          ...                 [0.005, 0.99, 0.005],
          ...                 [0.2, 0.3, 0.5],
          ...                 [0.1, 0.1, 0.8],
          ...                 [0.1, 0.7, 0.2]]
          >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, multi_class='ovr')
          >>> print(round(results['roc_auc'], 2))
          0.85
  
      Example 3:
          >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multilabel\")
          >>> refs = [[1, 1, 0],
          ...         [1, 1, 0],
          ...         [0, 1, 0],
          ...         [0, 0, 1],
          ...         [0, 1, 1],
          ...         [1, 0, 1]]
          >>> pred_scores = [[0.3, 0.5, 0.2],
          ...                 [0.7, 0.2, 0.1],
          ...                 [0.005, 0.99, 0.005],
          ...                 [0.2, 0.3, 0.5],
          ...                 [0.1, 0.1, 0.8],
          ...                 [0.1, 0.7, 0.2]]
          >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, average=None)
          >>> print([round(res, 2) for res in results['roc_auc']])
          [0.83, 0.38, 0.94]
  """
  
  _a      :				str											=					"""\
  @article{doi:10.1177/0272989X8900900307,
  author = {Donna Katzman McClish},
  title ={Analyzing a Portion of the ROC Curve},
  journal = {Medical Decision Making},
  volume = {9},
  number = {3},
  pages = {190-195},
  year = {1989},
  doi = {10.1177/0272989X8900900307},
      note ={PMID: 2668680},
  URL = {https://doi.org/10.1177/0272989X8900900307},
  eprint = {https://doi.org/10.1177/0272989X8900900307}
  }
  
  
  @article{10.1023/A:1010920819831,
  author = {Hand, David J. and Till, Robert J.},
  title = {A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems},
  year = {2001},
  issue_date = {November 2001},
  publisher = {Kluwer Academic Publishers},
  address = {USA},
  volume = {45},
  number = {2},
  issn = {0885-6125},
  url = {https://doi.org/10.1023/A:1010920819831},
  doi = {10.1023/A:1010920819831},
  journal = {Mach. Learn.},
  month = {oct},
  pages = {171–186},
  numpages = {16},
  keywords = {Gini index, AUC, error rate, ROC curve, receiver operating characteristic}
  }
  
  
  @article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
  }
  """
  
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,      _KWARGS_DESCRIPTION     )
  class 					_UpperCAmelCase							(		datasets.Metric     ):
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					return datasets.MetricInfo(
  					    description=_DESCRIPTION,citation=_CITATION,inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
  					        {
  					            """prediction_scores""": datasets.Sequence(datasets.Value("""float"""    )    ),
  					            """references""": datasets.Value("""int32"""    ),
  					        }
  					        if self.config_name == """multiclass"""
  					        else {
  					            """references""": datasets.Sequence(datasets.Value("""int32"""    )    ),
  					            """prediction_scores""": datasets.Sequence(datasets.Value("""float"""    )    ),
  					        }
  					        if self.config_name == """multilabel"""
  					        else {
  					            """references""": datasets.Value("""int32"""    ),
  					            """prediction_scores""": datasets.Value("""float"""    ),
  					        }    ),reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html"""],)
  
  
  
  
  
  
  
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE="macro",__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE="raise",__SCREAMING_SNAKE_CASE=None,):
  
  
  
  					'''simple docstring'''
  
  					return {
  					    "roc_auc": roc_auc_score(
  					        __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,average=__SCREAMING_SNAKE_CASE,sample_weight=__SCREAMING_SNAKE_CASE,max_fpr=__SCREAMING_SNAKE_CASE,multi_class=__SCREAMING_SNAKE_CASE,labels=__SCREAMING_SNAKE_CASE,)
  					}
  
  