



"""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     (			snake_case    ):




	"""simple docstring"""
	pass







def       __SCREAMING_SNAKE_CASE      (   __UpperCAmelCase					,							__UpperCAmelCase					,							__UpperCAmelCase=None					,							__UpperCAmelCase=False	):
		try:
				_lowercase							:      int			=					ast.parse(__UpperCAmelCase	)
		except SyntaxError as e:
				print("""The code generated by the agent is not valid.\n"""					,							__UpperCAmelCase	)
				return
		if state is None:
				_lowercase							:      List[str]			=					{}
		_lowercase							:      Union[str, Any]			=					None
		for idx, node in enumerate(expression.body	):
				try:
						_lowercase							:      str			=					evaluate_ast(__UpperCAmelCase					,							__UpperCAmelCase					,							__UpperCAmelCase	)
				except InterpretorError as e:
						_lowercase							:      Optional[int]			=					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(__UpperCAmelCase	)
						break
				if line_result is not None:
						_lowercase							:      Optional[Any]			=					line_result

		return result


def       __SCREAMING_SNAKE_CASE      (   __UpperCAmelCase					,							__UpperCAmelCase					,							__UpperCAmelCase	):
		if isinstance(__UpperCAmelCase					,							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(__UpperCAmelCase					,							__UpperCAmelCase					,							__UpperCAmelCase	)
		elif isinstance(__UpperCAmelCase					,							ast.Call	):
				# Function call -> we return the value of the function call
				return evaluate_call(__UpperCAmelCase					,							__UpperCAmelCase					,							__UpperCAmelCase	)
		elif isinstance(__UpperCAmelCase					,							ast.Constant	):
				# Constant -> just return the value
				return expression.value
		elif isinstance(__UpperCAmelCase					,							ast.Dict	):
				# Dict -> evaluate all keys and values
				_lowercase							:      Tuple			=					[evaluate_ast(__UpperCAmelCase					,							__UpperCAmelCase					,							__UpperCAmelCase	) for k in expression.keys]
				_lowercase							:      Tuple			=					[evaluate_ast(__UpperCAmelCase					,							__UpperCAmelCase					,							__UpperCAmelCase	) for v in expression.values]
				return dict(zip(__UpperCAmelCase					,							__UpperCAmelCase	)	)
		elif isinstance(__UpperCAmelCase					,							ast.Expr	):
				# Expression -> evaluate the content
				return evaluate_ast(expression.value					,							__UpperCAmelCase					,							__UpperCAmelCase	)
		elif isinstance(__UpperCAmelCase					,							ast.For	):
				# For loop -> execute the loop
				return evaluate_for(__UpperCAmelCase					,							__UpperCAmelCase					,							__UpperCAmelCase	)
		elif isinstance(__UpperCAmelCase					,							ast.FormattedValue	):
				# Formatted value (part of f-string) -> evaluate the content and return
				return evaluate_ast(expression.value					,							__UpperCAmelCase					,							__UpperCAmelCase	)
		elif isinstance(__UpperCAmelCase					,							ast.If	):
				# If -> execute the right branch
				return evaluate_if(__UpperCAmelCase					,							__UpperCAmelCase					,							__UpperCAmelCase	)
		elif hasattr(__UpperCAmelCase					,							"""Index"""	) and isinstance(__UpperCAmelCase					,							ast.Index	):
				return evaluate_ast(expression.value					,							__UpperCAmelCase					,							__UpperCAmelCase	)
		elif isinstance(__UpperCAmelCase					,							ast.JoinedStr	):
				return "".join([str(evaluate_ast(__UpperCAmelCase					,							__UpperCAmelCase					,							__UpperCAmelCase	)	) for v in expression.values]	)
		elif isinstance(__UpperCAmelCase					,							ast.List	):
				# List -> evaluate all elements
				return [evaluate_ast(__UpperCAmelCase					,							__UpperCAmelCase					,							__UpperCAmelCase	) for elt in expression.elts]
		elif isinstance(__UpperCAmelCase					,							ast.Name	):
				# Name -> pick up the value in the state
				return evaluate_name(__UpperCAmelCase					,							__UpperCAmelCase					,							__UpperCAmelCase	)
		elif isinstance(__UpperCAmelCase					,							ast.Subscript	):
				# Subscript -> return the value of the indexing
				return evaluate_subscript(__UpperCAmelCase					,							__UpperCAmelCase					,							__UpperCAmelCase	)
		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       __SCREAMING_SNAKE_CASE      (   __UpperCAmelCase					,							__UpperCAmelCase					,							__UpperCAmelCase	):
		_lowercase							:      List[Any]			=					assign.targets
		_lowercase							:      Dict			=					evaluate_ast(assign.value					,							__UpperCAmelCase					,							__UpperCAmelCase	)

		if len(__UpperCAmelCase	) == 1:
				_lowercase							:      List[Any]			=					result
		else:
				if len(__UpperCAmelCase	) != len(__UpperCAmelCase	):
						raise InterpretorError(F"""Expected {len(__UpperCAmelCase	)} values but got {len(__UpperCAmelCase	)}."""	)
				for var_name, r in zip(__UpperCAmelCase					,							__UpperCAmelCase	):
						_lowercase							:      Optional[int]			=					r
		return result


def       __SCREAMING_SNAKE_CASE      (   __UpperCAmelCase					,							__UpperCAmelCase					,							__UpperCAmelCase	):
		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	)}."""	)
		_lowercase							:      Tuple			=					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})."""	)

		_lowercase							:      List[Any]			=					tools[func_name]
		# Todo deal with args
		_lowercase							:      Union[str, Any]			=					[evaluate_ast(__UpperCAmelCase					,							__UpperCAmelCase					,							__UpperCAmelCase	) for arg in call.args]
		_lowercase							:      Optional[int]			=					{keyword.arg: evaluate_ast(keyword.value					,							__UpperCAmelCase					,							__UpperCAmelCase	) for keyword in call.keywords}
		return func(*__UpperCAmelCase					,							**__UpperCAmelCase	)


def       __SCREAMING_SNAKE_CASE      (   __UpperCAmelCase					,							__UpperCAmelCase					,							__UpperCAmelCase	):
		_lowercase							:      Union[str, Any]			=					evaluate_ast(subscript.slice					,							__UpperCAmelCase					,							__UpperCAmelCase	)
		_lowercase							:      Optional[int]			=					evaluate_ast(subscript.value					,							__UpperCAmelCase					,							__UpperCAmelCase	)
		if isinstance(__UpperCAmelCase					,							(list, tuple)	):
				return value[int(__UpperCAmelCase	)]
		if index in value:
				return value[index]
		if isinstance(__UpperCAmelCase					,							__UpperCAmelCase	) and isinstance(__UpperCAmelCase					,							__UpperCAmelCase	):
				_lowercase							:      Dict			=					difflib.get_close_matches(__UpperCAmelCase					,							list(value.keys()	)	)
				if len(__UpperCAmelCase	) > 0:
						return value[close_matches[0]]

		raise InterpretorError(F"""Could not index {value} with '{index}'."""	)


def       __SCREAMING_SNAKE_CASE      (   __UpperCAmelCase					,							__UpperCAmelCase					,							__UpperCAmelCase	):
		if name.id in state:
				return state[name.id]
		_lowercase							:      Dict			=					difflib.get_close_matches(name.id					,							list(state.keys()	)	)
		if len(__UpperCAmelCase	) > 0:
				return state[close_matches[0]]
		raise InterpretorError(F"""The variable `{name.id}` is not defined."""	)


def       __SCREAMING_SNAKE_CASE      (   __UpperCAmelCase					,							__UpperCAmelCase					,							__UpperCAmelCase	):
		if len(condition.ops	) > 1:
				raise InterpretorError("""Cannot evaluate conditions with multiple operators"""	)

		_lowercase							:      Any			=					evaluate_ast(condition.left					,							__UpperCAmelCase					,							__UpperCAmelCase	)
		_lowercase							:      str			=					condition.ops[0]
		_lowercase							:      Union[str, Any]			=					evaluate_ast(condition.comparators[0]					,							__UpperCAmelCase					,							__UpperCAmelCase	)

		if isinstance(__UpperCAmelCase					,							ast.Eq	):
				return left == right
		elif isinstance(__UpperCAmelCase					,							ast.NotEq	):
				return left != right
		elif isinstance(__UpperCAmelCase					,							ast.Lt	):
				return left < right
		elif isinstance(__UpperCAmelCase					,							ast.LtE	):
				return left <= right
		elif isinstance(__UpperCAmelCase					,							ast.Gt	):
				return left > right
		elif isinstance(__UpperCAmelCase					,							ast.GtE	):
				return left >= right
		elif isinstance(__UpperCAmelCase					,							ast.Is	):
				return left is right
		elif isinstance(__UpperCAmelCase					,							ast.IsNot	):
				return left is not right
		elif isinstance(__UpperCAmelCase					,							ast.In	):
				return left in right
		elif isinstance(__UpperCAmelCase					,							ast.NotIn	):
				return left not in right
		else:
				raise InterpretorError(F"""Operator not supported: {comparator}"""	)


def       __SCREAMING_SNAKE_CASE      (   __UpperCAmelCase					,							__UpperCAmelCase					,							__UpperCAmelCase	):
		_lowercase							:      Tuple			=					None
		if evaluate_condition(if_statement.test					,							__UpperCAmelCase					,							__UpperCAmelCase	):
				for line in if_statement.body:
						_lowercase							:      Union[str, Any]			=					evaluate_ast(__UpperCAmelCase					,							__UpperCAmelCase					,							__UpperCAmelCase	)
						if line_result is not None:
								_lowercase							:      Union[str, Any]			=					line_result
		else:
				for line in if_statement.orelse:
						_lowercase							:      Optional[Any]			=					evaluate_ast(__UpperCAmelCase					,							__UpperCAmelCase					,							__UpperCAmelCase	)
						if line_result is not None:
								_lowercase							:      int			=					line_result
		return result


def       __SCREAMING_SNAKE_CASE      (   __UpperCAmelCase					,							__UpperCAmelCase					,							__UpperCAmelCase	):
		_lowercase							:      Optional[Any]			=					None
		_lowercase							:      Union[str, Any]			=					evaluate_ast(for_loop.iter					,							__UpperCAmelCase					,							__UpperCAmelCase	)
		for counter in iterator:
				_lowercase							:      Dict			=					counter
				for expression in for_loop.body:
						_lowercase							:      Tuple			=					evaluate_ast(__UpperCAmelCase					,							__UpperCAmelCase					,							__UpperCAmelCase	)
						if line_result is not None:
								_lowercase							:      str			=					line_result
		return result










"""simple docstring"""




from sklearn.metrics import mean_absolute_error

import datasets


UpperCamelCase_           ="""\
@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}
}
"""

UpperCamelCase_           ="""\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


UpperCamelCase_           ="""
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 \"raw_values\" : Returns a full set of errors in case of multioutput input.

                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric(\"mae\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.5}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results)
    {'mae': array([0.5, 1. ])}
"""




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,				_KWARGS_DESCRIPTION				)
class  _a						(      datasets.Metric				):






				def 				snake_case	( self		:    Optional[int]							)	->      List[Any]:




										'''simple docstring'''



										return datasets.MetricInfo(
										    description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features(self._get_feature_types()							), reference_urls=[
										        '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html'''
										    ], )






				def 				snake_case	( self		:    Dict							)	->      Union[str, Any]:




										'''simple docstring'''



										if self.config_name == "multilist":
																return {
																    "predictions": datasets.Sequence(datasets.Value('''float'''							)							),
																    "references": datasets.Sequence(datasets.Value('''float'''							)							),
																}
										else:
																return {
																    "predictions": datasets.Value('''float'''							),
																    "references": datasets.Value('''float'''							),
																}






				def 				snake_case	( self		:    List[Any], lowerCAmelCase__		:    Dict, lowerCAmelCase__		:    Union[str, Any], lowerCAmelCase__		:    Tuple=None, lowerCAmelCase__		:    Tuple="uniform_average"							)	->      Optional[int]:




										'''simple docstring'''



										_UpperCamelCase			:					List[str]     =    mean_absolute_error(lowerCAmelCase__, lowerCAmelCase__, sample_weight=lowerCAmelCase__, multioutput=lowerCAmelCase__							)

										return {"mae": mae_score}



