


# 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     snake_case__  (      _A  ):
				pass






def  __SCREAMING_SNAKE_CASE       (				lowercase__  :str      ,						lowercase__  :Dict[str, Callable]      ,						lowercase__  :Union[str, Any]=None      ,						lowercase__  :str=False				)       ->  List[str]:
						try:
												__magic_name__				:							str				=      ast.parse(lowercase__				)
						except SyntaxError as e:
												print('The code generated by the agent is not valid.\n'      ,						lowercase__				)
												return
						if state is None:
												__magic_name__				:							Optional[Any]				=      {}
						__magic_name__				:							Dict				=      None
						for idx, node in enumerate(expression.body				):
												try:
																		__magic_name__				:							Any				=      evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				)
												except InterpretorError as e:
																		__magic_name__				:							Tuple				=      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:
																		__magic_name__				:							List[str]				=      line_result

						return result
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :ast.AST      ,						lowercase__  :Dict[str, Any]      ,						lowercase__  :Dict[str, Callable]				)       ->  Dict:
						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
												__magic_name__				:							Any				=      [evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				) for k in expression.keys]
												__magic_name__				:							Dict				=      [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  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[Any]      ,						lowercase__  :Optional[Any]      ,						lowercase__  :Union[str, Any]				)       ->  List[Any]:
						__magic_name__				:							Union[str, Any]				=      assign.targets
						__magic_name__				:							int				=      evaluate_ast(assign.value      ,						lowercase__      ,						lowercase__				)

						if len(lowercase__				) == 1:
												__magic_name__				:							Any				=      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__				):
																		__magic_name__				:							str				=      r
						return result
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[str]      ,						lowercase__  :List[str]      ,						lowercase__  :Tuple				)       ->  Optional[Any]:
						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				)}."				)
						__magic_name__				:							Dict				=      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})."				)

						__magic_name__				:							Tuple				=      tools[func_name]
						# Todo deal with args
						__magic_name__				:							List[str]				=      [evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				) for arg in call.args]
						__magic_name__				:							int				=      {keyword.arg: evaluate_ast(keyword.value      ,						lowercase__      ,						lowercase__				) for keyword in call.keywords}
						return func(*lowercase__      ,						**lowercase__				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Union[str, Any]      ,						lowercase__  :int      ,						lowercase__  :int				)       ->  List[Any]:
						__magic_name__				:							Optional[int]				=      evaluate_ast(subscript.slice      ,						lowercase__      ,						lowercase__				)
						__magic_name__				:							Union[str, Any]				=      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__				):
												__magic_name__				:							Any				=      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  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[str]      ,						lowercase__  :Optional[Any]      ,						lowercase__  :Optional[int]				)       ->  int:
						if name.id in state:
												return state[name.id]
						__magic_name__				:							Union[str, Any]				=      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  __SCREAMING_SNAKE_CASE       (				lowercase__  :Any      ,						lowercase__  :Union[str, Any]      ,						lowercase__  :Optional[Any]				)       ->  List[str]:
						if len(condition.ops				) > 1:
												raise InterpretorError('Cannot evaluate conditions with multiple operators'				)

						__magic_name__				:							int				=      evaluate_ast(condition.left      ,						lowercase__      ,						lowercase__				)
						__magic_name__				:							Optional[int]				=      condition.ops[0]
						__magic_name__				:							Union[str, Any]				=      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  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[int]      ,						lowercase__  :str      ,						lowercase__  :Tuple				)       ->  int:
						__magic_name__				:							Optional[int]				=      None
						if evaluate_condition(if_statement.test      ,						lowercase__      ,						lowercase__				):
												for line in if_statement.body:
																		__magic_name__				:							List[Any]				=      evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				)
																		if line_result is not None:
																								__magic_name__				:							Tuple				=      line_result
						else:
												for line in if_statement.orelse:
																		__magic_name__				:							Optional[int]				=      evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				)
																		if line_result is not None:
																								__magic_name__				:							List[Any]				=      line_result
						return result

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






import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets
@contextmanager
def  __SCREAMING_SNAKE_CASE       (				)       ->  Tuple:
						def filter_log(lowercase__  :List[Any]				):
												return False if "This IS expected if you are initializing" in record.msg else True

						__magic_name__				:							Any				=      datasets.utils.logging.get_logger('transformers.modeling_utils'				)
						logger.addFilter(lowercase__				)
						try:
												yield
						finally:
												logger.removeFilter(lowercase__				)


snake_case__     							= '\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n'

snake_case__     							= '\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.\n'

snake_case__     							= '\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. \'en\').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bertscore = datasets.load_metric("bertscore")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")\n    >>> print([round(v, 2) for v in results["f1"]])\n    [1.0, 1.0]\n'



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,       _KWARGS_DESCRIPTION  )
class     snake_case__  (      datasets.Metric  ):
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										return datasets.MetricInfo(
										    description=_DESCRIPTION     ,      citation=_CITATION     ,      homepage='https://github.com/Tiiiger/bert_score'     ,      inputs_description=_KWARGS_DESCRIPTION     ,      features=datasets.Features(
										        {
										            'predictions': datasets.Value('string'     ,      id='sequence'      ),
										            'references': datasets.Sequence(datasets.Value('string'     ,      id='sequence'      )     ,      id='references'      ),
										        }      )     ,      codebase_urls=['https://github.com/Tiiiger/bert_score']     ,      reference_urls=[
										        'https://github.com/Tiiiger/bert_score',
										        'https://arxiv.org/abs/1904.09675',
										    ]     ,      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=64     ,      _UpperCAmelCase=4     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=False     ,      ):




										"""simple docstring"""




										__magic_name__				:							List[str]				=      bert_score.utils.get_hash
										__magic_name__				:							Dict				=      bert_score.BERTScorer

										if version.parse(bert_score.__version__      ) >= version.parse('0.3.10'      ):
																__magic_name__				:							Any				=      functools.partial(_UpperCAmelCase     ,      use_fast_tokenizer=_UpperCAmelCase      )
																__magic_name__				:							Tuple				=      functools.partial(_UpperCAmelCase     ,      use_fast_tokenizer=_UpperCAmelCase      )
										elif use_fast_tokenizer:
																raise ImportWarning(
																    'To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn\'t match this condition.\n'
																    'You can install it with `pip install "bert-score>=0.3.10"`.'      )

										if model_type is None:
																assert lang is not None, "either lang or model_type should be specified"
																__magic_name__				:							List[Any]				=      bert_score.utils.langamodel[lang.lower()]

										if num_layers is None:
																__magic_name__				:							Optional[Any]				=      bert_score.utils.modelalayers[model_type]

										__magic_name__				:							List[str]				=      get_hash(
										    model=_UpperCAmelCase     ,      num_layers=_UpperCAmelCase     ,      idf=_UpperCAmelCase     ,      rescale_with_baseline=_UpperCAmelCase     ,      use_custom_baseline=baseline_path is not None     ,      )

										with filter_logging_context():
																if not hasattr(self     ,      'cached_bertscorer'      ) or self.cached_bertscorer.hash != hashcode:
																						__magic_name__				:							Optional[int]				=      scorer(
																						    model_type=_UpperCAmelCase     ,      num_layers=_UpperCAmelCase     ,      batch_size=_UpperCAmelCase     ,      nthreads=_UpperCAmelCase     ,      all_layers=_UpperCAmelCase     ,      idf=_UpperCAmelCase     ,      device=_UpperCAmelCase     ,      lang=_UpperCAmelCase     ,      rescale_with_baseline=_UpperCAmelCase     ,      baseline_path=_UpperCAmelCase     ,      )

										((__magic_name__)		, (__magic_name__)		, (__magic_name__))				:							List[Any]				=      self.cached_bertscorer.score(
										    cands=_UpperCAmelCase     ,      refs=_UpperCAmelCase     ,      verbose=_UpperCAmelCase     ,      batch_size=_UpperCAmelCase     ,      )
										__magic_name__				:							int				=      {
										    'precision': P.tolist(),
										    'recall': R.tolist(),
										    'f1': F.tolist(),
										    'hashcode': hashcode,
										}
										return output_dict
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=None     ,      **_UpperCAmelCase      ):




										"""simple docstring"""




										if references is not None:
																__magic_name__				:							List[str]				=      [[ref] if isinstance(_UpperCAmelCase     ,      _UpperCAmelCase      ) else ref for ref in references]
										super().add_batch(predictions=_UpperCAmelCase     ,      references=_UpperCAmelCase     ,      **_UpperCAmelCase      )





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=None     ,      **_UpperCAmelCase      ):




										"""simple docstring"""




										if isinstance(_UpperCAmelCase     ,      _UpperCAmelCase      ):
																__magic_name__				:							List[Any]				=      [reference]
										super().add(prediction=_UpperCAmelCase     ,      reference=_UpperCAmelCase     ,      **_UpperCAmelCase      )


