
import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets


@contextmanager
def 					__lowerCAmelCase    (    ) ->						str:
				def filter_log(a__						):
								return False if "This IS expected if you are initializing" in record.msg else True

				__a    							=					datasets.utils.logging.get_logger('''transformers.modeling_utils'''						)
				logger.addFilter(a__						)
				try:
								yield
				finally:
								logger.removeFilter(a__						)


A  :			Tuple     				=				'\\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'

A  :			Optional[int]     				=				'\\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'

A  :			Dict     				=				'\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     __A(				datasets.Metric    ):




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   str:


								'''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        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case	,  _snake_case=None	,  _snake_case=None	,  _snake_case=None	,  _snake_case=False	,  _snake_case=False	,  _snake_case=None	,  _snake_case=64	,  _snake_case=4	,  _snake_case=False	,  _snake_case=False	,  _snake_case=None	,  _snake_case=False	,  )					->   Dict:


								'''simple docstring'''




								__a    							=					bert_score.utils.get_hash
								__a    							=					bert_score.BERTScorer

								if version.parse(bert_score.__version__       ) >= version.parse('''0.3.10'''       ):
												__a    							=					functools.partial(_snake_case	,  use_fast_tokenizer=_snake_case       )
												__a    							=					functools.partial(_snake_case	,  use_fast_tokenizer=_snake_case       )
								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"
												__a    							=					bert_score.utils.langamodel[lang.lower()]

								if num_layers is None:
												__a    							=					bert_score.utils.modelalayers[model_type]

								__a    							=					get_hash(
								    model=_snake_case	,  num_layers=_snake_case	,  idf=_snake_case	,  rescale_with_baseline=_snake_case	,  use_custom_baseline=baseline_path is not None	,  )

								with filter_logging_context():
												if not hasattr(self	,  '''cached_bertscorer'''       ) or self.cached_bertscorer.hash != hashcode:
																__a    							=					scorer(
																    model_type=_snake_case	,  num_layers=_snake_case	,  batch_size=_snake_case	,  nthreads=_snake_case	,  all_layers=_snake_case	,  idf=_snake_case	,  device=_snake_case	,  lang=_snake_case	,  rescale_with_baseline=_snake_case	,  baseline_path=_snake_case	,  )

								((__a)							,  (__a)							,  (__a))    							=					self.cached_bertscorer.score(
								    cands=_snake_case	,  refs=_snake_case	,  verbose=_snake_case	,  batch_size=_snake_case	,  )
								__a    							=					{
								    '''precision''': P.tolist(),
								    '''recall''': R.tolist(),
								    '''f1''': F.tolist(),
								    '''hashcode''': hashcode,
								}
								return output_dict




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case=None	,  _snake_case=None	,  **_snake_case       )					->   Tuple:


								'''simple docstring'''




								if references is not None:
												__a    							=					[[ref] if isinstance(_snake_case	,  _snake_case       ) else ref for ref in references]
								super().add_batch(predictions=_snake_case	,  references=_snake_case	,  **_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case=None	,  _snake_case=None	,  **_snake_case       )					->   Union[str, Any]:


								'''simple docstring'''




								if isinstance(_snake_case	,  _snake_case       ):
												__a    							=					[reference]
								super().add(prediction=_snake_case	,  reference=_snake_case	,  **_snake_case       )

# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
					import tensorflow as tf







class     __A(				TensorFormatter[Mapping, '''tf.Tensor''', Mapping]    ):




				def __init__(  self	,  _snake_case=None	,  **_snake_case       )					->   Dict:


								'''simple docstring'''




								super().__init__(features=_snake_case       )
								__a    							=					tf_tensor_kwargs
								import tensorflow as tf  # noqa: import tf at initialization




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Optional[Any]:


								'''simple docstring'''




								import tensorflow as tf

								if isinstance(_snake_case	,  _snake_case       ) and column:
												if all(
												    isinstance(_snake_case	,  tf.Tensor       ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column       ):
																return tf.stack(_snake_case       )
												elif all(
												    isinstance(_snake_case	,  (tf.Tensor, tf.RaggedTensor)       ) and x.ndim == 1 and x.dtype == column[0].dtype
												    for x in column       ):
																# only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
																return tf.ragged.stack(_snake_case       )

								return column




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Optional[Any]:


								'''simple docstring'''




								import tensorflow as tf

								if value is None:
												return value

								__a    							=					{}

								if isinstance(_snake_case	,  (np.number, np.ndarray)       ) and np.issubdtype(value.dtype	,  np.integer       ):
												__a    							=					{'''dtype''': tf.intaa}
								elif isinstance(_snake_case	,  (np.number, np.ndarray)       ) and np.issubdtype(value.dtype	,  np.floating       ):
												__a    							=					{'''dtype''': tf.floataa}
								elif config.PIL_AVAILABLE and "PIL" in sys.modules:
												import PIL.Image

												if isinstance(_snake_case	,  PIL.Image.Image       ):
																__a    							=					np.asarray(_snake_case       )

								return tf.convert_to_tensor(_snake_case	,  **{**default_dtype, **self.tf_tensor_kwargs}       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   str:


								'''simple docstring'''




								import tensorflow as tf

								# support for torch, tf, jax etc.
								if config.TORCH_AVAILABLE and "torch" in sys.modules:
												import torch

												if isinstance(_snake_case	,  torch.Tensor       ):
																return self._tensorize(data_struct.detach().cpu().numpy()[()]       )
								if hasattr(_snake_case	,  '''__array__'''       ) and not isinstance(_snake_case	,  tf.Tensor       ):
												__a    							=					data_struct.__array__()
								# support for nested types like struct of list of struct
								if isinstance(_snake_case	,  np.ndarray       ):
												if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
																return self._consolidate([self.recursive_tensorize(_snake_case       ) for substruct in data_struct]       )
								elif isinstance(_snake_case	,  (list, tuple)       ):
												return self._consolidate([self.recursive_tensorize(_snake_case       ) for substruct in data_struct]       )
								return self._tensorize(_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tuple:


								'''simple docstring'''




								return map_nested(self._recursive_tensorize	,  _snake_case	,  map_list=_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Mapping:


								'''simple docstring'''




								__a    							=					self.numpy_arrow_extractor().extract_row(_snake_case       )
								__a    							=					self.python_features_decoder.decode_row(_snake_case       )
								return self.recursive_tensorize(_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   "tf.Tensor":


								'''simple docstring'''




								__a    							=					self.numpy_arrow_extractor().extract_column(_snake_case       )
								__a    							=					self.python_features_decoder.decode_column(_snake_case	,  pa_table.column_names[0]       )
								__a    							=					self.recursive_tensorize(_snake_case       )
								__a    							=					self._consolidate(_snake_case       )
								return column




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Mapping:


								'''simple docstring'''




								__a    							=					self.numpy_arrow_extractor().extract_batch(_snake_case       )
								__a    							=					self.python_features_decoder.decode_batch(_snake_case       )
								__a    							=					self.recursive_tensorize(_snake_case       )
								for column_name in batch:
												__a    							=					self._consolidate(batch[column_name]       )
								return batch