


"""simple docstring"""






import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets





@contextmanager
def 							A_					(   )				->     int:



			def filter_log(snake_case__		):
						return False if "This IS expected if you are initializing" in record.msg else True

			_UpperCamelCase				:Tuple				    =  datasets.utils.logging.get_logger('''transformers.modeling_utils'''		)
			logger.addFilter(snake_case__		)
			try:
						yield
			finally:
						logger.removeFilter(snake_case__		)


UpperCamelCase__							:List[str]       =  """\\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"""

UpperCamelCase__							:Optional[Any]       =  """\\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"""

UpperCamelCase__							:Optional[int]       =  """\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      _UpperCamelCase(   self      )			->  int:

					"""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				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__=None				,							SCREAMING_SNAKE_CASE__=None				,							SCREAMING_SNAKE_CASE__=None				,							SCREAMING_SNAKE_CASE__=False				,							SCREAMING_SNAKE_CASE__=False				,							SCREAMING_SNAKE_CASE__=None				,							SCREAMING_SNAKE_CASE__=64				,							SCREAMING_SNAKE_CASE__=4				,							SCREAMING_SNAKE_CASE__=False				,							SCREAMING_SNAKE_CASE__=False				,							SCREAMING_SNAKE_CASE__=None				,							SCREAMING_SNAKE_CASE__=False				,							)			->  Dict:

					"""simple docstring"""



					_UpperCamelCase				:Tuple				    =  bert_score.utils.get_hash
					_UpperCamelCase				:Any				    =  bert_score.BERTScorer

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

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

					_UpperCamelCase				:Union[str, Any]				    =  get_hash(
					    model=lowerCAmelCase_				,							num_layers=lowerCAmelCase_				,							idf=lowerCAmelCase_				,							rescale_with_baseline=lowerCAmelCase_				,							use_custom_baseline=baseline_path is not None				,							)

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

					((_UpperCamelCase)    ,			(_UpperCamelCase)    ,			(_UpperCamelCase))				:Optional[Any]				    =  self.cached_bertscorer.score(
					    cands=lowerCAmelCase_				,							refs=lowerCAmelCase_				,							verbose=lowerCAmelCase_				,							batch_size=lowerCAmelCase_				,							)
					_UpperCamelCase				:str				    =  {
					    '''precision''': P.tolist(),
					    '''recall''': R.tolist(),
					    '''f1''': F.tolist(),
					    '''hashcode''': hashcode,
					}
					return output_dict
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__=None				,							SCREAMING_SNAKE_CASE__=None				,							**SCREAMING_SNAKE_CASE__      )			->  str:

					"""simple docstring"""



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





		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__=None				,							SCREAMING_SNAKE_CASE__=None				,							**SCREAMING_SNAKE_CASE__      )			->  List[str]:

					"""simple docstring"""



					if isinstance(lowerCAmelCase_				,							lowerCAmelCase_      ):
								_UpperCamelCase				:List[str]				    =  [reference]
					super().add(prediction=lowerCAmelCase_				,							reference=lowerCAmelCase_				,							**lowerCAmelCase_      )





"""simple docstring"""






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available






@dataclass
class     A(     lowerCamelCase__		):

		"""simple docstring"""





		A         =       42


if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
					from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
					from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline

