





import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets
@contextmanager
def 			__UpperCamelCase				(			)	->      str:
    '''simple docstring'''





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

    lowerCAmelCase_					:	int						=   datasets.utils.logging.get_logger("""transformers.modeling_utils"""		)
    logger.addFilter(SCREAMING_SNAKE_CASE__		)
    try:
        yield
    finally:
        logger.removeFilter(SCREAMING_SNAKE_CASE__		)


__UpperCAmelCase						=      '\\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						=      '\\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						=      '\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  A							(     self	:   Any			):
           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  A							(     self	:   str					,			UpperCAmelCase	:   int					,			UpperCAmelCase	:   str					,			UpperCAmelCase	:   Optional[int]=None					,			UpperCAmelCase	:   List[Any]=None					,			UpperCAmelCase	:   Optional[int]=None					,			UpperCAmelCase	:   str=False					,			UpperCAmelCase	:   Tuple=False					,			UpperCAmelCase	:   Dict=None					,			UpperCAmelCase	:   int=64					,			UpperCAmelCase	:   Optional[int]=4					,			UpperCAmelCase	:   str=False					,			UpperCAmelCase	:   Union[str, Any]=False					,			UpperCAmelCase	:   Optional[int]=None					,			UpperCAmelCase	:   str=False					,			):
           lowerCAmelCase_					:	Union[str, Any]						=   bert_score.utils.get_hash
           lowerCAmelCase_					:	List[Any]						=   bert_score.BERTScorer

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

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

           lowerCAmelCase_					:	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:
                   lowerCAmelCase_					:	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__					,			)

           (lowerCAmelCase_)					:	Any						=   self.cached_bertscorer.score(
               cands=UpperCamelCase__					,			refs=UpperCamelCase__					,			verbose=UpperCamelCase__					,			batch_size=UpperCamelCase__					,			)
           lowerCAmelCase_					:	List[str]						=   {
               '''precision''': P.tolist(),
               '''recall''': R.tolist(),
               '''f1''': F.tolist(),
               '''hashcode''': hashcode,
           }
           return output_dict



       def  A							(     self	:   Any					,			UpperCAmelCase	:   Dict=None					,			UpperCAmelCase	:   int=None					,			**UpperCAmelCase	:   int			):







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



       def  A							(     self	:   List[str]					,			UpperCAmelCase	:   Tuple=None					,			UpperCAmelCase	:   List[Any]=None					,			**UpperCAmelCase	:   Any			):







           if isinstance(UpperCamelCase__					,			UpperCamelCase__			):
               lowerCAmelCase_					:	Optional[int]						=   [reference]
           super().add(prediction=UpperCamelCase__					,			reference=UpperCamelCase__					,			**UpperCamelCase__			)









import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest

class 		__a							(	__UpperCamelCase      ):
       __snake_case						:   Optional[Any]      =		(VQDiffusionScheduler,)



       def  A							(     self	:   Optional[int]					,			**UpperCAmelCase	:   List[Any]			):
           lowerCAmelCase_					:	Any						=   {
               """num_vec_classes""": 40_97,
               """num_train_timesteps""": 1_00,
           }

           config.update(**UpperCAmelCase			)
           return config



       def  A							(     self	:   str					,			UpperCAmelCase	:   List[Any]			):
           lowerCAmelCase_					:	Union[str, Any]						=   4
           lowerCAmelCase_					:	str						=   8
           lowerCAmelCase_					:	Optional[Any]						=   8

           lowerCAmelCase_					:	str						=   torch.randint(0					,			UpperCAmelCase					,			(batch_size, height * width)			)

           return sample



       @property
       def  A							(     self	:   Optional[int]			):
           assert False



       def  A							(     self	:   int					,			UpperCAmelCase	:   Dict			):
           def model(UpperCAmelCase	:   Union[str, Any]					,			UpperCAmelCase	:   List[str]					,			*UpperCAmelCase	:   List[Any]			):
               lowerCAmelCase_		,		lowerCAmelCase_					:	Tuple						=   sample.shape
               lowerCAmelCase_					:	int						=   torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)			)
               lowerCAmelCase_					:	str						=   F.log_softmax(logits.double()					,			dim=1			).float()
               return return_value

           return model



       def  A							(     self	:   Union[str, Any]			):
           for timesteps in [2, 5, 1_00, 10_00]:
               self.check_over_configs(num_train_timesteps=UpperCAmelCase			)



       def  A							(     self	:   str			):
           for num_vec_classes in [5, 1_00, 10_00, 40_00]:
               self.check_over_configs(num_vec_classes=UpperCAmelCase			)



       def  A							(     self	:   Optional[Any]			):
           for t in [0, 50, 99]:
               self.check_over_forward(time_step=UpperCAmelCase			)



       def  A							(     self	:   int			):
           pass


