


import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets

@contextmanager
def 		_UpperCAmelCase							(     ):
     def filter_log(SCREAMING_SNAKE_CASE__     :   Tuple							):
          return False if "This IS expected if you are initializing" in record.msg else True

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


_A									=		'\\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									=		'\\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									=		'\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        UpperCAmelCase__			(   datasets.Metric						):



    """simple docstring"""






    def _a   (     self       )    ->			List[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   ,  A_   ,  A_   ,  A_=None   ,  A_=None   ,  A_=None   ,  A_=False   ,  A_=False   ,  A_=None   ,  A_=64   ,  A_=4   ,  A_=False   ,  A_=False   ,  A_=None   ,  A_=False   ,  )    ->			Tuple:
         __UpperCamelCase       				=bert_score.utils.get_hash
         __UpperCamelCase       				=bert_score.BERTScorer

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

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

         __UpperCamelCase       				=get_hash(
             model=A_   ,  num_layers=A_   ,  idf=A_   ,  rescale_with_baseline=A_   ,  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       				=scorer(
                       model_type=A_   ,  num_layers=A_   ,  batch_size=A_   ,  nthreads=A_   ,  all_layers=A_   ,  idf=A_   ,  device=A_   ,  lang=A_   ,  rescale_with_baseline=A_   ,  baseline_path=A_   ,  )

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






    def _a   (     self   ,  A_=None   ,  A_=None   ,  **A_       )    ->			str:
         # References can be strings or lists of strings
         # Let's change strings to lists of strings with one element
         if references is not None:
              __UpperCamelCase       				=[[ref] if isinstance(A_   ,  A_       ) else ref for ref in references]
         super().add_batch(predictions=A_   ,  references=A_   ,  **A_       )






    def _a   (     self   ,  A_=None   ,  A_=None   ,  **A_       )    ->			int:
         # References can be strings or lists of strings
         # Let's change strings to lists of strings with one element
         if isinstance(A_   ,  A_       ):
              __UpperCamelCase       				=[reference]
         super().add(prediction=A_   ,  reference=A_   ,  **A_       )









import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   List[Any]     ,	SCREAMING_SNAKE_CASE__     :   Union[str, Any]     ,	SCREAMING_SNAKE_CASE__     :   Any							):
     # Initialise PyTorch model
     __UpperCamelCase       				=json.load(open(SCREAMING_SNAKE_CASE__     ,	'r'							)							)
     __UpperCamelCase       				=GPTNeoConfig(
         hidden_size=config_json['n_embd']     ,	num_layers=config_json['n_layer']     ,	num_heads=config_json['n_head']     ,	attention_types=config_json['attention_types']     ,	max_position_embeddings=config_json['n_positions']     ,	resid_dropout=config_json['res_dropout']     ,	embed_dropout=config_json['embed_dropout']     ,	attention_dropout=config_json['attn_dropout']     ,	)
     print(F'Building PyTorch model from configuration: {config}'							)
     __UpperCamelCase       				=GPTNeoForCausalLM(SCREAMING_SNAKE_CASE__							)

     # Load weights from tf checkpoint
     load_tf_weights_in_gpt_neo(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

     # Save pytorch-model
     print(F'Save PyTorch model to {pytorch_dump_path}'							)
     model.save_pretrained(SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
   _A									=		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
   )
   parser.add_argument(
       '--config_file',
       default=None,
       type=str,
       required=True,
       help=(
           'The config json file corresponding to the pre-trained mesh-tf model. \n'
           'This specifies the model architecture.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   _A									=		parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





