
import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowercase__							:							Optional[Any]													=	version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
				from nltk import word_tokenize


lowercase__							:							List[str]													=	"\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n"

lowercase__							:							Union[str, Any]													=	"\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"

lowercase__							:							str													=	"\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    'meteor': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric('meteor')\n    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results[\"meteor\"], 4))\n    0.6944\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,						_KWARGS_DESCRIPTION			)
class 				SCREAMING_SNAKE_CASE__			(							datasets.Metric			):

							"""simple docstring"""






							def 			A__						(     self	)->      int:





													'''simple docstring'''






													return datasets.MetricInfo(
													    description=_DESCRIPTION   ,		citation=_CITATION   ,		inputs_description=_KWARGS_DESCRIPTION   ,		features=datasets.Features(
													        {
													            '''predictions''': datasets.Value('''string'''   ,		id='''sequence'''	),
													            '''references''': datasets.Value('''string'''   ,		id='''sequence'''	),
													        }	)   ,		codebase_urls=['''https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py''']   ,		reference_urls=[
													        '''https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score''',
													        '''https://en.wikipedia.org/wiki/METEOR''',
													    ]   ,		)



							def 			A__						(     self   ,		SCREAMING_SNAKE_CASE_	)->      int:





													'''simple docstring'''






													import nltk

													nltk.download('''wordnet'''	)
													if NLTK_VERSION >= version.Version('''3.6.5'''	):
																			nltk.download('''punkt'''	)
													if NLTK_VERSION >= version.Version('''3.6.6'''	):
																			nltk.download('''omw-1.4'''	)



							def 			A__						(     self   ,		SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_=0.9   ,		SCREAMING_SNAKE_CASE_=3   ,		SCREAMING_SNAKE_CASE_=0.5	)->      Union[str, Any]:





													'''simple docstring'''






													if NLTK_VERSION >= version.Version('''3.6.5'''	):
																			__UpperCamelCase        =	[
																			    meteor_score.single_meteor_score(
																			        word_tokenize(__UpperCAmelCase	)   ,		word_tokenize(__UpperCAmelCase	)   ,		alpha=__UpperCAmelCase   ,		beta=__UpperCAmelCase   ,		gamma=__UpperCAmelCase	)
																			    for ref, pred in zip(__UpperCAmelCase   ,		__UpperCAmelCase	)
																			]
													else:
																			__UpperCamelCase        =	[
																			    meteor_score.single_meteor_score(__UpperCAmelCase   ,		__UpperCAmelCase   ,		alpha=__UpperCAmelCase   ,		beta=__UpperCAmelCase   ,		gamma=__UpperCAmelCase	)
																			    for ref, pred in zip(__UpperCAmelCase   ,		__UpperCAmelCase	)
																			]

													return {"meteor": np.mean(__UpperCAmelCase	)}











"""simple docstring"""





import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Dict:





  """simple docstring"""


  lowerCAmelCase__			:str											=							BertConfig.from_json_file(_SCREAMING_SNAKE_CASE			)
  print(F"Building PyTorch model from configuration: {config}"			)
  lowerCAmelCase__			:int											=							BertForPreTraining(_SCREAMING_SNAKE_CASE			)

  # Load weights from tf checkpoint
  load_tf_weights_in_bert(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)

  # Save pytorch-model
  print(F"Save PyTorch model to {pytorch_dump_path}"			)
  torch.save(model.state_dict()     ,   _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(
      """--bert_config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained BERT 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.bert_config_file, args.pytorch_dump_path)
