


"""simple docstring"""






import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


__snake_case							:     Union[str, Any]   =					"\\n@inproceedings{lin-2004-rouge,\n    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",\n    author = \"Lin, Chin-Yew\",\n    booktitle = \"Text Summarization Branches Out\",\n    month = jul,\n    year = \"2004\",\n    address = \"Barcelona, Spain\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W04-1013\",\n    pages = \"74--81\",\n}\n"

__snake_case							:     Optional[int]   =					"\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n"

__snake_case							:     str   =					"\nCalculates average rouge scores for a list of hypotheses and 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    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,\n        `\"rougeL\"`: Longest common subsequence based scoring.\n        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric('rouge')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']\n    >>> print(results[\"rouge1\"])\n    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n    >>> print(results[\"rouge1\"].mid.fmeasure)\n    1.0\n"



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




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int])       -> Union[str, Any]:

				"""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/google-research/google-research/tree/master/rouge"] ,    reference_urls=[
				        "https://en.wikipedia.org/wiki/ROUGE_(metric)",
				        "https://github.com/google-research/google-research/tree/master/rouge",
				    ] ,    )







	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	List[Any] ,    _SCREAMING_SNAKE_CASE:	List[str] ,    _SCREAMING_SNAKE_CASE:	str=None ,    _SCREAMING_SNAKE_CASE:	Optional[int]=True ,    _SCREAMING_SNAKE_CASE:	List[Any]=False)       -> int:

				"""simple docstring"""

				if rouge_types is None:
							__lowerCAmelCase   :						str				= ["""rouge1""", """rouge2""", """rougeL""", """rougeLsum"""]

				__lowerCAmelCase   :						Dict				= rouge_scorer.RougeScorer(rouge_types=_UpperCamelCase ,    use_stemmer=_UpperCamelCase)
				if use_aggregator:
							__lowerCAmelCase   :						int				= scoring.BootstrapAggregator()
				else:
							__lowerCAmelCase   :						Union[str, Any]				= []

				for ref, pred in zip(_UpperCamelCase ,    _UpperCamelCase):
							__lowerCAmelCase   :						Dict				= scorer.score(_UpperCamelCase ,    _UpperCamelCase)
							if use_aggregator:
										aggregator.add_scores(_UpperCamelCase)
							else:
										scores.append(_UpperCamelCase)

				if use_aggregator:
							__lowerCAmelCase   :						str				= aggregator.aggregate()
				else:
							__lowerCAmelCase   :						Dict				= {}
							for key in scores[0]:
										__lowerCAmelCase   :						str				= [score[key] for score in scores]

				return result




"""simple docstring"""






import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Tuple:
			# Initialise PyTorch model
			__lowerCAmelCase   :						Any				= AlbertConfig.from_json_file(__snake_case			)
			print(F"""Building PyTorch model from configuration: {config}"""			)
			__lowerCAmelCase   :						str				= AlbertForPreTraining(__snake_case			)

			# Load weights from tf checkpoint
			load_tf_weights_in_albert(__snake_case      ,__snake_case      ,__snake_case			)

			# Save pytorch-model
			print(F"""Save PyTorch model to {pytorch_dump_path}"""			)
			torch.save(model.state_dict()      ,__snake_case			)


if __name__ == "__main__":
	__snake_case							:     Union[str, Any]   =					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(
	    '--albert_config_file',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        'The config json file corresponding to the pre-trained ALBERT 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.'
	)
	__snake_case							:     str   =					parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)