import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


_SCREAMING_SNAKE_CASE      						=	version.parse(importlib_metadata.version("""nltk"""))
if NLTK_VERSION >= version.Version("""3.6.4"""):
     from nltk import word_tokenize


_SCREAMING_SNAKE_CASE      						=	"\\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"

_SCREAMING_SNAKE_CASE      						=	"\\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"

_SCREAMING_SNAKE_CASE      						=	"\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						):







   def 		lowerCamelCase_     (			self		:		List[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/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 		lowerCamelCase_     (			self		:		Any					,					lowerCamelCase_		:		List[str]							):





    """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 		lowerCamelCase_     (			self		:		int					,					lowerCamelCase_		:		List[Any]					,					lowerCamelCase_		:		List[str]					,					lowerCamelCase_		:		Tuple=0.9					,					lowerCamelCase_		:		int=3					,					lowerCamelCase_		:		List[Any]=0.5							):





    """simple docstring"""


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

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





"""simple docstring"""






import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def      snake_case__   (		__lowerCamelCase				:    List[Any]							,		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    List[Any]	):
			"""simple docstring"""
			# Construct model
			if gpta_config_file == "":
						lowerCamelCase__		:		Dict			=GPTaConfig()
			else:
						lowerCamelCase__		:		Tuple			=GPTaConfig.from_json_file(__lowerCamelCase	)
			lowerCamelCase__		:		Optional[Any]			=GPTaModel(__lowerCamelCase	)

			# Load weights from numpy
			load_tf_weights_in_gpta(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase	)

			# Save pytorch-model
			lowerCamelCase__		:		List[str]			=pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
			lowerCamelCase__		:		int			=pytorch_dump_folder_path + '''/''' + CONFIG_NAME
			print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''	)
			torch.save(model.state_dict()							,		__lowerCamelCase	)
			print(f'''Save configuration file to {pytorch_config_dump_path}'''	)
			with open(__lowerCamelCase							,		'''w'''							,		encoding='''utf-8'''	) as f:
						f.write(config.to_json_string()	)


if __name__ == "__main__":
		_lowercase : Optional[Any]      						=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--gpt2_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		parser.add_argument(
		    "--gpt2_config_file",
		    default="",
		    type=str,
		    help=(
		        "An optional config json file corresponding to the pre-trained OpenAI model. \n"
		        "This specifies the model architecture."
		    ),
		)
		_lowercase : Any      						=						parser.parse_args()
		convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)




