



"""simple docstring"""






import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


_lowercase				:				List[str]   		=		version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
		from nltk import word_tokenize


_lowercase				:				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				:				int   		=		"\\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				:				int   		=		"\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 	snake_case ( self							:						Dict  )->      List[str]:
								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 	snake_case ( self							:						str,						lowerCamelCase							:						Dict  )->      Optional[int]:
								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 	snake_case ( self							:						int,						lowerCamelCase							:						Optional[int],						lowerCamelCase							:						Optional[int],						lowerCamelCase							:						List[str]=0.9,						lowerCamelCase							:						str=3,						lowerCamelCase							:						Union[str, Any]=0.5  )->      int:
								if NLTK_VERSION >= version.Version('''3.6.5'''  ):
											lowerCamelCase__	:	Tuple				=[
											    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:
											lowerCamelCase__	:	List[Any]				=[
											    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

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def   snake_case__     (	__lowerCamelCase       :					str							,	__lowerCamelCase       :					str							,	__lowerCamelCase       :					Tuple			):
			"""simple docstring"""
			lowerCamelCase__	:	Union[str, Any]				=AutoConfig.from_pretrained(__lowerCamelCase			)
			lowerCamelCase__	:	Any				=FlaxAutoModelForSeqaSeqLM.from_config(config=__lowerCamelCase			)
			lowerCamelCase__	:	Union[str, Any]				=checkpoints.load_tax_checkpoint(__lowerCamelCase			)

			lowerCamelCase__	:	Union[str, Any]				='''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

			if config.model_type == "t5":
						lowerCamelCase__	:	List[str]				='''SelfAttention'''
			if config.model_type == "longt5" and config.encoder_attention_type == "local":
						lowerCamelCase__	:	List[Any]				='''LocalSelfAttention'''
			elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						lowerCamelCase__	:	Optional[Any]				='''TransientGlobalSelfAttention'''
			else:
						raise ValueError(
						    '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
						    ''' attribute with a value from [\'local\', \'transient-global].'''			)

			# Encoder
			for layer_index in range(config.num_layers			):
						lowerCamelCase__	:	List[Any]				=f'''layers_{str(__lowerCamelCase			)}'''

						# Self-Attention
						lowerCamelCase__	:	List[str]				=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
						lowerCamelCase__	:	Optional[int]				=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
						lowerCamelCase__	:	List[str]				=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
						lowerCamelCase__	:	List[Any]				=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

						# Global input layer norm
						if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
									lowerCamelCase__	:	str				=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

						# Layer Normalization
						lowerCamelCase__	:	List[Any]				=tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

						if split_mlp_wi:
									lowerCamelCase__	:	Optional[Any]				=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
									lowerCamelCase__	:	Dict				=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
						else:
									lowerCamelCase__	:	List[str]				=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

						lowerCamelCase__	:	Union[str, Any]				=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

						# Layer Normalization
						lowerCamelCase__	:	Tuple				=tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

						# Assigning
						lowerCamelCase__	:	str				=flax_model.params['''encoder''']['''block'''][str(__lowerCamelCase			)]['''layer''']
						lowerCamelCase__	:	int				=tax_attention_key
						lowerCamelCase__	:	Optional[int]				=tax_attention_out
						lowerCamelCase__	:	List[Any]				=tax_attention_query
						lowerCamelCase__	:	Optional[Any]				=tax_attention_value

						lowerCamelCase__	:	List[str]				=tax_attention_layer_norm

						# Global input layer norm
						if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
									lowerCamelCase__	:	Optional[int]				=tax_global_layer_norm

						if split_mlp_wi:
									lowerCamelCase__	:	Optional[int]				=tax_mlp_wi_a
									lowerCamelCase__	:	Optional[int]				=tax_mlp_wi_a
						else:
									lowerCamelCase__	:	Union[str, Any]				=tax_mlp_wi

						lowerCamelCase__	:	str				=tax_mlp_wo
						lowerCamelCase__	:	Optional[Any]				=tax_mlp_layer_norm

						lowerCamelCase__	:	Optional[int]				=flax_model_encoder_layer_block

			# Only for layer 0:
			lowerCamelCase__	:	Union[str, Any]				=tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
			lowerCamelCase__	:	str				=tax_encoder_rel_embedding

			# Side/global relative position_bias + layer norm
			if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						lowerCamelCase__	:	Optional[int]				=tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
						lowerCamelCase__	:	Optional[int]				=tax_encoder_global_rel_embedding

			# Assigning
			lowerCamelCase__	:	int				=tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
			lowerCamelCase__	:	List[Any]				=tax_encoder_norm

			# Decoder
			for layer_index in range(config.num_layers			):
						lowerCamelCase__	:	Dict				=f'''layers_{str(__lowerCamelCase			)}'''

						# Self-Attention
						lowerCamelCase__	:	Dict				=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
						lowerCamelCase__	:	List[Any]				=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
						lowerCamelCase__	:	Optional[int]				=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
						lowerCamelCase__	:	Union[str, Any]				=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

						# Layer Normalization
						lowerCamelCase__	:	int				=tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
						    '''scale'''
						]

						# Encoder-Decoder-Attention
						lowerCamelCase__	:	int				=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
						lowerCamelCase__	:	List[Any]				=tax_enc_dec_attention_module['''key''']['''kernel''']
						lowerCamelCase__	:	Any				=tax_enc_dec_attention_module['''out''']['''kernel''']
						lowerCamelCase__	:	Dict				=tax_enc_dec_attention_module['''query''']['''kernel''']
						lowerCamelCase__	:	List[str]				=tax_enc_dec_attention_module['''value''']['''kernel''']

						# Layer Normalization
						lowerCamelCase__	:	Dict				=tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

						# MLP
						if split_mlp_wi:
									lowerCamelCase__	:	str				=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
									lowerCamelCase__	:	Any				=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
						else:
									lowerCamelCase__	:	List[Any]				=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

						lowerCamelCase__	:	Optional[Any]				=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

						# Layer Normalization
						lowerCamelCase__	:	str				=tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

						# Assigning
						lowerCamelCase__	:	str				=flax_model.params['''decoder''']['''block'''][str(__lowerCamelCase			)]['''layer''']
						lowerCamelCase__	:	Union[str, Any]				=tax_attention_key
						lowerCamelCase__	:	str				=tax_attention_out
						lowerCamelCase__	:	Optional[int]				=tax_attention_query
						lowerCamelCase__	:	Dict				=tax_attention_value

						lowerCamelCase__	:	List[str]				=tax_pre_attention_layer_norm

						lowerCamelCase__	:	List[Any]				=tax_enc_dec_attention_key
						lowerCamelCase__	:	Any				=tax_enc_dec_attention_out
						lowerCamelCase__	:	Any				=tax_enc_dec_attention_query
						lowerCamelCase__	:	Optional[int]				=tax_enc_dec_attention_value

						lowerCamelCase__	:	Dict				=tax_cross_layer_norm

						if split_mlp_wi:
									lowerCamelCase__	:	Tuple				=tax_mlp_wi_a
									lowerCamelCase__	:	int				=tax_mlp_wi_a
						else:
									lowerCamelCase__	:	List[Any]				=tax_mlp_wi

						lowerCamelCase__	:	Dict				=tax_mlp_wo

						lowerCamelCase__	:	Tuple				=txa_mlp_layer_norm

						lowerCamelCase__	:	Optional[Any]				=flax_model_decoder_layer_block

			# Decoder Normalization
			lowerCamelCase__	:	Dict				=tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
			lowerCamelCase__	:	int				=txa_decoder_norm

			# Only for layer 0:
			lowerCamelCase__	:	Tuple				=tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
			lowerCamelCase__	:	Tuple				=tax_decoder_rel_embedding

			# Token Embeddings
			lowerCamelCase__	:	Union[str, Any]				=tax_model['''target''']['''token_embedder''']['''embedding''']
			lowerCamelCase__	:	Dict				=txa_token_embeddings

			# LM Head (only in v1.1 and LongT5 checkpoints)
			if "logits_dense" in tax_model["target"]["decoder"]:
						lowerCamelCase__	:	int				=tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

			flax_model.save_pretrained(__lowerCamelCase			)
			print('''T5X Model was sucessfully converted!'''			)


if __name__ == "__main__":
		_lowercase				:				Tuple   		=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the T5X checkpoint."
		)
		parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of LongT5/T5 model.")
		parser.add_argument(
		    "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
		)
		_lowercase				:				List[Any]   		=		parser.parse_args()
		convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




