





"""simple docstring"""




import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


a						: List[str]     	=			[
    # tf -> hf
    ('''/''', '''.'''),
    ('''layer_''', '''layers.'''),
    ('''kernel''', '''weight'''),
    ('''beta''', '''bias'''),
    ('''gamma''', '''weight'''),
    ('''pegasus''', '''model'''),
]
a						: Any     	=			[
    ('''.output.dense''', '''.fc2'''),
    ('''intermediate.LayerNorm''', '''final_layer_norm'''),
    ('''intermediate.dense''', '''fc1'''),
]

a						: List[str]     	=			(
    INIT_COMMON
    + [
        ('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
        ('''attention.output.dense''', '''self_attn.out_proj'''),
        ('''attention.self''', '''self_attn'''),
        ('''attention.encdec.LayerNorm''', '''encoder_attn_layer_norm'''),
        ('''attention.encdec_output.dense''', '''encoder_attn.out_proj'''),
        ('''attention.encdec''', '''encoder_attn'''),
        ('''key''', '''k_proj'''),
        ('''value''', '''v_proj'''),
        ('''query''', '''q_proj'''),
        ('''decoder.LayerNorm''', '''decoder.layernorm_embedding'''),
    ]
    + END_COMMON
)

a						: str     	=			(
    INIT_COMMON
    + [
        ('''embeddings.word_embeddings''', '''shared.weight'''),
        ('''embeddings.position_embeddings''', '''embed_positions.weight'''),
        ('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
        ('''attention.output.dense''', '''self_attn.output'''),
        ('''attention.self''', '''self_attn.self'''),
        ('''encoder.LayerNorm''', '''encoder.layernorm_embedding'''),
    ]
    + END_COMMON
)

a						: Any     	=			[
    '''encdec/key/bias''',
    '''encdec/query/bias''',
    '''encdec/value/bias''',
    '''self/key/bias''',
    '''self/query/bias''',
    '''self/value/bias''',
    '''encdec_output/dense/bias''',
    '''attention/output/dense/bias''',
]






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






	'''simple docstring'''






	for tf_name, hf_name in patterns:
		a      :		List[Any]	=				k.replace(_lowercase    ,       _lowercase					)
	return k






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    dict    ,       _lowercase						:    dict					) ->BigBirdPegasusForConditionalGeneration:






	'''simple docstring'''






	a      :		List[Any]	=				BigBirdPegasusConfig(**_lowercase					)
	a      :		Any	=				BigBirdPegasusForConditionalGeneration(_lowercase					)
	a      :		Optional[Any]	=				torch_model.state_dict()
	a      :		Tuple	=				{}

	# separating decoder weights
	a      :		int	=				{k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder"					)}
	a      :		Tuple	=				{k: tf_weights[k] for k in tf_weights if not k.startswith("pegasus/decoder"					)}

	for k, v in tqdm(decoder_weights.items()    ,       "tf -> hf conversion"					):
		a      :		str	=				[k.endswith(_lowercase					) for ending in KEYS_TO_IGNORE]
		if any(_lowercase					):
			continue
		a      :		Union[str, Any]	=				DECODER_PATTERNS
		a      :		List[Any]	=				rename_state_dict_key(_lowercase    ,       _lowercase					)
		if new_k not in state_dict:
			raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})"""					)
		if any(True if i in k else False for i in ["dense", "query", "key", "value"]					):
			a      :		Optional[Any]	=				v.T
		a      :		List[str]	=				torch.from_numpy(_lowercase					)
		assert v.shape == state_dict[new_k].shape, F"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""

	for k, v in tqdm(remaining_weights.items()    ,       "tf -> hf conversion"					):
		a      :		Dict	=				[k.endswith(_lowercase					) for ending in KEYS_TO_IGNORE]
		if any(_lowercase					):
			continue
		a      :		Any	=				REMAINING_PATTERNS
		a      :		Optional[Any]	=				rename_state_dict_key(_lowercase    ,       _lowercase					)
		if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
			raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})"""					)
		if any(True if i in k else False for i in ["dense", "query", "key", "value"]					):
			a      :		Dict	=				v.T
		a      :		Optional[Any]	=				torch.from_numpy(_lowercase					)
		if k != "pegasus/embeddings/position_embeddings":
			assert v.shape == state_dict[new_k].shape, F"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""

	a      :		str	=				mapping["model.embed_positions.weight"]
	a      :		Union[str, Any]	=				mapping.pop("model.embed_positions.weight"					)
	a,       a      :		Optional[Any]	=				torch_model.load_state_dict(_lowercase    ,       strict=_lowercase					)
	a      :		Optional[Any]	=				[
	    k
	    for k in missing
	    if k
	    not in [
	        "final_logits_bias",
	        "model.encoder.embed_tokens.weight",
	        "model.decoder.embed_tokens.weight",
	        "lm_head.weight",
	    ]
	]
	assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
	assert extra == [], F"""no matches found for the following tf keys {extra}"""
	return torch_model






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Optional[Any]					) ->Dict:






	'''simple docstring'''






	a      :		int	=				tf.train.list_variables(_lowercase					)
	a      :		str	=				{}
	a      :		Optional[Any]	=				["global_step"]
	for name, shape in tqdm(_lowercase    ,       desc="converting tf checkpoint to dict"					):
		a      :		List[Any]	=				any(pat in name for pat in ignore_name					)
		if skip_key:
			continue
		a      :		Optional[int]	=				tf.train.load_variable(_lowercase    ,       _lowercase					)
		a      :		Optional[Any]	=				array
	return tf_weights






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str    ,       _lowercase						:    str    ,       _lowercase						:    dict					) ->List[Any]:






	'''simple docstring'''






	a      :		List[Any]	=				get_tf_weights_as_numpy(_lowercase					)
	a      :		Union[str, Any]	=				convert_bigbird_pegasus(_lowercase    ,       _lowercase					)
	torch_model.save_pretrained(_lowercase					)


if __name__ == "__main__":
		a						: List[Any]     	=			argparse.ArgumentParser()
		parser.add_argument('''--tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
		parser.add_argument('''--save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
		a						: Optional[Any]     	=			parser.parse_args()
		a						: Optional[Any]     	=			{}
		convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)





'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


lowerCamelCase :      Any					 =     "\\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"

lowerCamelCase :      Optional[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"

lowerCamelCase :      Optional[Any]					 =     "\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 							A__    (       datasets.Metric  ):





  def 						A  (		self      :	Tuple )	->    str:





       '''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      :	Union[str, Any]							,					_a      :	Union[str, Any] )	->    Optional[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      :	int							,					_a      :	Tuple							,					_a      :	List[str]							,					_a      :	List[str]=0.9							,					_a      :	Dict=3							,					_a      :	Optional[int]=0.5 )	->    Optional[int]:





       '''simple docstring'''






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

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