





'''simple docstring'''







import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


UpperCamelCase    :      Optional[Any]  			=		[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['memory_attention', 'encoder_attn'],
    ['attention', 'attn'],
    ['/', '.'],
    ['.LayerNorm.gamma', '_layer_norm.weight'],
    ['.LayerNorm.beta', '_layer_norm.bias'],
    ['r.layer_', 'r.layers.'],
    ['output_proj', 'out_proj'],
    ['ffn.dense_1.', 'fc2.'],
    ['ffn.dense.', 'fc1.'],
    ['ffn_layer_norm', 'final_layer_norm'],
    ['kernel', 'weight'],
    ['encoder_layer_norm.', 'encoder.layer_norm.'],
    ['decoder_layer_norm.', 'decoder.layer_norm.'],
    ['embeddings.weights', 'shared.weight'],
]
def 						A__			(				__lowerCAmelCase		:			str						):






						for pegasus_name, hf_name in PATTERNS:
												lowerCamelCase__         =  k.replace(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE						)
						return k
def 						A__			(				__lowerCAmelCase		:			Any   ,    __lowerCAmelCase		:			Any						):
						lowerCamelCase__         =  DEFAULTS.copy()
						cfg_kwargs.update(__SCREAMING_SNAKE_CASE						)
						lowerCamelCase__         =  PegasusConfig(**__SCREAMING_SNAKE_CASE						)
						lowerCamelCase__         =  PegasusForConditionalGeneration(__SCREAMING_SNAKE_CASE						)
						lowerCamelCase__         =  torch_model.model.state_dict()
						lowerCamelCase__         =  {}
						for k, v in tf_weights.items():
												lowerCamelCase__         =  rename_state_dict_key(__SCREAMING_SNAKE_CASE						)
												if new_k not in sd:
																		raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})'''						)

												if "dense" in k or "proj" in new_k:
																		lowerCamelCase__         =  v.T
												lowerCamelCase__         =  torch.tensor(__SCREAMING_SNAKE_CASE   ,    dtype=sd[new_k].dtype						)
												assert v.shape == sd[new_k].shape, F'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
						# make sure embedding.padding_idx is respected
						lowerCamelCase__         =  torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1]						)
						lowerCamelCase__         =  mapping["shared.weight"]
						lowerCamelCase__         =  mapping["shared.weight"]
						lowerCamelCase__         =  {k: torch.zeros_like(__SCREAMING_SNAKE_CASE						) for k, v in sd.items() if k.endswith("""bias"""						) and k not in mapping}
						mapping.update(**__SCREAMING_SNAKE_CASE						)
						lowerCamelCase__         =  torch_model.model.load_state_dict(__SCREAMING_SNAKE_CASE   ,    strict=__SCREAMING_SNAKE_CASE						)
						lowerCamelCase__         =  [
						    k for k in missing if k not in ["encoder.embed_positions.weight", "decoder.embed_positions.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 						A__			(				__lowerCAmelCase		:			List[Any]="./ckpt/aeslc/model.ckpt-32000"						):
						lowerCamelCase__         =  tf.train.list_variables(__SCREAMING_SNAKE_CASE						)
						lowerCamelCase__         =  {}
						lowerCamelCase__         =  ["Adafactor", "global_step"]
						for name, shape in tqdm(__SCREAMING_SNAKE_CASE   ,    desc="""converting tf checkpoint to dict"""						):
												lowerCamelCase__         =  any(pat in name for pat in ignore_name						)
												if skip_key:
																		continue
												lowerCamelCase__         =  tf.train.load_variable(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE						)
												lowerCamelCase__         =  array
						return tf_weights
def 						A__			(				__lowerCAmelCase		:			Tuple   ,    __lowerCAmelCase		:			Optional[int]						):
						lowerCamelCase__         =  Path(__SCREAMING_SNAKE_CASE						).parent.name
						lowerCamelCase__         =  task_specific_params[F'''summarization_{dataset}''']["max_position_embeddings"]
						lowerCamelCase__         =  PegasusTokenizer.from_pretrained("""sshleifer/pegasus"""   ,    model_max_length=__SCREAMING_SNAKE_CASE						)
						assert tok.model_max_length == desired_max_model_length
						tok.save_pretrained(__SCREAMING_SNAKE_CASE						)

						# convert model
						lowerCamelCase__         =  get_tf_weights_as_numpy(__SCREAMING_SNAKE_CASE						)
						lowerCamelCase__         =  task_specific_params[F'''summarization_{dataset}''']
						if dataset == "large":
												lowerCamelCase__         =  task_specific_params
						lowerCamelCase__         =  convert_pegasus(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE						)
						torch_model.save_pretrained(__SCREAMING_SNAKE_CASE						)
						lowerCamelCase__         =  torch_model.state_dict()
						sd.pop("""model.decoder.embed_positions.weight"""						)
						sd.pop("""model.encoder.embed_positions.weight"""						)
						torch.save(__SCREAMING_SNAKE_CASE   ,    Path(__SCREAMING_SNAKE_CASE						) / """pytorch_model.bin"""						)


if __name__ == "__main__":
							UpperCamelCase    :      Dict  			=		argparse.ArgumentParser()
							# Required parameters
							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.')
							UpperCamelCase    :      Any  			=		parser.parse_args()
							if args.save_dir is None:
														UpperCamelCase    :      Any  			=		Path(args.tf_ckpt_path).parent.name
														UpperCamelCase    :      Dict  			=		os.path.join('pegasus', dataset)
							convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)









'''simple docstring'''







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase    :      int  			=		logging.get_logger(__name__)

UpperCamelCase    :      Optional[int]  			=		{'vocab_file': 'sentencepiece.bpe.model'}

UpperCamelCase    :      Dict  			=		{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

UpperCamelCase    :      List[Any]  			=		{
    'camembert-base': 5_12,
}

UpperCamelCase    :      List[str]  			=		'▁'




class 		UpperCamelCase__    (a			):




		'''simple docstring'''





		_UpperCamelCase				  =					VOCAB_FILES_NAMES
		_UpperCamelCase				  =					PRETRAINED_VOCAB_FILES_MAP
		_UpperCamelCase				  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_UpperCamelCase				  =					['input_ids', 'attention_mask']






		def __init__(      self   ,_lowerCAmelCase   ,_lowerCAmelCase="<s>"   ,_lowerCAmelCase="</s>"   ,_lowerCAmelCase="</s>"   ,_lowerCAmelCase="<s>"   ,_lowerCAmelCase="<unk>"   ,_lowerCAmelCase="<pad>"   ,_lowerCAmelCase="<mask>"   ,_lowerCAmelCase=["<s>NOTUSED", "</s>NOTUSED"]   ,_lowerCAmelCase = None   ,**_lowerCAmelCase   ,):
								# Mask token behave like a normal word, i.e. include the space before it
								lowerCamelCase__         =  AddedToken(_lowerCAmelCase   ,lstrip=_lowerCAmelCase   ,rstrip=_lowerCAmelCase       ) if isinstance(_lowerCAmelCase   ,_lowerCAmelCase       ) else mask_token

								lowerCamelCase__         =  {} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=_lowerCAmelCase   ,eos_token=_lowerCAmelCase   ,unk_token=_lowerCAmelCase   ,sep_token=_lowerCAmelCase   ,cls_token=_lowerCAmelCase   ,pad_token=_lowerCAmelCase   ,mask_token=_lowerCAmelCase   ,additional_special_tokens=_lowerCAmelCase   ,sp_model_kwargs=self.sp_model_kwargs   ,**_lowerCAmelCase   ,)
								lowerCamelCase__         =  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
								self.sp_model.Load(str(_lowerCAmelCase       )       )
								lowerCamelCase__         =  vocab_file
								# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
								# sentencepiece vocabulary (this is the case for <s> and </s>
								lowerCamelCase__         =  {"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
								lowerCamelCase__         =  len(self.fairseq_tokens_to_ids       )
								lowerCamelCase__         =  len(self.sp_model       ) + len(self.fairseq_tokens_to_ids       )
								lowerCamelCase__         =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = None       ):

								if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								lowerCamelCase__         =  [self.cls_token_id]
								lowerCamelCase__         =  [self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = None   ,_lowerCAmelCase = False       ):
								if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=_lowerCAmelCase   ,token_ids_a=_lowerCAmelCase   ,already_has_special_tokens=_lowerCAmelCase       )

								if token_ids_a is None:
														return [1] + ([0] * len(_lowerCAmelCase       )) + [1]
								return [1] + ([0] * len(_lowerCAmelCase       )) + [1, 1] + ([0] * len(_lowerCAmelCase       )) + [1]






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = None       ):
								lowerCamelCase__         =  [self.sep_token_id]
								lowerCamelCase__         =  [self.cls_token_id]

								if token_ids_a is None:
														return len(cls + token_ids_a + sep       ) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]






		@property
		def 			UpperCamelCase_					(      self       ):
								return len(self.fairseq_tokens_to_ids       ) + len(self.sp_model       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  {self.convert_ids_to_tokens(_lowerCAmelCase       ): i for i in range(self.vocab_size       )}
								vocab.update(self.added_tokens_encoder       )
								return vocab






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								return self.sp_model.encode(_lowerCAmelCase   ,out_type=_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								if token in self.fairseq_tokens_to_ids:
														return self.fairseq_tokens_to_ids[token]
								elif self.sp_model.PieceToId(_lowerCAmelCase       ) == 0:
														# Convert sentence piece unk token to fairseq unk token index
														return self.unk_token_id
								return self.fairseq_offset + self.sp_model.PieceToId(_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								if index in self.fairseq_ids_to_tokens:
														return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(index - self.fairseq_offset       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  []
								lowerCamelCase__         =  """"""
								lowerCamelCase__         =  False
								for token in tokens:
														# make sure that special tokens are not decoded using sentencepiece model
														if token in self.all_special_tokens:
																				if not prev_is_special:
																										out_string += " "
																				out_string += self.sp_model.decode(_lowerCAmelCase       ) + token
																				lowerCamelCase__         =  True
																				lowerCamelCase__         =  []
														else:
																				current_sub_tokens.append(_lowerCAmelCase       )
																				lowerCamelCase__         =  False
								out_string += self.sp_model.decode(_lowerCAmelCase       )
								return out_string.strip()






		def __getstate__(      self       ):
								lowerCamelCase__         =  self.__dict__.copy()
								lowerCamelCase__         =  None
								return state






		def __setstate__(      self   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  d

								# for backward compatibility
								if not hasattr(self   ,"""sp_model_kwargs"""       ):
														lowerCamelCase__         =  {}

								lowerCamelCase__         =  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
								self.sp_model.Load(self.vocab_file       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = None       ):
								if not os.path.isdir(_lowerCAmelCase       ):
														logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
														return
								lowerCamelCase__         =  os.path.join(
								    _lowerCAmelCase   ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )

								if os.path.abspath(self.vocab_file       ) != os.path.abspath(_lowerCAmelCase       ) and os.path.isfile(self.vocab_file       ):
														copyfile(self.vocab_file   ,_lowerCAmelCase       )
								elif not os.path.isfile(self.vocab_file       ):
														with open(_lowerCAmelCase   ,"""wb"""       ) as fi:
																				lowerCamelCase__         =  self.sp_model.serialized_model_proto()
																				fi.write(_lowerCAmelCase       )

								return (out_vocab_file,)

