



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


lowerCAmelCase__		:					str							=logging.get_logger(__name__)

lowerCAmelCase__		:					str							={'''vocab_file''': '''sentencepiece.bpe.model'''}

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

lowerCAmelCase__		:					Any							={
    '''camembert-base''': 512,
}

lowerCAmelCase__		:					Optional[int]							='''▁'''

class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			Dict      =VOCAB_FILES_NAMES
						UpperCamelCase__      :			Dict      =PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase__      :			str      =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase__      :			List[Any]      =['''input_ids''', '''attention_mask''']






						def __init__(   self				,							_A				,							_A="<s>"				,							_A="</s>"				,							_A="</s>"				,							_A="<s>"				,							_A="<unk>"				,							_A="<pad>"				,							_A="<mask>"				,							_A=["<s>NOTUSED", "</s>NOTUSED"]				,							_A = None				,							**_A				,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= AddedToken(_A				,							lstrip=_A				,							rstrip=_A		) if isinstance(_A				,							_A		) else mask_token

													__SCREAMING_SNAKE_CASE									= {} if sp_model_kwargs is None else sp_model_kwargs

													super().__init__(
													    bos_token=_A				,							eos_token=_A				,							unk_token=_A				,							sep_token=_A				,							cls_token=_A				,							pad_token=_A				,							mask_token=_A				,							additional_special_tokens=_A				,							sp_model_kwargs=self.sp_model_kwargs				,							**_A				,							)
													__SCREAMING_SNAKE_CASE									= spm.SentencePieceProcessor(**self.sp_model_kwargs		)
													self.sp_model.Load(str(_A		)		)
													__SCREAMING_SNAKE_CASE									= 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>
													__SCREAMING_SNAKE_CASE									= {'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
													__SCREAMING_SNAKE_CASE									= len(self.fairseq_tokens_to_ids		)
													__SCREAMING_SNAKE_CASE									= len(self.sp_model		) + len(self.fairseq_tokens_to_ids		)
													__SCREAMING_SNAKE_CASE									= {v: k for k, v in self.fairseq_tokens_to_ids.items()}






						def 	_A     (   self				,							_A				,							_A = None		):







													'''simple docstring'''

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






						def 	_A     (   self				,							_A				,							_A = None				,							_A = False		):







													'''simple docstring'''

													if already_has_special_tokens:
																				return super().get_special_tokens_mask(
																				    token_ids_a=_A				,							token_ids_a=_A				,							already_has_special_tokens=_A		)

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






						def 	_A     (   self				,							_A				,							_A = None		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= [self.sep_token_id]
													__SCREAMING_SNAKE_CASE									= [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 	_A     (   self		):







													'''simple docstring'''

													return len(self.fairseq_tokens_to_ids		) + len(self.sp_model		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= {self.convert_ids_to_tokens(_A		): i for i in range(self.vocab_size		)}
													vocab.update(self.added_tokens_encoder		)
													return vocab






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													return self.sp_model.encode(_A				,							out_type=_A		)






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

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






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													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 	_A     (   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= []
													__SCREAMING_SNAKE_CASE									= ''
													__SCREAMING_SNAKE_CASE									= 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(_A		) + token
																											__SCREAMING_SNAKE_CASE									= True
																											__SCREAMING_SNAKE_CASE									= []
																				else:
																											current_sub_tokens.append(_A		)
																											__SCREAMING_SNAKE_CASE									= False
													out_string += self.sp_model.decode(_A		)
													return out_string.strip()






						def __getstate__(   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.__dict__.copy()
													__SCREAMING_SNAKE_CASE									= None
													return state






						def __setstate__(   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= d

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

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






						def 	_A     (   self				,							_A				,							_A = None		):







													'''simple docstring'''

													if not os.path.isdir(_A		):
																				logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
																				return
													__SCREAMING_SNAKE_CASE									= os.path.join(
													    _A				,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

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

													return (out_vocab_file,)











from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser

@dataclass
class        UpperCAmelCase_  :

						'''simple docstring'''




						UpperCamelCase__      :			str      =  field(
						    metadata={'''help''': '''The output directory where the model will be written.'''}   ,   )
						UpperCamelCase__      :			str      =  field(
						    metadata={
						        '''help''': (
						            '''The encoder model checkpoint for weights initialization.'''
						            '''Don\'t set if you want to train an encoder model from scratch.'''
						        )
						    }   ,   )
						UpperCamelCase__      :			str      =  field(
						    metadata={
						        '''help''': (
						            '''The decoder model checkpoint for weights initialization.'''
						            '''Don\'t set if you want to train a decoder model from scratch.'''
						        )
						    }   ,   )
						UpperCamelCase__      :			Optional[str]      =  field(
						    default=UpperCamelCase_   ,   metadata={'''help''': '''Pretrained encoder config name or path if not the same as encoder_model_name'''}	)
						UpperCamelCase__      :			Optional[str]      =  field(
						    default=UpperCamelCase_   ,   metadata={'''help''': '''Pretrained decoder config name or path if not the same as decoder_model_name'''}	)







def      __lowercase       (			)    ->			Union[str, Any]:
							__SCREAMING_SNAKE_CASE									= HfArgumentParser((ModelArguments,)       )
							((__SCREAMING_SNAKE_CASE)							,	)									= parser.parse_args_into_dataclasses()

							# Load pretrained model and tokenizer

							# Use explicit specified encoder config
							if model_args.encoder_config_name:
														__SCREAMING_SNAKE_CASE									= AutoConfig.from_pretrained(model_args.encoder_config_name       )
							# Use pretrained encoder model's config
							else:
														__SCREAMING_SNAKE_CASE									= AutoConfig.from_pretrained(model_args.encoder_model_name_or_path       )

							# Use explicit specified decoder config
							if model_args.decoder_config_name:
														__SCREAMING_SNAKE_CASE									= AutoConfig.from_pretrained(model_args.decoder_config_name       )
							# Use pretrained decoder model's config
							else:
														__SCREAMING_SNAKE_CASE									= AutoConfig.from_pretrained(model_args.decoder_model_name_or_path       )

							# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
							__SCREAMING_SNAKE_CASE									= True
							__SCREAMING_SNAKE_CASE									= True

							__SCREAMING_SNAKE_CASE									= FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
							    encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path							,       decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path							,       encoder_config=a__							,       decoder_config=a__							,       )

							# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
							__SCREAMING_SNAKE_CASE									= decoder_config.decoder_start_token_id
							__SCREAMING_SNAKE_CASE									= decoder_config.pad_token_id
							if decoder_start_token_id is None:
														__SCREAMING_SNAKE_CASE									= decoder_config.bos_token_id
							if pad_token_id is None:
														__SCREAMING_SNAKE_CASE									= decoder_config.eos_token_id

							# This is necessary to make Flax's generate() work
							__SCREAMING_SNAKE_CASE									= decoder_config.eos_token_id
							__SCREAMING_SNAKE_CASE									= decoder_start_token_id
							__SCREAMING_SNAKE_CASE									= pad_token_id

							__SCREAMING_SNAKE_CASE									= AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path       )

							__SCREAMING_SNAKE_CASE									= AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path       )
							__SCREAMING_SNAKE_CASE									= tokenizer.convert_ids_to_tokens(model.config.pad_token_id       )

							model.save_pretrained(model_args.output_dir       )
							image_processor.save_pretrained(model_args.output_dir       )
							tokenizer.save_pretrained(model_args.output_dir       )


if __name__ == "__main__":
			main()






