



import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
			import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__		:					Optional[int]							=logging.get_logger(__name__)

lowerCAmelCase__		:					Optional[Any]							={'''vocab_file''': '''vocab.txt'''}

lowerCAmelCase__		:					Dict							={
    '''vocab_file''': {
        '''openbmb/cpm-ant-10b''': '''https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt''',
    },
}

lowerCAmelCase__		:					Any							={
    '''openbmb/cpm-ant-10b''': 1024,
}






def      __lowercase       (			a__       )    ->			Dict:
							__SCREAMING_SNAKE_CASE									= collections.OrderedDict()
							with open(lowercase_							,       'r'							,       encoding='utf-8'       ) as reader:
														__SCREAMING_SNAKE_CASE									= reader.readlines()
							for index, token in enumerate(lowercase_       ):
														__SCREAMING_SNAKE_CASE									= token.rstrip('\n'       )
														__SCREAMING_SNAKE_CASE									= index
							return vocab

class        UpperCAmelCase_  (       A__	):

						'''simple docstring'''




						def __init__(   self				,							_A				,							_A="<unk>"				,							_A=200		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= vocab
													__SCREAMING_SNAKE_CASE									= unk_token
													__SCREAMING_SNAKE_CASE									= max_input_chars_per_word






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= list(lowerCamelCase__		)
													if len(lowerCamelCase__		) > self.max_input_chars_per_word:
																				return [self.unk_token]

													__SCREAMING_SNAKE_CASE									= 0
													__SCREAMING_SNAKE_CASE									= []
													while start < len(lowerCamelCase__		):
																				__SCREAMING_SNAKE_CASE									= len(lowerCamelCase__		)
																				__SCREAMING_SNAKE_CASE									= None
																				while start < end:
																											__SCREAMING_SNAKE_CASE									= ''.join(chars[start:end]		)
																											if substr in self.vocab:
																																		__SCREAMING_SNAKE_CASE									= substr
																																		break
																											end -= 1
																				if cur_substr is None:
																											sub_tokens.append(self.unk_token		)
																											start += 1
																				else:
																											sub_tokens.append(lowerCamelCase__		)
																											__SCREAMING_SNAKE_CASE									= end

													return sub_tokens







class        UpperCAmelCase_  (       A__	):

						'''simple docstring'''




						UpperCamelCase__      :			int      =VOCAB_FILES_NAMES
						UpperCamelCase__      :			Union[str, Any]      =PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase__      :			Tuple      =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase__      :			List[str]      =['input_ids', 'attention_mask']
						UpperCamelCase__      :			int      =False






						def __init__(   self				,							_A				,							_A="<d>"				,							_A="</d>"				,							_A="<s>"				,							_A="</s>"				,							_A="<pad>"				,							_A="<unk>"				,							_A="</n>"				,							_A="</_>"				,							_A="left"				,							**_A				,							):







													'''simple docstring'''

													requires_backends(self				,							['jieba']		)
													super().__init__(
													    bod_token=lowerCamelCase__				,							eod_token=lowerCamelCase__				,							bos_token=lowerCamelCase__				,							eos_token=lowerCamelCase__				,							pad_token=lowerCamelCase__				,							unk_token=lowerCamelCase__				,							line_token=lowerCamelCase__				,							space_token=lowerCamelCase__				,							padding_side=lowerCamelCase__				,							**lowerCamelCase__				,							)
													__SCREAMING_SNAKE_CASE									= bod_token
													__SCREAMING_SNAKE_CASE									= eod_token
													__SCREAMING_SNAKE_CASE									= load_vocab(lowerCamelCase__		)
													__SCREAMING_SNAKE_CASE									= self.encoder[space_token]
													__SCREAMING_SNAKE_CASE									= self.encoder[line_token]

													del self.encoder[space_token]
													del self.encoder[line_token]

													__SCREAMING_SNAKE_CASE									= collections.OrderedDict(sorted(self.encoder.items()				,							key=lambda _A		: x[1]		)		)
													__SCREAMING_SNAKE_CASE									= {v: k for k, v in self.encoder.items()}

													__SCREAMING_SNAKE_CASE									= WordpieceTokenizer(vocab=self.encoder				,							unk_token=self.unk_token		)






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return self.encoder[self.bod_token]






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return self.encoder[self.eod_token]






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return self.encoder["\n"]






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return len(self.encoder		)






						def 	_A     (   self		):







													'''simple docstring'''

													return dict(self.encoder				,							**self.added_tokens_encoder		)






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= []
													for x in jieba.cut(lowerCamelCase__				,							cut_all=lowerCamelCase__		):
																				output_tokens.extend(self.wordpiece_tokenizer.tokenize(lowerCamelCase__		)		)
													return output_tokens






						def 	_A     (   self				,							_A				,							**_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= [i for i in token_ids if i >= 0]
													__SCREAMING_SNAKE_CASE									= [
													    x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
													]
													return super()._decode(lowerCamelCase__				,							**lowerCamelCase__		)






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													return token in self.encoder






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													return "".join(lowerCamelCase__		)






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													return self.encoder.get(lowerCamelCase__				,							self.encoder.get(self.unk_token		)		)






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													return self.decoder.get(lowerCamelCase__				,							self.unk_token		)






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







													'''simple docstring'''

													if os.path.isdir(lowerCamelCase__		):
																				__SCREAMING_SNAKE_CASE									= os.path.join(
																				    lowerCamelCase__				,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)
													else:
																				__SCREAMING_SNAKE_CASE									= (filename_prefix + '-' if filename_prefix else '') + save_directory
													__SCREAMING_SNAKE_CASE									= 0
													if " " in self.encoder:
																				__SCREAMING_SNAKE_CASE									= self.encoder[' ']
																				del self.encoder[" "]
													if "\n" in self.encoder:
																				__SCREAMING_SNAKE_CASE									= self.encoder['\n']
																				del self.encoder["\n"]
													__SCREAMING_SNAKE_CASE									= collections.OrderedDict(sorted(self.encoder.items()				,							key=lambda _A		: x[1]		)		)
													with open(lowerCamelCase__				,							'w'				,							encoding='utf-8'		) as writer:
																				for token, token_index in self.encoder.items():
																											if index != token_index:
																																		logger.warning(
																																		    f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
																																		    ' Please check that the vocabulary is not corrupted!'		)
																																		__SCREAMING_SNAKE_CASE									= token_index
																											writer.write(token + '\n'		)
																											index += 1
													return (vocab_file,)






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







													'''simple docstring'''

													if token_ids_a is None:
																				return [self.bos_token_id] + token_ids_a
													return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a






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







													'''simple docstring'''

													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 not None:
																				return [1] + ([0] * len(lowerCamelCase__		)) + [1] + ([0] * len(lowerCamelCase__		))
													return [1] + ([0] * len(lowerCamelCase__		))











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()






