





'''simple docstring'''
from dataclasses import dataclass, field
from typing import Optional

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

@dataclass
class      snake_case     :




 """simple docstring"""



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






def 				lowercase__   (   ) ->      Any:



       """simple docstring"""






       __UpperCamelCase       =  HfArgumentParser((ModelArguments,)     )
       ((__UpperCamelCase)       ,						)       =  parser.parse_args_into_dataclasses()

       # Load pretrained model and tokenizer

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

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

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

       __UpperCamelCase       =  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=__lowercase				,  decoder_config=__lowercase				,  )

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

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

       __UpperCamelCase       =  AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path     )

       __UpperCamelCase       =  AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path     )
       __UpperCamelCase       =  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()




"""simple docstring"""







import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						_A  (  _a    ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase       :							str	=       LayoutLMTokenizer
    UpperCAmelCase       :							int	=       LayoutLMTokenizerFast
    UpperCAmelCase       :							Union[str, Any]	=       True
    UpperCAmelCase       :							Optional[Any]	=       True






    def 	__snake_case							(					self				:    Optional[int]):
      super().setUp()

      a						:		Tuple      							=			[
          "[UNK]",
          "[CLS]",
          "[SEP]",
          "want",
          "##want",
          "##ed",
          "wa",
          "un",
          "runn",
          "##ing",
          ",",
          "low",
          "lowest",
      ]
      a						:		Optional[Any]      							=			os.path.join(self.tmpdirname				, VOCAB_FILES_NAMES["vocab_file"])
      with open(self.vocab_file				, "w"				, encoding="utf-8") as vocab_writer:
        vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))






    def 	__snake_case							(					self				:    Optional[int]				, **__UpperCAmelCase				:    Tuple):
      return LayoutLMTokenizer.from_pretrained(self.tmpdirname				, **__UpperCAmelCase)






    def 	__snake_case							(					self				:    Union[str, Any]				, __UpperCAmelCase				:    str):
      a						:		Tuple      							=			"UNwant\u00E9d,running"
      a						:		Dict      							=			"unwanted, running"
      return input_text, output_text






    def 	__snake_case							(					self				:    Any):
      a						:		List[Any]      							=			self.tokenizer_class(self.vocab_file)

      a						:		str      							=			tokenizer.tokenize("UNwant\u00E9d,running")
      self.assertListEqual(__UpperCAmelCase				, ["un", "##want", "##ed", ",", "runn", "##ing"])
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase)				, [7, 4, 5, 10, 8, 9])






    def 	__snake_case							(					self				:    Dict):
      pass






