


import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							["image_processor", "tokenizer"]
       _a   							=							"AutoImageProcessor"
       _a   							=							"AutoTokenizer"



       def __init__(						self				:				str		,				__a				:				Optional[int]=None		,				__a				:				int=None		,				**__a				:				Optional[Any]							)					->str:
          lowerCamelCase_						:   Optional[int]		= None
          if "feature_extractor" in kwargs:
             warnings.warn(
                 """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                 """ instead."""		,				__a		,				)
             lowerCamelCase_						:   Dict		= kwargs.pop("""feature_extractor"""							)

          lowerCamelCase_						:   str		= image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
             raise ValueError("""You need to specify an `image_processor`."""							)
          if tokenizer is None:
             raise ValueError("""You need to specify a `tokenizer`."""							)

          super().__init__(__a		,				__a							)
          lowerCamelCase_						:   Optional[Any]		= self.image_processor
          lowerCamelCase_						:   Optional[Any]		= False



       def __call__(						self				:				List[Any]		,				*__a				:				Optional[Any]		,				**__a				:				List[Any]							)					->Optional[Any]:
          # For backward compatibility
          if self._in_target_context_manager:
             return self.current_processor(*__a		,				**__a							)

          lowerCamelCase_						:   Any		= kwargs.pop("""images"""		,				__a							)
          lowerCamelCase_						:   Union[str, Any]		= kwargs.pop("""text"""		,				__a							)
          if len(__a							) > 0:
             lowerCamelCase_						:   List[str]		= args[0]
             lowerCamelCase_						:   Optional[Any]		= args[1:]

          if images is None and text is None:
             raise ValueError("""You need to specify either an `images` or `text` input to process."""							)

          if images is not None:
             lowerCamelCase_						:   Dict		= self.image_processor(__a		,				*__a		,				**__a							)
          if text is not None:
             lowerCamelCase_						:   Any		= self.tokenizer(__a		,				**__a							)

          if text is None:
             return inputs
          elif images is None:
             return encodings
          else:
             lowerCamelCase_						:   Tuple		= encodings["""input_ids"""]
             return inputs



       def 				_lowerCAmelCase							(						self				:				int		,				*__a				:				Tuple		,				**__a				:				Optional[int]							)					->Optional[int]:
          return self.tokenizer.batch_decode(*__a		,				**__a							)



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]		,				*__a				:				Any		,				**__a				:				Tuple							)					->List[str]:
          return self.tokenizer.decode(*__a		,				**__a							)



       @contextmanager
       def 				_lowerCAmelCase							(						self				:				Optional[int]							)					->Any:
          warnings.warn(
              """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
              """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
              """your images inputs, or in a separate call."""							)
          lowerCamelCase_						:   List[str]		= True
          lowerCamelCase_						:   Optional[Any]		= self.tokenizer
          yield
          lowerCamelCase_						:   int		= self.image_processor
          lowerCamelCase_						:   Optional[int]		= False



       def 				_lowerCAmelCase							(						self				:				Optional[Any]		,				__a				:				List[Any]		,				__a				:				List[Any]=False		,				__a				:				Union[str, Any]=None							)					->Any:
          if added_vocab is None:
             lowerCamelCase_						:   Tuple		= self.tokenizer.get_added_vocab()

          lowerCamelCase_						:   List[str]		= {}

          while tokens:
             lowerCamelCase_						:   Optional[int]		= re.search(r"""<s_(.*?)>"""		,				__a		,				re.IGNORECASE							)
             if start_token is None:
                break
             lowerCamelCase_						:   Union[str, Any]		= start_token.group(1							)
             lowerCamelCase_						:   Any		= re.search(rF'''</s_{key}>'''		,				__a		,				re.IGNORECASE							)
             lowerCamelCase_						:   List[str]		= start_token.group()
             if end_token is None:
                lowerCamelCase_						:   Optional[int]		= tokens.replace(__a		,				""""""							)
             else:
                lowerCamelCase_						:   Optional[Any]		= end_token.group()
                lowerCamelCase_						:   int		= re.escape(__a							)
                lowerCamelCase_						:   Tuple		= re.escape(__a							)
                lowerCamelCase_						:   str		= re.search(F'''{start_token_escaped}(.*?){end_token_escaped}'''		,				__a		,				re.IGNORECASE							)
                if content is not None:
                   lowerCamelCase_						:   Optional[Any]		= content.group(1							).strip()
                   if r"<s_" in content and r"</s_" in content:  # non-leaf node
                      lowerCamelCase_						:   Optional[int]		= self.tokenajson(__a		,				is_inner_value=__a		,				added_vocab=__a							)
                      if value:
                         if len(__a							) == 1:
                            lowerCamelCase_						:   Union[str, Any]		= value[0]
                         lowerCamelCase_						:   Any		= value
                   else:  # leaf nodes
                      lowerCamelCase_						:   str		= []
                      for leaf in content.split(r"""<sep/>"""							):
                         lowerCamelCase_						:   Optional[Any]		= leaf.strip()
                         if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                            lowerCamelCase_						:   int		= leaf[1:-2]  # for categorical special tokens
                         output[key].append(__a							)
                      if len(output[key]							) == 1:
                         lowerCamelCase_						:   int		= output[key][0]

                lowerCamelCase_						:   str		= tokens[tokens.find(__a							) + len(__a							) :].strip()
                if tokens[:6] == r"<sep/>":  # non-leaf nodes
                   return [output] + self.tokenajson(tokens[6:]		,				is_inner_value=__a		,				added_vocab=__a							)

          if len(__a							):
             return [output] if is_inner_value else output
          else:
             return [] if is_inner_value else {"text_sequence": tokens}



       @property
       def 				_lowerCAmelCase							(						self				:				Tuple							)					->Optional[int]:
          warnings.warn(
              """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""		,				__a		,				)
          return self.image_processor_class




       @property
       def 				_lowerCAmelCase							(						self				:				List[str]							)					->Union[str, Any]:
          warnings.warn(
              """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""		,				__a		,				)
          return self.image_processor









from dataclasses import dataclass, field
from typing import Optional

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





@dataclass
class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



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







def    __lowerCamelCase   (      )     ->    Optional[Any]:
   lowerCamelCase_						:   List[Any]		= HfArgumentParser((ModelArguments,)					)
   ((lowerCamelCase_),	)						:   List[Any]		= parser.parse_args_into_dataclasses()

   # Load pretrained model and tokenizer

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

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

   # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
   lowerCamelCase_						:   Optional[Any]		= True
   lowerCamelCase_						:   List[Any]		= True

   lowerCamelCase_						:   List[Any]		= 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
   lowerCamelCase_						:   Tuple		= decoder_config.decoder_start_token_id
   lowerCamelCase_						:   int		= decoder_config.pad_token_id
   if decoder_start_token_id is None:
      lowerCamelCase_						:   Any		= decoder_config.bos_token_id
   if pad_token_id is None:
      lowerCamelCase_						:   List[str]		= decoder_config.eos_token_id

   # This is necessary to make Flax's generate() work
   lowerCamelCase_						:   List[str]		= decoder_config.eos_token_id
   lowerCamelCase_						:   Tuple		= decoder_start_token_id
   lowerCamelCase_						:   Optional[Any]		= pad_token_id

   lowerCamelCase_						:   str		= AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path					)

   lowerCamelCase_						:   Any		= AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path					)
   lowerCamelCase_						:   Dict		= 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()





