from dataclasses import dataclass, field
from typing import Optional

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


@dataclass
class 	lowerCamelCase_     :






    '''simple docstring'''






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



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :int			  =		HfArgumentParser((ModelArguments,)	)
    ((__UpperCamelCase      )  ,             )     :Any			  =		parser.parse_args_into_dataclasses()

    # Load pretrained model and tokenizer

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

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

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

    __UpperCamelCase     :Union[str, 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=SCREAMING_SNAKE_CASE__  ,			decoder_config=SCREAMING_SNAKE_CASE__  ,			)

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

    # This is necessary to make Flax's generate() work
    __UpperCamelCase     :Union[str, Any]			  =		decoder_config.eos_token_id
    __UpperCamelCase     :Optional[int]			  =		decoder_start_token_id
    __UpperCamelCase     :Optional[Any]			  =		pad_token_id

    __UpperCamelCase     :Optional[Any]			  =		AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path	)

    __UpperCamelCase     :List[str]			  =		AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path	)
    __UpperCamelCase     :Optional[int]			  =		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()









_A		       =						{0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

_A		       =						{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					dict[int, list[int]]			,     SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					list[bool]				):
     __UpperCamelCase    							=True
     __UpperCamelCase    							=[]

     for neighbour in graph[vert]:
          if not visited[neighbour]:
               order += topology_sort(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)

     order.append(SCREAMING_SNAKE_CASE__				)

     return order

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					dict[int, list[int]]			,     SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					list[bool]				):
     __UpperCamelCase    							=True
     __UpperCamelCase    							=[vert]

     for neighbour in reversed_graph[vert]:
          if not visited[neighbour]:
               component += find_components(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)

     return component





def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					dict[int, list[int]]				):
     __UpperCamelCase    							=len(SCREAMING_SNAKE_CASE__				) * [False]
     __UpperCamelCase    							={vert: [] for vert in range(len(SCREAMING_SNAKE_CASE__				)				)}

     for vert, neighbours in graph.items():
          for neighbour in neighbours:
               reversed_graph[neighbour].append(SCREAMING_SNAKE_CASE__				)

     __UpperCamelCase    							=[]
     for i, was_visited in enumerate(SCREAMING_SNAKE_CASE__				):
          if not was_visited:
               order += topology_sort(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)

     __UpperCamelCase    							=[]
     __UpperCamelCase    							=len(SCREAMING_SNAKE_CASE__				) * [False]

     for i in range(len(SCREAMING_SNAKE_CASE__				)				):
          __UpperCamelCase    							=order[len(SCREAMING_SNAKE_CASE__				) - i - 1]
          if not visited[vert]:
               __UpperCamelCase    							=find_components(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
               components_list.append(SCREAMING_SNAKE_CASE__				)

     return components_list





