


from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

lowerCAmelCase__      		= TypeVar('T')







class      lowerCAmelCase__       (				Generic[T]):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= 42  # Cache store of keys
     __SCREAMING_SNAKE_CASE  			= 42  # References of the keys in cache
     __SCREAMING_SNAKE_CASE  			= 10  # Maximum capacity of cache


     def __init__(						self ,						__lowerCamelCase)			->				None:
            _A      :       List[Any]				    = deque()
            _A      :       Union[str, Any]				    = set()
            if not n:
                   _A      :       List[Any]				    = sys.maxsize
            elif n < 0:
                   raise ValueError("n should be an integer greater than 0.")
            else:
                   _A      :       str				    = n


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				None:
            if x not in self.key_reference:
                   if len(self.dq_store) == LRUCache._MAX_CAPACITY:
                          _A      :       Optional[Any]				    = self.dq_store.pop()
                          self.key_reference.remove(__lowerCamelCase)
            else:
                   self.dq_store.remove(__lowerCamelCase)

            self.dq_store.appendleft(__lowerCamelCase)
            self.key_reference.add(__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				None:
            for k in self.dq_store:
                   print(__lowerCamelCase)


     def __repr__(						self)			->				str:
            return F"LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store)}"


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    lowerCAmelCase__      		= LRUCache(4)
    lru_cache.refer('A')
    lru_cache.refer(2)
    lru_cache.refer(3)
    lru_cache.refer('A')
    lru_cache.refer(4)
    lru_cache.refer(5)
    lru_cache.display()

    print(lru_cache)
    assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"






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=_UpperCamelCase    ,       metadata={"""help""": """Pretrained encoder config name or path if not the same as encoder_model_name"""}   )
				SCREAMING_SNAKE_CASE_       :							Optional[str]							 =					field(
				    default=_UpperCamelCase    ,       metadata={"""help""": """Pretrained decoder config name or path if not the same as decoder_model_name"""}   )




def 			__magic_name__				( ):






			'''simple docstring'''







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

			# Load pretrained model and tokenizer

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

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

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

			a    =			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
			a    =			decoder_config.decoder_start_token_id
			a    =			decoder_config.pad_token_id
			if decoder_start_token_id is None:
						a    =			decoder_config.bos_token_id
			if pad_token_id is None:
						a    =			decoder_config.eos_token_id

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

			a    =			AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path   )

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






