



from dataclasses import dataclass, field
from typing import Optional

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


@dataclass
class 	__snake_case   :
		SCREAMING_SNAKE_CASE__								=	field(
		    metadata={'help': 'The output directory where the model will be written.'}							, )
		SCREAMING_SNAKE_CASE__								=	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__								=	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__								=	field(
		    default=SCREAMING_SNAKE_CASE							, metadata={'help': 'Pretrained encoder config name or path if not the same as encoder_model_name'} )
		SCREAMING_SNAKE_CASE__								=	field(
		    default=SCREAMING_SNAKE_CASE							, metadata={'help': 'Pretrained decoder config name or path if not the same as decoder_model_name'} )







def     UpperCAmelCase_		(					)	->     Optional[Any]:



				"""simple docstring"""





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

				# Load pretrained model and tokenizer

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

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

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

				lowerCAmelCase__		  =  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=snake_case__    ,			decoder_config=snake_case__    ,			)

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

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

				lowerCAmelCase__		  =  AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path					)

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





from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

_lowerCAmelCase	:   List[str]    =   TypeVar("T")


class 	__snake_case   ( Generic[T] ):
		def __init__(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  data
						lowerCAmelCase__		  =  None



		def __str__(							self						):







						"""simple docstring"""
						return f'{self.data}'



class 	__snake_case   ( Generic[T] ):
		def __init__(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  None
		def __iter__(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.top
						while node:
										yield node.data
										lowerCAmelCase__		  =  node.next
		def __str__(							self						):







						"""simple docstring"""
						return "->".join([str(a_						) for item in self]						)
		def __len__(							self						):







						"""simple docstring"""
						return len(tuple(iter(self						)						)						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.top is None
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  Node(a_						)
						if not self.is_empty():
										lowerCAmelCase__		  =  self.top
						lowerCAmelCase__		  =  node
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						if self.is_empty():
										raise IndexError('pop from empty stack'						)
						assert isinstance(self.top    ,a_						)
						lowerCAmelCase__		  =  self.top
						lowerCAmelCase__		  =  self.top.next
						return pop_node.data
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						if self.is_empty():
										raise IndexError('peek from empty stack'						)

						assert self.top is not None
						return self.top.data



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  None


if __name__ == "__main__":
	from doctest import testmod

	testmod()
