




'''simple docstring'''


from dataclasses import dataclass, field
from typing import Optional

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

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



def 						snake_case_       ():





							'''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=UpperCamelCase					,		decoder_config=UpperCamelCase					,		)

							# 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()







'''simple docstring'''


from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass

@dataclass
class      A       :
							lowercase_							     =							42
							lowercase_							     =							42



class      A       :







							def __init__(    self			:    Optional[Any]				,     lowerCAmelCase_			:    int   )     ->				str:


														"""simple docstring"""


														_a      	=							[[] for _ in range(lowerCAmelCase_   )]
														_a      	=							size







							def __getitem__(    self			:    Any				,     lowerCAmelCase_			:    int   )     ->				Iterator[Edge]:


														"""simple docstring"""


														return iter(self._graph[vertex]   )







							@property
							def   __lowerCAmelCase     (    self			:    str   )     ->				Tuple:


														"""simple docstring"""


														return self._size







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    int   )     ->				Dict:


														"""simple docstring"""


														if weight not in (0, 1):
																					raise ValueError('''Edge weight must be either 0 or 1.'''   )

														if to_vertex < 0 or to_vertex >= self.size:
																					raise ValueError('''Vertex indexes must be in [0; size).'''   )

														self._graph[from_vertex].append(Edge(lowerCAmelCase_				,     lowerCAmelCase_   )   )







							def   __lowerCAmelCase     (    self			:    Tuple				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    int   )     ->				int | None:


														"""simple docstring"""


														_a      	=							deque([start_vertex]   )
														_a      	=							[None] * self.size
														_a      	=							0

														while queue:
																					_a      	=							queue.popleft()
																					_a      	=							distances[current_vertex]
																					if current_distance is None:
																												continue

																					for edge in self[current_vertex]:
																												_a      	=							current_distance + edge.weight
																												_a      	=							distances[edge.destination_vertex]
																												if (
																												    isinstance(lowerCAmelCase_				,     lowerCAmelCase_   )
																												    and new_distance >= dest_vertex_distance
																												):
																																			continue
																												_a      	=							new_distance
																												if edge.weight == 0:
																																			queue.appendleft(edge.destination_vertex   )
																												else:
																																			queue.append(edge.destination_vertex   )

														if distances[finish_vertex] is None:
																					raise ValueError('''No path from start_vertex to finish_vertex.'''   )

														return distances[finish_vertex]


if __name__ == "__main__":
				import doctest

				doctest.testmod()

