





"""simple docstring"""





from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""
				__snake_case :							str									=		field(
				    metadata={"""help""": """The output directory where the model will be written."""}							,	)
				__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."""
				        )
				    }							,	)
				__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."""
				        )
				    }							,	)
				__snake_case :							Optional[str]									=		field(
				    default=snake_case__							,	metadata={"""help""": """Pretrained encoder config name or path if not the same as encoder_model_name"""} )
				__snake_case :							Optional[str]									=		field(
				    default=snake_case__							,	metadata={"""help""": """Pretrained decoder config name or path if not the same as decoder_model_name"""} )



def 					lowerCAmelCase_      (   ):


							'''simple docstring'''







							__lowerCamelCase   :   List[str]       =HfArgumentParser((ModelArguments,) )
							((__lowerCamelCase)     ,    )   :   int       =parser.parse_args_into_dataclasses()

							# Load pretrained model and tokenizer

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

							# Use explicit specified decoder config
							if model_args.decoder_config_name:
														__lowerCamelCase   :   Optional[Any]       =AutoConfig.from_pretrained(model_args.decoder_config_name )
							# Use pretrained decoder model's config
							else:
														__lowerCamelCase   :   Optional[Any]       =AutoConfig.from_pretrained(model_args.decoder_model_name_or_path )

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

							__lowerCamelCase   :   Optional[int]       =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
							__lowerCamelCase   :   Optional[int]       =decoder_config.decoder_start_token_id
							__lowerCamelCase   :   Optional[Any]       =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   :   Tuple       =decoder_config.eos_token_id

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

							__lowerCamelCase   :   Dict       =AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path )

							__lowerCamelCase   :   Optional[Any]       =AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path )
							__lowerCamelCase   :   str       =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"""





import numpy as np



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				np.array ):


							'''simple docstring'''







							return 1 / (1 + np.exp(-vector ))


if __name__ == "__main__":
							import doctest

							doctest.testmod()




