
from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class 					lowercase__:



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




def 							a		(				):


				'''simple docstring'''
				lowercase_	    =   HfArgumentParser((ModelArguments,)   )
				((lowercase_)    ,      )	    =   parser.parse_args_into_dataclasses()

				# Load pretrained model and tokenizer

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

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

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

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

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

				lowercase_	    =   AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path   )

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

__a         =   8.988E9  # units = N * m^s * C^-2




def 							a		(				snake_case__:   float		,  snake_case__:   float		,  snake_case__:   float		,  snake_case__:   float   ):


				'''simple docstring'''
				lowercase_	    =   abs(chargea * chargea   )

				if (force, chargea, chargea, distance).count(0   ) != 1:
								raise ValueError('''One and only one argument must be 0'''   )
				if distance < 0:
								raise ValueError('''Distance cannot be negative'''   )
				if force == 0:
								lowercase_	    =   COULOMBS_CONSTANT * charge_product / (distance**2)
								return {"force": force}
				elif chargea == 0:
								lowercase_	    =   abs(snake_case__   ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
								return {"charge1": chargea}
				elif chargea == 0:
								lowercase_	    =   abs(snake_case__   ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
								return {"charge2": chargea}
				elif distance == 0:
								lowercase_	    =   (COULOMBS_CONSTANT * charge_product / abs(snake_case__   )) ** 0.5
								return {"distance": distance}
				raise ValueError('''Exactly one argument must be 0'''   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()




