



'''simple docstring'''

from __future__ import annotations

import os
from typing import Any

import requests

__lowerCAmelCase						      =       '''https://api.github.com'''

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
__lowerCAmelCase						      =       BASE_URL + '''/user'''

# https://github.com/settings/tokens
__lowerCAmelCase						      =       os.environ.get('''USER_TOKEN''', '''''')



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				dict[Any, Any]:
		_a					:				Union[str, Any]				=							{
		    'Authorization': f"""token {auth_token}""",
		    'Accept': 'application/vnd.github.v3+json',
		}
		return requests.get(lowerCAmelCase_						,      headers=lowerCAmelCase_  ).json()


if __name__ == "__main__":  # pragma: no cover
							if USER_TOKEN:
														for key, value in fetch_github_info(USER_TOKEN).items():
																					print(f"""{key}: {value}""")
							else:
														raise ValueError('''\'USER_TOKEN\' field cannot be empty.''')









'''simple docstring'''

from dataclasses import dataclass, field
from typing import Optional

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

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



def 					__lowerCamelCase					(				)      ->				Union[str, Any]:
		_a					:				Any				=							HfArgumentParser((ModelArguments,)  )
		((_a)	, )					:				Dict				=							parser.parse_args_into_dataclasses()

		# Load pretrained model and tokenizer

		# Use explicit specified encoder config
		if model_args.encoder_config_name:
				_a					:				Optional[Any]				=							AutoConfig.from_pretrained(model_args.encoder_config_name  )
		# Use pretrained encoder model's config
		else:
				_a					:				Optional[Any]				=							AutoConfig.from_pretrained(model_args.encoder_model_name_or_path  )

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

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

		_a					:				Any				=							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=lowerCAmelCase_						,      decoder_config=lowerCAmelCase_						,      )

		# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
		_a					:				List[str]				=							decoder_config.decoder_start_token_id
		_a					:				Optional[int]				=							decoder_config.pad_token_id
		if decoder_start_token_id is None:
				_a					:				Tuple				=							decoder_config.bos_token_id
		if pad_token_id is None:
				_a					:				List[Any]				=							decoder_config.eos_token_id

		# This is necessary to make Flax's generate() work
		_a					:				Any				=							decoder_config.eos_token_id
		_a					:				Tuple				=							decoder_start_token_id
		_a					:				Any				=							pad_token_id

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

		_a					:				Dict				=							AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path  )
		_a					:				int				=							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()




