"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional

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



@dataclass
class   lowerCAmelCase_    :



				"""simple docstring"""
				_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=lowerCAmelCase     ,    metadata={"""help""": """Pretrained encoder config name or path if not the same as encoder_model_name"""}					)
				_lowerCAmelCase     :    Optional[str]			  =						field(
				    default=lowerCAmelCase     ,    metadata={"""help""": """Pretrained decoder config name or path if not the same as decoder_model_name"""}					)





def    lowerCAmelCase__    (			)					->				List[Any]:





						"""simple docstring"""






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

						# Load pretrained model and tokenizer

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

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

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

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

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

						snake_case								=			AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path					)

						snake_case								=			AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path					)
						snake_case								=			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 math





def    lowerCAmelCase__    (			_UpperCamelCase       :				int					)					->				bool:





						"""simple docstring"""






						return math.sqrt(_UpperCamelCase					) * math.sqrt(_UpperCamelCase					) == num





def    lowerCAmelCase__    (			_UpperCamelCase       :				int					)					->				bool:





						"""simple docstring"""






						snake_case								=			0
						snake_case								=			n
						while left <= right:
												snake_case								=			(left + right) // 2
												if mid**2 == n:
																		return True
												elif mid**2 > n:
																		snake_case								=			mid - 1
												else:
																		snake_case								=			mid + 1
						return False


if __name__ == "__main__":
					import doctest

					doctest.testmod()


