
from dataclasses import dataclass, field
from typing import Optional

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




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





def 			lowerCamelCase_ (				):
				lowerCamelCase_						=			HfArgumentParser((ModelArguments,)    )
				((lowerCamelCase_)	,    )						=			parser.parse_args_into_dataclasses()

				# Load pretrained model and tokenizer

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

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

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

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

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

				lowerCamelCase_						=			AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path    )

				lowerCamelCase_						=			AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path    )
				lowerCamelCase_						=			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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A				={
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=['''BloomTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''BloomForCausalLM''',
			    '''BloomModel''',
			    '''BloomPreTrainedModel''',
			    '''BloomForSequenceClassification''',
			    '''BloomForTokenClassification''',
			    '''BloomForQuestionAnswering''',
			]

if TYPE_CHECKING:
			from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bloom_fast import BloomTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bloom import (
						    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BloomForCausalLM,
						    BloomForQuestionAnswering,
						    BloomForSequenceClassification,
						    BloomForTokenClassification,
						    BloomModel,
						    BloomPreTrainedModel,
						)

else:
			import sys

			__A				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


