



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__		:					str							={
    '''configuration_longformer''': [
        '''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LongformerConfig''',
        '''LongformerOnnxConfig''',
    ],
    '''tokenization_longformer''': ['''LongformerTokenizer'''],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Dict							=['''LongformerTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Optional[int]							=[
			    '''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''LongformerForMaskedLM''',
			    '''LongformerForMultipleChoice''',
			    '''LongformerForQuestionAnswering''',
			    '''LongformerForSequenceClassification''',
			    '''LongformerForTokenClassification''',
			    '''LongformerModel''',
			    '''LongformerPreTrainedModel''',
			    '''LongformerSelfAttention''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					List[Any]							=[
			    '''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFLongformerForMaskedLM''',
			    '''TFLongformerForMultipleChoice''',
			    '''TFLongformerForQuestionAnswering''',
			    '''TFLongformerForSequenceClassification''',
			    '''TFLongformerForTokenClassification''',
			    '''TFLongformerModel''',
			    '''TFLongformerPreTrainedModel''',
			    '''TFLongformerSelfAttention''',
			]


if TYPE_CHECKING:
			from .configuration_longformer import (
			    LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    LongformerConfig,
			    LongformerOnnxConfig,
			)
			from .tokenization_longformer import LongformerTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_longformer_fast import LongformerTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_longformer import (
						    LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    LongformerForMaskedLM,
						    LongformerForMultipleChoice,
						    LongformerForQuestionAnswering,
						    LongformerForSequenceClassification,
						    LongformerForTokenClassification,
						    LongformerModel,
						    LongformerPreTrainedModel,
						    LongformerSelfAttention,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_longformer import (
						    TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFLongformerForMaskedLM,
						    TFLongformerForMultipleChoice,
						    TFLongformerForQuestionAnswering,
						    TFLongformerForSequenceClassification,
						    TFLongformerForTokenClassification,
						    TFLongformerModel,
						    TFLongformerPreTrainedModel,
						    TFLongformerSelfAttention,
						)

else:
			import sys

			lowerCAmelCase__		:					Tuple							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)











from dataclasses import dataclass, field
from typing import Optional

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

@dataclass
class        UpperCAmelCase_  :

						'''simple docstring'''




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







def      __lowercase       (			)    ->			Union[str, Any]:
							__SCREAMING_SNAKE_CASE									= HfArgumentParser((ModelArguments,)       )
							((__SCREAMING_SNAKE_CASE)							,	)									= parser.parse_args_into_dataclasses()

							# Load pretrained model and tokenizer

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

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

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

							__SCREAMING_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=a__							,       decoder_config=a__							,       )

							# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
							__SCREAMING_SNAKE_CASE									= decoder_config.decoder_start_token_id
							__SCREAMING_SNAKE_CASE									= decoder_config.pad_token_id
							if decoder_start_token_id is None:
														__SCREAMING_SNAKE_CASE									= decoder_config.bos_token_id
							if pad_token_id is None:
														__SCREAMING_SNAKE_CASE									= decoder_config.eos_token_id

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

							__SCREAMING_SNAKE_CASE									= AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path       )

							__SCREAMING_SNAKE_CASE									= AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path       )
							__SCREAMING_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()






