



'''simple docstring'''

import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()







def     __UpperCamelCase					(				_lowercase,					_lowercase,					_lowercase,					_lowercase,					_lowercase       )			->      Union[str, Any]:
	# Initialise PyTorch model
	_lowercase       :							str  	=		BertConfig.from_pretrained(
	    'bert-large-cased',					vocab_size=_lowercase,					max_position_embeddings=512,					is_decoder=_lowercase,					add_cross_attention=_lowercase,					)
	_lowercase       :							Tuple  	=		bert_config.to_dict()
	del bert_config_dict["type_vocab_size"]
	_lowercase       :							Optional[int]  	=		BertGenerationConfig(**_lowercase       )
	if is_encoder:
		_lowercase       :							Dict  	=		BertGenerationEncoder(_lowercase       )
	else:
		_lowercase       :							List[Any]  	=		BertGenerationDecoder(_lowercase       )
	print(f'''Building PyTorch model from configuration: {config}'''       )

	# Load weights from tf checkpoint
	load_tf_weights_in_bert_generation(
	    _lowercase,					_lowercase,					model_class='bert',					is_encoder_named_decoder=_lowercase,					is_encoder=_lowercase,					)

	# Save pytorch-model
	print(f'''Save PyTorch model and config to {pytorch_dump_path}'''       )
	model.save_pretrained(_lowercase       )


if __name__ == "__main__":
		_A					:     Dict      =argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--tf_hub_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		parser.add_argument(
		    '''--is_encoder_named_decoder''',
		    action='''store_true''',
		    help='''If decoder has to be renamed to encoder in PyTorch model.''',
		)
		parser.add_argument('''--is_encoder''', action='''store_true''', help='''If model is an encoder.''')
		parser.add_argument('''--vocab_size''', default=5_0_3_5_8, type=int, help='''Vocab size of model''')
		_A					:     int      =parser.parse_args()
		convert_tf_checkpoint_to_pytorch(
		    args.tf_hub_path,
		    args.pytorch_dump_path,
		    args.is_encoder_named_decoder,
		    args.vocab_size,
		    is_encoder=args.is_encoder,
		)





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A					:     List[Any]      ={'''configuration_umt5''': ['''UMT5Config''', '''UMT5OnnxConfig''']}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A					:     Optional[Any]      =[
		    '''UMT5EncoderModel''',
		    '''UMT5ForConditionalGeneration''',
		    '''UMT5ForQuestionAnswering''',
		    '''UMT5Model''',
		    '''UMT5PreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_umta import UMTaConfig, UMTaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_umta import (
				    UMTaEncoderModel,
				    UMTaForConditionalGeneration,
				    UMTaForQuestionAnswering,
				    UMTaModel,
				    UMTaPreTrainedModel,
				)
else:
		import sys

		_A					:     Optional[int]      =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
