




'''simple docstring'''

import argparse

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


logging.set_verbosity_info()




def    snake_case__     (  a       ,      a       ,      a       ,      a       ,      a	)			->       List[str]:





			'''simple docstring'''






			snake_case__          =						BertConfig.from_pretrained(
			    """bert-large-cased"""       ,      vocab_size=a       ,      max_position_embeddings=512       ,      is_decoder=a       ,      add_cross_attention=a       ,      )
			snake_case__          =						bert_config.to_dict()
			del bert_config_dict["type_vocab_size"]
			snake_case__          =						BertGenerationConfig(**a	)
			if is_encoder:
						snake_case__          =						BertGenerationEncoder(a	)
			else:
						snake_case__          =						BertGenerationDecoder(a	)
			print(F"""Building PyTorch model from configuration: {config}"""	)

			# Load weights from tf checkpoint
			load_tf_weights_in_bert_generation(
			    a       ,      a       ,      model_class="""bert"""       ,      is_encoder_named_decoder=a       ,      is_encoder=a       ,      )

			# Save pytorch-model
			print(F"""Save PyTorch model and config to {pytorch_dump_path}"""	)
			model.save_pretrained(a	)


if __name__ == "__main__":
		a__         =     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=50_358, type=int, help='''Vocab size of model''')
		a__         =     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_tf_available, is_torch_available


a__         =     {'''configuration_cvt''': ['''CVT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CvtConfig''']}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CvtForImageClassification''',
		    '''CvtModel''',
		    '''CvtPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFCvtForImageClassification''',
		    '''TFCvtModel''',
		    '''TFCvtPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_cvt import (
				    CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CvtForImageClassification,
				    CvtModel,
				    CvtPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_cvt import (
				    TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFCvtForImageClassification,
				    TFCvtModel,
				    TFCvtPreTrainedModel,
				)


else:
		import sys

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