
'''simple docstring'''

import argparse

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


logging.set_verbosity_info()



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

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

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


if __name__ == "__main__":
   __UpperCAmelCase												=   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_0358, type=int, help='Vocab size of model')
   __UpperCAmelCase												=   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,
   )



from typing import TYPE_CHECKING

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


UpperCamelCase    				=		{'''configuration_vit_hybrid''': ['''VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTHybridConfig''']}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase    				=		[
          '''VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ViTHybridForImageClassification''',
          '''ViTHybridModel''',
          '''ViTHybridPreTrainedModel''',
      ]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase    				=		['''ViTHybridImageProcessor''']


if TYPE_CHECKING:
      from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_vit_hybrid import (
                VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
                ViTHybridForImageClassification,
                ViTHybridModel,
                ViTHybridPreTrainedModel,
            )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
      import sys

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



