


import argparse

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


logging.set_verbosity_info()







def 	A (		A__		,		A__		,		A__		,		A__		,		A__			):
     # Initialise PyTorch model
     UpperCAmelCase    :					Dict			=      BertConfig.from_pretrained(
         "bert-large-cased"		,		vocab_size=A__		,		max_position_embeddings=5_1_2		,		is_decoder=A__		,		add_cross_attention=A__		,		)
     UpperCAmelCase    :					Tuple			=      bert_config.to_dict()
     del bert_config_dict["type_vocab_size"]
     UpperCAmelCase    :					List[Any]			=      BertGenerationConfig(**A__			)
     if is_encoder:
          UpperCAmelCase    :					List[Any]			=      BertGenerationEncoder(A__			)
     else:
          UpperCAmelCase    :					Optional[Any]			=      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__":
      _snake_case				=			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")
      _snake_case				=			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


_snake_case				=			{
    "configuration_oneformer": ["ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "OneFormerConfig"],
    "processing_oneformer": ["OneFormerProcessor"],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case				=			["OneFormerImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case				=			[
          "ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "OneFormerForUniversalSegmentation",
          "OneFormerModel",
          "OneFormerPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig
      from .processing_oneformer import OneFormerProcessor

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_oneformer import OneFormerImageProcessor
      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_oneformer import (
                ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                OneFormerForUniversalSegmentation,
                OneFormerModel,
                OneFormerPreTrainedModel,
            )


else:
      import sys

      _snake_case				=			_LazyModule(__name__, globals()["__file__"], _import_structure)



