


'''simple docstring'''
from typing import TYPE_CHECKING

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


lowerCAmelCase  =				{
    """configuration_segformer""": ["""SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SegformerConfig""", """SegformerOnnxConfig"""]
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase  =				["""SegformerFeatureExtractor"""]
    lowerCAmelCase  =				["""SegformerImageProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase  =				[
        """SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """SegformerDecodeHead""",
        """SegformerForImageClassification""",
        """SegformerForSemanticSegmentation""",
        """SegformerLayer""",
        """SegformerModel""",
        """SegformerPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase  =				[
        """TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFSegformerDecodeHead""",
        """TFSegformerForImageClassification""",
        """TFSegformerForSemanticSegmentation""",
        """TFSegformerModel""",
        """TFSegformerPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_segformer import SegformerFeatureExtractor
        from .image_processing_segformer import SegformerImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_segformer import (
            SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            SegformerDecodeHead,
            SegformerForImageClassification,
            SegformerForSemanticSegmentation,
            SegformerLayer,
            SegformerModel,
            SegformerPreTrainedModel,
        )
    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_segformer import (
            TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFSegformerDecodeHead,
            TFSegformerForImageClassification,
            TFSegformerForSemanticSegmentation,
            TFSegformerModel,
            TFSegformerPreTrainedModel,
        )

else:
    import sys

    lowerCAmelCase  =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









'''simple docstring'''
import argparse

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


logging.set_verbosity_info()

def 				lowerCamelCase_   (   __UpperCamelCase				:					Tuple       ,						__UpperCamelCase				:					Any       ,						__UpperCamelCase				:					Dict       ,						__UpperCamelCase				:					List[str]       ,						__UpperCamelCase				:					str		)				->  List[str]:







     """simple docstring"""
     # Initialise PyTorch model
     _A				=       BertConfig.from_pretrained(
         'bert-large-cased'       ,						vocab_size=__UpperCamelCase       ,						max_position_embeddings=5_1_2       ,						is_decoder=__UpperCamelCase       ,						add_cross_attention=__UpperCamelCase       ,						)
     _A				=       bert_config.to_dict()
     del bert_config_dict["type_vocab_size"]
     _A				=       BertGenerationConfig(**__UpperCamelCase		)
     if is_encoder:
          _A				=       BertGenerationEncoder(__UpperCamelCase		)
     else:
          _A				=       BertGenerationDecoder(__UpperCamelCase		)
     print(F'Building PyTorch model from configuration: {config}'		)

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

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


if __name__ == "__main__":
    lowerCAmelCase  =				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""")
    lowerCAmelCase  =				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,
    )





