
"""simple docstring"""


import argparse

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


logging.set_verbosity_info()






def        lowerCAmelCase_       (		snake_case_		:      int							,   snake_case_		:      int							,   snake_case_		:      Dict							,   snake_case_		:      Union[str, Any]							,   snake_case_		:      Optional[Any]      )       ->Tuple:
			# Initialise PyTorch model
			lowerCamelCase__     :					Dict		=BertConfig.from_pretrained(
			    'bert-large-cased'							,   vocab_size=snake_case_							,   max_position_embeddings=5_1_2							,   is_decoder=snake_case_							,   add_cross_attention=snake_case_							,   )
			lowerCamelCase__     :					Any		=bert_config.to_dict()
			del bert_config_dict["type_vocab_size"]
			lowerCamelCase__     :					List[str]		=BertGenerationConfig(**snake_case_      )
			if is_encoder:
						lowerCamelCase__     :					Any		=BertGenerationEncoder(snake_case_      )
			else:
						lowerCamelCase__     :					str		=BertGenerationDecoder(snake_case_      )
			print(f"""Building PyTorch model from configuration: {config}"""      )

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

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


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=5_03_58, 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,
							)

"""simple docstring"""


from typing import TYPE_CHECKING

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


lowerCAmelCase								=						{
    """configuration_mobilenet_v1""": [
        """MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileNetV1Config""",
        """MobileNetV1OnnxConfig""",
    ],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						["""MobileNetV1FeatureExtractor"""]
							lowerCAmelCase								=						["""MobileNetV1ImageProcessor"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    """MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MobileNetV1ForImageClassification""",
							    """MobileNetV1Model""",
							    """MobileNetV1PreTrainedModel""",
							    """load_tf_weights_in_mobilenet_v1""",
							]


if TYPE_CHECKING:
							from .configuration_mobilenet_va import (
							    MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    MobileNetVaConfig,
							    MobileNetVaOnnxConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
														from .image_processing_mobilenet_va import MobileNetVaImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mobilenet_va import (
														    MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MobileNetVaForImageClassification,
														    MobileNetVaModel,
														    MobileNetVaPreTrainedModel,
														    load_tf_weights_in_mobilenet_va,
														)


else:
							import sys

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