



import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			# Initialise PyTorch model
			A							:					Optional[Any]              =						MobileBertConfig.from_json_file(_lowerCamelCase    )
			print(f"""Building PyTorch model from configuration: {config}"""    )
			A							:					Union[str, Any]              =						MobileBertForPreTraining(_lowerCamelCase    )
			# Load weights from tf checkpoint
			A							:					Union[str, Any]              =						load_tf_weights_in_mobilebert(_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    )
			# Save pytorch-model
			print(f"""Save PyTorch model to {pytorch_dump_path}"""    )
			torch.save(model.state_dict() ,    _lowerCamelCase    )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE           =  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
							)
							parser.add_argument(
							    """--mobilebert_config_file""",
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        """The config json file corresponding to the pre-trained MobileBERT model. \n"""
							        """This specifies the model architecture."""
							    ),
							)
							parser.add_argument(
							    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							__SCREAMING_SNAKE_CASE           =  parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE           =  {
    """configuration_instructblip""": [
        """INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InstructBlipConfig""",
        """InstructBlipQFormerConfig""",
        """InstructBlipVisionConfig""",
    ],
    """processing_instructblip""": ["""InstructBlipProcessor"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE           =  [
							    """INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """InstructBlipQFormerModel""",
							    """InstructBlipPreTrainedModel""",
							    """InstructBlipForConditionalGeneration""",
							    """InstructBlipVisionModel""",
							]

if TYPE_CHECKING:
							from .configuration_instructblip import (
							    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    InstructBlipConfig,
							    InstructBlipQFormerConfig,
							    InstructBlipVisionConfig,
							)
							from .processing_instructblip import InstructBlipProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_instructblip import (
														    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    InstructBlipForConditionalGeneration,
														    InstructBlipPreTrainedModel,
														    InstructBlipQFormerModel,
														    InstructBlipVisionModel,
														)

else:
							import sys

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