





from typing import TYPE_CHECKING

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


lowerCamelCase_						: List[Any]         =  {
    """configuration_efficientformer""": [
        """EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EfficientFormerConfig""",
    ]
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Dict         =  ["""EfficientFormerImageProcessor"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Union[str, Any]         =  [
							    """EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """EfficientFormerForImageClassification""",
							    """EfficientFormerForImageClassificationWithTeacher""",
							    """EfficientFormerModel""",
							    """EfficientFormerPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: List[str]         =  [
							    """TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFEfficientFormerForImageClassification""",
							    """TFEfficientFormerForImageClassificationWithTeacher""",
							    """TFEfficientFormerModel""",
							    """TFEfficientFormerPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_efficientformer import EfficientFormerImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_efficientformer import (
														    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    EfficientFormerForImageClassification,
														    EfficientFormerForImageClassificationWithTeacher,
														    EfficientFormerModel,
														    EfficientFormerPreTrainedModel,
														)
							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_efficientformer import (
														    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFEfficientFormerForImageClassification,
														    TFEfficientFormerForImageClassificationWithTeacher,
														    TFEfficientFormerModel,
														    TFEfficientFormerPreTrainedModel,
														)

else:
							import sys

							lowerCamelCase_						: Optional[int]         =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Union[str, Any]:
				# Initialise PyTorch model
				UpperCamelCase_:							List[Any]	=						TaConfig.from_json_file(lowerCamelCase						)
				print(F'''Building PyTorch model from configuration: {config}'''						)
				UpperCamelCase_:							Any	=						TaForConditionalGeneration(lowerCamelCase						)

				# Load weights from tf checkpoint
				load_tf_weights_in_ta(lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)

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


if __name__ == "__main__":
							lowerCamelCase_						: Tuple         =  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(
							    """--config_file""",
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
							    ),
							)
							parser.add_argument(
							    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							lowerCamelCase_						: Union[str, Any]         =  parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






