import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def        SCREAMING_SNAKE_CASE_      (		lowerCAmelCase_:			Optional[int]			,						lowerCAmelCase_:			str			,						lowerCAmelCase_:			Union[str, Any]  ):



      if openai_config_file == "":
            snake_case_  :      Tuple  	=							OpenAIGPTConfig()
      else:
            snake_case_  :      Optional[Any]  	=							OpenAIGPTConfig.from_json_file(lowerCAmelCase_  )
      snake_case_  :      Dict  	=							OpenAIGPTModel(lowerCAmelCase_  )

      # Load weights from numpy
      load_tf_weights_in_openai_gpt(lowerCAmelCase_			,						lowerCAmelCase_			,						lowerCAmelCase_  )

      # Save pytorch-model
      snake_case_  :      List[str]  	=							pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
      snake_case_  :      Tuple  	=							pytorch_dump_folder_path + """/""" + CONFIG_NAME
      print(f"Save PyTorch model to {pytorch_weights_dump_path}"  )
      torch.save(model.state_dict()			,						lowerCAmelCase_  )
      print(f"Save configuration file to {pytorch_config_dump_path}"  )
      with open(lowerCAmelCase_			,						"w"			,						encoding="utf-8"  ) as f:
            f.write(config.to_json_string()  )


if __name__ == "__main__":
     UpperCAmelCase					      =							argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--openai_checkpoint_folder_path",
         default=None,
         type=str,
         required=True,
         help="Path to the TensorFlow checkpoint path.",
     )
     parser.add_argument(
         "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
     )
     parser.add_argument(
         "--openai_config_file",
         default="",
         type=str,
         help=(
             "An optional config json file corresponding to the pre-trained OpenAI model. \n"
             "This specifies the model architecture."
         ),
     )
     UpperCAmelCase					      =							parser.parse_args()
     convert_openai_checkpoint_to_pytorch(
         args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
     )










from typing import TYPE_CHECKING

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


UpperCAmelCase_							=				{
    """configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
    """configuration_maskformer_swin""": ["""MaskFormerSwinConfig"""],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_							=				["""MaskFormerFeatureExtractor"""]
							UpperCAmelCase_							=				["""MaskFormerImageProcessor"""]


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_							=				[
							    """MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MaskFormerForInstanceSegmentation""",
							    """MaskFormerModel""",
							    """MaskFormerPreTrainedModel""",
							]
							UpperCAmelCase_							=				[
							    """MaskFormerSwinBackbone""",
							    """MaskFormerSwinModel""",
							    """MaskFormerSwinPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
							from .configuration_maskformer_swin import MaskFormerSwinConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_maskformer import MaskFormerFeatureExtractor
														from .image_processing_maskformer import MaskFormerImageProcessor
							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_maskformer import (
														    MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MaskFormerForInstanceSegmentation,
														    MaskFormerModel,
														    MaskFormerPreTrainedModel,
														)
														from .modeling_maskformer_swin import (
														    MaskFormerSwinBackbone,
														    MaskFormerSwinModel,
														    MaskFormerSwinPreTrainedModel,
														)


else:
							import sys

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