


import argparse

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


logging.set_verbosity_info()



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Optional[int]:
    # Initialise PyTorch model
    _lowercase			:				Optional[int] 						=						TaConfig.from_json_file(lowerCamelCase_					)
    print(F'''Building PyTorch model from configuration: {config}'''					)
    _lowercase			:				Union[str, 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__":
 SCREAMING_SNAKE_CASE			: Optional[int]				   =					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."
 )
 SCREAMING_SNAKE_CASE			: Any				   =					parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE			: Optional[Any]				   =					{
    "configuration_conditional_detr": [
        "CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ConditionalDetrConfig",
        "ConditionalDetrOnnxConfig",
    ]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					["ConditionalDetrFeatureExtractor"]
 SCREAMING_SNAKE_CASE			: Optional[Any]				   =					["ConditionalDetrImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE			: Any				   =					[
     "CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ConditionalDetrForObjectDetection",
     "ConditionalDetrForSegmentation",
     "ConditionalDetrModel",
     "ConditionalDetrPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_conditional_detr import (
     CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ConditionalDetrConfig,
     ConditionalDetrOnnxConfig,
 )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
  from .image_processing_conditional_detr import ConditionalDetrImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_conditional_detr import (
      CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConditionalDetrForObjectDetection,
      ConditionalDetrForSegmentation,
      ConditionalDetrModel,
      ConditionalDetrPreTrainedModel,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE			: str				   =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


