






import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()






def   UpperCamelCase  (       snake_case__     :   int	,snake_case__     :   Optional[int]	,snake_case__     :   str	,snake_case__     :   Tuple  ):




     '''simple docstring'''






     __snake_case			:List[str]          =						FunnelConfig.from_json_file(_SCREAMING_SNAKE_CASE  )
     print(f'''Building PyTorch model from configuration: {config}'''  )
     __snake_case			:str          =						FunnelBaseModel(_SCREAMING_SNAKE_CASE  ) if base_model else FunnelModel(_SCREAMING_SNAKE_CASE  )

     # Load weights from tf checkpoint
     load_tf_weights_in_funnel(_SCREAMING_SNAKE_CASE	,_SCREAMING_SNAKE_CASE	,_SCREAMING_SNAKE_CASE  )

     # Save pytorch-model
     print(f'''Save PyTorch model to {pytorch_dump_path}'''  )
     torch.save(model.state_dict()	,_SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
     lowerCamelCase__							=     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 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."""
     )
     parser.add_argument(
         """--base_model""", action="""store_true""", help="""Whether you want just the base model (no decoder) or not."""
     )
     lowerCamelCase__							=     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(
         args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
     )







from typing import TYPE_CHECKING

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


lowerCamelCase__							=     {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfig"""]}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__							=     ["""DPTFeatureExtractor"""]
     lowerCamelCase__							=     ["""DPTImageProcessor"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__							=     [
         """DPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """DPTForDepthEstimation""",
         """DPTForSemanticSegmentation""",
         """DPTModel""",
         """DPTPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_dpt import DPTFeatureExtractor
          from .image_processing_dpt import DPTImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_dpt import (
              DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              DPTForDepthEstimation,
              DPTForSemanticSegmentation,
              DPTModel,
              DPTPreTrainedModel,
          )


else:
     import sys

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


