


import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowerCamelCase         =			logging.get_logger(__name__)

__lowerCamelCase         =			{
    '''facebook/detr-resnet-50''': '''https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json''',
    # See all DETR models at https://huggingface.co/models?filter=detr
}


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""detr"""
       _lowerCamelCase			    =				["""past_key_values"""]
       _lowerCamelCase			    =				{
           """hidden_size""": """d_model""",
           """num_attention_heads""": """encoder_attention_heads""",
       }



       def __init__(     self     ,lowercase=True     ,lowercase=None     ,lowercase=3     ,lowercase=100     ,lowercase=6     ,lowercase=2048     ,lowercase=8     ,lowercase=6     ,lowercase=2048     ,lowercase=8     ,lowercase=0.0     ,lowercase=0.0     ,lowercase=True     ,lowercase="relu"     ,lowercase=256     ,lowercase=0.1     ,lowercase=0.0     ,lowercase=0.0     ,lowercase=0.02     ,lowercase=1.0     ,lowercase=False     ,lowercase="sine"     ,lowercase="resnet50"     ,lowercase=True     ,lowercase=False     ,lowercase=1     ,lowercase=5     ,lowercase=2     ,lowercase=1     ,lowercase=1     ,lowercase=5     ,lowercase=2     ,lowercase=0.1     ,**lowercase     ,):

             """simple docstring"""





             if backbone_config is not None and use_timm_backbone:
                   raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`.")

             if not use_timm_backbone:
                   if backbone_config is None:
                         logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.")
                         UpperCAmelCase_  :	Tuple				      =		CONFIG_MAPPING["resnet"](out_features=["stage4"])
                   elif isinstance(lowercase     ,lowercase):
                         UpperCAmelCase_  :	Optional[Any]				      =		backbone_config.get("model_type")
                         UpperCAmelCase_  :	Optional[int]				      =		CONFIG_MAPPING[backbone_model_type]
                         UpperCAmelCase_  :	int				      =		config_class.from_dict(lowercase)
                   # set timm attributes to None
                   UpperCAmelCase_  :	Optional[Any]				      =		None, None, None

             UpperCAmelCase_  :	int				      =		use_timm_backbone
             UpperCAmelCase_  :	List[str]				      =		backbone_config
             UpperCAmelCase_  :	List[Any]				      =		num_channels
             UpperCAmelCase_  :	List[Any]				      =		num_queries
             UpperCAmelCase_  :	str				      =		d_model
             UpperCAmelCase_  :	int				      =		encoder_ffn_dim
             UpperCAmelCase_  :	Optional[int]				      =		encoder_layers
             UpperCAmelCase_  :	Dict				      =		encoder_attention_heads
             UpperCAmelCase_  :	Optional[int]				      =		decoder_ffn_dim
             UpperCAmelCase_  :	Any				      =		decoder_layers
             UpperCAmelCase_  :	Optional[int]				      =		decoder_attention_heads
             UpperCAmelCase_  :	Union[str, Any]				      =		dropout
             UpperCAmelCase_  :	int				      =		attention_dropout
             UpperCAmelCase_  :	Any				      =		activation_dropout
             UpperCAmelCase_  :	Any				      =		activation_function
             UpperCAmelCase_  :	Tuple				      =		init_std
             UpperCAmelCase_  :	Tuple				      =		init_xavier_std
             UpperCAmelCase_  :	int				      =		encoder_layerdrop
             UpperCAmelCase_  :	Dict				      =		decoder_layerdrop
             UpperCAmelCase_  :	Optional[Any]				      =		encoder_layers
             UpperCAmelCase_  :	List[Any]				      =		auxiliary_loss
             UpperCAmelCase_  :	List[str]				      =		position_embedding_type
             UpperCAmelCase_  :	List[Any]				      =		backbone
             UpperCAmelCase_  :	Union[str, Any]				      =		use_pretrained_backbone
             UpperCAmelCase_  :	Any				      =		dilation
             # Hungarian matcher
             UpperCAmelCase_  :	Dict				      =		class_cost
             UpperCAmelCase_  :	List[str]				      =		bbox_cost
             UpperCAmelCase_  :	Optional[int]				      =		giou_cost
             # Loss coefficients
             UpperCAmelCase_  :	str				      =		mask_loss_coefficient
             UpperCAmelCase_  :	int				      =		dice_loss_coefficient
             UpperCAmelCase_  :	Optional[int]				      =		bbox_loss_coefficient
             UpperCAmelCase_  :	List[Any]				      =		giou_loss_coefficient
             UpperCAmelCase_  :	str				      =		eos_coefficient
             super().__init__(is_encoder_decoder=lowercase     ,**lowercase)



       @property
       def  A_   (     self):

             """simple docstring"""





             return self.encoder_attention_heads



       @property
       def  A_   (     self):

             """simple docstring"""





             return self.d_model



       @classmethod
       def  A_   (     cls     ,lowercase     ,**lowercase):

             """simple docstring"""





             return cls(backbone_config=lowercase     ,**lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		copy.deepcopy(self.__dict__)
             if output["backbone_config"] is not None:
                   UpperCAmelCase_  :	Dict				      =		self.backbone_config.to_dict()
             UpperCAmelCase_  :	int				      =		self.__class__.model_type
             return output


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				version.parse("""1.11"""  )



       @property
       def  A_   (     self):

             """simple docstring"""





             return OrderedDict(
                 [
                     ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                     ("pixel_mask", {0: "batch"}),
                 ])



       @property
       def  A_   (     self):

             """simple docstring"""





             return 1E-5



       @property
       def  A_   (     self):

             """simple docstring"""





             return 12





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    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case    )     ->     Optional[Any]:






      '''simple docstring'''







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

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

      # Save pytorch-model
      print(F"""Save PyTorch model to {pytorch_dump_path}"""    )
      torch.save(model.state_dict()					,  __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
   )
