import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase      :			Any							       =		logging.get_logger()







@dataclass
class __snake_case    :
   lowerCAmelCase_														=							42
   lowerCAmelCase_														=							field(default_factory=lowerCamelCase_		)
   lowerCAmelCase_														=							field(default_factory=lowerCamelCase_		)




   def      __a						(   self      :    List[str]	,     _lowercase      :    str	,     _lowercase      :    Tensor	,     _lowercase      :    Tensor				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				len(list(m.modules()				)				) == 1 or isinstance(_lowercase	,     nn.Convad				) or isinstance(_lowercase	,     nn.BatchNormad				)
     if has_not_submodules:
       self.traced.append(_lowercase				)




   def __call__(   self      :    Optional[Any]	,     _lowercase      :    Tensor				):



     """simple docstring"""






     for m in self.module.modules():
       self.handles.append(m.register_forward_hook(self._forward_hook				)				)
     self.module(_lowercase				)
     [x.remove() for x in self.handles]
     return self




   @property
   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     return list(filter(lambda _lowercase				: len(list(x.state_dict().keys()				)				) > 0	,     self.traced				)				)







@dataclass
class __snake_case    :
   lowerCAmelCase_														=							42
   lowerCAmelCase_														=							42
   lowerCAmelCase_														=							0
   lowerCAmelCase_														=							field(default_factory=lowerCamelCase_		)
   lowerCAmelCase_														=							field(default_factory=lowerCamelCase_		)




   def __call__(   self      :    int	,     _lowercase      :    Tensor				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Tracker(self.dest				)(_lowercase				).parametrized
     SCREAMING_SNAKE_CASE__      							=				Tracker(self.src				)(_lowercase				).parametrized

     SCREAMING_SNAKE_CASE__      							=				list(filter(lambda _lowercase				: type(_lowercase				) not in self.src_skip	,     _lowercase				)				)
     SCREAMING_SNAKE_CASE__      							=				list(filter(lambda _lowercase				: type(_lowercase				) not in self.dest_skip	,     _lowercase				)				)

     if len(_lowercase				) != len(_lowercase				):
       raise Exception(
           f"""Numbers of operations are different. Source module has {len(_lowercase				)} operations while"""
           f""" destination module has {len(_lowercase				)}."""				)

     for dest_m, src_m in zip(_lowercase	,     _lowercase				):
       dest_m.load_state_dict(src_m.state_dict()				)
       if self.verbose == 1:
         print(f"""Transfered from={src_m} to={dest_m}"""				)





def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	str			,					__UpperCamelCase   :	ResNetConfig			,					__UpperCamelCase   :	Path			,					__UpperCamelCase   :	bool = True     )							-> Union[str, Any]:







  """simple docstring"""





  print(f"""Converting {name}..."""     )
  with torch.no_grad():
    SCREAMING_SNAKE_CASE__      							=				timm.create_model(__UpperCamelCase			,					pretrained=__UpperCamelCase     ).eval()
    SCREAMING_SNAKE_CASE__      							=				ResNetForImageClassification(__UpperCamelCase     ).eval()
    SCREAMING_SNAKE_CASE__      							=				ModuleTransfer(src=__UpperCamelCase			,					dest=__UpperCamelCase     )
    SCREAMING_SNAKE_CASE__      							=				torch.randn((1, 3, 2_24, 2_24)     )
    module_transfer(__UpperCamelCase     )

  assert torch.allclose(from_model(__UpperCamelCase     )			,					our_model(__UpperCamelCase     ).logits     ), "The model logits don't match the original one."

  SCREAMING_SNAKE_CASE__      							=				f"""resnet{'-'.join(name.split('resnet'     )     )}"""
  print(__UpperCamelCase     )

  if push_to_hub:
    our_model.push_to_hub(
        repo_path_or_name=save_directory / checkpoint_name			,					commit_message="""Add model"""			,					use_temp_dir=__UpperCamelCase			,					)

    # we can use the convnext one
    SCREAMING_SNAKE_CASE__      							=				AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""     )
    image_processor.push_to_hub(
        repo_path_or_name=save_directory / checkpoint_name			,					commit_message="""Add image processor"""			,					use_temp_dir=__UpperCamelCase			,					)

    print(f"""Pushed {checkpoint_name}"""     )



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Path			,					__UpperCamelCase   :	str = None			,					__UpperCamelCase   :	bool = True     )							-> Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				"""imagenet-1k-id2label.json"""
  SCREAMING_SNAKE_CASE__      							=				10_00
  SCREAMING_SNAKE_CASE__      							=				(1, num_labels)

  SCREAMING_SNAKE_CASE__      							=				"""huggingface/label-files"""
  SCREAMING_SNAKE_CASE__      							=				num_labels
  SCREAMING_SNAKE_CASE__      							=				json.load(open(hf_hub_download(__UpperCamelCase			,					__UpperCamelCase			,					repo_type="""dataset"""     )			,					"""r"""     )     )
  SCREAMING_SNAKE_CASE__      							=				{int(__UpperCamelCase     ): v for k, v in idalabel.items()}

  SCREAMING_SNAKE_CASE__      							=				idalabel
  SCREAMING_SNAKE_CASE__      							=				{v: k for k, v in idalabel.items()}

  SCREAMING_SNAKE_CASE__      							=				partial(__UpperCamelCase			,					num_labels=__UpperCamelCase			,					idalabel=__UpperCamelCase			,					labelaid=__UpperCamelCase     )

  SCREAMING_SNAKE_CASE__      							=				{
      """resnet18""": ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]			,					hidden_sizes=[64, 1_28, 2_56, 5_12]			,					layer_type="""basic"""     ),
      """resnet26""": ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]			,					hidden_sizes=[2_56, 5_12, 10_24, 20_48]			,					layer_type="""bottleneck"""     ),
      """resnet34""": ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]			,					hidden_sizes=[64, 1_28, 2_56, 5_12]			,					layer_type="""basic"""     ),
      """resnet50""": ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]			,					hidden_sizes=[2_56, 5_12, 10_24, 20_48]			,					layer_type="""bottleneck"""     ),
      """resnet101""": ImageNetPreTrainedConfig(
          depths=[3, 4, 23, 3]			,					hidden_sizes=[2_56, 5_12, 10_24, 20_48]			,					layer_type="""bottleneck"""     ),
      """resnet152""": ImageNetPreTrainedConfig(
          depths=[3, 8, 36, 3]			,					hidden_sizes=[2_56, 5_12, 10_24, 20_48]			,					layer_type="""bottleneck"""     ),
  }

  if model_name:
    convert_weight_and_push(__UpperCamelCase			,					names_to_config[model_name]			,					__UpperCamelCase			,					__UpperCamelCase     )
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase     )
  return config, expected_shape


if __name__ == "__main__":
   __lowerCamelCase      :			Any							       =		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--model_name''',
       default=None,
       type=str,
       help=(
           '''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
           ''' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'''
       ),
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''',
       default=None,
       type=Path,
       required=True,
       help='''Path to the output PyTorch model directory.''',
   )
   parser.add_argument(
       '''--push_to_hub''',
       default=True,
       type=bool,
       required=False,
       help='''If True, push model and image processor to the hub.''',
   )

   __lowerCamelCase      :			List[str]							       =		parser.parse_args()
   __lowerCamelCase      :			Path							       =		args.pytorch_dump_folder_path
   pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
   convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)

from typing import TYPE_CHECKING

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


__lowerCamelCase      :			Optional[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      :			Optional[int]							       =		[
       '''EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''EfficientFormerForImageClassification''',
       '''EfficientFormerForImageClassificationWithTeacher''',
       '''EfficientFormerModel''',
       '''EfficientFormerPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Any							       =		[
       '''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      :			List[Any]							       =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
