





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()
UpperCAmelCase	:	Any		     =       logging.get_logger()







@dataclass
class 				__lowerCAmelCase       :
      _lowercase				:					nn.Module
      _lowercase				:					List[nn.Module]						=			field(default_factory=UpperCamelCase__)
      _lowercase				:					list						=			field(default_factory=UpperCamelCase__)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						str:



        '''simple docstring'''




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







      def __call__(    self		,      lowerCAmelCase__							)   ->						List[Any]:



        '''simple docstring'''




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







      @property
      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




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







@dataclass
class 				__lowerCAmelCase       :
      _lowercase				:					nn.Module
      _lowercase				:					nn.Module
      _lowercase				:					int						=			0
      _lowercase				:					List						=			field(default_factory=UpperCamelCase__)
      _lowercase				:					List						=			field(default_factory=UpperCamelCase__)







      def __call__(    self		,      lowerCAmelCase__							)   ->						Tuple:



        '''simple docstring'''




        a__	:					Any										=Tracker(self.dest							)(lowerCAmelCase__							).parametrized
        a__	:					List[str]										=Tracker(self.src							)(lowerCAmelCase__							).parametrized

        a__	:					Tuple										=list(filter(lambda lowerCAmelCase__							: type(lowerCAmelCase__							) not in self.src_skip		,      lowerCAmelCase__							)							)
        a__	:					Any										=list(filter(lambda lowerCAmelCase__							: type(lowerCAmelCase__							) not in self.dest_skip		,      lowerCAmelCase__							)							)

        if len(lowerCAmelCase__							) != len(lowerCAmelCase__							):
          raise Exception(
              F'''Numbers of operations are different. Source module has {len(lowerCAmelCase__							)} operations while'''
              F''' destination module has {len(lowerCAmelCase__							)}.'''							)

        for dest_m, src_m in zip(lowerCAmelCase__		,      lowerCAmelCase__							):
          dest_m.load_state_dict(src_m.state_dict()							)
          if self.verbose == 1:
            print(F'''Transfered from={src_m} to={dest_m}'''							)
def      _A       (		SCREAMING_SNAKE_CASE     :   str       ,       SCREAMING_SNAKE_CASE     :   ResNetConfig       ,       SCREAMING_SNAKE_CASE     :   Path       ,       SCREAMING_SNAKE_CASE     :   bool = True						):





  """simple docstring"""



  print(f'''Converting {name}...'''						)
  with torch.no_grad():
    a__	:					Tuple										=timm.create_model(SCREAMING_SNAKE_CASE       ,       pretrained=SCREAMING_SNAKE_CASE						).eval()
    a__	:					str										=ResNetForImageClassification(SCREAMING_SNAKE_CASE						).eval()
    a__	:					List[str]										=ModuleTransfer(src=SCREAMING_SNAKE_CASE       ,       dest=SCREAMING_SNAKE_CASE						)
    a__	:					Optional[Any]										=torch.randn((1, 3, 224, 224)						)
    module_transfer(SCREAMING_SNAKE_CASE						)

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

  a__	:					Union[str, Any]										=f'''resnet{"-".join(name.split("resnet"						)						)}'''
  print(SCREAMING_SNAKE_CASE						)

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

    # we can use the convnext one
    a__	:					Optional[int]										=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=SCREAMING_SNAKE_CASE       ,       )

    print(f'''Pushed {checkpoint_name}'''						)
def      _A       (		SCREAMING_SNAKE_CASE     :   Path       ,       SCREAMING_SNAKE_CASE     :   str = None       ,       SCREAMING_SNAKE_CASE     :   bool = True						):





  """simple docstring"""



  a__	:					int										="imagenet-1k-id2label.json"
  a__	:					List[str]										=1_000
  a__	:					List[Any]										=(1, num_labels)

  a__	:					str										="huggingface/label-files"
  a__	:					Optional[int]										=num_labels
  a__	:					Union[str, Any]										=json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       repo_type="dataset"						)       ,       "r"						)						)
  a__	:					str										={int(SCREAMING_SNAKE_CASE						): v for k, v in idalabel.items()}

  a__	:					int										=idalabel
  a__	:					Dict										={v: k for k, v in idalabel.items()}

  a__	:					str										=partial(SCREAMING_SNAKE_CASE       ,       num_labels=SCREAMING_SNAKE_CASE       ,       idalabel=SCREAMING_SNAKE_CASE       ,       labelaid=SCREAMING_SNAKE_CASE						)

  a__	:					Optional[Any]										={
      "resnet18": ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]       ,       hidden_sizes=[64, 128, 256, 512]       ,       layer_type="basic"						),
      "resnet26": ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]       ,       hidden_sizes=[256, 512, 1_024, 2_048]       ,       layer_type="bottleneck"						),
      "resnet34": ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]       ,       hidden_sizes=[64, 128, 256, 512]       ,       layer_type="basic"						),
      "resnet50": ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]       ,       hidden_sizes=[256, 512, 1_024, 2_048]       ,       layer_type="bottleneck"						),
      "resnet101": ImageNetPreTrainedConfig(
          depths=[3, 4, 23, 3]       ,       hidden_sizes=[256, 512, 1_024, 2_048]       ,       layer_type="bottleneck"						),
      "resnet152": ImageNetPreTrainedConfig(
          depths=[3, 8, 36, 3]       ,       hidden_sizes=[256, 512, 1_024, 2_048]       ,       layer_type="bottleneck"						),
  }

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


if __name__ == "__main__":
     UpperCAmelCase	:	str		     =       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.""",
     )

     UpperCAmelCase	:	str		     =       parser.parse_args()
     UpperCAmelCase	:	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)













'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__							:							List[Any] ={
    '''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a__							:							Optional[int] =[
        '''TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TimesformerModel''',
        '''TimesformerForVideoClassification''',
        '''TimesformerPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_timesformer import (
            TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            TimesformerForVideoClassification,
            TimesformerModel,
            TimesformerPreTrainedModel,
        )

else:
    import sys

    a__							:							Optional[int] =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
