


"""simple docstring"""






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							:    Optional[Any]					   =	logging.get_logger()




@dataclass
class 							A_      :
  lowerCAmelCase__      			=	4_2
  lowerCAmelCase__      			=	field(default_factory=lowerCAmelCase__ )
  lowerCAmelCase__      			=	field(default_factory=lowerCAmelCase__ )


  def 							_lowercase					(			self:						List[str]							,__lowerCAmelCase:						Any							,__lowerCAmelCase:						Dict							,__lowerCAmelCase:						Tuple    ):







     '''simple docstring'''







     _lowerCamelCase   :				str							=     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:						int							,__lowerCAmelCase:						Optional[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:						Tuple    ):







     '''simple docstring'''







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




@dataclass
class 							A_      :
  lowerCAmelCase__      			=	4_2
  lowerCAmelCase__      			=	4_2
  lowerCAmelCase__      			=	0
  lowerCAmelCase__      			=	field(default_factory=lowerCAmelCase__ )
  lowerCAmelCase__      			=	field(default_factory=lowerCAmelCase__ )


  def __call__(			self:						Any							,__lowerCAmelCase:						Union[str, Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				int							=     Tracker(self.dest    )(_lowerCamelCase    ).parametrized
     _lowerCamelCase   :				int							=     Tracker(self.src    )(_lowerCamelCase    ).parametrized

     _lowerCamelCase   :				Optional[int]							=     list(filter(lambda __lowerCAmelCase    : type(_lowerCamelCase    ) not in self.src_skip							,_lowerCamelCase    )    )
     _lowerCamelCase   :				Optional[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        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase = True       )    ->     Any:
   '''simple docstring'''





   print(F"""Converting {name}..."""       )
   with torch.no_grad():
      _lowerCamelCase   :				Optional[int]							=     timm.create_model(_lowerCamelCase			,		pretrained=_lowerCamelCase       ).eval()
      _lowerCamelCase   :				str							=     ResNetForImageClassification(_lowerCamelCase       ).eval()
      _lowerCamelCase   :				List[str]							=     ModuleTransfer(src=_lowerCamelCase			,		dest=_lowerCamelCase       )
      _lowerCamelCase   :				Any							=     torch.randn((1, 3, 224, 224)       )
      module_transfer(_lowerCamelCase       )

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

   _lowerCamelCase   :				List[str]							=     F"""resnet{'-'.join(name.split('resnet'       )       )}"""
   print(_lowerCamelCase       )

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

      # we can use the convnext one
      _lowerCamelCase   :				Dict							=     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=_lowerCamelCase			,		)

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







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase = None			,		_lowerCamelCase = True       )    ->     Dict:
   '''simple docstring'''





   _lowerCamelCase   :				Any							=     """imagenet-1k-id2label.json"""
   _lowerCamelCase   :				Any							=     1000
   _lowerCamelCase   :				List[str]							=     (1, num_labels)

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

   _lowerCamelCase   :				str							=     idalabel
   _lowerCamelCase   :				Tuple							=     {v: k for k, v in idalabel.items()}

   _lowerCamelCase   :				Dict							=     partial(_lowerCamelCase			,		num_labels=_lowerCamelCase			,		idalabel=_lowerCamelCase			,		labelaid=_lowerCamelCase       )

   _lowerCamelCase   :				Dict							=     {
       """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, 1024, 2048]			,		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, 1024, 2048]			,		layer_type="bottleneck"       ),
       """resnet101""": ImageNetPreTrainedConfig(
           depths=[3, 4, 23, 3]			,		hidden_sizes=[256, 512, 1024, 2048]			,		layer_type="bottleneck"       ),
       """resnet152""": ImageNetPreTrainedConfig(
           depths=[3, 8, 36, 3]			,		hidden_sizes=[256, 512, 1024, 2048]			,		layer_type="bottleneck"       ),
   }

   if model_name:
      convert_weight_and_push(_lowerCamelCase			,		names_to_config[model_name]			,		_lowerCamelCase			,		_lowerCamelCase       )
   else:
      for model_name, config in names_to_config.items():
         convert_weight_and_push(_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )
   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							:    Dict					   =	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							=  {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A							=  [
        """SEW_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """SEWForCTC""",
        """SEWForSequenceClassification""",
        """SEWModel""",
        """SEWPreTrainedModel""",
    ]

if TYPE_CHECKING:
    from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_sew import (
            SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
            SEWForCTC,
            SEWForSequenceClassification,
            SEWModel,
            SEWPreTrainedModel,
        )


else:
    import sys

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

