


"""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()
__lowercase    =				logging.get_logger()






@dataclass
class 						_A  :







    """simple docstring"""




    UpperCAmelCase	:							nn.Module
    UpperCAmelCase	:							List[nn.Module]     =	field(default_factory=_a  )
    UpperCAmelCase	:							list     =	field(default_factory=_a  )






    def      __snake_case							( self    :   Dict    ,			__UpperCAmelCase    :   str    ,			__UpperCAmelCase    :   Tensor    ,			__UpperCAmelCase    :   Tensor):
      a						:		Any											=      len(list(m.modules())) == 1 or isinstance(__UpperCAmelCase    ,			nn.Convad) or isinstance(__UpperCAmelCase    ,			nn.BatchNormad)
      if has_not_submodules:
        self.traced.append(__UpperCAmelCase)






    def __call__( self    :   Dict    ,			__UpperCAmelCase    :   Tensor):
      for m in self.module.modules():
        self.handles.append(m.register_forward_hook(self._forward_hook))
      self.module(__UpperCAmelCase)
      [x.remove() for x in self.handles]
      return self






    @property
    def      __snake_case							( self    :   Any):
      # check the len of the state_dict keys to see if we have learnable params
      return list(filter(lambda __UpperCAmelCase: len(list(x.state_dict().keys())) > 0    ,			self.traced))






@dataclass
class 						_A  :







    """simple docstring"""




    UpperCAmelCase	:							nn.Module
    UpperCAmelCase	:							nn.Module
    UpperCAmelCase	:							int     =	0
    UpperCAmelCase	:							List     =	field(default_factory=_a  )
    UpperCAmelCase	:							List     =	field(default_factory=_a  )






    def __call__( self    :   str    ,			__UpperCAmelCase    :   Tensor):
      a						:		Any											=      Tracker(self.dest)(__UpperCAmelCase).parametrized
      a						:		Union[str, Any]											=      Tracker(self.src)(__UpperCAmelCase).parametrized

      a						:		Any											=      list(filter(lambda __UpperCAmelCase: type(__UpperCAmelCase) not in self.src_skip    ,			__UpperCAmelCase))
      a						:		Dict											=      list(filter(lambda __UpperCAmelCase: type(__UpperCAmelCase) not in self.dest_skip    ,			__UpperCAmelCase))

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

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






def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_ = True	)->				int:
  '''simple docstring'''







  print(F'''Converting {name}...'''	)
  with torch.no_grad():
    a						:		Dict											=      timm.create_model(A_  ,       pretrained=A_	).eval()
    a						:		Tuple											=      ResNetForImageClassification(A_	).eval()
    a						:		str											=      ModuleTransfer(src=A_  ,       dest=A_	)
    a						:		Any											=      torch.randn((1, 3, 224, 224)	)
    module_transfer(A_	)

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

  a						:		List[Any]											=      F'''resnet{"-".join(name.split("resnet"	)	)}'''
  print(A_	)

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

    # we can use the convnext one
    a						:		str											=      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=A_  ,       )

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


def 					lowercase      (    A_  ,       A_ = None  ,       A_ = True	)->				Optional[Any]:
  '''simple docstring'''







  a						:		str											=      "imagenet-1k-id2label.json"
  a						:		Any											=      1_000
  a						:		Dict											=      (1, num_labels)

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

  a						:		Union[str, Any]											=      idalabel
  a						:		str											=      {v: k for k, v in idalabel.items()}

  a						:		List[Any]											=      partial(A_  ,       num_labels=A_  ,       idalabel=A_  ,       labelaid=A_	)

  a						:		Optional[int]											=      {
      "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(A_  ,       names_to_config[model_name]  ,       A_  ,       A_	)
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(A_  ,       A_  ,       A_  ,       A_	)
  return config, expected_shape


if __name__ == "__main__":
 __lowercase    =				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.""",
 )

 __lowercase    =				parser.parse_args()
 __lowercase    =				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"""







__lowercase    =				frozenset(
    [
        """prompt""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
        """cross_attention_kwargs""",
    ]
)

__lowercase    =				frozenset(["""prompt""", """negative_prompt"""])

__lowercase    =				frozenset([])

__lowercase    =				frozenset(["""image"""])

__lowercase    =				frozenset(
    [
        """image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

__lowercase    =				frozenset(["""image"""])

__lowercase    =				frozenset(
    [
        """prompt""",
        """image""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
    ]
)

__lowercase    =				frozenset(["""prompt""", """image""", """negative_prompt"""])

__lowercase    =				frozenset(
    [
        # Text guided image variation with an image mask
        """prompt""",
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
    ]
)

__lowercase    =				frozenset(["""prompt""", """image""", """mask_image""", """negative_prompt"""])

__lowercase    =				frozenset(
    [
        # image variation with an image mask
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

__lowercase    =				frozenset(["""image""", """mask_image"""])

__lowercase    =				frozenset(
    [
        """example_image""",
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

__lowercase    =				frozenset(["""example_image""", """image""", """mask_image"""])

__lowercase    =				frozenset(["""class_labels"""])

__lowercase    =				frozenset(["""class_labels"""])

__lowercase    =				frozenset(["""batch_size"""])

__lowercase    =				frozenset([])

__lowercase    =				frozenset(["""batch_size"""])

__lowercase    =				frozenset([])

__lowercase    =				frozenset(
    [
        """prompt""",
        """audio_length_in_s""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
        """cross_attention_kwargs""",
    ]
)

__lowercase    =				frozenset(["""prompt""", """negative_prompt"""])
__lowercase    =				frozenset(["""input_tokens"""])

__lowercase    =				frozenset(["""input_tokens"""])






