




'''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()
UpperCAmelCase_ 		=      logging.get_logger()





@dataclass
class    __lowercase						:
   _a							=      42
   _a							=      field(default_factory=__magic_name__ )
   _a							=      field(default_factory=__magic_name__ )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    Optional[Any]:
     __a			=					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      ,							UpperCamelCase      )				->    str:
     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 		UpperCamelCase__			(	self      )				->    Optional[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    __lowercase						:
   _a							=      42
   _a							=      42
   _a							=      0
   _a							=      field(default_factory=__magic_name__ )
   _a							=      field(default_factory=__magic_name__ )







   def __call__(	self      ,							UpperCamelCase      )				->    List[str]:
     __a			=					Tracker(self.dest      )(UpperCamelCase      ).parametrized
     __a			=					Tracker(self.src      )(UpperCamelCase      ).parametrized

     __a			=					list(filter(lambda UpperCamelCase      : type(UpperCamelCase      ) not in self.src_skip      ,							UpperCamelCase      )      )
     __a			=					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 						SCREAMING_SNAKE_CASE       (      a_	:							str				,					a_	:							ResNetConfig				,					a_	:							Path				,					a_	:							bool = True			):
  print(f"Converting {name}..."			)
  with torch.no_grad():
    __a			=					timm.create_model(a_				,					pretrained=a_			).eval()
    __a			=					ResNetForImageClassification(a_			).eval()
    __a			=					ModuleTransfer(src=a_				,					dest=a_			)
    __a			=					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			=					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			=					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 						SCREAMING_SNAKE_CASE       (      a_	:							Path				,					a_	:							str = None				,					a_	:							bool = True			):
  __a			=					'imagenet-1k-id2label.json'
  __a			=					1000
  __a			=					(1, num_labels)

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

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

  __a			=					partial(a_				,					num_labels=a_				,					idalabel=a_				,					labelaid=a_			)

  __a			=					{
      '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(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__":
  UpperCAmelCase_ 		=      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_ 		=      parser.parse_args()
  UpperCAmelCase_ 		=      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 Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


UpperCAmelCase_ 		=      logging.get_logger(__name__)





class    __lowercase						(  __magic_name__ ):
   _a							=      ["""pixel_values"""]







   def __init__(	self      ,							UpperCamelCase = True      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = PILImageResampling.BILINEAR      ,							UpperCamelCase = True      ,							UpperCamelCase = 1 / 255      ,							UpperCamelCase = True      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    None:
     super().__init__(**UpperCamelCase      )
     __a			=					size if size is not None else {'shortest_edge': 384}
     __a			=					get_size_dict(UpperCamelCase      ,							default_to_square=UpperCamelCase      )

     __a			=					do_resize
     __a			=					size
     # Default value set here for backwards compatibility where the value in config is None
     __a			=					crop_pct if crop_pct is not None else 224 / 256
     __a			=					resample
     __a			=					do_rescale
     __a			=					rescale_factor
     __a			=					do_normalize
     __a			=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     __a			=					image_std if image_std is not None else IMAGENET_STANDARD_STD







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase = PILImageResampling.BICUBIC      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    np.ndarray:
     __a			=					get_size_dict(UpperCamelCase      ,							default_to_square=UpperCamelCase      )
     if "shortest_edge" not in size:
       raise ValueError(f"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"      )
     __a			=					size['shortest_edge']

     if shortest_edge < 384:
       # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
       __a			=					int(shortest_edge / crop_pct      )
       __a			=					get_resize_output_image_size(UpperCamelCase      ,							size=UpperCamelCase      ,							default_to_square=UpperCamelCase      )
       __a			=					resize(image=UpperCamelCase      ,							size=UpperCamelCase      ,							resample=UpperCamelCase      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )
       # then crop to (shortest_edge, shortest_edge)
       return center_crop(image=UpperCamelCase      ,							size=(shortest_edge, shortest_edge)      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )
     else:
       # warping (no cropping) when evaluated at 384 or larger
       return resize(
           UpperCamelCase      ,							size=(shortest_edge, shortest_edge)      ,							resample=UpperCamelCase      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    Any:
     return rescale(UpperCamelCase      ,							scale=UpperCamelCase      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    np.ndarray:
     return normalize(UpperCamelCase      ,							mean=UpperCamelCase      ,							std=UpperCamelCase      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = ChannelDimension.FIRST      ,							**UpperCamelCase      ,							)				->    PIL.Image.Image:
     __a			=					do_resize if do_resize is not None else self.do_resize
     __a			=					crop_pct if crop_pct is not None else self.crop_pct
     __a			=					resample if resample is not None else self.resample
     __a			=					do_rescale if do_rescale is not None else self.do_rescale
     __a			=					rescale_factor if rescale_factor is not None else self.rescale_factor
     __a			=					do_normalize if do_normalize is not None else self.do_normalize
     __a			=					image_mean if image_mean is not None else self.image_mean
     __a			=					image_std if image_std is not None else self.image_std

     __a			=					size if size is not None else self.size
     __a			=					get_size_dict(UpperCamelCase      ,							default_to_square=UpperCamelCase      )

     __a			=					make_list_of_images(UpperCamelCase      )

     if not valid_images(UpperCamelCase      ):
       raise ValueError(
           'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
           'torch.Tensor, tf.Tensor or jax.ndarray.'      )

     if do_resize and size is None or resample is None:
       raise ValueError('Size and resample must be specified if do_resize is True.'      )

     if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
       raise ValueError('crop_pct must be specified if size < 384.'      )

     if do_rescale and rescale_factor is None:
       raise ValueError('Rescale factor must be specified if do_rescale is True.'      )

     if do_normalize and (image_mean is None or image_std is None):
       raise ValueError('Image mean and std must be specified if do_normalize is True.'      )

     # All transformations expect numpy arrays.
     __a			=					[to_numpy_array(UpperCamelCase      ) for image in images]

     if do_resize:
       __a			=					[self.resize(image=UpperCamelCase      ,							size=UpperCamelCase      ,							crop_pct=UpperCamelCase      ,							resample=UpperCamelCase      ) for image in images]

     if do_rescale:
       __a			=					[self.rescale(image=UpperCamelCase      ,							scale=UpperCamelCase      ) for image in images]

     if do_normalize:
       __a			=					[self.normalize(image=UpperCamelCase      ,							mean=UpperCamelCase      ,							std=UpperCamelCase      ) for image in images]

     __a			=					[to_channel_dimension_format(UpperCamelCase      ,							UpperCamelCase      ) for image in images]

     __a			=					{'pixel_values': images}
     return BatchFeature(data=UpperCamelCase      ,							tensor_type=UpperCamelCase      )



