



'''simple docstring'''






import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def 				lowerCamelCase__	(						_A    ):
  a						: Dict					       =					SwinConfig()
  a						: Optional[int]					       =					swin_name.split('_'    )

  a						: List[Any]					       =					name_split[1]
  a						: Any					       =					int(name_split[4]    )
  a						: Optional[int]					       =					int(name_split[3][-1]    )

  if model_size == "tiny":
    a						: Tuple					       =					96
    a						: Optional[int]					       =					(2, 2, 6, 2)
    a						: int					       =					(3, 6, 12, 24)
  elif model_size == "small":
    a						: Optional[Any]					       =					96
    a						: Optional[int]					       =					(2, 2, 18, 2)
    a						: int					       =					(3, 6, 12, 24)
  elif model_size == "base":
    a						: List[str]					       =					128
    a						: List[str]					       =					(2, 2, 18, 2)
    a						: int					       =					(4, 8, 16, 32)
  else:
    a						: Dict					       =					192
    a						: List[Any]					       =					(2, 2, 18, 2)
    a						: List[Any]					       =					(6, 12, 24, 48)

  if "in22k" in swin_name:
    a						: int					       =					2_1841
  else:
    a						: Tuple					       =					1000
    a						: List[Any]					       =					'huggingface/label-files'
    a						: int					       =					'imagenet-1k-id2label.json'
    a						: Any					       =					json.load(open(hf_hub_download(_A			,      _A			,      repo_type='dataset'    )			,      'r'    )    )
    a						: Union[str, Any]					       =					{int(_A    ): v for k, v in idalabel.items()}
    a						: Dict					       =					idalabel
    a						: Optional[int]					       =					{v: k for k, v in idalabel.items()}

  a						: List[str]					       =					img_size
  a						: Tuple					       =					num_classes
  a						: List[Any]					       =					embed_dim
  a						: List[Any]					       =					depths
  a						: List[Any]					       =					num_heads
  a						: Dict					       =					window_size

  return config
def 				lowerCamelCase__	(						_A    ):
  if "patch_embed.proj" in name:
    a						: Any					       =					name.replace('patch_embed.proj'			,      'embeddings.patch_embeddings.projection'    )
  if "patch_embed.norm" in name:
    a						: str					       =					name.replace('patch_embed.norm'			,      'embeddings.norm'    )
  if "layers" in name:
    a						: Dict					       =					'encoder.' + name
  if "attn.proj" in name:
    a						: str					       =					name.replace('attn.proj'			,      'attention.output.dense'    )
  if "attn" in name:
    a						: Dict					       =					name.replace('attn'			,      'attention.self'    )
  if "norm1" in name:
    a						: Optional[Any]					       =					name.replace('norm1'			,      'layernorm_before'    )
  if "norm2" in name:
    a						: Optional[int]					       =					name.replace('norm2'			,      'layernorm_after'    )
  if "mlp.fc1" in name:
    a						: List[Any]					       =					name.replace('mlp.fc1'			,      'intermediate.dense'    )
  if "mlp.fc2" in name:
    a						: List[Any]					       =					name.replace('mlp.fc2'			,      'output.dense'    )

  if name == "norm.weight":
    a						: Optional[int]					       =					'layernorm.weight'
  if name == "norm.bias":
    a						: Any					       =					'layernorm.bias'

  if "head" in name:
    a						: Optional[int]					       =					name.replace('head'			,      'classifier'    )
  else:
    a						: Optional[int]					       =					'swin.' + name

  return name
def 				lowerCamelCase__	(						_A			,      _A    ):
  for key in orig_state_dict.copy().keys():
    a						: int					       =					orig_state_dict.pop(_A    )

    if "mask" in key:
      continue
    elif "qkv" in key:
      a						: List[Any]					       =					key.split('.'    )
      a						: List[Any]					       =					int(key_split[1]    )
      a						: Tuple					       =					int(key_split[3]    )
      a						: Any					       =					model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

      if "weight" in key:
        a						: Optional[int]					       =					val[:dim, :]
        a						: List[Any]					       =					val[
            dim : dim * 2, :
        ]
        a						: Optional[Any]					       =					val[-dim:, :]
      else:
        a						: Tuple					       =					val[
            :dim
        ]
        a						: Optional[int]					       =					val[
            dim : dim * 2
        ]
        a						: int					       =					val[
            -dim:
        ]
    else:
      a						: str					       =					val

  return orig_state_dict
def 				lowerCamelCase__	(						_A			,      _A    ):
  a						: Dict					       =					timm.create_model(_A			,      pretrained=_A    )
  timm_model.eval()

  a						: Dict					       =					get_swin_config(_A    )
  a						: Any					       =					SwinForImageClassification(_A    )
  model.eval()

  a						: Optional[Any]					       =					convert_state_dict(timm_model.state_dict()			,      _A    )
  model.load_state_dict(_A    )

  a						: Optional[Any]					       =					'http://images.cocodataset.org/val2017/000000039769.jpg'

  a						: int					       =					AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_'			,      '-'    )    )    )
  a						: Optional[Any]					       =					Image.open(requests.get(_A			,      stream=_A    ).raw    )
  a						: List[Any]					       =					image_processor(images=_A			,      return_tensors='pt'    )

  a						: List[Any]					       =					timm_model(inputs['pixel_values']    )
  a						: List[str]					       =					model(**_A    ).logits

  assert torch.allclose(_A			,      _A			,      atol=1E-3    )

  print(f"""Saving model {swin_name} to {pytorch_dump_folder_path}"""    )
  model.save_pretrained(_A    )

  print(f"""Saving image processor to {pytorch_dump_folder_path}"""    )
  image_processor.save_pretrained(_A    )


if __name__ == "__main__":
     lowerCAmelCase: Tuple							=						argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--swin_name',
         default='swin_tiny_patch4_window7_224',
         type=str,
         help='Name of the Swin timm model you\'d like to convert.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
     )

     lowerCAmelCase: List[Any]							=						parser.parse_args()
     convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)




'''simple docstring'''






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase: Any							=						logging.get_logger(__name__)

class 							a__( lowerCamelCase__     ):
    lowercase__			=   ["""pixel_values"""]







    def __init__(				self			:  List[str]						,      __snake_case			:  bool = True						,      __snake_case			:  int = 32						,      __snake_case			:  Union[str, Any]=PILImageResampling.BILINEAR						,      __snake_case			:  bool = True						,      **__snake_case			:  List[Any]						,      ):
      a						: Optional[Any]					       =					do_resize
      a						: Union[str, Any]					       =					do_rescale
      a						: Union[str, Any]					       =					size_divisor
      a						: List[Any]					       =					resample
      super().__init__(**__snake_case     )







    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  np.ndarray						,      __snake_case			:  int						,      __snake_case			:  Tuple						,      __snake_case			:  Optional[ChannelDimension] = None						,      **__snake_case			:  Tuple     ):
      a       ,     a						: Optional[int]					       =					get_image_size(__snake_case     )
      # Rounds the height and width down to the closest multiple of size_divisor
      a						: int					       =					height // size_divisor * size_divisor
      a						: int					       =					width // size_divisor * size_divisor
      a						: Any					       =					resize(__snake_case						,      (new_h, new_w)						,      resample=__snake_case						,      data_format=__snake_case						,      **__snake_case     )
      return image







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  np.ndarray						,      __snake_case			:  float						,      __snake_case			:  Optional[ChannelDimension] = None						,      **__snake_case			:  Optional[Any]     ):
      return rescale(image=__snake_case						,      scale=__snake_case						,      data_format=__snake_case						,      **__snake_case     )







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]						,      __snake_case			:  Optional[bool] = None						,      __snake_case			:  Optional[int] = None						,      __snake_case			:  Dict=None						,      __snake_case			:  Optional[bool] = None						,      __snake_case			:  Optional[Union[TensorType, str]] = None						,      __snake_case			:  ChannelDimension = ChannelDimension.FIRST						,      **__snake_case			:  Any						,      ):
      a						: List[str]					       =					do_resize if do_resize is not None else self.do_resize
      a						: Tuple					       =					do_rescale if do_rescale is not None else self.do_rescale
      a						: Optional[Any]					       =					size_divisor if size_divisor is not None else self.size_divisor
      a						: Union[str, Any]					       =					resample if resample is not None else self.resample

      if do_resize and size_divisor is None:
        raise ValueError('size_divisor is required for resizing'     )

      a						: Tuple					       =					make_list_of_images(__snake_case     )

      if not valid_images(__snake_case     ):
        raise ValueError('Invalid image(s)'     )

      # All transformations expect numpy arrays.
      a						: str					       =					[to_numpy_array(__snake_case     ) for img in images]

      if do_resize:
        a						: int					       =					[self.resize(__snake_case						,      size_divisor=__snake_case						,      resample=__snake_case     ) for image in images]

      if do_rescale:
        a						: List[str]					       =					[self.rescale(__snake_case						,      scale=1 / 2_55     ) for image in images]

      a						: Any					       =					[to_channel_dimension_format(__snake_case						,      __snake_case     ) for image in images]

      a						: Any					       =					{'pixel_values': images}
      return BatchFeature(data=__snake_case						,      tensor_type=__snake_case     )