




import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

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


if is_torch_available():
     import torch

if is_vision_available():
     import PIL


__a          =			logging.get_logger(__name__)







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     def constraint_to_multiple_of(_lowercase			,						_lowercase			,						_lowercase=0			,						_lowercase=None				):
          UpperCAmelCase_      :							Any       =      round(val / multiple				) * multiple

          if max_val is not None and x > max_val:
               UpperCAmelCase_      :							Optional[int]       =      math.floor(val / multiple				) * multiple

          if x < min_val:
               UpperCAmelCase_      :							List[str]       =      math.ceil(val / multiple				) * multiple

          return x

     UpperCAmelCase_      :							Any       =      (output_size, output_size) if isinstance(_lowercase			,						_lowercase				) else output_size

     UpperCAmelCase_,   UpperCAmelCase_      :							Optional[int]       =      get_image_size(_lowercase				)
     UpperCAmelCase_,   UpperCAmelCase_      :							str       =      output_size

     # determine new height and width
     UpperCAmelCase_      :							Union[str, Any]       =      output_height / input_height
     UpperCAmelCase_      :							List[Any]       =      output_width / input_width

     if keep_aspect_ratio:
          # scale as little as possible
          if abs(1 - scale_width				) < abs(1 - scale_height				):
               # fit width
               UpperCAmelCase_      :							Optional[int]       =      scale_width
          else:
               # fit height
               UpperCAmelCase_      :							int       =      scale_height

     UpperCAmelCase_      :							List[Any]       =      constraint_to_multiple_of(scale_height * input_height			,						multiple=_lowercase				)
     UpperCAmelCase_      :							int       =      constraint_to_multiple_of(scale_width * input_width			,						multiple=_lowercase				)

     return (new_height, new_width)



class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				['''pixel_values''']





    def __init__(				self       ,_SCREAMING_SNAKE_CASE = True       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR       ,_SCREAMING_SNAKE_CASE = False       ,_SCREAMING_SNAKE_CASE = 1       ,_SCREAMING_SNAKE_CASE = True       ,_SCREAMING_SNAKE_CASE = 1 / 255       ,_SCREAMING_SNAKE_CASE = True       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,**_SCREAMING_SNAKE_CASE       ,)			->		None:
         super().__init__(**_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Optional[int]       =      size if size is not None else {'''height''': 384, '''width''': 384}
         UpperCAmelCase_      :							Dict       =      get_size_dict(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Optional[int]       =      do_resize
         UpperCAmelCase_      :							str       =      size
         UpperCAmelCase_      :							Dict       =      keep_aspect_ratio
         UpperCAmelCase_      :							Dict       =      ensure_multiple_of
         UpperCAmelCase_      :							int       =      resample
         UpperCAmelCase_      :							Optional[int]       =      do_rescale
         UpperCAmelCase_      :							Union[str, Any]       =      rescale_factor
         UpperCAmelCase_      :							Optional[Any]       =      do_normalize
         UpperCAmelCase_      :							str       =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         UpperCAmelCase_      :							Dict       =      image_std if image_std is not None else IMAGENET_STANDARD_STD





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = False       ,_SCREAMING_SNAKE_CASE = 1       ,_SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC       ,_SCREAMING_SNAKE_CASE = None       ,**_SCREAMING_SNAKE_CASE       ,)			->		np.ndarray:
         UpperCAmelCase_      :							Optional[Any]       =      get_size_dict(_SCREAMING_SNAKE_CASE  )
         if "height" not in size or "width" not in size:
              raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''  )
         UpperCAmelCase_      :							List[Any]       =      get_resize_output_image_size(
             _SCREAMING_SNAKE_CASE       ,output_size=(size['''height'''], size['''width'''])       ,keep_aspect_ratio=_SCREAMING_SNAKE_CASE       ,multiple=_SCREAMING_SNAKE_CASE       ,)
         return resize(_SCREAMING_SNAKE_CASE       ,size=_SCREAMING_SNAKE_CASE       ,resample=_SCREAMING_SNAKE_CASE       ,data_format=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )





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





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





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST       ,**_SCREAMING_SNAKE_CASE       ,)			->		PIL.Image.Image:
         UpperCAmelCase_      :							Optional[int]       =      do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase_      :							Tuple       =      size if size is not None else self.size
         UpperCAmelCase_      :							Union[str, Any]       =      get_size_dict(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[str]       =      keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
         UpperCAmelCase_      :							str       =      ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
         UpperCAmelCase_      :							Optional[int]       =      resample if resample is not None else self.resample
         UpperCAmelCase_      :							Union[str, Any]       =      do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase_      :							Any       =      rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase_      :							List[str]       =      do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase_      :							Union[str, Any]       =      image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase_      :							Optional[Any]       =      image_std if image_std is not None else self.image_std

         UpperCAmelCase_      :							str       =      make_list_of_images(_SCREAMING_SNAKE_CASE  )

         if not valid_images(_SCREAMING_SNAKE_CASE  ):
              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_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.
         UpperCAmelCase_      :							List[Any]       =      [to_numpy_array(_SCREAMING_SNAKE_CASE  ) for image in images]

         if do_resize:
              UpperCAmelCase_      :							Tuple       =      [self.resize(image=_SCREAMING_SNAKE_CASE       ,size=_SCREAMING_SNAKE_CASE       ,resample=_SCREAMING_SNAKE_CASE  ) for image in images]

         if do_rescale:
              UpperCAmelCase_      :							List[Any]       =      [self.rescale(image=_SCREAMING_SNAKE_CASE       ,scale=_SCREAMING_SNAKE_CASE  ) for image in images]

         if do_normalize:
              UpperCAmelCase_      :							int       =      [self.normalize(image=_SCREAMING_SNAKE_CASE       ,mean=_SCREAMING_SNAKE_CASE       ,std=_SCREAMING_SNAKE_CASE  ) for image in images]

         UpperCAmelCase_      :							Tuple       =      [to_channel_dimension_format(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ) for image in images]

         UpperCAmelCase_      :							Dict       =      {'''pixel_values''': images}
         return BatchFeature(data=_SCREAMING_SNAKE_CASE       ,tensor_type=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None  )			->		Dict:
         UpperCAmelCase_      :							Union[str, Any]       =      outputs.logits

         # Resize logits and compute semantic segmentation maps
         if target_sizes is not None:
              if len(_SCREAMING_SNAKE_CASE  ) != len(_SCREAMING_SNAKE_CASE  ):
                   raise ValueError(
                       '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''  )

              if is_torch_tensor(_SCREAMING_SNAKE_CASE  ):
                   UpperCAmelCase_      :							List[Any]       =      target_sizes.numpy()

              UpperCAmelCase_      :							List[str]       =      []

              for idx in range(len(_SCREAMING_SNAKE_CASE  )  ):
                   UpperCAmelCase_      :							Optional[int]       =      torch.nn.functional.interpolate(
                       logits[idx].unsqueeze(dim=0  )       ,size=target_sizes[idx]       ,mode='''bilinear'''       ,align_corners=_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							Union[str, Any]       =      resized_logits[0].argmax(dim=0  )
                   semantic_segmentation.append(_SCREAMING_SNAKE_CASE  )
         else:
              UpperCAmelCase_      :							Optional[Any]       =      logits.argmax(dim=1  )
              UpperCAmelCase_      :							str       =      [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]  )]

         return semantic_segmentation





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__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Optional[Any]       =      SwinConfig()
     UpperCAmelCase_      :							Dict       =      swin_name.split('''_'''				)

     UpperCAmelCase_      :							List[Any]       =      name_split[1]
     UpperCAmelCase_      :							Optional[Any]       =      int(name_split[4]				)
     UpperCAmelCase_      :							Union[str, Any]       =      int(name_split[3][-1]				)

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

     if "in22k" in swin_name:
          UpperCAmelCase_      :							int       =      21841
     else:
          UpperCAmelCase_      :							Tuple       =      1000
          UpperCAmelCase_      :							Any       =      '''huggingface/label-files'''
          UpperCAmelCase_      :							int       =      '''imagenet-1k-id2label.json'''
          UpperCAmelCase_      :							str       =      json.load(open(hf_hub_download(_lowercase			,						_lowercase			,						repo_type='''dataset'''				)			,						'''r'''				)				)
          UpperCAmelCase_      :							Optional[int]       =      {int(_lowercase				): v for k, v in idalabel.items()}
          UpperCAmelCase_      :							List[Any]       =      idalabel
          UpperCAmelCase_      :							int       =      {v: k for k, v in idalabel.items()}

     UpperCAmelCase_      :							Tuple       =      img_size
     UpperCAmelCase_      :							List[Any]       =      num_classes
     UpperCAmelCase_      :							str       =      embed_dim
     UpperCAmelCase_      :							Optional[int]       =      depths
     UpperCAmelCase_      :							Dict       =      num_heads
     UpperCAmelCase_      :							Optional[int]       =      window_size

     return config







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


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

     if name == "norm.weight":
          UpperCAmelCase_      :							Optional[Any]       =      '''layernorm.weight'''
     if name == "norm.bias":
          UpperCAmelCase_      :							List[str]       =      '''layernorm.bias'''

     if "head" in name:
          UpperCAmelCase_      :							Union[str, Any]       =      name.replace('''head'''			,						'''classifier'''				)
     else:
          UpperCAmelCase_      :							Union[str, Any]       =      '''swin.''' + name

     return name







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     for key in orig_state_dict.copy().keys():
          UpperCAmelCase_      :							str       =      orig_state_dict.pop(_lowercase				)

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

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

     return orig_state_dict







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      timm.create_model(_lowercase			,						pretrained=_lowercase				)
     timm_model.eval()

     UpperCAmelCase_      :							Any       =      get_swin_config(_lowercase				)
     UpperCAmelCase_      :							Union[str, Any]       =      SwinForImageClassification(_lowercase				)
     model.eval()

     UpperCAmelCase_      :							Tuple       =      convert_state_dict(timm_model.state_dict()			,						_lowercase				)
     model.load_state_dict(_lowercase				)

     UpperCAmelCase_      :							List[Any]       =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''

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

     UpperCAmelCase_      :							Optional[Any]       =      timm_model(inputs['''pixel_values''']				)
     UpperCAmelCase_      :							Dict       =      model(**_lowercase				).logits

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

     print(f'''Saving model {swin_name} to {pytorch_dump_folder_path}'''				)
     model.save_pretrained(_lowercase				)

     print(f'''Saving image processor to {pytorch_dump_folder_path}'''				)
     image_processor.save_pretrained(_lowercase				)


if __name__ == "__main__":
     __a          =			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.'
     )

     __a          =			parser.parse_args()
     convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)