




'''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       _UpperCamelCase			(_lowerCamelCase				:  List[Any]						)-> str:




     '''simple docstring'''





     __snake_case            =	SwinConfig()
     __snake_case            =	swin_name.split('''_'''						)

     __snake_case            =	name_split[1]
     __snake_case            =	int(name_split[4]						)
     __snake_case            =	int(name_split[3][-1]						)

     if model_size == "tiny":
          __snake_case            =	96
          __snake_case            =	(2, 2, 6, 2)
          __snake_case            =	(3, 6, 12, 24)
     elif model_size == "small":
          __snake_case            =	96
          __snake_case            =	(2, 2, 18, 2)
          __snake_case            =	(3, 6, 12, 24)
     elif model_size == "base":
          __snake_case            =	1_28
          __snake_case            =	(2, 2, 18, 2)
          __snake_case            =	(4, 8, 16, 32)
     else:
          __snake_case            =	1_92
          __snake_case            =	(2, 2, 18, 2)
          __snake_case            =	(6, 12, 24, 48)

     if "in22k" in swin_name:
          __snake_case            =	2_18_41
     else:
          __snake_case            =	10_00
          __snake_case            =	'''huggingface/label-files'''
          __snake_case            =	'''imagenet-1k-id2label.json'''
          __snake_case            =	json.load(open(hf_hub_download(_lowerCamelCase ,						_lowerCamelCase ,						repo_type='''dataset'''						) ,						'''r'''						)						)
          __snake_case            =	{int(_lowerCamelCase						): v for k, v in idalabel.items()}
          __snake_case            =	idalabel
          __snake_case            =	{v: k for k, v in idalabel.items()}

     __snake_case            =	img_size
     __snake_case            =	num_classes
     __snake_case            =	embed_dim
     __snake_case            =	depths
     __snake_case            =	num_heads
     __snake_case            =	window_size

     return config







def       _UpperCamelCase			(_lowerCamelCase				:  str						)-> List[str]:




     '''simple docstring'''





     if "patch_embed.proj" in name:
          __snake_case            =	name.replace('''patch_embed.proj''' ,						'''embeddings.patch_embeddings.projection'''						)
     if "patch_embed.norm" in name:
          __snake_case            =	name.replace('''patch_embed.norm''' ,						'''embeddings.norm'''						)
     if "layers" in name:
          __snake_case            =	'''encoder.''' + name
     if "attn.proj" in name:
          __snake_case            =	name.replace('''attn.proj''' ,						'''attention.output.dense'''						)
     if "attn" in name:
          __snake_case            =	name.replace('''attn''' ,						'''attention.self'''						)
     if "norm1" in name:
          __snake_case            =	name.replace('''norm1''' ,						'''layernorm_before'''						)
     if "norm2" in name:
          __snake_case            =	name.replace('''norm2''' ,						'''layernorm_after'''						)
     if "mlp.fc1" in name:
          __snake_case            =	name.replace('''mlp.fc1''' ,						'''intermediate.dense'''						)
     if "mlp.fc2" in name:
          __snake_case            =	name.replace('''mlp.fc2''' ,						'''output.dense'''						)

     if name == "norm.weight":
          __snake_case            =	'''layernorm.weight'''
     if name == "norm.bias":
          __snake_case            =	'''layernorm.bias'''

     if "head" in name:
          __snake_case            =	name.replace('''head''' ,						'''classifier'''						)
     else:
          __snake_case            =	'''swin.''' + name

     return name







def       _UpperCamelCase			(_lowerCamelCase				:  str ,						_lowerCamelCase				:  Union[str, Any]						)-> Dict:




     '''simple docstring'''





     for key in orig_state_dict.copy().keys():
          __snake_case            =	orig_state_dict.pop(_lowerCamelCase						)

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

               if "weight" in key:
                    __snake_case            =	val[:dim, :]
                    __snake_case            =	val[
                        dim : dim * 2, :
                    ]
                    __snake_case            =	val[-dim:, :]
               else:
                    __snake_case            =	val[
                        :dim
                    ]
                    __snake_case            =	val[
                        dim : dim * 2
                    ]
                    __snake_case            =	val[
                        -dim:
                    ]
          else:
               __snake_case            =	val

     return orig_state_dict







def       _UpperCamelCase			(_lowerCamelCase				:  str ,						_lowerCamelCase				:  Any						)-> List[Any]:




     '''simple docstring'''





     __snake_case            =	timm.create_model(_lowerCamelCase ,						pretrained=_lowerCamelCase						)
     timm_model.eval()

     __snake_case            =	get_swin_config(_lowerCamelCase						)
     __snake_case            =	SwinForImageClassification(_lowerCamelCase						)
     model.eval()

     __snake_case            =	convert_state_dict(timm_model.state_dict() ,						_lowerCamelCase						)
     model.load_state_dict(_lowerCamelCase						)

     __snake_case            =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''

     __snake_case            =	AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_''' ,						'''-'''						)						)						)
     __snake_case            =	Image.open(requests.get(_lowerCamelCase ,						stream=_lowerCamelCase						).raw						)
     __snake_case            =	image_processor(images=_lowerCamelCase ,						return_tensors='''pt'''						)

     __snake_case            =	timm_model(inputs['''pixel_values''']						)
     __snake_case            =	model(**_lowerCamelCase						).logits

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

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

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


if __name__ == "__main__":
     UpperCAmelCase_							:	List[Any]	     =		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.'''
     )

     UpperCAmelCase_							:	Optional[Any]	     =		parser.parse_args()
     convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)












'''simple docstring'''



import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


UpperCAmelCase_							:	Tuple	     =		logging.get_logger(__name__)  # pylint: disable=invalid-name







def       _UpperCamelCase			(_lowerCamelCase				:  Union[List, PIL.Image.Image, torch.Tensor]						)-> Optional[Any]:




     '''simple docstring'''





     warnings.warn(
         '''The preprocess method is deprecated and will be removed in a future version. Please'''
         ''' use VaeImageProcessor.preprocess instead''' ,						_lowerCamelCase ,						)
     if isinstance(_lowerCamelCase ,						torch.Tensor						):
          return image
     elif isinstance(_lowerCamelCase ,						PIL.Image.Image						):
          __snake_case            =	[image]

     if isinstance(image[0] ,						PIL.Image.Image						):
          __snake_case       ,     __snake_case            =	image[0].size
          __snake_case       ,     __snake_case            =	(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

          __snake_case            =	[np.array(i.resize((w, h) ,						resample=PIL_INTERPOLATION['''lanczos''']						)						)[None, :] for i in image]
          __snake_case            =	np.concatenate(_lowerCamelCase ,						axis=0						)
          __snake_case            =	np.array(_lowerCamelCase						).astype(np.floataa						) / 255.0
          __snake_case            =	image.transpose(0 ,						3 ,						1 ,						2						)
          __snake_case            =	2.0 * image - 1.0
          __snake_case            =	torch.from_numpy(_lowerCamelCase						)
     elif isinstance(image[0] ,						torch.Tensor						):
          __snake_case            =	torch.cat(_lowerCamelCase ,						dim=0						)
     return image







def       _UpperCamelCase			(_lowerCamelCase				:  Union[List, PIL.Image.Image, torch.Tensor]						)-> Optional[Any]:




     '''simple docstring'''





     if isinstance(_lowerCamelCase ,						torch.Tensor						):
          return mask
     elif isinstance(_lowerCamelCase ,						PIL.Image.Image						):
          __snake_case            =	[mask]

     if isinstance(mask[0] ,						PIL.Image.Image						):
          __snake_case       ,     __snake_case            =	mask[0].size
          __snake_case       ,     __snake_case            =	(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
          __snake_case            =	[np.array(m.convert('''L'''						).resize((w, h) ,						resample=PIL_INTERPOLATION['''nearest''']						)						)[None, :] for m in mask]
          __snake_case            =	np.concatenate(_lowerCamelCase ,						axis=0						)
          __snake_case            =	mask.astype(np.floataa						) / 255.0
          __snake_case            =	0
          __snake_case            =	1
          __snake_case            =	torch.from_numpy(_lowerCamelCase						)
     elif isinstance(mask[0] ,						torch.Tensor						):
          __snake_case            =	torch.cat(_lowerCamelCase ,						dim=0						)
     return mask







class      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   UNetaDModel
       __lowercase	:   RePaintScheduler


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	str:



            '''simple docstring'''




            super().__init__()
            self.register_modules(unet=__SCREAMING_SNAKE_CASE     ,	scheduler=__SCREAMING_SNAKE_CASE	)




       @torch.no_grad()
       def __call__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = 250     ,	__SCREAMING_SNAKE_CASE = 0.0     ,	__SCREAMING_SNAKE_CASE = 10     ,	__SCREAMING_SNAKE_CASE = 10     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = "pil"     ,	__SCREAMING_SNAKE_CASE = True     ,	)   ->	Union[ImagePipelineOutput, Tuple]:



            '''simple docstring'''




            __snake_case            =	image

            __snake_case            =	_preprocess_image(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	original_image.to(device=self.device     ,	dtype=self.unet.dtype	)
            __snake_case            =	_preprocess_mask(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	mask_image.to(device=self.device     ,	dtype=self.unet.dtype	)

            __snake_case            =	original_image.shape[0]

            # sample gaussian noise to begin the loop
            if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	) and len(__SCREAMING_SNAKE_CASE	) != batch_size:
                 raise ValueError(
                     F'''You have passed a list of generators of length {len(__SCREAMING_SNAKE_CASE	)}, but requested an effective batch'''
                     F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''	)

            __snake_case            =	original_image.shape
            __snake_case            =	randn_tensor(__SCREAMING_SNAKE_CASE     ,	generator=__SCREAMING_SNAKE_CASE     ,	device=self.device     ,	dtype=self.unet.dtype	)

            # set step values
            self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	self.device	)
            __snake_case            =	eta

            __snake_case            =	self.scheduler.timesteps[0] + 1
            __snake_case            =	generator[0] if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	) else generator
            for i, t in enumerate(self.progress_bar(self.scheduler.timesteps	)	):
                 if t < t_last:
                      # predict the noise residual
                      __snake_case            =	self.unet(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	).sample
                      # compute previous image: x_t -> x_t-1
                      __snake_case            =	self.scheduler.step(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	).prev_sample

                 else:
                      # compute the reverse: x_t-1 -> x_t
                      __snake_case            =	self.scheduler.undo_step(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	t

            __snake_case            =	(image / 2 + 0.5).clamp(0     ,	1	)
            __snake_case            =	image.cpu().permute(0     ,	2     ,	3     ,	1	).numpy()
            if output_type == "pil":
                 __snake_case            =	self.numpy_to_pil(__SCREAMING_SNAKE_CASE	)

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE	)






