






'''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						:						Optional[int]  =					logging.get_logger(__name__)  # pylint: disable=invalid-name
def __UpperCAmelCase   (					A					:			Union[List, PIL.Image.Image, torch.Tensor]		)    ->   int:
			warnings.warn(
			    '''The preprocess method is deprecated and will be removed in a future version. Please'''
			    ''' use VaeImageProcessor.preprocess instead'''			,       A			,       )
			if isinstance(A			,       torch.Tensor		):
						return image
			elif isinstance(A			,       PIL.Image.Image		):
						UpperCAmelCase_      :       Tuple          =       [image]

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

						UpperCAmelCase_      :       Optional[int]          =       [np.array(i.resize((w, h)			,       resample=PIL_INTERPOLATION['''lanczos''']		)		)[None, :] for i in image]
						UpperCAmelCase_      :       Optional[Any]          =       np.concatenate(A			,       axis=0		)
						UpperCAmelCase_      :       Tuple          =       np.array(A		).astype(np.floataa		) / 255.0
						UpperCAmelCase_      :       Dict          =       image.transpose(0			,       3			,       1			,       2		)
						UpperCAmelCase_      :       Dict          =       2.0 * image - 1.0
						UpperCAmelCase_      :       Optional[int]          =       torch.from_numpy(A		)
			elif isinstance(image[0]			,       torch.Tensor		):
						UpperCAmelCase_      :       str          =       torch.cat(A			,       dim=0		)
			return image
def __UpperCAmelCase   (					A					:			Union[List, PIL.Image.Image, torch.Tensor]		)    ->   Dict:





			if isinstance(A			,       torch.Tensor		):
						return mask
			elif isinstance(A			,       PIL.Image.Image		):
						UpperCAmelCase_      :       Optional[int]          =       [mask]

			if isinstance(mask[0]			,       PIL.Image.Image		):
						UpperCAmelCase_      :       List[Any]          =       mask[0].size
						UpperCAmelCase_      :       Union[str, Any]          =       (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
						UpperCAmelCase_      :       Dict          =       [np.array(m.convert('''L'''		).resize((w, h)			,       resample=PIL_INTERPOLATION['''nearest''']		)		)[None, :] for m in mask]
						UpperCAmelCase_      :       Tuple          =       np.concatenate(A			,       axis=0		)
						UpperCAmelCase_      :       Optional[Any]          =       mask.astype(np.floataa		) / 255.0
						UpperCAmelCase_      :       List[Any]          =       0
						UpperCAmelCase_      :       List[str]          =       1
						UpperCAmelCase_      :       Dict          =       torch.from_numpy(A		)
			elif isinstance(mask[0]			,       torch.Tensor		):
						UpperCAmelCase_      :       Optional[int]          =       torch.cat(A			,       dim=0		)
			return mask







class  snake_case__      (					snake_case_):
					a_        =					42
					a_        =					42




					def __init__(  self  :		Any				, _A  :		List[str]				, _A  :		Dict      )    ->							List[Any]:
								super().__init__()
								self.register_modules(unet=UpperCamelCase__				, scheduler=UpperCamelCase__      )





					@torch.no_grad()
					def __call__(  self  :		Dict				, _A  :		Union[str, Any]				, _A  :		Tuple				, _A  :		str = 2_50				, _A  :		List[Any] = 0.0				, _A  :		Optional[Any] = 10				, _A  :		int = 10				, _A  :		Optional[int] = None				, _A  :		List[str] = "pil"				, _A  :		Tuple = True				, )    ->							Union[ImagePipelineOutput, Tuple]:
								UpperCAmelCase_      :       Optional[Any]          =       image

								UpperCAmelCase_      :       int          =       _preprocess_image(UpperCamelCase__      )
								UpperCAmelCase_      :       List[Any]          =       original_image.to(device=self.device				, dtype=self.unet.dtype      )
								UpperCAmelCase_      :       Tuple          =       _preprocess_mask(UpperCamelCase__      )
								UpperCAmelCase_      :       str          =       mask_image.to(device=self.device				, dtype=self.unet.dtype      )

								UpperCAmelCase_      :       int          =       original_image.shape[0]

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

								UpperCAmelCase_      :       Dict          =       original_image.shape
								UpperCAmelCase_      :       Tuple          =       randn_tensor(UpperCamelCase__				, generator=UpperCamelCase__				, device=self.device				, dtype=self.unet.dtype      )

								# set step values
								self.scheduler.set_timesteps(UpperCamelCase__				, UpperCamelCase__				, UpperCamelCase__				, self.device      )
								UpperCAmelCase_      :       str          =       eta

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

											else:
														# compute the reverse: x_t-1 -> x_t
														UpperCAmelCase_      :       Optional[int]          =       self.scheduler.undo_step(UpperCamelCase__				, UpperCamelCase__				, UpperCamelCase__      )
											UpperCAmelCase_      :       Optional[Any]          =       t

								UpperCAmelCase_      :       List[str]          =       (image / 2 + 0.5).clamp(0				, 1      )
								UpperCAmelCase_      :       str          =       image.cpu().permute(0				, 2				, 3				, 1      ).numpy()
								if output_type == "pil":
											UpperCAmelCase_      :       Union[str, Any]          =       self.numpy_to_pil(UpperCamelCase__      )

								if not return_dict:
											return (image,)

								return ImagePipelineOutput(images=UpperCamelCase__      )






"""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		(		lowercase    :    Optional[int]					)      ->		Tuple:


    """simple docstring"""





    snake_case       : Optional[Any]    =       SwinConfig()
    snake_case       : Union[str, Any]    =       swin_name.split("_"					)

    snake_case       : str    =       name_split[1]
    snake_case       : Tuple    =       int(name_split[4]					)
    snake_case       : str    =       int(name_split[3][-1]					)

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

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

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

    return config







def     __lowerCAmelCase		(		lowercase    :    Optional[Any]					)      ->		Any:


    """simple docstring"""





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

    if name == "norm.weight":
        snake_case       : Optional[int]    =       "layernorm.weight"
    if name == "norm.bias":
        snake_case       : Tuple    =       "layernorm.bias"

    if "head" in name:
        snake_case       : Any    =       name.replace("head"		,				"classifier"					)
    else:
        snake_case       : Tuple    =       "swin." + name

    return name







def     __lowerCAmelCase		(		lowercase    :    List[Any]		,				lowercase    :    List[Any]					)      ->		Any:


    """simple docstring"""





    for key in orig_state_dict.copy().keys():
        snake_case       : int    =       orig_state_dict.pop(lowercase					)

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

            if "weight" in key:
                snake_case       : Dict    =       val[:dim, :]
                snake_case       : Tuple    =       val[
                    dim : dim * 2, :
                ]
                snake_case       : List[str]    =       val[-dim:, :]
            else:
                snake_case       : Dict    =       val[
                    :dim
                ]
                snake_case       : List[str]    =       val[
                    dim : dim * 2
                ]
                snake_case       : Union[str, Any]    =       val[
                    -dim:
                ]
        else:
            snake_case       : Any    =       val

    return orig_state_dict







def     __lowerCAmelCase		(		lowercase    :    Tuple		,				lowercase    :    Any					)      ->		Dict:


    """simple docstring"""





    snake_case       : str    =       timm.create_model(lowercase		,				pretrained=lowercase					)
    timm_model.eval()

    snake_case       : int    =       get_swin_config(lowercase					)
    snake_case       : Optional[int]    =       SwinForImageClassification(lowercase					)
    model.eval()

    snake_case       : int    =       convert_state_dict(timm_model.state_dict()		,				lowercase					)
    model.load_state_dict(lowercase					)

    snake_case       : Dict    =       "http://images.cocodataset.org/val2017/000000039769.jpg"

    snake_case       : Tuple    =       AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"		,				"-"					)					)					)
    snake_case       : Optional[int]    =       Image.open(requests.get(lowercase		,				stream=lowercase					).raw					)
    snake_case       : Dict    =       image_processor(images=lowercase		,				return_tensors="pt"					)

    snake_case       : str    =       timm_model(inputs["pixel_values"]					)
    snake_case       : Tuple    =       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__":
  __snake_case       =       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."""
  )

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





