





import argparse
import json
from pathlib import Path

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

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case  :   Dict						   =  logging.get_logger(__name__)

_snake_case  :   Union[str, Any]						   =  torch.device('cpu')

def   a_   (			):
   __lowerCAmelCase							     =     'http://images.cocodataset.org/val2017/000000039769.jpg'
   __lowerCAmelCase							     =     Image.open(requests.get(lowerCAmelCase_,     stream=lowerCAmelCase_     ).raw     )
   return im

def   a_   (			lowerCAmelCase_				:      int     ):
   if swiftformer_name == "swiftformer_xs":
      return torch.tensor([-2.17_03E00, 2.11_07E00, -2.08_11E00, 8.86_85E-01, 2.43_60E-01]     )

   elif swiftformer_name == "swiftformer_s":
      return torch.tensor([3.96_36E-01, 2.34_78E-01, -1.69_63E00, -1.73_81E00, -8.63_37E-01]     )

   elif swiftformer_name == "swiftformer_l1":
      return torch.tensor([-4.27_68E-01, -4.74_29E-01, -1.08_97E00, -1.02_48E00, 3.55_23E-02]     )

   elif swiftformer_name == "swiftformer_l3":
      return torch.tensor([-2.53_30E-01, 2.42_11E-01, -6.01_85E-01, -8.27_89E-01, -6.04_46E-02]     )

def   a_   (			lowerCAmelCase_				:      Tuple,     lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      List[Any]     ):
   __lowerCAmelCase							     =     dct.pop(lowerCAmelCase_     )
   __lowerCAmelCase							     =     val

def   a_   (			lowerCAmelCase_				:      str     ):
   __lowerCAmelCase							     =     []
   for k in state_dict.keys():
      __lowerCAmelCase							     =     k
      if ".pwconv" in k:
         __lowerCAmelCase							     =     k_new.replace('.pwconv',     '.point_wise_conv'     )
      if ".dwconv" in k:
         __lowerCAmelCase							     =     k_new.replace('.dwconv',     '.depth_wise_conv'     )
      if ".Proj." in k:
         __lowerCAmelCase							     =     k_new.replace('.Proj.',     '.proj.'     )
      if "patch_embed" in k_new:
         __lowerCAmelCase							     =     k_new.replace('patch_embed',     'swiftformer.patch_embed.patch_embedding'     )
      if "network" in k_new:
         __lowerCAmelCase							     =     k_new.split('.'     )
         if ls[2].isdigit():
            __lowerCAmelCase							     =     'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]     )
         else:
            __lowerCAmelCase							     =     k_new.replace('network',     'swiftformer.encoder.network'     )
      rename_keys.append((k, k_new)     )
   return rename_keys

@torch.no_grad()
def   a_   (			lowerCAmelCase_				:      str,     lowerCAmelCase_				:      Optional[int],     lowerCAmelCase_				:      Optional[Any]     ):
   __lowerCAmelCase							     =     SwiftFormerConfig()

   # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
   __lowerCAmelCase							     =     1000
   __lowerCAmelCase							     =     'huggingface/label-files'
   __lowerCAmelCase							     =     'imagenet-1k-id2label.json'
   __lowerCAmelCase							     =     json.load(open(hf_hub_download(lowerCAmelCase_,     lowerCAmelCase_,     repo_type='dataset'     ),     'r'     )     )
   __lowerCAmelCase							     =     {int(lowerCAmelCase_     ): v for k, v in idalabel.items()}
   __lowerCAmelCase							     =     idalabel
   __lowerCAmelCase							     =     {v: k for k, v in idalabel.items()}

   # size of the architecture
   if swiftformer_name == "swiftformer_xs":
      __lowerCAmelCase							     =     [3, 3, 6, 4]
      __lowerCAmelCase							     =     [48, 56, 112, 220]

   elif swiftformer_name == "swiftformer_s":
      __lowerCAmelCase							     =     [3, 3, 9, 6]
      __lowerCAmelCase							     =     [48, 64, 168, 224]

   elif swiftformer_name == "swiftformer_l1":
      __lowerCAmelCase							     =     [4, 3, 10, 5]
      __lowerCAmelCase							     =     [48, 96, 192, 384]

   elif swiftformer_name == "swiftformer_l3":
      __lowerCAmelCase							     =     [4, 4, 12, 6]
      __lowerCAmelCase							     =     [64, 128, 320, 512]

   # load state_dict of original model, remove and rename some keys
   if original_ckpt:
      if original_ckpt.startswith('https'     ):
         __lowerCAmelCase							     =     torch.hub.load_state_dict_from_url(lowerCAmelCase_,     map_location='cpu',     check_hash=lowerCAmelCase_     )
      else:
         __lowerCAmelCase							     =     torch.load(lowerCAmelCase_,     map_location='cpu'     )
   __lowerCAmelCase							     =     checkpoint

   __lowerCAmelCase							     =     create_rename_keys(lowerCAmelCase_     )
   for rename_key_src, rename_key_dest in rename_keys:
      rename_key(lowerCAmelCase_,     lowerCAmelCase_,     lowerCAmelCase_     )

   # load HuggingFace model
   __lowerCAmelCase							     =     SwiftFormerForImageClassification(lowerCAmelCase_     ).eval()
   hf_model.load_state_dict(lowerCAmelCase_     )

   # prepare test inputs
   __lowerCAmelCase							     =     prepare_img()
   __lowerCAmelCase							     =     ViTImageProcessor.from_pretrained('preprocessor_config'     )
   __lowerCAmelCase							     =     processor(images=lowerCAmelCase_,     return_tensors='pt'     )

   # compare outputs from both models
   __lowerCAmelCase							     =     get_expected_output(lowerCAmelCase_     )
   __lowerCAmelCase							     =     hf_model(inputs['pixel_values']     ).logits

   assert hf_logits.shape == torch.Size([1, 1000]     )
   assert torch.allclose(hf_logits[0, 0:5],     lowerCAmelCase_,     atol=1E-3     )

   Path(lowerCAmelCase_     ).mkdir(exist_ok=lowerCAmelCase_     )
   print(F"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}"""     )
   hf_model.save_pretrained(lowerCAmelCase_     )


if __name__ == "__main__":
     _snake_case  :   Dict						   =  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--swiftformer_name',
         default='swiftformer_xs',
         choices=['swiftformer_xs', 'swiftformer_s', 'swiftformer_l1', 'swiftformer_l3'],
         type=str,
         help='Name of the SwiftFormer model you\'d like to convert.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path',
         default='./converted_outputs/',
         type=str,
         help='Path to the output PyTorch model directory.',
     )
     parser.add_argument('--original_ckpt', default=None, type=str, help='Path to the original model checkpoint.')

     _snake_case  :   Optional[int]						   =  parser.parse_args()
     convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)








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


_snake_case  :   Union[str, Any]						   =  logging.get_logger(__name__)  # pylint: disable=invalid-name

def   a_   (			lowerCAmelCase_				:      Union[List, PIL.Image.Image, torch.Tensor]     ):
   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     ):
      __lowerCAmelCase							     =     [image]

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

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

def   a_   (			lowerCAmelCase_				:      Union[List, PIL.Image.Image, torch.Tensor]     ):
   if isinstance(lowerCAmelCase_,     torch.Tensor     ):
      return mask
   elif isinstance(lowerCAmelCase_,     PIL.Image.Image     ):
      __lowerCAmelCase							     =     [mask]

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




class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			42
  a_      =			42






  def __init__(    self			:		Optional[Any] ,  lowerCAmelCase_			:		List[str] ,  lowerCAmelCase_			:		List[Any]					)							->						Optional[int]:
     super().__init__()
     self.register_modules(unet=lowerCAmelCase_ ,  scheduler=lowerCAmelCase_					)






  @torch.no_grad()
  def __call__(    self			:		Dict ,  lowerCAmelCase_			:		Union[torch.Tensor, PIL.Image.Image] ,  lowerCAmelCase_			:		Union[torch.Tensor, PIL.Image.Image] ,  lowerCAmelCase_			:		int = 2_5_0 ,  lowerCAmelCase_			:		float = 0.0 ,  lowerCAmelCase_			:		int = 1_0 ,  lowerCAmelCase_			:		int = 1_0 ,  lowerCAmelCase_			:		Optional[Union[torch.Generator, List[torch.Generator]]] = None ,  lowerCAmelCase_			:		Optional[str] = "pil" ,  lowerCAmelCase_			:		bool = True ,  )							->						Union[ImagePipelineOutput, Tuple]:
     __lowerCAmelCase							     =     image

     __lowerCAmelCase							     =     _preprocess_image(lowerCAmelCase_					)
     __lowerCAmelCase							     =     original_image.to(device=self.device ,  dtype=self.unet.dtype					)
     __lowerCAmelCase							     =     _preprocess_mask(lowerCAmelCase_					)
     __lowerCAmelCase							     =     mask_image.to(device=self.device ,  dtype=self.unet.dtype					)

     __lowerCAmelCase							     =     original_image.shape[0]

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

     __lowerCAmelCase							     =     original_image.shape
     __lowerCAmelCase							     =     randn_tensor(lowerCAmelCase_ ,  generator=lowerCAmelCase_ ,  device=self.device ,  dtype=self.unet.dtype					)

     # set step values
     self.scheduler.set_timesteps(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  self.device					)
     __lowerCAmelCase							     =     eta

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

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

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

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=lowerCAmelCase_					)

