'''simple docstring'''





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()
SCREAMING_SNAKE_CASE_:					Union[str, Any]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					List[Any]  =torch.device('cpu')





def      lowerCAmelCase_    ( )   ->				Any:


     '''simple docstring'''




     UpperCAmelCase_	   =       "http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCAmelCase_	   =       Image.open(requests.get(snake_case_ ,   stream=snake_case_      ).raw      )
     return im





def      lowerCAmelCase_    ( snake_case_	:			List[str]      )   ->				List[Any]:


     '''simple docstring'''




     if swiftformer_name == "swiftformer_xs":
          return torch.tensor([-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0, 8.8_6_8_5E-0_1, 2.4_3_6_0E-0_1]      )

     elif swiftformer_name == "swiftformer_s":
          return torch.tensor([3.9_6_3_6E-0_1, 2.3_4_7_8E-0_1, -1.6_9_6_3E0_0, -1.7_3_8_1E0_0, -8.6_3_3_7E-0_1]      )

     elif swiftformer_name == "swiftformer_l1":
          return torch.tensor([-4.2_7_6_8E-0_1, -4.7_4_2_9E-0_1, -1.0_8_9_7E0_0, -1.0_2_4_8E0_0, 3.5_5_2_3E-0_2]      )

     elif swiftformer_name == "swiftformer_l3":
          return torch.tensor([-2.5_3_3_0E-0_1, 2.4_2_1_1E-0_1, -6.0_1_8_5E-0_1, -8.2_7_8_9E-0_1, -6.0_4_4_6E-0_2]      )





def      lowerCAmelCase_    ( snake_case_	:			List[Any] ,   snake_case_	:			Union[str, Any] ,   snake_case_	:			Tuple      )   ->				int:


     '''simple docstring'''




     UpperCAmelCase_	   =       dct.pop(snake_case_      )
     UpperCAmelCase_	   =       val





def      lowerCAmelCase_    ( snake_case_	:			Optional[Any]      )   ->				Optional[int]:


     '''simple docstring'''




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





@torch.no_grad()
def      lowerCAmelCase_    ( snake_case_	:			int ,   snake_case_	:			List[str] ,   snake_case_	:			Optional[Any]      )   ->				Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       SwiftFormerConfig()

     # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
     UpperCAmelCase_	   =       10_00
     UpperCAmelCase_	   =       "huggingface/label-files"
     UpperCAmelCase_	   =       "imagenet-1k-id2label.json"
     UpperCAmelCase_	   =       json.load(open(hf_hub_download(snake_case_ ,   snake_case_ ,   repo_type="dataset"      ) ,   "r"      )      )
     UpperCAmelCase_	   =       {int(snake_case_      ): v for k, v in idalabel.items()}
     UpperCAmelCase_	   =       idalabel
     UpperCAmelCase_	   =       {v: k for k, v in idalabel.items()}

     # size of the architecture
     if swiftformer_name == "swiftformer_xs":
          UpperCAmelCase_	   =       [3, 3, 6, 4]
          UpperCAmelCase_	   =       [48, 56, 1_12, 2_20]

     elif swiftformer_name == "swiftformer_s":
          UpperCAmelCase_	   =       [3, 3, 9, 6]
          UpperCAmelCase_	   =       [48, 64, 1_68, 2_24]

     elif swiftformer_name == "swiftformer_l1":
          UpperCAmelCase_	   =       [4, 3, 10, 5]
          UpperCAmelCase_	   =       [48, 96, 1_92, 3_84]

     elif swiftformer_name == "swiftformer_l3":
          UpperCAmelCase_	   =       [4, 4, 12, 6]
          UpperCAmelCase_	   =       [64, 1_28, 3_20, 5_12]

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

     UpperCAmelCase_	   =       create_rename_keys(snake_case_      )
     for rename_key_src, rename_key_dest in rename_keys:
          rename_key(snake_case_ ,   snake_case_ ,   snake_case_      )

     # load HuggingFace model
     UpperCAmelCase_	   =       SwiftFormerForImageClassification(snake_case_      ).eval()
     hf_model.load_state_dict(snake_case_      )

     # prepare test inputs
     UpperCAmelCase_	   =       prepare_img()
     UpperCAmelCase_	   =       ViTImageProcessor.from_pretrained("preprocessor_config"      )
     UpperCAmelCase_	   =       processor(images=snake_case_ ,   return_tensors="pt"      )

     # compare outputs from both models
     UpperCAmelCase_	   =       get_expected_output(snake_case_      )
     UpperCAmelCase_	   =       hf_model(inputs["pixel_values"]      ).logits

     assert hf_logits.shape == torch.Size([1, 10_00]      )
     assert torch.allclose(hf_logits[0, 0:5] ,   snake_case_ ,   atol=1E-3      )

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


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

    SCREAMING_SNAKE_CASE_:					str  =parser.parse_args()
    convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)





'''simple docstring'''





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

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 	__A				(			UpperCamelCase__     ):



    def __init__(self   :   Union[str, Any]       ,      __a   :   VQModel       ,      __a   :   UNetaDModel       ,      __a   :   DDIMScheduler			):
         super().__init__()
         self.register_modules(vqvae=__a       ,      unet=__a       ,      scheduler=__a			)






    @torch.no_grad()
    def __call__(self   :   str       ,      __a   :   int = 1       ,      __a   :   Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,      __a   :   float = 0.0       ,      __a   :   int = 50       ,      __a   :   Optional[str] = "pil"       ,      __a   :   bool = True       ,      **__a   :   Optional[int]       ,      ):
         UpperCAmelCase_	   =       randn_tensor(
             (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,      generator=__a       ,      )
         UpperCAmelCase_	   =       latents.to(self.device			)

         # scale the initial noise by the standard deviation required by the scheduler
         UpperCAmelCase_	   =       latents * self.scheduler.init_noise_sigma

         self.scheduler.set_timesteps(__a			)

         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         UpperCAmelCase_	   =       "eta" in set(inspect.signature(self.scheduler.step			).parameters.keys()			)

         UpperCAmelCase_	   =       {}
         if accepts_eta:
              UpperCAmelCase_	   =       eta

         for t in self.progress_bar(self.scheduler.timesteps			):
              UpperCAmelCase_	   =       self.scheduler.scale_model_input(__a       ,      __a			)
              # predict the noise residual
              UpperCAmelCase_	   =       self.unet(__a       ,      __a			).sample
              # compute the previous noisy sample x_t -> x_t-1
              UpperCAmelCase_	   =       self.scheduler.step(__a       ,      __a       ,      __a       ,      **__a			).prev_sample

         # decode the image latents with the VAE
         UpperCAmelCase_	   =       self.vqvae.decode(__a			).sample

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

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=__a			)




