



"""simple docstring"""
from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


A:   str								=		logging.get_logger(__name__)  # pylint: disable=invalid-name



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     )  ->      List[Any]:





    '''simple docstring'''




    super().__init__()

    if hasattr(scheduler.config      ,     """steps_offset"""						) and scheduler.config.steps_offset != 1:
     UpperCAmelCase	:					Any	=	(
         F"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
         F" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
         """to update the config accordingly as leaving `steps_offset` might led to incorrect results"""
         """ in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"""
         """ it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"""
         """ file"""
     )
     deprecate("""steps_offset!=1"""      ,     """1.0.0"""      ,     _SCREAMING_SNAKE_CASE      ,     standard_warn=_SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					Tuple	=	dict(scheduler.config						)
     UpperCAmelCase	:					Tuple	=	1
     UpperCAmelCase	:					Tuple	=	FrozenDict(_SCREAMING_SNAKE_CASE						)

    if hasattr(scheduler.config      ,     """skip_prk_steps"""						) and scheduler.config.skip_prk_steps is False:
     UpperCAmelCase	:					Dict	=	(
         F"The configuration file of this scheduler: {scheduler} has not set the configuration"
         """ `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"""
         """ sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"""
         """ incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"""
         """ Hub, it would be very nice if you could open a Pull request for the"""
         """ `scheduler/scheduler_config.json` file"""
     )
     deprecate("""skip_prk_steps not set"""      ,     """1.0.0"""      ,     _SCREAMING_SNAKE_CASE      ,     standard_warn=_SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					Union[str, Any]	=	dict(scheduler.config						)
     UpperCAmelCase	:					Optional[Any]	=	True
     UpperCAmelCase	:					str	=	FrozenDict(_SCREAMING_SNAKE_CASE						)

    if safety_checker is None:
     logger.warning(
         F"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
         """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
         """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
         """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
         """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
         """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""						)

    self.register_modules(
        segmentation_model=_SCREAMING_SNAKE_CASE      ,     segmentation_processor=_SCREAMING_SNAKE_CASE      ,     vae=_SCREAMING_SNAKE_CASE      ,     text_encoder=_SCREAMING_SNAKE_CASE      ,     tokenizer=_SCREAMING_SNAKE_CASE      ,     unet=_SCREAMING_SNAKE_CASE      ,     scheduler=_SCREAMING_SNAKE_CASE      ,     safety_checker=_SCREAMING_SNAKE_CASE      ,     feature_extractor=_SCREAMING_SNAKE_CASE      ,     )

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE = "auto"						)  ->      List[str]:





    '''simple docstring'''




    if slice_size == "auto":
     # half the attention head size is usually a good trade-off between
     # speed and memory
     UpperCAmelCase	:					List[Any]	=	self.unet.config.attention_head_dim // 2
    self.unet.set_attention_slice(_SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    self.enable_attention_slicing(_SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    if is_accelerate_available():
     from accelerate import cpu_offload
    else:
     raise ImportError("""Please install accelerate via `pip install accelerate`"""						)

    UpperCAmelCase	:					List[str]	=	torch.device("""cuda"""						)

    for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
     if cpu_offloaded_model is not None:
      cpu_offload(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)

   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Union[str, Any]:





    '''simple docstring'''




    if self.device != torch.device("""meta"""						) or not hasattr(self.unet      ,     """_hf_hook"""						):
     return self.device
    for module in self.unet.modules():
     if (
         hasattr(_SCREAMING_SNAKE_CASE      ,     """_hf_hook"""						)
         and hasattr(module._hf_hook      ,     """execution_device"""						)
         and module._hf_hook.execution_device is not None
     ):
      return torch.device(module._hf_hook.execution_device						)
    return self.device




   @torch.no_grad()
   def __call__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = 512      ,     _SCREAMING_SNAKE_CASE = 512      ,     _SCREAMING_SNAKE_CASE = 50      ,     _SCREAMING_SNAKE_CASE = 7.5      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = 1      ,     _SCREAMING_SNAKE_CASE = 0.0      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = "pil"      ,     _SCREAMING_SNAKE_CASE = True      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = 1      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      Optional[int]:





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	self.segmentation_processor(
        text=[text]      ,     images=[image]      ,     padding="""max_length"""      ,     return_tensors="""pt"""						).to(self.device						)
    UpperCAmelCase	:					str	=	self.segmentation_model(**_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[Any]	=	torch.sigmoid(outputs.logits						).cpu().detach().unsqueeze(-1						).numpy()
    UpperCAmelCase	:					Optional[Any]	=	self.numpy_to_pil(_SCREAMING_SNAKE_CASE						)[0].resize(image.size						)

    # Run inpainting pipeline with the generated mask
    UpperCAmelCase	:					Tuple	=	StableDiffusionInpaintPipeline(
        vae=self.vae      ,     text_encoder=self.text_encoder      ,     tokenizer=self.tokenizer      ,     unet=self.unet      ,     scheduler=self.scheduler      ,     safety_checker=self.safety_checker      ,     feature_extractor=self.feature_extractor      ,     )
    return inpainting_pipeline(
        prompt=_SCREAMING_SNAKE_CASE      ,     image=_SCREAMING_SNAKE_CASE      ,     mask_image=_SCREAMING_SNAKE_CASE      ,     height=_SCREAMING_SNAKE_CASE      ,     width=_SCREAMING_SNAKE_CASE      ,     num_inference_steps=_SCREAMING_SNAKE_CASE      ,     guidance_scale=_SCREAMING_SNAKE_CASE      ,     negative_prompt=_SCREAMING_SNAKE_CASE      ,     num_images_per_prompt=_SCREAMING_SNAKE_CASE      ,     eta=_SCREAMING_SNAKE_CASE      ,     generator=_SCREAMING_SNAKE_CASE      ,     latents=_SCREAMING_SNAKE_CASE      ,     output_type=_SCREAMING_SNAKE_CASE      ,     return_dict=_SCREAMING_SNAKE_CASE      ,     callback=_SCREAMING_SNAKE_CASE      ,     callback_steps=_SCREAMING_SNAKE_CASE      ,     )





"""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()
A:   Optional[int]								=		logging.get_logger(__name__)

A:   Optional[int]								=		torch.device("cpu")

def       _snake_case						(						):
 UpperCAmelCase	:					List[Any]	=	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 UpperCAmelCase	:					Tuple	=	Image.open(requests.get(UpperCamelCase			,		stream=UpperCamelCase						).raw						)
 return im

def       _snake_case						(						UpperCamelCase						:		int						):
 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       _snake_case						(						UpperCamelCase						:		Optional[Any]			,		UpperCamelCase						:		List[str]			,		UpperCamelCase						:		str						):
 UpperCAmelCase	:					int	=	dct.pop(UpperCamelCase						)
 UpperCAmelCase	:					Any	=	val

def       _snake_case						(						UpperCamelCase						:		Union[str, Any]						):
 UpperCAmelCase	:					Optional[int]	=	[]
 for k in state_dict.keys():
  UpperCAmelCase	:					Optional[Any]	=	k
  if ".pwconv" in k:
   UpperCAmelCase	:					int	=	k_new.replace(""".pwconv"""			,		""".point_wise_conv"""						)
  if ".dwconv" in k:
   UpperCAmelCase	:					Tuple	=	k_new.replace(""".dwconv"""			,		""".depth_wise_conv"""						)
  if ".Proj." in k:
   UpperCAmelCase	:					List[Any]	=	k_new.replace(""".Proj."""			,		""".proj."""						)
  if "patch_embed" in k_new:
   UpperCAmelCase	:					Any	=	k_new.replace("""patch_embed"""			,		"""swiftformer.patch_embed.patch_embedding"""						)
  if "network" in k_new:
   UpperCAmelCase	:					int	=	k_new.split("""."""						)
   if ls[2].isdigit():
    UpperCAmelCase	:					List[Any]	=	"""swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:]						)
   else:
    UpperCAmelCase	:					Any	=	k_new.replace("""network"""			,		"""swiftformer.encoder.network"""						)
  rename_keys.append((k, k_new)						)
 return rename_keys

@torch.no_grad()
def       _snake_case						(						UpperCamelCase						:		int			,		UpperCamelCase						:		int			,		UpperCamelCase						:		Optional[int]						):
 UpperCAmelCase	:					List[Any]	=	SwiftFormerConfig()

 # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
 UpperCAmelCase	:					Optional[Any]	=	1000
 UpperCAmelCase	:					Tuple	=	"""huggingface/label-files"""
 UpperCAmelCase	:					List[str]	=	"""imagenet-1k-id2label.json"""
 UpperCAmelCase	:					Dict	=	json.load(open(hf_hub_download(UpperCamelCase			,		UpperCamelCase			,		repo_type="""dataset"""						)			,		"""r"""						)						)
 UpperCAmelCase	:					Dict	=	{int(UpperCamelCase						): v for k, v in idalabel.items()}
 UpperCAmelCase	:					str	=	idalabel
 UpperCAmelCase	:					Dict	=	{v: k for k, v in idalabel.items()}

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

 elif swiftformer_name == "swiftformer_s":
  UpperCAmelCase	:					Dict	=	[3, 3, 9, 6]
  UpperCAmelCase	:					Union[str, Any]	=	[48, 64, 168, 224]

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

 elif swiftformer_name == "swiftformer_l3":
  UpperCAmelCase	:					Union[str, Any]	=	[4, 4, 12, 6]
  UpperCAmelCase	:					List[Any]	=	[64, 128, 320, 512]

 # load state_dict of original model, remove and rename some keys
 if original_ckpt:
  if original_ckpt.startswith("""https"""						):
   UpperCAmelCase	:					List[str]	=	torch.hub.load_state_dict_from_url(UpperCamelCase			,		map_location="""cpu"""			,		check_hash=UpperCamelCase						)
  else:
   UpperCAmelCase	:					Any	=	torch.load(UpperCamelCase			,		map_location="""cpu"""						)
 UpperCAmelCase	:					Optional[Any]	=	checkpoint

 UpperCAmelCase	:					Dict	=	create_rename_keys(UpperCamelCase						)
 for rename_key_src, rename_key_dest in rename_keys:
  rename_key(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)

 # load HuggingFace model
 UpperCAmelCase	:					List[Any]	=	SwiftFormerForImageClassification(UpperCamelCase						).eval()
 hf_model.load_state_dict(UpperCamelCase						)

 # prepare test inputs
 UpperCAmelCase	:					Dict	=	prepare_img()
 UpperCAmelCase	:					Tuple	=	ViTImageProcessor.from_pretrained("""preprocessor_config"""						)
 UpperCAmelCase	:					Optional[int]	=	processor(images=UpperCamelCase			,		return_tensors="""pt"""						)

 # compare outputs from both models
 UpperCAmelCase	:					Optional[int]	=	get_expected_output(UpperCamelCase						)
 UpperCAmelCase	:					List[str]	=	hf_model(inputs["""pixel_values"""]						).logits

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

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


if __name__ == "__main__":
      A:   List[str]								=		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.")

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