



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

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

A:   Dict								=		"\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")\n        >>> pipe_prior.to(\"cuda\")\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")\n        >>> pipe.to(\"cuda\")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save(\"cat.png\")\n        ```\n"

def       _snake_case						(						UpperCamelCase						:		Any			,		UpperCamelCase						:		Any			,		UpperCamelCase						:		Any=8						):
 UpperCAmelCase	:					Any	=	height // scale_factor**2
 if height % scale_factor**2 != 0:
  new_height += 1
 UpperCAmelCase	:					Optional[Any]	=	width // scale_factor**2
 if width % scale_factor**2 != 0:
  new_width += 1
 return new_height * scale_factor, new_width * scale_factor



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):

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





    '''simple docstring'''




    super().__init__()

    self.register_modules(
        unet=_SCREAMING_SNAKE_CASE      ,     scheduler=_SCREAMING_SNAKE_CASE      ,     movq=_SCREAMING_SNAKE_CASE      ,     )
    UpperCAmelCase	:					Any	=	2 ** (len(self.movq.config.block_out_channels						) - 1)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      Optional[Any]:





    '''simple docstring'''




    if latents is None:
     UpperCAmelCase	:					Optional[Any]	=	randn_tensor(_SCREAMING_SNAKE_CASE      ,     generator=_SCREAMING_SNAKE_CASE      ,     device=_SCREAMING_SNAKE_CASE      ,     dtype=_SCREAMING_SNAKE_CASE						)
    else:
     if latents.shape != shape:
      raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"						)
     UpperCAmelCase	:					Optional[int]	=	latents.to(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					List[Any]	=	latents * scheduler.init_noise_sigma
    return latents

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE=0						)  ->      Optional[int]:





    '''simple docstring'''




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

    UpperCAmelCase	:					Optional[int]	=	torch.device(F"cuda:{gpu_id}"						)

    UpperCAmelCase	:					Dict	=	[
        self.unet,
        self.movq,
    ]
    for cpu_offloaded_model in models:
     if cpu_offloaded_model is not None:
      cpu_offload(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE=0						)  ->      str:





    '''simple docstring'''




    if is_accelerate_available() and is_accelerate_version(""">="""      ,     """0.17.0.dev0"""						):
     from accelerate import cpu_offload_with_hook
    else:
     raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""						)

    UpperCAmelCase	:					int	=	torch.device(F"cuda:{gpu_id}"						)

    if self.device.type != "cpu":
     self.to("""cpu"""      ,     silence_dtype_warnings=_SCREAMING_SNAKE_CASE						)
     torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

    UpperCAmelCase	:					Union[str, Any]	=	None
    for cpu_offloaded_model in [self.unet, self.movq]:
     UpperCAmelCase	,		UpperCAmelCase	:					Optional[Any]	=	cpu_offload_with_hook(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     prev_module_hook=_SCREAMING_SNAKE_CASE						)

    # We'll offload the last model manually.
    UpperCAmelCase	:					List[str]	=	hook

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





    '''simple docstring'''




    if 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()
   @replace_example_docstring(_SCREAMING_SNAKE_CASE						)
   def __call__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = 512      ,     _SCREAMING_SNAKE_CASE = 512      ,     _SCREAMING_SNAKE_CASE = 100      ,     _SCREAMING_SNAKE_CASE = 4.0      ,     _SCREAMING_SNAKE_CASE = 1      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = "pil"      ,     _SCREAMING_SNAKE_CASE = True      ,     )  ->      Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	self._execution_device

    UpperCAmelCase	:					Optional[Any]	=	guidance_scale > 1.0

    if isinstance(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						):
     UpperCAmelCase	:					Optional[Any]	=	torch.cat(_SCREAMING_SNAKE_CASE      ,     dim=0						)
    UpperCAmelCase	:					Any	=	image_embeds.shape[0] * num_images_per_prompt
    if isinstance(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						):
     UpperCAmelCase	:					Optional[int]	=	torch.cat(_SCREAMING_SNAKE_CASE      ,     dim=0						)

    if do_classifier_free_guidance:
     UpperCAmelCase	:					Union[str, Any]	=	image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE      ,     dim=0						)
     UpperCAmelCase	:					Dict	=	negative_image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE      ,     dim=0						)

    UpperCAmelCase	:					Optional[Any]	=	torch.cat([negative_image_embeds, image_embeds]      ,     dim=0						).to(dtype=self.unet.dtype      ,     device=_SCREAMING_SNAKE_CASE						)

    self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE      ,     device=_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Dict	=	self.scheduler.timesteps

    UpperCAmelCase	:					Optional[int]	=	self.unet.config.in_channels

    UpperCAmelCase	,		UpperCAmelCase	:					Optional[int]	=	downscale_height_and_width(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     self.movq_scale_factor						)

    # create initial latent
    UpperCAmelCase	:					List[str]	=	self.prepare_latents(
        (batch_size, num_channels_latents, height, width)      ,     image_embeds.dtype      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     self.scheduler      ,     )

    for i, t in enumerate(self.progress_bar(_SCREAMING_SNAKE_CASE						)						):
     # expand the latents if we are doing classifier free guidance
     UpperCAmelCase	:					str	=	torch.cat([latents] * 2						) if do_classifier_free_guidance else latents

     UpperCAmelCase	:					Any	=	{"""image_embeds""": image_embeds}
     UpperCAmelCase	:					Dict	=	self.unet(
         sample=_SCREAMING_SNAKE_CASE      ,     timestep=_SCREAMING_SNAKE_CASE      ,     encoder_hidden_states=_SCREAMING_SNAKE_CASE      ,     added_cond_kwargs=_SCREAMING_SNAKE_CASE      ,     return_dict=_SCREAMING_SNAKE_CASE      ,     )[0]

     if do_classifier_free_guidance:
      UpperCAmelCase	,		UpperCAmelCase	:					Tuple	=	noise_pred.split(latents.shape[1]      ,     dim=1						)
      UpperCAmelCase	,		UpperCAmelCase	:					List[str]	=	noise_pred.chunk(2						)
      UpperCAmelCase	,		UpperCAmelCase	:					Dict	=	variance_pred.chunk(2						)
      UpperCAmelCase	:					Optional[int]	=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
      UpperCAmelCase	:					Optional[Any]	=	torch.cat([noise_pred, variance_pred_text]      ,     dim=1						)

     if not (
         hasattr(self.scheduler.config      ,     """variance_type"""						)
         and self.scheduler.config.variance_type in ["learned", "learned_range"]
     ):
      UpperCAmelCase	,		UpperCAmelCase	:					Union[str, Any]	=	noise_pred.split(latents.shape[1]      ,     dim=1						)

     # compute the previous noisy sample x_t -> x_t-1
     UpperCAmelCase	:					Optional[int]	=	self.scheduler.step(
         _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     generator=_SCREAMING_SNAKE_CASE      ,     )[0]
    # post-processing
    UpperCAmelCase	:					str	=	self.movq.decode(_SCREAMING_SNAKE_CASE      ,     force_not_quantize=_SCREAMING_SNAKE_CASE						)["""sample"""]

    if output_type not in ["pt", "np", "pil"]:
     raise ValueError(F"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"						)

    if output_type in ["np", "pil"]:
     UpperCAmelCase	:					int	=	image * 0.5 + 0.5
     UpperCAmelCase	:					Optional[int]	=	image.clamp(0      ,     1						)
     UpperCAmelCase	:					int	=	image.cpu().permute(0      ,     2      ,     3      ,     1						).float().numpy()

    if output_type == "pil":
     UpperCAmelCase	:					Union[str, Any]	=	self.numpy_to_pil(_SCREAMING_SNAKE_CASE						)

    if not return_dict:
     return (image,)

    return ImagePipelineOutput(images=_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)
