

'''simple docstring'''

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

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class        A_ (				_snake_case							):






     '''simple docstring'''



     def __init__(		self :     Optional[Any]     ,			lowercase_ :     TransformeraDModel     ,			lowercase_ :     AutoencoderKL     ,			lowercase_ :     KarrasDiffusionSchedulers     ,			lowercase_ :     Optional[Dict[int, str]] = None     ,			)						->	Optional[int]:
      super().__init__()
      self.register_modules(transformer=lowercase_     ,			vae=lowercase_     ,			scheduler=lowercase_       )

      # create a imagenet -> id dictionary for easier use
      UpperCAmelCase       :	Optional[Any]											= {}
      if idalabel is not None:
       for key, value in idalabel.items():
        for label in value.split(','       ):
         UpperCAmelCase       :	List[Any]											= int(lowercase_       )
       UpperCAmelCase       :	int											= dict(sorted(self.labels.items()       )       )


     def    UpperCAmelCase_       (		self :     int     ,			lowercase_ :     Union[str, List[str]]       )						->	Optional[int]:






      if not isinstance(lowercase_     ,			lowercase_       ):
       UpperCAmelCase       :	List[str]											= list(lowercase_       )

      for l in label:
       if l not in self.labels:
        raise ValueError(
            f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""       )

      return [self.labels[l] for l in label]




     @torch.no_grad()
     def __call__(		self :     Any     ,			lowercase_ :     List[int]     ,			lowercase_ :     float = 4.0     ,			lowercase_ :     Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,			lowercase_ :     int = 50     ,			lowercase_ :     Optional[str] = "pil"     ,			lowercase_ :     bool = True     ,			)						->	Dict:
      UpperCAmelCase       :	int											= len(lowercase_       )
      UpperCAmelCase       :	str											= self.transformer.config.sample_size
      UpperCAmelCase       :	Optional[Any]											= self.transformer.config.in_channels

      UpperCAmelCase       :	Dict											= randn_tensor(
          shape=(batch_size, latent_channels, latent_size, latent_size)     ,			generator=lowercase_     ,			device=self.device     ,			dtype=self.transformer.dtype     ,			)
      UpperCAmelCase       :	List[str]											= torch.cat([latents] * 2       ) if guidance_scale > 1 else latents

      UpperCAmelCase       :	Optional[int]											= torch.tensor(lowercase_     ,			device=self.device       ).reshape(-1       )
      UpperCAmelCase       :	Dict											= torch.tensor([1_000] * batch_size     ,			device=self.device       )
      UpperCAmelCase       :	Union[str, Any]											= torch.cat([class_labels, class_null]     ,			0       ) if guidance_scale > 1 else class_labels

      # set step values
      self.scheduler.set_timesteps(lowercase_       )

      for t in self.progress_bar(self.scheduler.timesteps       ):
       if guidance_scale > 1:
        UpperCAmelCase       :	List[Any]											= latent_model_input[: len(lowercase_       ) // 2]
        UpperCAmelCase       :	Optional[int]											= torch.cat([half, half]     ,			dim=0       )
       UpperCAmelCase       :	Tuple											= self.scheduler.scale_model_input(lowercase_     ,			lowercase_       )

       UpperCAmelCase       :	Optional[int]											= t
       if not torch.is_tensor(lowercase_       ):
        # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
        # This would be a good case for the `match` statement (Python 3.10+)
        UpperCAmelCase       :	Any											= latent_model_input.device.type == """mps"""
        if isinstance(lowercase_     ,			lowercase_       ):
         UpperCAmelCase       :	List[Any]											= torch.floataa if is_mps else torch.floataa
        else:
         UpperCAmelCase       :	Union[str, Any]											= torch.intaa if is_mps else torch.intaa
        UpperCAmelCase       :	Tuple											= torch.tensor([timesteps]     ,			dtype=lowercase_     ,			device=latent_model_input.device       )
       elif len(timesteps.shape       ) == 0:
        UpperCAmelCase       :	Tuple											= timesteps[None].to(latent_model_input.device       )
       # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
       UpperCAmelCase       :	List[Any]											= timesteps.expand(latent_model_input.shape[0]       )
       # predict noise model_output
       UpperCAmelCase       :	List[str]											= self.transformer(
           lowercase_     ,			timestep=lowercase_     ,			class_labels=lowercase_       ).sample

       # perform guidance
       if guidance_scale > 1:
        UpperCAmelCase       :	int											= noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
        UpperCAmelCase       :	List[str]											= torch.split(lowercase_     ,			len(lowercase_       ) // 2     ,			dim=0       )

        UpperCAmelCase       :	str											= uncond_eps + guidance_scale * (cond_eps - uncond_eps)
        UpperCAmelCase       :	Tuple											= torch.cat([half_eps, half_eps]     ,			dim=0       )

        UpperCAmelCase       :	Optional[int]											= torch.cat([eps, rest]     ,			dim=1       )

       # learned sigma
       if self.transformer.config.out_channels // 2 == latent_channels:
        UpperCAmelCase       :	Any											= torch.split(lowercase_     ,			lowercase_     ,			dim=1       )
       else:
        UpperCAmelCase       :	List[Any]											= noise_pred

       # compute previous image: x_t -> x_t-1
       UpperCAmelCase       :	List[Any]											= self.scheduler.step(lowercase_     ,			lowercase_     ,			lowercase_       ).prev_sample

      if guidance_scale > 1:
       UpperCAmelCase       :	Any											= latent_model_input.chunk(2     ,			dim=0       )
      else:
       UpperCAmelCase       :	int											= latent_model_input

      UpperCAmelCase       :	int											= 1 / self.vae.config.scaling_factor * latents
      UpperCAmelCase       :	int											= self.vae.decode(lowercase_       ).sample

      UpperCAmelCase       :	str											= (samples / 2 + 0.5).clamp(0     ,			1       )

      # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
      UpperCAmelCase       :	Union[str, Any]											= samples.cpu().permute(0     ,			2     ,			3     ,			1       ).float().numpy()

      if output_type == "pil":
       UpperCAmelCase       :	Optional[Any]											= self.numpy_to_pil(lowercase_       )

      if not return_dict:
       return (samples,)

      return ImagePipelineOutput(images=lowercase_       )








"""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()
UpperCAmelCase    :     Dict   						=		logging.get_logger(__name__)

UpperCAmelCase    :     Union[str, Any]   						=		torch.device('cpu')
def      lowerCamelCase       (     )		->       str:

							'''simple docstring'''



							__UpperCAmelCase   :						Optional[int]      							=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							__UpperCAmelCase   :						Optional[Any]      							=					Image.open(requests.get(_UpperCamelCase       ,			stream=_UpperCamelCase							).raw							)
							return im
def      lowerCamelCase       (     _UpperCamelCase		:      Union[str, Any]							)		->       Union[str, Any]:

							'''simple docstring'''



							if swiftformer_name == "swiftformer_xs":
														return torch.tensor([-2.1_703E00, 2.1_107E00, -2.0_811E00, 8.8_685E-01, 2.4_360E-01]							)

							elif swiftformer_name == "swiftformer_s":
														return torch.tensor([3.9_636E-01, 2.3_478E-01, -1.6_963E00, -1.7_381E00, -8.6_337E-01]							)

							elif swiftformer_name == "swiftformer_l1":
														return torch.tensor([-4.2_768E-01, -4.7_429E-01, -1.0_897E00, -1.0_248E00, 3.5_523E-02]							)

							elif swiftformer_name == "swiftformer_l3":
														return torch.tensor([-2.5_330E-01, 2.4_211E-01, -6.0_185E-01, -8.2_789E-01, -6.0_446E-02]							)
def      lowerCamelCase       (     _UpperCamelCase		:      Union[str, Any]       ,			_UpperCamelCase		:      Optional[int]       ,			_UpperCamelCase		:      List[Any]							)		->       Union[str, Any]:

							'''simple docstring'''



							__UpperCAmelCase   :						Any      							=					dct.pop(_UpperCamelCase							)
							__UpperCAmelCase   :						List[str]      							=					val
def      lowerCamelCase       (     _UpperCamelCase		:      List[Any]							)		->       List[Any]:

							'''simple docstring'''



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

							'''simple docstring'''



							__UpperCAmelCase   :						Optional[int]      							=					SwiftFormerConfig()

							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							__UpperCAmelCase   :						Optional[int]      							=					1_0_0_0
							__UpperCAmelCase   :						Optional[int]      							=					"""huggingface/label-files"""
							__UpperCAmelCase   :						str      							=					"""imagenet-1k-id2label.json"""
							__UpperCAmelCase   :						List[Any]      							=					json.load(open(hf_hub_download(_UpperCamelCase       ,			_UpperCamelCase       ,			repo_type="""dataset"""							)       ,			"""r"""							)							)
							__UpperCAmelCase   :						Any      							=					{int(_UpperCamelCase							): v for k, v in idalabel.items()}
							__UpperCAmelCase   :						Optional[Any]      							=					idalabel
							__UpperCAmelCase   :						Optional[int]      							=					{v: k for k, v in idalabel.items()}

							# size of the architecture
							if swiftformer_name == "swiftformer_xs":
														__UpperCAmelCase   :						Tuple      							=					[3, 3, 6, 4]
														__UpperCAmelCase   :						List[Any]      							=					[4_8, 5_6, 1_1_2, 2_2_0]

							elif swiftformer_name == "swiftformer_s":
														__UpperCAmelCase   :						Optional[int]      							=					[3, 3, 9, 6]
														__UpperCAmelCase   :						Tuple      							=					[4_8, 6_4, 1_6_8, 2_2_4]

							elif swiftformer_name == "swiftformer_l1":
														__UpperCAmelCase   :						Dict      							=					[4, 3, 1_0, 5]
														__UpperCAmelCase   :						Optional[Any]      							=					[4_8, 9_6, 1_9_2, 3_8_4]

							elif swiftformer_name == "swiftformer_l3":
														__UpperCAmelCase   :						str      							=					[4, 4, 1_2, 6]
														__UpperCAmelCase   :						str      							=					[6_4, 1_2_8, 3_2_0, 5_1_2]

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

							__UpperCAmelCase   :						Union[str, Any]      							=					create_rename_keys(_UpperCamelCase							)
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase							)

							# load HuggingFace model
							__UpperCAmelCase   :						int      							=					SwiftFormerForImageClassification(_UpperCamelCase							).eval()
							hf_model.load_state_dict(_UpperCamelCase							)

							# prepare test inputs
							__UpperCAmelCase   :						List[Any]      							=					prepare_img()
							__UpperCAmelCase   :						int      							=					ViTImageProcessor.from_pretrained("""preprocessor_config"""							)
							__UpperCAmelCase   :						Optional[Any]      							=					processor(images=_UpperCamelCase       ,			return_tensors="""pt"""							)

							# compare outputs from both models
							__UpperCAmelCase   :						Tuple      							=					get_expected_output(_UpperCamelCase							)
							__UpperCAmelCase   :						Dict      							=					hf_model(inputs["""pixel_values"""]							).logits

							assert hf_logits.shape == torch.Size([1, 1_0_0_0]							)
							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__":
							UpperCAmelCase    :     Optional[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.')

							UpperCAmelCase    :     Tuple   						=		parser.parse_args()
							convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)
