






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

a					:							Tuple    =						torch.device("""cpu""")





def 		lowercase__()							->int:




						"""simple docstring"""



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





def 		lowercase__(A		)							->int:




						"""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 		lowercase__(A   ,						A   ,						A		)							->Optional[Any]:




						"""simple docstring"""



						lowercase__							: Tuple=			dct.pop(A__		)
						lowercase__							: Optional[Any]=			val





def 		lowercase__(A		)							->Optional[Any]:




						"""simple docstring"""



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





@torch.no_grad()
def 		lowercase__(A   ,						A   ,						A		)							->Dict:




						"""simple docstring"""



						lowercase__							: Optional[int]=			SwiftFormerConfig()

						# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
						lowercase__							: Optional[Any]=			1_000
						lowercase__							: str=			"huggingface/label-files"
						lowercase__							: str=			"imagenet-1k-id2label.json"
						lowercase__							: List[str]=			json.load(open(hf_hub_download(A__   ,						A__   ,						repo_type="dataset"		)   ,						"r"		)		)
						lowercase__							: Tuple=			{int(A__		): v for k, v in idalabel.items()}
						lowercase__							: List[Any]=			idalabel
						lowercase__							: Optional[Any]=			{v: k for k, v in idalabel.items()}

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

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

						elif swiftformer_name == "swiftformer_l1":
												lowercase__							: int=			[4, 3, 10, 5]
												lowercase__							: Union[str, Any]=			[48, 96, 192, 384]

						elif swiftformer_name == "swiftformer_l3":
												lowercase__							: Dict=			[4, 4, 12, 6]
												lowercase__							: Optional[int]=			[64, 128, 320, 512]

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

						lowercase__							: Dict=			create_rename_keys(A__		)
						for rename_key_src, rename_key_dest in rename_keys:
												rename_key(A__   ,						A__   ,						A__		)

						# load HuggingFace model
						lowercase__							: Optional[int]=			SwiftFormerForImageClassification(A__		).eval()
						hf_model.load_state_dict(A__		)

						# prepare test inputs
						lowercase__							: Tuple=			prepare_img()
						lowercase__							: int=			ViTImageProcessor.from_pretrained("preprocessor_config"		)
						lowercase__							: int=			processor(images=A__   ,						return_tensors="pt"		)

						# compare outputs from both models
						lowercase__							: List[Any]=			get_expected_output(A__		)
						lowercase__							: int=			hf_model(inputs["pixel_values"]		).logits

						assert hf_logits.shape == torch.Size([1, 1_000]		)
						assert torch.allclose(hf_logits[0, 0:5]   ,						A__   ,						atol=1e-3		)

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


if __name__ == "__main__":
			a					:							Tuple    =						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					:							List[Any]    =						parser.parse_args()
			convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)














"""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      UpperCamelCase_			(__A	):





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

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





	def 	_SCREAMING_SNAKE_CASE ( self   :					str      ,      lowerCAmelCase_   :					Union[str, List[str]]		)		->     List[int]:

				if not isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		):
							UpperCAmelCase_	:					str     	=   list(lowerCAmelCase_		)

				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   :					List[str]      ,      lowerCAmelCase_   :					List[int]      ,      lowerCAmelCase_   :					float = 4.0      ,      lowerCAmelCase_   :					Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,      lowerCAmelCase_   :					int = 50      ,      lowerCAmelCase_   :					Optional[str] = "pil"      ,      lowerCAmelCase_   :					bool = True      ,      )		->     Union[ImagePipelineOutput, Tuple]:
				UpperCAmelCase_	:					Optional[int]     	=   len(lowerCAmelCase_		)
				UpperCAmelCase_	:					int     	=   self.transformer.config.sample_size
				UpperCAmelCase_	:					Optional[Any]     	=   self.transformer.config.in_channels

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

				UpperCAmelCase_	:					List[Any]     	=   torch.tensor(lowerCAmelCase_      ,      device=self.device		).reshape(-1		)
				UpperCAmelCase_	:					Optional[Any]     	=   torch.tensor([1_000] * batch_size      ,      device=self.device		)
				UpperCAmelCase_	:					int     	=   torch.cat([class_labels, class_null]      ,      0		) if guidance_scale > 1 else class_labels

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

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

							UpperCAmelCase_	:					Union[str, Any]     	=   t
							if not torch.is_tensor(lowerCAmelCase_		):
										# 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_	:					List[Any]     	=   latent_model_input.device.type == "mps"
										if isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		):
													UpperCAmelCase_	:					Optional[int]     	=   torch.floataa if is_mps else torch.floataa
										else:
													UpperCAmelCase_	:					int     	=   torch.intaa if is_mps else torch.intaa
										UpperCAmelCase_	:					Optional[int]     	=   torch.tensor([timesteps]      ,      dtype=lowerCAmelCase_      ,      device=latent_model_input.device		)
							elif len(timesteps.shape		) == 0:
										UpperCAmelCase_	:					List[Any]     	=   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[Any]     	=   self.transformer(
							    lowerCAmelCase_      ,      timestep=lowerCAmelCase_      ,      class_labels=lowerCAmelCase_		).sample

							# perform guidance
							if guidance_scale > 1:
										UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[int]     	=   noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
										UpperCAmelCase_	,      UpperCAmelCase_	:					Union[str, Any]     	=   torch.split(lowerCAmelCase_      ,      len(lowerCAmelCase_		) // 2      ,      dim=0		)

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

										UpperCAmelCase_	:					Optional[Any]     	=   torch.cat([eps, rest]      ,      dim=1		)

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

							# compute previous image: x_t -> x_t-1
							UpperCAmelCase_	:					Union[str, Any]     	=   self.scheduler.step(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_		).prev_sample

				if guidance_scale > 1:
							UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[Any]     	=   latent_model_input.chunk(2      ,      dim=0		)
				else:
							UpperCAmelCase_	:					Optional[int]     	=   latent_model_input

				UpperCAmelCase_	:					Dict     	=   1 / self.vae.config.scaling_factor * latents
				UpperCAmelCase_	:					Dict     	=   self.vae.decode(lowerCAmelCase_		).sample

				UpperCAmelCase_	:					Tuple     	=   (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_	:					Optional[Any]     	=   samples.cpu().permute(0      ,      2      ,      3      ,      1		).float().numpy()

				if output_type == "pil":
							UpperCAmelCase_	:					Optional[int]     	=   self.numpy_to_pil(lowerCAmelCase_		)

				if not return_dict:
							return (samples,)

				return ImagePipelineOutput(images=lowerCAmelCase_		)



