



'''simple docstring'''




# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput

@dataclass
class 			UpperCamelCase_ (							__magic_name__			):
	lowercase          = 42
	lowercase          = 42




class 			UpperCamelCase_ (							__magic_name__      ,     __magic_name__			):
	lowercase          = 1





	@register_to_config
	def __init__(  self		,		A = 2000		,		A = 0.1_5		,		A = 0.0_1		,		A = 1_3_4_8.0		,		A = 1e-5		,		A = 1		,		)			->			Optional[Any]:
								# standard deviation of the initial noise distribution
								UpperCAmelCase     :		Any							     =		sigma_max

								# setable values
								UpperCAmelCase     :		Tuple							     =		None

								self.set_sigmas(A		,		A		,		A		,		A    )





	def  _lowercase(  self		,		A		,		A = None    )			->			torch.FloatTensor:
								return sample





	def  _lowercase(  self		,		A		,		A = None		,		A = None    )			->			Optional[Any]:
								UpperCAmelCase     :		int							     =		sampling_eps if sampling_eps is not None else self.config.sampling_eps

								UpperCAmelCase     :		List[Any]							     =		torch.linspace(1		,		A		,		A		,		device=A    )





	def  _lowercase(  self		,		A		,		A = None		,		A = None		,		A = None    )			->			Dict:
								UpperCAmelCase     :		Union[str, Any]							     =		sigma_min if sigma_min is not None else self.config.sigma_min
								UpperCAmelCase     :		Tuple							     =		sigma_max if sigma_max is not None else self.config.sigma_max
								UpperCAmelCase     :		Optional[int]							     =		sampling_eps if sampling_eps is not None else self.config.sampling_eps
								if self.timesteps is None:
															self.set_timesteps(A		,		A    )

								UpperCAmelCase     :		Dict							     =		sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
								UpperCAmelCase     :		Any							     =		torch.exp(torch.linspace(math.log(A    )		,		math.log(A    )		,		A    )    )
								UpperCAmelCase     :		Any							     =		torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]    )





	def  _lowercase(  self		,		A		,		A    )			->			List[str]:
								return torch.where(
								    timesteps == 0		,		torch.zeros_like(t.to(timesteps.device    )    )		,		self.discrete_sigmas[timesteps - 1].to(timesteps.device    )		,		)





	def  _lowercase(  self		,		A		,		A		,		A		,		A = None		,		A = True		,		)			->			Union[SdeVeOutput, Tuple]:
								if self.timesteps is None:
															raise ValueError(
															    """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""    )

								UpperCAmelCase     :		str							     =		timestep * torch.ones(
								    sample.shape[0]		,		device=sample.device    )  # torch.repeat_interleave(timestep, sample.shape[0])
								UpperCAmelCase     :		Optional[int]							     =		(timestep * (len(self.timesteps    ) - 1)).long()

								# mps requires indices to be in the same device, so we use cpu as is the default with cuda
								UpperCAmelCase     :		List[str]							     =		timesteps.to(self.discrete_sigmas.device    )

								UpperCAmelCase     :		Union[str, Any]							     =		self.discrete_sigmas[timesteps].to(sample.device    )
								UpperCAmelCase     :		Optional[int]							     =		self.get_adjacent_sigma(A		,		A    ).to(sample.device    )
								UpperCAmelCase     :		Tuple							     =		torch.zeros_like(A    )
								UpperCAmelCase     :		Dict							     =		(sigma**2 - adjacent_sigma**2) ** 0.5

								# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
								# also equation 47 shows the analog from SDE models to ancestral sampling methods
								UpperCAmelCase     :		Optional[Any]							     =		diffusion.flatten()
								while len(diffusion.shape    ) < len(sample.shape    ):
															UpperCAmelCase     :		Optional[int]							     =		diffusion.unsqueeze(-1    )
								UpperCAmelCase     :		Union[str, Any]							     =		drift - diffusion**2 * model_output

								#  equation 6: sample noise for the diffusion term of
								UpperCAmelCase     :		Optional[Any]							     =		randn_tensor(
								    sample.shape		,		layout=sample.layout		,		generator=A		,		device=sample.device		,		dtype=sample.dtype    )
								UpperCAmelCase     :		Any							     =		sample - drift  # subtract because `dt` is a small negative timestep
								# TODO is the variable diffusion the correct scaling term for the noise?
								UpperCAmelCase     :		Tuple							     =		prev_sample_mean + diffusion * noise  # add impact of diffusion field g

								if not return_dict:
															return (prev_sample, prev_sample_mean)

								return SdeVeOutput(prev_sample=A		,		prev_sample_mean=A    )





	def  _lowercase(  self		,		A		,		A		,		A = None		,		A = True		,		)			->			Union[SchedulerOutput, Tuple]:
								if self.timesteps is None:
															raise ValueError(
															    """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""    )

								# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
								# sample noise for correction
								UpperCAmelCase     :		Any							     =		randn_tensor(sample.shape		,		layout=sample.layout		,		generator=A    ).to(sample.device    )

								# compute step size from the model_output, the noise, and the snr
								UpperCAmelCase     :		str							     =		torch.norm(model_output.reshape(model_output.shape[0]		,		-1    )		,		dim=-1    ).mean()
								UpperCAmelCase     :		str							     =		torch.norm(noise.reshape(noise.shape[0]		,		-1    )		,		dim=-1    ).mean()
								UpperCAmelCase     :		Dict							     =		(self.config.snr * noise_norm / grad_norm) ** 2 * 2
								UpperCAmelCase     :		int							     =		step_size * torch.ones(sample.shape[0]    ).to(sample.device    )
								# self.repeat_scalar(step_size, sample.shape[0])

								# compute corrected sample: model_output term and noise term
								UpperCAmelCase     :		Union[str, Any]							     =		step_size.flatten()
								while len(step_size.shape    ) < len(sample.shape    ):
															UpperCAmelCase     :		List[str]							     =		step_size.unsqueeze(-1    )
								UpperCAmelCase     :		Optional[Any]							     =		sample + step_size * model_output
								UpperCAmelCase     :		str							     =		prev_sample_mean + ((step_size * 2) ** 0.5) * noise

								if not return_dict:
															return (prev_sample,)

								return SchedulerOutput(prev_sample=A    )





	def  _lowercase(  self		,		A		,		A		,		A		,		)			->			torch.FloatTensor:
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								UpperCAmelCase     :		int							     =		timesteps.to(original_samples.device    )
								UpperCAmelCase     :		Any							     =		self.discrete_sigmas.to(original_samples.device    )[timesteps]
								UpperCAmelCase     :		Union[str, Any]							     =		(
								    noise * sigmas[:, None, None, None]
								    if noise is not None
								    else torch.randn_like(A    ) * sigmas[:, None, None, None]
								)
								UpperCAmelCase     :		Dict							     =		noise + original_samples
								return noisy_samples






	def __len__(  self    )			->			Optional[int]:
								return self.config.num_train_timesteps






'''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   :			List[Any]					  =    logging.get_logger(__name__)

a   :			Union[str, Any]					  =    torch.device("""cpu""")



def       __lowerCamelCase  (					)      ->			Any:
							UpperCAmelCase     :		Optional[Any]							     =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							UpperCAmelCase     :		Dict							     =		Image.open(requests.get(_lowercase	,				stream=_lowercase		).raw		)
							return im



def       __lowerCamelCase  (					_lowercase		)      ->			Dict:
							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  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			str:
							UpperCAmelCase     :		Union[str, Any]							     =		dct.pop(_lowercase		)
							UpperCAmelCase     :		str							     =		val



def       __lowerCamelCase  (					_lowercase		)      ->			List[str]:
							UpperCAmelCase     :		Tuple							     =		[]
							for k in state_dict.keys():
														UpperCAmelCase     :		Dict							     =		k
														if ".pwconv" in k:
																					UpperCAmelCase     :		Union[str, Any]							     =		k_new.replace(""".pwconv"""	,				""".point_wise_conv"""		)
														if ".dwconv" in k:
																					UpperCAmelCase     :		Dict							     =		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     :		Dict							     =		k_new.replace("""patch_embed"""	,				"""swiftformer.patch_embed.patch_embedding"""		)
														if "network" in k_new:
																					UpperCAmelCase     :		Optional[Any]							     =		k_new.split("""."""		)
																					if ls[2].isdigit():
																												UpperCAmelCase     :		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       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			Optional[int]:
							UpperCAmelCase     :		Optional[Any]							     =		SwiftFormerConfig()

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

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

							elif swiftformer_name == "swiftformer_s":
														UpperCAmelCase     :		str							     =		[3, 3, 9, 6]
														UpperCAmelCase     :		str							     =		[4_8, 6_4, 1_6_8, 2_2_4]

							elif swiftformer_name == "swiftformer_l1":
														UpperCAmelCase     :		List[Any]							     =		[4, 3, 1_0, 5]
														UpperCAmelCase     :		Union[str, Any]							     =		[4_8, 9_6, 1_9_2, 3_8_4]

							elif swiftformer_name == "swiftformer_l3":
														UpperCAmelCase     :		Any							     =		[4, 4, 1_2, 6]
														UpperCAmelCase     :		List[Any]							     =		[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     :		Dict							     =		torch.hub.load_state_dict_from_url(_lowercase	,				map_location="""cpu"""	,				check_hash=_lowercase		)
														else:
																					UpperCAmelCase     :		Dict							     =		torch.load(_lowercase	,				map_location="""cpu"""		)
							UpperCAmelCase     :		str							     =		checkpoint

							UpperCAmelCase     :		Tuple							     =		create_rename_keys(_lowercase		)
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(_lowercase	,				_lowercase	,				_lowercase		)

							# load HuggingFace model
							UpperCAmelCase     :		str							     =		SwiftFormerForImageClassification(_lowercase		).eval()
							hf_model.load_state_dict(_lowercase		)

							# prepare test inputs
							UpperCAmelCase     :		Any							     =		prepare_img()
							UpperCAmelCase     :		List[Any]							     =		ViTImageProcessor.from_pretrained("""preprocessor_config"""		)
							UpperCAmelCase     :		List[str]							     =		processor(images=_lowercase	,				return_tensors="""pt"""		)

							# compare outputs from both models
							UpperCAmelCase     :		List[str]							     =		get_expected_output(_lowercase		)
							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]	,				_lowercase	,				atol=1e-3		)

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


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   :			Union[str, Any]					  =    parser.parse_args()
							convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)

