
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

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




def 						A_			(				snake_case				: Optional[int]			,   snake_case				: Optional[Any]=0.999			,   snake_case				: List[str]="cosine"			,   )   ->     Optional[int]:



						'''simple docstring'''

						if alpha_transform_type == "cosine":

												def alpha_bar_fn(snake_case				: Tuple			):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2			) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(snake_case				: Dict			):
																		return math.exp(t * -12.0			)

						else:
												raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"			)

						__UpperCamelCase  	=      []
						for i in range(snake_case			):
												__UpperCamelCase  	=      i / num_diffusion_timesteps
												__UpperCamelCase  	=      (i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(snake_case			) / alpha_bar_fn(snake_case			)			,   snake_case			)			)
						return torch.tensor(snake_case			,   dtype=torch.floataa			)






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							_snake_case  		=	[e.name for e in KarrasDiffusionSchedulers]
							_snake_case  		=	2



							@register_to_config
							def __init__(	self  ,						SCREAMING_SNAKE_CASE_ = 1000  ,						SCREAMING_SNAKE_CASE_ = 0.0_0_0_8_5  ,						SCREAMING_SNAKE_CASE_ = 0.0_1_2  ,						SCREAMING_SNAKE_CASE_ = "linear"  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = "epsilon"  ,						SCREAMING_SNAKE_CASE_ = "linspace"  ,						SCREAMING_SNAKE_CASE_ = 0  ,						)-> List[Any]:





													'''simple docstring'''






													if trained_betas is not None:
																			__UpperCamelCase  	=      torch.tensor(SCREAMING_SNAKE_CASE_  ,						dtype=torch.floataa      )
													elif beta_schedule == "linear":
																			__UpperCamelCase  	=      torch.linspace(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						dtype=torch.floataa      )
													elif beta_schedule == "scaled_linear":
																			# this schedule is very specific to the latent diffusion model.
																			__UpperCamelCase  	=      (
																			    torch.linspace(beta_start**0.5  ,						beta_end**0.5  ,						SCREAMING_SNAKE_CASE_  ,						dtype=torch.floataa      ) ** 2
																			)
													elif beta_schedule == "squaredcos_cap_v2":
																			# Glide cosine schedule
																			__UpperCamelCase  	=      betas_for_alpha_bar(SCREAMING_SNAKE_CASE_      )
													else:
																			raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"      )

													__UpperCamelCase  	=      1.0 - self.betas
													__UpperCamelCase  	=      torch.cumprod(self.alphas  ,						dim=0      )

													#  set all values
													self.set_timesteps(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=None      )-> List[str]:





													'''simple docstring'''






													if schedule_timesteps is None:
																			__UpperCamelCase  	=      self.timesteps

													__UpperCamelCase  	=      (schedule_timesteps == timestep).nonzero()

													# The sigma index that is taken for the **very** first `step`
													# is always the second index (or the last index if there is only 1)
													# This way we can ensure we don't accidentally skip a sigma in
													# case we start in the middle of the denoising schedule (e.g. for image-to-image)
													if len(self._index_counter      ) == 0:
																			__UpperCamelCase  	=      1 if len(SCREAMING_SNAKE_CASE_      ) > 1 else 0
													else:
																			__UpperCamelCase  	=      timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_      ) else timestep
																			__UpperCamelCase  	=      self._index_counter[timestep_int]

													return indices[pos].item()



							@property
							def      A__		(	self      )-> List[str]:





													'''simple docstring'''






													if self.config.timestep_spacing in ["linspace", "trailing"]:
																			return self.sigmas.max()

													return (self.sigmas.max() ** 2 + 1) ** 0.5



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						)-> torch.FloatTensor:





													'''simple docstring'''






													__UpperCamelCase  	=      self.index_for_timestep(SCREAMING_SNAKE_CASE_      )

													if self.state_in_first_order:
																			__UpperCamelCase  	=      self.sigmas[step_index]
													else:
																			__UpperCamelCase  	=      self.sigmas_interpol[step_index - 1]

													__UpperCamelCase  	=      sample / ((sigma**2 + 1) ** 0.5)
													return sample



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						)-> List[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      num_inference_steps

													__UpperCamelCase  	=      num_train_timesteps or self.config.num_train_timesteps

													# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
													if self.config.timestep_spacing == "linspace":
																			__UpperCamelCase  	=      np.linspace(0  ,						num_train_timesteps - 1  ,						SCREAMING_SNAKE_CASE_  ,						dtype=SCREAMING_SNAKE_CASE_      )[::-1].copy()
													elif self.config.timestep_spacing == "leading":
																			__UpperCamelCase  	=      num_train_timesteps // self.num_inference_steps
																			# creates integer timesteps by multiplying by ratio
																			# casting to int to avoid issues when num_inference_step is power of 3
																			__UpperCamelCase  	=      (np.arange(0  ,						SCREAMING_SNAKE_CASE_      ) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE_      )
																			timesteps += self.config.steps_offset
													elif self.config.timestep_spacing == "trailing":
																			__UpperCamelCase  	=      num_train_timesteps / self.num_inference_steps
																			# creates integer timesteps by multiplying by ratio
																			# casting to int to avoid issues when num_inference_step is power of 3
																			__UpperCamelCase  	=      (np.arange(SCREAMING_SNAKE_CASE_  ,						0  ,						-step_ratio      )).round().copy().astype(SCREAMING_SNAKE_CASE_      )
																			timesteps -= 1
													else:
																			raise ValueError(
																			    F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."      )

													__UpperCamelCase  	=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
													__UpperCamelCase  	=      torch.from_numpy(np.log(SCREAMING_SNAKE_CASE_      )      ).to(SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      np.interp(SCREAMING_SNAKE_CASE_  ,						np.arange(0  ,						len(SCREAMING_SNAKE_CASE_      )      )  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
													__UpperCamelCase  	=      torch.from_numpy(SCREAMING_SNAKE_CASE_      ).to(device=SCREAMING_SNAKE_CASE_      )

													# compute up and down sigmas
													__UpperCamelCase  	=      sigmas.roll(-1      )
													__UpperCamelCase  	=      0.0
													__UpperCamelCase  	=      (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
													__UpperCamelCase  	=      (sigmas_next**2 - sigmas_up**2) ** 0.5
													__UpperCamelCase  	=      0.0

													# compute interpolated sigmas
													__UpperCamelCase  	=      sigmas.log().lerp(sigmas_down.log()  ,						0.5      ).exp()
													__UpperCamelCase  	=      0.0

													# set sigmas
													__UpperCamelCase  	=      torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2      ), sigmas[-1:]]      )
													__UpperCamelCase  	=      torch.cat(
													    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2      ), sigmas_interpol[-1:]]      )
													__UpperCamelCase  	=      torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2      ), sigmas_up[-1:]]      )
													__UpperCamelCase  	=      torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2      ), sigmas_down[-1:]]      )

													if str(SCREAMING_SNAKE_CASE_      ).startswith('''mps'''      ):
																			# mps does not support float64
																			__UpperCamelCase  	=      torch.from_numpy(SCREAMING_SNAKE_CASE_      ).to(SCREAMING_SNAKE_CASE_  ,						dtype=torch.floataa      )
													else:
																			__UpperCamelCase  	=      torch.from_numpy(SCREAMING_SNAKE_CASE_      ).to(SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      self.sigma_to_t(SCREAMING_SNAKE_CASE_      ).to(SCREAMING_SNAKE_CASE_  ,						dtype=timesteps.dtype      )
													__UpperCamelCase  	=      torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])  ,						dim=-1      ).flatten()

													__UpperCamelCase  	=      torch.cat([timesteps[:1], interleaved_timesteps]      )

													__UpperCamelCase  	=      None

													# for exp beta schedules, such as the one for `pipeline_shap_e.py`
													# we need an index counter
													__UpperCamelCase  	=      defaultdict(SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> List[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      sigma.log()

													# get distribution
													__UpperCamelCase  	=      log_sigma - self.log_sigmas[:, None]

													# get sigmas range
													__UpperCamelCase  	=      dists.ge(0      ).cumsum(dim=0      ).argmax(dim=0      ).clamp(max=self.log_sigmas.shape[0] - 2      )
													__UpperCamelCase  	=      low_idx + 1

													__UpperCamelCase  	=      self.log_sigmas[low_idx]
													__UpperCamelCase  	=      self.log_sigmas[high_idx]

													# interpolate sigmas
													__UpperCamelCase  	=      (low - log_sigma) / (low - high)
													__UpperCamelCase  	=      w.clamp(0  ,						1      )

													# transform interpolation to time range
													__UpperCamelCase  	=      (1 - w) * low_idx + w * high_idx
													__UpperCamelCase  	=      t.view(sigma.shape      )
													return t



							@property
							def      A__		(	self      )-> List[Any]:





													'''simple docstring'''






													return self.sample is None



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = True  ,						)-> Union[SchedulerOutput, Tuple]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.index_for_timestep(SCREAMING_SNAKE_CASE_      )

													# advance index counter by 1
													__UpperCamelCase  	=      timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_      ) else timestep
													self._index_counter[timestep_int] += 1

													if self.state_in_first_order:
																			__UpperCamelCase  	=      self.sigmas[step_index]
																			__UpperCamelCase  	=      self.sigmas_interpol[step_index]
																			__UpperCamelCase  	=      self.sigmas_up[step_index]
																			__UpperCamelCase  	=      self.sigmas_down[step_index - 1]
													else:
																			# 2nd order / KPDM2's method
																			__UpperCamelCase  	=      self.sigmas[step_index - 1]
																			__UpperCamelCase  	=      self.sigmas_interpol[step_index - 1]
																			__UpperCamelCase  	=      self.sigmas_up[step_index - 1]
																			__UpperCamelCase  	=      self.sigmas_down[step_index - 1]

													# currently only gamma=0 is supported. This usually works best anyways.
													# We can support gamma in the future but then need to scale the timestep before
													# passing it to the model which requires a change in API
													__UpperCamelCase  	=      0
													__UpperCamelCase  	=      sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

													__UpperCamelCase  	=      model_output.device
													__UpperCamelCase  	=      randn_tensor(model_output.shape  ,						dtype=model_output.dtype  ,						device=SCREAMING_SNAKE_CASE_  ,						generator=SCREAMING_SNAKE_CASE_      )

													# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
													if self.config.prediction_type == "epsilon":
																			__UpperCamelCase  	=      sigma_hat if self.state_in_first_order else sigma_interpol
																			__UpperCamelCase  	=      sample - sigma_input * model_output
													elif self.config.prediction_type == "v_prediction":
																			__UpperCamelCase  	=      sigma_hat if self.state_in_first_order else sigma_interpol
																			__UpperCamelCase  	=      model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																			    sample / (sigma_input**2 + 1)
																			)
													elif self.config.prediction_type == "sample":
																			raise NotImplementedError('''prediction_type not implemented yet: sample'''      )
													else:
																			raise ValueError(
																			    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"      )

													if self.state_in_first_order:
																			# 2. Convert to an ODE derivative for 1st order
																			__UpperCamelCase  	=      (sample - pred_original_sample) / sigma_hat
																			# 3. delta timestep
																			__UpperCamelCase  	=      sigma_interpol - sigma_hat

																			# store for 2nd order step
																			__UpperCamelCase  	=      sample
																			__UpperCamelCase  	=      dt
																			__UpperCamelCase  	=      sample + derivative * dt
													else:
																			# DPM-Solver-2
																			# 2. Convert to an ODE derivative for 2nd order
																			__UpperCamelCase  	=      (sample - pred_original_sample) / sigma_interpol
																			# 3. delta timestep
																			__UpperCamelCase  	=      sigma_down - sigma_hat

																			__UpperCamelCase  	=      self.sample
																			__UpperCamelCase  	=      None

																			__UpperCamelCase  	=      sample + derivative * dt
																			__UpperCamelCase  	=      prev_sample + noise * sigma_up

													if not return_dict:
																			return (prev_sample,)

													return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						)-> torch.FloatTensor:





													'''simple docstring'''






													__UpperCamelCase  	=      self.sigmas.to(device=original_samples.device  ,						dtype=original_samples.dtype      )
													if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE_      ):
																			# mps does not support float64
																			__UpperCamelCase  	=      self.timesteps.to(original_samples.device  ,						dtype=torch.floataa      )
																			__UpperCamelCase  	=      timesteps.to(original_samples.device  ,						dtype=torch.floataa      )
													else:
																			__UpperCamelCase  	=      self.timesteps.to(original_samples.device      )
																			__UpperCamelCase  	=      timesteps.to(original_samples.device      )

													__UpperCamelCase  	=      [self.index_for_timestep(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ) for t in timesteps]

													__UpperCamelCase  	=      sigmas[step_indices].flatten()
													while len(sigma.shape      ) < len(original_samples.shape      ):
																			__UpperCamelCase  	=      sigma.unsqueeze(-1      )

													__UpperCamelCase  	=      original_samples + noise * sigma
													return noisy_samples



							def __len__(	self      )-> Optional[int]:





													'''simple docstring'''






													return self.config.num_train_timesteps











'''simple docstring'''







import argparse
import json
import os

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
snake_case_  :							Optional[int]            =	logging.get_logger(__name__)





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Optional[int] )			->						Dict:
						UpperCAmelCase_      :   int      =	ConvNextVaConfig()

						if "atto" in checkpoint_url:
												UpperCAmelCase_      :   Tuple      =	[2, 2, 6, 2]
												UpperCAmelCase_      :   Tuple      =	[40, 80, 160, 320]
						if "femto" in checkpoint_url:
												UpperCAmelCase_      :   List[Any]      =	[2, 2, 6, 2]
												UpperCAmelCase_      :   Any      =	[48, 96, 192, 384]
						if "pico" in checkpoint_url:
												UpperCAmelCase_      :   Union[str, Any]      =	[2, 2, 6, 2]
												UpperCAmelCase_      :   int      =	[64, 128, 256, 512]
						if "nano" in checkpoint_url:
												UpperCAmelCase_      :   str      =	[2, 2, 8, 2]
												UpperCAmelCase_      :   List[str]      =	[80, 160, 320, 640]
						if "tiny" in checkpoint_url:
												UpperCAmelCase_      :   Optional[Any]      =	[3, 3, 9, 3]
												UpperCAmelCase_      :   Any      =	[96, 192, 384, 768]
						if "base" in checkpoint_url:
												UpperCAmelCase_      :   Union[str, Any]      =	[3, 3, 27, 3]
												UpperCAmelCase_      :   Optional[int]      =	[128, 256, 512, 1024]
						if "large" in checkpoint_url:
												UpperCAmelCase_      :   int      =	[3, 3, 27, 3]
												UpperCAmelCase_      :   List[Any]      =	[192, 384, 768, 1536]
						if "huge" in checkpoint_url:
												UpperCAmelCase_      :   Tuple      =	[3, 3, 27, 3]
												UpperCAmelCase_      :   Dict      =	[352, 704, 1408, 2816]

						UpperCAmelCase_      :   Union[str, Any]      =	1000
						UpperCAmelCase_      :   Optional[Any]      =	'''imagenet-1k-id2label.json'''
						UpperCAmelCase_      :   int      =	(1, 1000)

						UpperCAmelCase_      :   Any      =	'''huggingface/label-files'''
						UpperCAmelCase_      :   List[str]      =	num_labels
						UpperCAmelCase_      :   Optional[int]      =	json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      repo_type='''dataset''' ),      '''r''' ) )
						UpperCAmelCase_      :   Tuple      =	{int(SCREAMING_SNAKE_CASE__ ): v for k, v in idalabel.items()}

						UpperCAmelCase_      :   Union[str, Any]      =	idalabel
						UpperCAmelCase_      :   List[Any]      =	{v: k for k, v in idalabel.items()}
						UpperCAmelCase_      :   str      =	hidden_sizes
						UpperCAmelCase_      :   Dict      =	depths

						return config, expected_shape





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Dict )			->						Optional[Any]:
						if "downsample_layers.0.0" in name:
												UpperCAmelCase_      :   str      =	name.replace('''downsample_layers.0.0''',      '''embeddings.patch_embeddings''' )
						if "downsample_layers.0.1" in name:
												UpperCAmelCase_      :   Optional[Any]      =	name.replace('''downsample_layers.0.1''',      '''embeddings.norm''' )  # we rename to layernorm later on
						if "downsample_layers.1.0" in name:
												UpperCAmelCase_      :   Dict      =	name.replace('''downsample_layers.1.0''',      '''stages.1.downsampling_layer.0''' )
						if "downsample_layers.1.1" in name:
												UpperCAmelCase_      :   Any      =	name.replace('''downsample_layers.1.1''',      '''stages.1.downsampling_layer.1''' )
						if "downsample_layers.2.0" in name:
												UpperCAmelCase_      :   Optional[int]      =	name.replace('''downsample_layers.2.0''',      '''stages.2.downsampling_layer.0''' )
						if "downsample_layers.2.1" in name:
												UpperCAmelCase_      :   List[Any]      =	name.replace('''downsample_layers.2.1''',      '''stages.2.downsampling_layer.1''' )
						if "downsample_layers.3.0" in name:
												UpperCAmelCase_      :   Optional[int]      =	name.replace('''downsample_layers.3.0''',      '''stages.3.downsampling_layer.0''' )
						if "downsample_layers.3.1" in name:
												UpperCAmelCase_      :   Optional[int]      =	name.replace('''downsample_layers.3.1''',      '''stages.3.downsampling_layer.1''' )
						if "stages" in name and "downsampling_layer" not in name:
												# stages.0.0. for instance should be renamed to stages.0.layers.0.
												UpperCAmelCase_      :   Any      =	name[: len('''stages.0''' )] + '''.layers''' + name[len('''stages.0''' ) :]
						if "gamma" in name:
												UpperCAmelCase_      :   Optional[Any]      =	name.replace('''gamma''',      '''weight''' )
						if "beta" in name:
												UpperCAmelCase_      :   Dict      =	name.replace('''beta''',      '''bias''' )
						if "stages" in name:
												UpperCAmelCase_      :   str      =	name.replace('''stages''',      '''encoder.stages''' )
						if "norm" in name:
												UpperCAmelCase_      :   Union[str, Any]      =	name.replace('''norm''',      '''layernorm''' )
						if "head" in name:
												UpperCAmelCase_      :   Union[str, Any]      =	name.replace('''head''',      '''classifier''' )

						return name





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





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Optional[int] )			->						int:
						if "224" in checkpoint_url:
												UpperCAmelCase_      :   Optional[Any]      =	224
												UpperCAmelCase_      :   Any      =	224 / 256
						elif "384" in checkpoint_url:
												UpperCAmelCase_      :   int      =	384
												UpperCAmelCase_      :   Optional[Any]      =	None
						else:
												UpperCAmelCase_      :   List[Any]      =	512
												UpperCAmelCase_      :   Union[str, Any]      =	None

						return ConvNextImageProcessor(
						    size=SCREAMING_SNAKE_CASE__,      crop_pct=SCREAMING_SNAKE_CASE__,      image_mean=[0.4_85, 0.4_56, 0.4_06],      image_std=[0.2_29, 0.2_24, 0.2_25],      resample=PILImageResampling.BICUBIC,      )





@torch.no_grad()
def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      List[Any],      SCREAMING_SNAKE_CASE__   :      List[Any],      SCREAMING_SNAKE_CASE__   :      List[Any],      SCREAMING_SNAKE_CASE__   :      str )			->						Optional[Any]:
						print('''Downloading original model from checkpoint...''' )
						# define ConvNeXTV2 configuration based on URL
						UpperCAmelCase_    ,							UpperCAmelCase_      :   Tuple      =	get_convnextva_config(SCREAMING_SNAKE_CASE__ )
						# load original state_dict from URL
						UpperCAmelCase_      :   int      =	torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__ )['''model''']

						print('''Converting model parameters...''' )
						# rename keys
						for key in state_dict.copy().keys():
												UpperCAmelCase_      :   Optional[Any]      =	state_dict.pop(SCREAMING_SNAKE_CASE__ )
												UpperCAmelCase_      :   List[str]      =	val
						# add prefix to all keys expect classifier head
						for key in state_dict.copy().keys():
												UpperCAmelCase_      :   List[str]      =	state_dict.pop(SCREAMING_SNAKE_CASE__ )
												if not key.startswith('''classifier''' ):
																		UpperCAmelCase_      :   Optional[Any]      =	'''convnextv2.''' + key
												UpperCAmelCase_      :   Union[str, Any]      =	val

						# load HuggingFace model
						UpperCAmelCase_      :   Optional[int]      =	ConvNextVaForImageClassification(SCREAMING_SNAKE_CASE__ )
						model.load_state_dict(SCREAMING_SNAKE_CASE__ )
						model.eval()

						# Check outputs on an image, prepared by ConvNextImageProcessor
						UpperCAmelCase_      :   Any      =	convert_preprocessor(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   Tuple      =	preprocessor(images=prepare_img(),      return_tensors='''pt''' )
						UpperCAmelCase_      :   Optional[Any]      =	model(**SCREAMING_SNAKE_CASE__ ).logits

						# note: the logits below were obtained without center cropping
						if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
												UpperCAmelCase_      :   Optional[Any]      =	torch.tensor([-0.39_30, 0.17_47, -0.52_46, 0.41_77, 0.42_95] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
												UpperCAmelCase_      :   List[Any]      =	torch.tensor([-0.17_27, -0.53_41, -0.78_18, -0.47_45, -0.65_66] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
												UpperCAmelCase_      :   Dict      =	torch.tensor([-0.03_33, 0.15_63, -0.91_37, 0.10_54, 0.03_81] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
												UpperCAmelCase_      :   Tuple      =	torch.tensor([-0.17_44, -0.15_55, -0.07_13, 0.09_50, -0.14_31] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
												UpperCAmelCase_      :   Optional[int]      =	torch.tensor([0.99_96, 0.19_66, -0.43_86, -0.34_72, 0.66_61] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
												UpperCAmelCase_      :   Optional[Any]      =	torch.tensor([-0.25_53, -0.67_08, -0.13_59, 0.25_18, -0.24_88] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
												UpperCAmelCase_      :   int      =	torch.tensor([-0.06_73, -0.56_27, -0.37_53, -0.27_22, 0.01_78] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
												UpperCAmelCase_      :   Optional[int]      =	torch.tensor([-0.63_77, -0.74_58, -0.21_50, 0.11_84, -0.05_97] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
												UpperCAmelCase_      :   List[Any]      =	torch.tensor([1.07_99, 0.23_22, -0.88_60, 1.02_19, 0.62_31] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
												UpperCAmelCase_      :   Optional[int]      =	torch.tensor([0.37_66, 0.49_17, -1.14_26, 0.99_42, 0.60_24] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
												UpperCAmelCase_      :   Dict      =	torch.tensor([0.42_20, -0.69_19, -0.43_17, -0.28_81, -0.66_09] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
												UpperCAmelCase_      :   Dict      =	torch.tensor([0.10_82, -0.82_86, -0.50_95, 0.46_81, -0.80_85] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
												UpperCAmelCase_      :   Optional[int]      =	torch.tensor([-0.24_19, -0.62_21, 0.21_76, -0.09_80, -0.75_27] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
												UpperCAmelCase_      :   Optional[int]      =	torch.tensor([0.03_91, -0.43_71, 0.37_86, 0.12_51, -0.27_84] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
												UpperCAmelCase_      :   Any      =	torch.tensor([-0.05_04, 0.56_36, -0.17_29, -0.65_07, -0.39_49] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
												UpperCAmelCase_      :   Optional[Any]      =	torch.tensor([0.35_60, 0.94_86, 0.31_49, -0.26_67, -0.51_38] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
												UpperCAmelCase_      :   List[Any]      =	torch.tensor([-0.24_69, -0.45_50, -0.58_53, -0.08_10, 0.03_09] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
												UpperCAmelCase_      :   Tuple      =	torch.tensor([-0.30_90, 0.08_02, -0.06_82, -0.19_79, -0.28_26] )
						else:
												raise ValueError(F"""Unknown URL: {checkpoint_url}""" )

						assert torch.allclose(logits[0, :5],      SCREAMING_SNAKE_CASE__,      atol=1E-3 )
						assert logits.shape == expected_shape
						print('''Model outputs match the original results!''' )

						if save_model:
												print('''Saving model to local...''' )
												# Create folder to save model
												if not os.path.isdir(SCREAMING_SNAKE_CASE__ ):
																		os.mkdir(SCREAMING_SNAKE_CASE__ )

												model.save_pretrained(SCREAMING_SNAKE_CASE__ )
												preprocessor.save_pretrained(SCREAMING_SNAKE_CASE__ )

						UpperCAmelCase_      :   Dict      =	'''convnextv2'''
						if "atto" in checkpoint_url:
												model_name += "-atto"
						if "femto" in checkpoint_url:
												model_name += "-femto"
						if "pico" in checkpoint_url:
												model_name += "-pico"
						if "nano" in checkpoint_url:
												model_name += "-nano"
						elif "tiny" in checkpoint_url:
												model_name += "-tiny"
						elif "base" in checkpoint_url:
												model_name += "-base"
						elif "large" in checkpoint_url:
												model_name += "-large"
						elif "huge" in checkpoint_url:
												model_name += "-huge"
						if "22k" in checkpoint_url and "1k" not in checkpoint_url:
												model_name += "-22k"
						elif "22k" in checkpoint_url and "1k" in checkpoint_url:
												model_name += "-22k-1k"
						elif "1k" in checkpoint_url:
												model_name += "-1k"
						if "224" in checkpoint_url:
												model_name += "-224"
						elif "384" in checkpoint_url:
												model_name += "-384"
						elif "512" in checkpoint_url:
												model_name += "-512"

						if push_to_hub:
												print(F"""Pushing {model_name} to the hub...""" )
												model.push_to_hub(SCREAMING_SNAKE_CASE__ )
												preprocessor.push_to_hub(SCREAMING_SNAKE_CASE__ )


if __name__ == "__main__":
					snake_case_  :							Tuple            =	argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--checkpoint_url",
					    default="https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt",
					    type=str,
					    help="URL of the original ConvNeXTV2 checkpoint you'd like to convert.",
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path",
					    default="model",
					    type=str,
					    help="Path to the output PyTorch model directory.",
					)
					parser.add_argument("--save_model", action="store_true", help="Save model to local")
					parser.add_argument("--push_to_hub", action="store_true", help="Push model and image preprocessor to the hub")

					snake_case_  :							Optional[int]            =	parser.parse_args()
					convert_convnextva_checkpoint(
					    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
					)





