





"""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 ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


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






def  A__    (      UpperCamelCase	):
							A		=  ConvNextConfig()

							if "tiny" in checkpoint_url:
														A		=  [3, 3, 9, 3]
														A		=  [96, 192, 384, 768]
							if "small" in checkpoint_url:
														A		=  [3, 3, 27, 3]
														A		=  [96, 192, 384, 768]
							if "base" in checkpoint_url:
														A		=  [3, 3, 27, 3]
														A		=  [128, 256, 512, 1_024]
							if "large" in checkpoint_url:
														A		=  [3, 3, 27, 3]
														A		=  [192, 384, 768, 1_536]
							if "xlarge" in checkpoint_url:
														A		=  [3, 3, 27, 3]
														A		=  [256, 512, 1_024, 2_048]

							if "1k" in checkpoint_url:
														A		=  1_000
														A		=  '''imagenet-1k-id2label.json'''
														A		=  (1, 1_000)
							else:
														A		=  21_841
														A		=  '''imagenet-22k-id2label.json'''
														A		=  (1, 21_841)

							A		=  '''huggingface/label-files'''
							A		=  num_labels
							A		=  json.load(open(hf_hub_download(UpperCamelCase ,  UpperCamelCase ,  repo_type="dataset"	) ,  "r"	)	)
							A		=  {int(UpperCamelCase	): v for k, v in idalabel.items()}
							if "1k" not in checkpoint_url:
														# this dataset contains 21843 labels but the model only has 21841
														# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
														del idalabel[9_205]
														del idalabel[15_027]
							A		=  idalabel
							A		=  {v: k for k, v in idalabel.items()}
							A		=  hidden_sizes
							A		=  depths

							return config, expected_shape






def  A__    (      UpperCamelCase	):
							if "downsample_layers.0.0" in name:
														A		=  name.replace("downsample_layers.0.0" ,  "embeddings.patch_embeddings"	)
							if "downsample_layers.0.1" in name:
														A		=  name.replace("downsample_layers.0.1" ,  "embeddings.norm"	)  # we rename to layernorm later on
							if "downsample_layers.1.0" in name:
														A		=  name.replace("downsample_layers.1.0" ,  "stages.1.downsampling_layer.0"	)
							if "downsample_layers.1.1" in name:
														A		=  name.replace("downsample_layers.1.1" ,  "stages.1.downsampling_layer.1"	)
							if "downsample_layers.2.0" in name:
														A		=  name.replace("downsample_layers.2.0" ,  "stages.2.downsampling_layer.0"	)
							if "downsample_layers.2.1" in name:
														A		=  name.replace("downsample_layers.2.1" ,  "stages.2.downsampling_layer.1"	)
							if "downsample_layers.3.0" in name:
														A		=  name.replace("downsample_layers.3.0" ,  "stages.3.downsampling_layer.0"	)
							if "downsample_layers.3.1" in name:
														A		=  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.
														A		=  name[: len("stages.0"	)] + '''.layers''' + name[len("stages.0"	) :]
							if "stages" in name:
														A		=  name.replace("stages" ,  "encoder.stages"	)
							if "norm" in name:
														A		=  name.replace("norm" ,  "layernorm"	)
							if "gamma" in name:
														A		=  name.replace("gamma" ,  "layer_scale_parameter"	)
							if "head" in name:
														A		=  name.replace("head" ,  "classifier"	)

							return name






def  A__    (      ):
							A		=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
							A		=  Image.open(requests.get(UpperCamelCase ,  stream=UpperCamelCase	).raw	)
							return im






@torch.no_grad()
def  A__    (      UpperCamelCase ,  UpperCamelCase	):
							A		=  get_convnext_config(UpperCamelCase	)
							# load original state_dict from URL
							A		=  torch.hub.load_state_dict_from_url(UpperCamelCase	)['''model''']
							# rename keys
							for key in state_dict.copy().keys():
														A		=  state_dict.pop(UpperCamelCase	)
														A		=  val
							# add prefix to all keys expect classifier head
							for key in state_dict.copy().keys():
														A		=  state_dict.pop(UpperCamelCase	)
														if not key.startswith("classifier"	):
																					A		=  '''convnext.''' + key
														A		=  val

							# load HuggingFace model
							A		=  ConvNextForImageClassification(UpperCamelCase	)
							model.load_state_dict(UpperCamelCase	)
							model.eval()

							# Check outputs on an image, prepared by ConvNextImageProcessor
							A		=  224 if '''224''' in checkpoint_url else 384
							A		=  ConvNextImageProcessor(size=UpperCamelCase	)
							A		=  image_processor(images=prepare_img() ,  return_tensors="pt"	).pixel_values

							A		=  model(UpperCamelCase	).logits

							# note: the logits below were obtained without center cropping
							if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
														A		=  torch.tensor([-0.12_10, -0.66_05, 0.19_18]	)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
														A		=  torch.tensor([-0.44_73, -0.18_47, -0.63_65]	)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
														A		=  torch.tensor([0.45_25, 0.75_39, 0.03_08]	)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
														A		=  torch.tensor([0.35_61, 0.63_50, -0.03_84]	)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
														A		=  torch.tensor([0.41_74, -0.09_89, 0.14_89]	)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
														A		=  torch.tensor([0.25_13, -0.13_49, -0.16_13]	)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
														A		=  torch.tensor([1.29_80, 0.36_31, -0.11_98]	)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
														A		=  torch.tensor([1.29_63, 0.12_27, 0.17_23]	)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
														A		=  torch.tensor([1.79_56, 0.83_90, 0.28_20]	)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
														A		=  torch.tensor([-0.28_22, -0.05_02, -0.08_78]	)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
														A		=  torch.tensor([-0.56_72, -0.07_30, -0.43_48]	)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
														A		=  torch.tensor([0.26_81, 0.23_65, 0.62_46]	)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
														A		=  torch.tensor([-0.26_42, 0.39_31, 0.51_16]	)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
														A		=  torch.tensor([-0.66_77, -0.18_73, -0.83_79]	)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
														A		=  torch.tensor([-0.77_49, -0.29_67, -0.64_44]	)
							else:
														raise ValueError(F"Unknown URL: {checkpoint_url}"	)

							assert torch.allclose(logits[0, :3] ,  UpperCamelCase ,  atol=1E-3	)
							assert logits.shape == expected_shape

							Path(UpperCamelCase	).mkdir(exist_ok=UpperCamelCase	)
							print(F"Saving model to {pytorch_dump_folder_path}"	)
							model.save_pretrained(UpperCamelCase	)
							print(F"Saving image processor to {pytorch_dump_folder_path}"	)
							image_processor.save_pretrained(UpperCamelCase	)

							print("Pushing model to the hub..."	)
							A		=  '''convnext'''
							if "tiny" in checkpoint_url:
														model_name += "-tiny"
							elif "small" in checkpoint_url:
														model_name += "-small"
							elif "base" in checkpoint_url:
														model_name += "-base"
							elif "xlarge" in checkpoint_url:
														model_name += "-xlarge"
							elif "large" in checkpoint_url:
														model_name += "-large"
							if "224" in checkpoint_url:
														model_name += "-224"
							elif "384" in checkpoint_url:
														model_name += "-384"
							if "22k" in checkpoint_url and "1k" not in checkpoint_url:
														model_name += "-22k"
							if "22k" in checkpoint_url and "1k" in checkpoint_url:
														model_name += "-22k-1k"

							model.push_to_hub(
							    repo_path_or_name=Path(UpperCamelCase ,  UpperCamelCase	) ,  organization="nielsr" ,  commit_message="Add model" ,  )


if __name__ == "__main__":
					_snake_case					:		str							=			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--checkpoint_url',
					    default='https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth',
					    type=str,
					    help='URL of the original ConvNeXT checkpoint you\'d like to convert.',
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path',
					    default=None,
					    type=str,
					    required=True,
					    help='Path to the output PyTorch model directory.',
					)

					_snake_case					:		Any							=			parser.parse_args()
					convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

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


_snake_case					:		str							=			logging.get_logger(__name__)  # pylint: disable=invalid-name






@dataclass
class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							42







class  _UpperCAmelCase       (     lowercase_					,							lowercase_			):
					UpperCamelCase								=							1


					@register_to_config
					def __init__( self      :Tuple					,	__UpperCamelCase      :int = 40					,	__UpperCamelCase      :float = 0.002					,	__UpperCamelCase      :float = 80.0					,	__UpperCamelCase      :float = 0.5					,	__UpperCamelCase      :float = 1.0					,	__UpperCamelCase      :float = 7.0					,	__UpperCamelCase      :bool = True					,	):
												# standard deviation of the initial noise distribution
												A		=  sigma_max

												A		=  np.linspace(0					,	1					,	__UpperCamelCase		)
												A		=  self._convert_to_karras(__UpperCamelCase		)
												A		=  self.sigma_to_t(__UpperCamelCase		)

												# setable values
												A		=  None
												A		=  torch.from_numpy(__UpperCamelCase		)
												A		=  torch.from_numpy(__UpperCamelCase		)
												A		=  False
												A		=  False


					def      lowerCamelCase	( self      :List[Any]					,	__UpperCamelCase      :Optional[Any]					,	__UpperCamelCase      :Dict=None		):
												if schedule_timesteps is None:
																			A		=  self.timesteps

												A		=  (schedule_timesteps == timestep).nonzero()
												return indices.item()


					def      lowerCamelCase	( self      :List[Any]					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :Union[float, torch.FloatTensor]		):
												# Get sigma corresponding to timestep
												if isinstance(__UpperCamelCase					,	torch.Tensor		):
																			A		=  timestep.to(self.timesteps.device		)
												A		=  self.index_for_timestep(__UpperCamelCase		)
												A		=  self.sigmas[step_idx]

												A		=  sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

												A		=  True
												return sample


					def      lowerCamelCase	( self      :Tuple					,	__UpperCamelCase      :Union[float, np.ndarray]		):
												if not isinstance(__UpperCamelCase					,	np.ndarray		):
																			A		=  np.array(__UpperCamelCase					,	dtype=np.floataa		)

												A		=  10_00 * 0.25 * np.log(sigmas + 1e-44		)

												return timesteps


					def      lowerCamelCase	( self      :str					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :Union[str, torch.device] = None					,	__UpperCamelCase      :Optional[List[int]] = None					,	):
												if num_inference_steps is None and timesteps is None:
																			raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied."		)

												if num_inference_steps is not None and timesteps is not None:
																			raise ValueError("Can only pass one of `num_inference_steps` or `timesteps`."		)

												# Follow DDPMScheduler custom timesteps logic
												if timesteps is not None:
																			for i in range(1					,	len(__UpperCamelCase		)		):
																										if timesteps[i] >= timesteps[i - 1]:
																																	raise ValueError("`timesteps` must be in descending order."		)

																			if timesteps[0] >= self.config.num_train_timesteps:
																										raise ValueError(
																										    f"`timesteps` must start before `self.config.train_timesteps`:"
																										    f" {self.config.num_train_timesteps}."		)

																			A		=  np.array(__UpperCamelCase					,	dtype=np.intaa		)
																			A		=  True
												else:
																			if num_inference_steps > self.config.num_train_timesteps:
																										raise ValueError(
																										    f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
																										    f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
																										    f" maximal {self.config.num_train_timesteps} timesteps."		)

																			A		=  num_inference_steps

																			A		=  self.config.num_train_timesteps // self.num_inference_steps
																			A		=  (np.arange(0					,	__UpperCamelCase		) * step_ratio).round()[::-1].copy().astype(np.intaa		)
																			A		=  False

												# Map timesteps to Karras sigmas directly for multistep sampling
												# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
												A		=  self.config.num_train_timesteps
												A		=  timesteps[::-1].copy()
												A		=  ramp / (num_train_timesteps - 1)
												A		=  self._convert_to_karras(__UpperCamelCase		)
												A		=  self.sigma_to_t(__UpperCamelCase		)

												A		=  np.concatenate([sigmas, [self.sigma_min]]		).astype(np.floataa		)
												A		=  torch.from_numpy(__UpperCamelCase		).to(device=__UpperCamelCase		)

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


					def      lowerCamelCase	( self      :Optional[Any]					,	__UpperCamelCase      :Union[str, Any]		):
												A		=  self.config.sigma_min
												A		=  self.config.sigma_max

												A		=  self.config.rho
												A		=  sigma_min ** (1 / rho)
												A		=  sigma_max ** (1 / rho)
												A		=  (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
												return sigmas


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :Dict		):
												A		=  self.config.sigma_data

												A		=  sigma_data**2 / (sigma**2 + sigma_data**2)
												A		=  sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
												return c_skip, c_out


					def      lowerCamelCase	( self      :Any					,	__UpperCamelCase      :Tuple		):
												A		=  self.config.sigma_min
												A		=  self.config.sigma_data

												A		=  sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
												A		=  (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
												return c_skip, c_out


					def      lowerCamelCase	( self      :Optional[int]					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :Union[float, torch.FloatTensor]					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :Optional[torch.Generator] = None					,	__UpperCamelCase      :bool = True					,	):

												if (
												    isinstance(__UpperCamelCase					,	__UpperCamelCase		)
												    or isinstance(__UpperCamelCase					,	torch.IntTensor		)
												    or isinstance(__UpperCamelCase					,	torch.LongTensor		)
												):
																			raise ValueError(
																			    (
																			        "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
																			        f" `{self.__class__}.step()` is not supported. Make sure to pass"
																			        " one of the `scheduler.timesteps` as a timestep."
																			    )					,	)

												if not self.is_scale_input_called:
																			logger.warning(
																			    "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
																			    "See `StableDiffusionPipeline` for a usage example."		)

												if isinstance(__UpperCamelCase					,	torch.Tensor		):
																			A		=  timestep.to(self.timesteps.device		)

												A		=  self.config.sigma_min
												A		=  self.config.sigma_max

												A		=  self.index_for_timestep(__UpperCamelCase		)

												# sigma_next corresponds to next_t in original implementation
												A		=  self.sigmas[step_index]
												if step_index + 1 < self.config.num_train_timesteps:
																			A		=  self.sigmas[step_index + 1]
												else:
																			# Set sigma_next to sigma_min
																			A		=  self.sigmas[-1]

												# Get scalings for boundary conditions
												A,     A		=  self.get_scalings_for_boundary_condition(__UpperCamelCase		)

												# 1. Denoise model output using boundary conditions
												A		=  c_out * model_output + c_skip * sample
												if self.config.clip_denoised:
																			A		=  denoised.clamp(-1					,	1		)

												# 2. Sample z ~ N(0, s_noise^2 * I)
												# Noise is not used for onestep sampling.
												if len(self.timesteps		) > 1:
																			A		=  randn_tensor(
																			    model_output.shape					,	dtype=model_output.dtype					,	device=model_output.device					,	generator=__UpperCamelCase		)
												else:
																			A		=  torch.zeros_like(__UpperCamelCase		)
												A		=  noise * self.config.s_noise

												A		=  sigma_next.clamp(min=__UpperCamelCase					,	max=__UpperCamelCase		)

												# 3. Return noisy sample
												# tau = sigma_hat, eps = sigma_min
												A		=  denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

												if not return_dict:
																			return (prev_sample,)

												return CMStochasticIterativeSchedulerOutput(prev_sample=__UpperCamelCase		)


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :torch.FloatTensor					,	):
												# Make sure sigmas and timesteps have the same device and dtype as original_samples
												A		=  self.sigmas.to(device=original_samples.device					,	dtype=original_samples.dtype		)
												if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCamelCase		):
																			# mps does not support float64
																			A		=  self.timesteps.to(original_samples.device					,	dtype=torch.floataa		)
																			A		=  timesteps.to(original_samples.device					,	dtype=torch.floataa		)
												else:
																			A		=  self.timesteps.to(original_samples.device		)
																			A		=  timesteps.to(original_samples.device		)

												A		=  [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

												A		=  original_samples + noise * sigma
												return noisy_samples


					def __len__( self      :str		):
												return self.config.num_train_timesteps


