"""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()
__lowerCamelCase									=	logging.get_logger(__name__)






def 	UpperCAmelCase       (						UpperCamelCase__					):






							"""simple docstring"""





							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 	UpperCAmelCase       (						UpperCamelCase__					):






							"""simple docstring"""





							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 	UpperCAmelCase       (						):






							"""simple docstring"""





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






@torch.no_grad()
def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							A__  ,	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([-0.1_2_1_0, -0.6_6_0_5, 0.1_9_1_8]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
														A__						  =				torch.tensor([-0.4_4_7_3, -0.1_8_4_7, -0.6_3_6_5]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
														A__						  =				torch.tensor([0.4_5_2_5, 0.7_5_3_9, 0.0_3_0_8]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
														A__						  =				torch.tensor([0.3_5_6_1, 0.6_3_5_0, -0.0_3_8_4]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
														A__						  =				torch.tensor([0.4_1_7_4, -0.0_9_8_9, 0.1_4_8_9]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
														A__						  =				torch.tensor([0.2_5_1_3, -0.1_3_4_9, -0.1_6_1_3]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
														A__						  =				torch.tensor([1.2_9_8_0, 0.3_6_3_1, -0.1_1_9_8]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
														A__						  =				torch.tensor([1.2_9_6_3, 0.1_2_2_7, 0.1_7_2_3]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
														A__						  =				torch.tensor([1.7_9_5_6, 0.8_3_9_0, 0.2_8_2_0]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
														A__						  =				torch.tensor([-0.2_8_2_2, -0.0_5_0_2, -0.0_8_7_8]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
														A__						  =				torch.tensor([-0.5_6_7_2, -0.0_7_3_0, -0.4_3_4_8]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
														A__						  =				torch.tensor([0.2_6_8_1, 0.2_3_6_5, 0.6_2_4_6]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
														A__						  =				torch.tensor([-0.2_6_4_2, 0.3_9_3_1, 0.5_1_1_6]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
														A__						  =				torch.tensor([-0.6_6_7_7, -0.1_8_7_3, -0.8_3_7_9]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
														A__						  =				torch.tensor([-0.7_7_4_9, -0.2_9_6_7, -0.6_4_4_4]					)
							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__":
				__lowerCamelCase									=	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.",
				)

				__lowerCamelCase									=	parser.parse_args()
				convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






"""simple docstring"""




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

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)
@flax.struct.dataclass
class      UpperCamelCase__:
						lowerCAmelCase__	:    CommonSchedulerState

						# setable values
						lowerCAmelCase__	:    jnp.ndarray
						lowerCAmelCase__	:    jnp.ndarray
						lowerCAmelCase__	:    jnp.ndarray
						lowerCAmelCase__	:    Optional[int]    				=		None

						# running values
						lowerCAmelCase__	:    Optional[jnp.ndarray]    				=		None





						@classmethod
						def 					snake_case__						(							cls		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					Optional[Any]:
													return cls(common=__UpperCAmelCase		,init_noise_sigma=__UpperCAmelCase		,timesteps=__UpperCAmelCase		,sigmas=__UpperCAmelCase  )






@dataclass
class      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    LMSDiscreteSchedulerState






class      UpperCamelCase__(      __A		,    __A			):
						lowerCAmelCase__	:    List[str]    				=		[e.name for e in FlaxKarrasDiffusionSchedulers]

						lowerCAmelCase__	:    jnp.dtype





						@property
						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													return True





						@register_to_config
						def __init__(							self		,__UpperCAmelCase = 10_00		,__UpperCAmelCase = 0.0_0_0_1		,__UpperCAmelCase = 0.0_2		,__UpperCAmelCase = "linear"		,__UpperCAmelCase = None		,__UpperCAmelCase = "epsilon"		,__UpperCAmelCase = jnp.floataa		,)       ->					str:
													A__						  =				dtype





						def 					snake_case__						(							self		,__UpperCAmelCase = None  )       ->					LMSDiscreteSchedulerState:
													if common is None:
																				A__						  =				CommonSchedulerState.create(self  )

													A__						  =				jnp.arange(0		,self.config.num_train_timesteps  ).round()[::-1]
													A__						  =				((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

													# standard deviation of the initial noise distribution
													A__						  =				sigmas.max()

													return LMSDiscreteSchedulerState.create(
													    common=__UpperCAmelCase		,init_noise_sigma=__UpperCAmelCase		,timesteps=__UpperCAmelCase		,sigmas=__UpperCAmelCase		,)





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					jnp.ndarray:
													((A__)  ,	)						  =				jnp.where(state.timesteps == timestep		,size=1  )
													A__						  =				step_index[0]

													A__						  =				state.sigmas[step_index]
													A__						  =				sample / ((sigma**2 + 1) ** 0.5)
													return sample





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					Optional[int]:

													def lms_derivative(__UpperCAmelCase  ):
																				A__						  =				1.0
																				for k in range(__UpperCAmelCase  ):
																											if current_order == k:
																																		continue
																											prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
																				return prod

													A__						  =				integrate.quad(__UpperCAmelCase		,state.sigmas[t]		,state.sigmas[t + 1]		,epsrel=1e-4  )[0]

													return integrated_coeff





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = ()  )       ->					LMSDiscreteSchedulerState:
													A__						  =				jnp.linspace(self.config.num_train_timesteps - 1		,0		,__UpperCAmelCase		,dtype=self.dtype  )

													A__						  =				jnp.floor(__UpperCAmelCase  ).astype(jnp.intaa  )
													A__						  =				jnp.ceil(__UpperCAmelCase  ).astype(jnp.intaa  )

													A__						  =				jnp.mod(__UpperCAmelCase		,1.0  )

													A__						  =				((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
													A__						  =				(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
													A__						  =				jnp.concatenate([sigmas, jnp.array([0.0]		,dtype=self.dtype  )]  )

													A__						  =				timesteps.astype(jnp.intaa  )

													# initial running values
													A__						  =				jnp.zeros((0,) + shape		,dtype=self.dtype  )

													return state.replace(
													    timesteps=__UpperCAmelCase		,sigmas=__UpperCAmelCase		,num_inference_steps=__UpperCAmelCase		,derivatives=__UpperCAmelCase		,)





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = 4		,__UpperCAmelCase = True		,)       ->					Union[FlaxLMSSchedulerOutput, Tuple]:
													if state.num_inference_steps is None:
																				raise ValueError(
																				    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'  )

													A__						  =				state.sigmas[timestep]

													# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
													if self.config.prediction_type == "epsilon":
																				A__						  =				sample - sigma * model_output
													elif self.config.prediction_type == "v_prediction":
																				# * c_out + input * c_skip
																				A__						  =				model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
													else:
																				raise ValueError(
																				    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''  )

													# 2. Convert to an ODE derivative
													A__						  =				(sample - pred_original_sample) / sigma
													A__						  =				state.replace(derivatives=jnp.append(state.derivatives		,__UpperCAmelCase  )  )
													if len(state.derivatives  ) > order:
																				A__						  =				state.replace(derivatives=jnp.delete(state.derivatives		,0  )  )

													# 3. Compute linear multistep coefficients
													A__						  =				min(timestep + 1		,__UpperCAmelCase  )
													A__						  =				[self.get_lms_coefficient(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  ) for curr_order in range(__UpperCAmelCase  )]

													# 4. Compute previous sample based on the derivatives path
													A__						  =				sample + sum(
													    coeff * derivative for coeff, derivative in zip(__UpperCAmelCase		,reversed(state.derivatives  )  )  )

													if not return_dict:
																				return (prev_sample, state)

													return FlaxLMSSchedulerOutput(prev_sample=__UpperCAmelCase		,state=__UpperCAmelCase  )





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,)       ->					jnp.ndarray:
													A__						  =				state.sigmas[timesteps].flatten()
													A__						  =				broadcast_to_shape_from_left(__UpperCAmelCase		,noise.shape  )

													A__						  =				original_samples + noise * sigma

													return noisy_samples





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





