



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()
__A       					=		logging.get_logger(__name__)



def  __a  (					lowerCAmelCase_     :      str       )   ->				Dict:






						'''simple docstring'''



						UpperCAmelCase_=     ConvNextConfig()

						if "tiny" in checkpoint_url:
												UpperCAmelCase_=     [3, 3, 9, 3]
												UpperCAmelCase_=     [96, 1_92, 3_84, 7_68]
						if "small" in checkpoint_url:
												UpperCAmelCase_=     [3, 3, 27, 3]
												UpperCAmelCase_=     [96, 1_92, 3_84, 7_68]
						if "base" in checkpoint_url:
												UpperCAmelCase_=     [3, 3, 27, 3]
												UpperCAmelCase_=     [1_28, 2_56, 5_12, 10_24]
						if "large" in checkpoint_url:
												UpperCAmelCase_=     [3, 3, 27, 3]
												UpperCAmelCase_=     [1_92, 3_84, 7_68, 15_36]
						if "xlarge" in checkpoint_url:
												UpperCAmelCase_=     [3, 3, 27, 3]
												UpperCAmelCase_=     [2_56, 5_12, 10_24, 20_48]

						if "1k" in checkpoint_url:
												UpperCAmelCase_=     10_00
												UpperCAmelCase_=     """imagenet-1k-id2label.json"""
												UpperCAmelCase_=     (1, 10_00)
						else:
												UpperCAmelCase_=     2_18_41
												UpperCAmelCase_=     """imagenet-22k-id2label.json"""
												UpperCAmelCase_=     (1, 2_18_41)

						UpperCAmelCase_=     """huggingface/label-files"""
						UpperCAmelCase_=     num_labels
						UpperCAmelCase_=     json.load(open(hf_hub_download(lowerCAmelCase_    ,lowerCAmelCase_    ,repo_type="""dataset"""       )    ,"""r"""       )       )
						UpperCAmelCase_=     {int(lowerCAmelCase_       ): 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[92_05]
												del idalabel[1_50_27]
						UpperCAmelCase_=     idalabel
						UpperCAmelCase_=     {v: k for k, v in idalabel.items()}
						UpperCAmelCase_=     hidden_sizes
						UpperCAmelCase_=     depths

						return config, expected_shape



def  __a  (					lowerCAmelCase_     :      str       )   ->				Any:






						'''simple docstring'''



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

						return name



def  __a  (					)   ->				int:






						'''simple docstring'''



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



@torch.no_grad()
def  __a  (					lowerCAmelCase_     :      Optional[Any]    ,lowerCAmelCase_     :      int       )   ->				Optional[int]:






						'''simple docstring'''



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

						# load HuggingFace model
						UpperCAmelCase_=     ConvNextForImageClassification(lowerCAmelCase_       )
						model.load_state_dict(lowerCAmelCase_       )
						model.eval()

						# Check outputs on an image, prepared by ConvNextImageProcessor
						UpperCAmelCase_=     2_24 if """224""" in checkpoint_url else 3_84
						UpperCAmelCase_=     ConvNextImageProcessor(size=lowerCAmelCase_       )
						UpperCAmelCase_=     image_processor(images=prepare_img()    ,return_tensors="""pt"""       ).pixel_values

						UpperCAmelCase_=     model(lowerCAmelCase_       ).logits

						# note: the logits below were obtained without center cropping
						if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
												UpperCAmelCase_=     torch.tensor([-0.1_210, -0.6_605, 0.1_918]       )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
												UpperCAmelCase_=     torch.tensor([-0.4_473, -0.1_847, -0.6_365]       )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
												UpperCAmelCase_=     torch.tensor([0.4_525, 0.7_539, 0.0_308]       )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
												UpperCAmelCase_=     torch.tensor([0.3_561, 0.6_350, -0.0_384]       )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
												UpperCAmelCase_=     torch.tensor([0.4_174, -0.0_989, 0.1_489]       )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
												UpperCAmelCase_=     torch.tensor([0.2_513, -0.1_349, -0.1_613]       )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
												UpperCAmelCase_=     torch.tensor([1.2_980, 0.3_631, -0.1_198]       )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
												UpperCAmelCase_=     torch.tensor([1.2_963, 0.1_227, 0.1_723]       )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
												UpperCAmelCase_=     torch.tensor([1.7_956, 0.8_390, 0.2_820]       )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
												UpperCAmelCase_=     torch.tensor([-0.2_822, -0.0_502, -0.0_878]       )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
												UpperCAmelCase_=     torch.tensor([-0.5_672, -0.0_730, -0.4_348]       )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
												UpperCAmelCase_=     torch.tensor([0.2_681, 0.2_365, 0.6_246]       )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
												UpperCAmelCase_=     torch.tensor([-0.2_642, 0.3_931, 0.5_116]       )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
												UpperCAmelCase_=     torch.tensor([-0.6_677, -0.1_873, -0.8_379]       )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
												UpperCAmelCase_=     torch.tensor([-0.7_749, -0.2_967, -0.6_444]       )
						else:
												raise ValueError(F"""Unknown URL: {checkpoint_url}"""       )

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

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

						print("""Pushing model to the hub..."""       )
						UpperCAmelCase_=     """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(lowerCAmelCase_    ,lowerCAmelCase_       )    ,organization="""nielsr"""    ,commit_message="""Add model"""    ,)


if __name__ == "__main__":
	__A       					=		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.''',
	)

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









# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)






@flax.struct.dataclass
class 	lowercase		:






	"""simple docstring"""


	a__   :      CommonSchedulerState
	a__   :      jnp.ndarray

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




	@classmethod
	def 						_SCREAMING_SNAKE_CASE	(			cls			:		Tuple		,						__UpperCAmelCase			:		CommonSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						)    ->				int:
							return cls(
							    common=__UpperCAmelCase		,						final_alpha_cumprod=__UpperCAmelCase		,						init_noise_sigma=__UpperCAmelCase		,						timesteps=__UpperCAmelCase		,						)






@dataclass
class 	lowercase		(				snake_case__):






	"""simple docstring"""


	a__   :      DDIMSchedulerState






class 	lowercase		(				snake_case__				,					snake_case__):






	"""simple docstring"""


	a__   :      Dict	     =							[e.name for e in FlaxKarrasDiffusionSchedulers]

	a__   :      jnp.dtype




	@property
	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				List[str]:
							return True




	@register_to_config
	def __init__(			self			:		List[Any]		,						__UpperCAmelCase			:		int = 1_000		,						__UpperCAmelCase			:		float = 0.0_001		,						__UpperCAmelCase			:		float = 0.02		,						__UpperCAmelCase			:		str = "linear"		,						__UpperCAmelCase			:		Optional[jnp.ndarray] = None		,						__UpperCAmelCase			:		bool = True		,						__UpperCAmelCase			:		int = 0		,						__UpperCAmelCase			:		str = "epsilon"		,						__UpperCAmelCase			:		jnp.dtype = jnp.floataa		,						)    ->				Union[str, Any]:
							UpperCAmelCase_=     dtype




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict		,						__UpperCAmelCase			:		Optional[CommonSchedulerState] = None  )    ->				DDIMSchedulerState:
							if common is None:
													UpperCAmelCase_=     CommonSchedulerState.create(self  )

							# At every step in ddim, we are looking into the previous alphas_cumprod
							# For the final step, there is no previous alphas_cumprod because we are already at 0
							# `set_alpha_to_one` decides whether we set this parameter simply to one or
							# whether we use the final alpha of the "non-previous" one.
							UpperCAmelCase_=     (
							    jnp.array(1.0		,						dtype=self.dtype  ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
							)

							# standard deviation of the initial noise distribution
							UpperCAmelCase_=     jnp.array(1.0		,						dtype=self.dtype  )

							UpperCAmelCase_=     jnp.arange(0		,						self.config.num_train_timesteps  ).round()[::-1]

							return DDIMSchedulerState.create(
							    common=__UpperCAmelCase		,						final_alpha_cumprod=__UpperCAmelCase		,						init_noise_sigma=__UpperCAmelCase		,						timesteps=__UpperCAmelCase		,						)




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		Optional[int] = None  )    ->				jnp.ndarray:
							return sample




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		Tuple = ()  )    ->				DDIMSchedulerState:
							UpperCAmelCase_=     self.config.num_train_timesteps // num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# rounding to avoid issues when num_inference_step is power of 3
							UpperCAmelCase_=     (jnp.arange(0		,						__UpperCAmelCase  ) * step_ratio).round()[::-1] + self.config.steps_offset

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




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Tuple  )    ->				int:
							UpperCAmelCase_=     state.common.alphas_cumprod[timestep]
							UpperCAmelCase_=     jnp.where(
							    prev_timestep >= 0		,						state.common.alphas_cumprod[prev_timestep]		,						state.final_alpha_cumprod  )
							UpperCAmelCase_=     1 - alpha_prod_t
							UpperCAmelCase_=     1 - alpha_prod_t_prev

							UpperCAmelCase_=     (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

							return variance




	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		float = 0.0		,						__UpperCAmelCase			:		bool = True		,						)    ->				Union[FlaxDDIMSchedulerOutput, 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"""  )

							# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
							# Ideally, read DDIM paper in-detail understanding

							# Notation (<variable name> -> <name in paper>
							# - pred_noise_t -> e_theta(x_t, t)
							# - pred_original_sample -> f_theta(x_t, t) or x_0
							# - std_dev_t -> sigma_t
							# - eta -> η
							# - pred_sample_direction -> "direction pointing to x_t"
							# - pred_prev_sample -> "x_t-1"

							# 1. get previous step value (=t-1)
							UpperCAmelCase_=     timestep - self.config.num_train_timesteps // state.num_inference_steps

							UpperCAmelCase_=     state.common.alphas_cumprod
							UpperCAmelCase_=     state.final_alpha_cumprod

							# 2. compute alphas, betas
							UpperCAmelCase_=     alphas_cumprod[timestep]
							UpperCAmelCase_=     jnp.where(prev_timestep >= 0		,						alphas_cumprod[prev_timestep]		,						__UpperCAmelCase  )

							UpperCAmelCase_=     1 - alpha_prod_t

							# 3. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							if self.config.prediction_type == "epsilon":
													UpperCAmelCase_=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
													UpperCAmelCase_=     model_output
							elif self.config.prediction_type == "sample":
													UpperCAmelCase_=     model_output
													UpperCAmelCase_=     (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
							elif self.config.prediction_type == "v_prediction":
													UpperCAmelCase_=     (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
													UpperCAmelCase_=     (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
							else:
													raise ValueError(
													    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
													    """ `v_prediction`"""  )

							# 4. compute variance: "sigma_t(η)" -> see formula (16)
							# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
							UpperCAmelCase_=     self._get_variance(__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )
							UpperCAmelCase_=     eta * variance ** 0.5

							# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							UpperCAmelCase_=     (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

							# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							UpperCAmelCase_=     alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

							if not return_dict:
													return (prev_sample, state)

							return FlaxDDIMSchedulerOutput(prev_sample=__UpperCAmelCase		,						state=__UpperCAmelCase  )




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						)    ->				jnp.ndarray:
							return add_noise_common(state.common		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )




	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						)    ->				jnp.ndarray:
							return get_velocity_common(state.common		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )




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




