


import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase	=							logging.get_logger(__name__)
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=False					):


		"""simple docstring"""




		lowercase__						=				[]

		# fmt: off
		# stem:
		rename_keys.append(('''cls_token''', '''vit.embeddings.cls_token''')					)
		rename_keys.append(('''pos_embed''', '''vit.embeddings.position_embeddings''')					)

		rename_keys.append(('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight''')					)
		rename_keys.append(('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias''')					)

		# backbone
		rename_keys.append(('''patch_embed.backbone.stem.conv.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight''')					)
		rename_keys.append(('''patch_embed.backbone.stem.norm.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight''')					)
		rename_keys.append(('''patch_embed.backbone.stem.norm.bias''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias''')					)

		for stage_idx in range(len(config.backbone_config.depths					)					):
				for layer_idx in range(config.backbone_config.depths[stage_idx]					):
						rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight')					)
						rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight')					)
						rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias')					)
						rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight')					)
						rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight')					)
						rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias')					)
						rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight')					)
						rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight')					)
						rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias')					)

				rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight')					)
				rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight')					)
				rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias')					)

		# transformer encoder
		for i in range(config.num_hidden_layers					):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append((f'blocks.{i}.norm1.weight', f'vit.encoder.layer.{i}.layernorm_before.weight')					)
				rename_keys.append((f'blocks.{i}.norm1.bias', f'vit.encoder.layer.{i}.layernorm_before.bias')					)
				rename_keys.append((f'blocks.{i}.attn.proj.weight', f'vit.encoder.layer.{i}.attention.output.dense.weight')					)
				rename_keys.append((f'blocks.{i}.attn.proj.bias', f'vit.encoder.layer.{i}.attention.output.dense.bias')					)
				rename_keys.append((f'blocks.{i}.norm2.weight', f'vit.encoder.layer.{i}.layernorm_after.weight')					)
				rename_keys.append((f'blocks.{i}.norm2.bias', f'vit.encoder.layer.{i}.layernorm_after.bias')					)
				rename_keys.append((f'blocks.{i}.mlp.fc1.weight', f'vit.encoder.layer.{i}.intermediate.dense.weight')					)
				rename_keys.append((f'blocks.{i}.mlp.fc1.bias', f'vit.encoder.layer.{i}.intermediate.dense.bias')					)
				rename_keys.append((f'blocks.{i}.mlp.fc2.weight', f'vit.encoder.layer.{i}.output.dense.weight')					)
				rename_keys.append((f'blocks.{i}.mlp.fc2.bias', f'vit.encoder.layer.{i}.output.dense.bias')					)

		if base_model:
				# layernorm + pooler
				rename_keys.extend(
				    [
				        ('''norm.weight''', '''layernorm.weight'''),
				        ('''norm.bias''', '''layernorm.bias'''),
				        ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
				        ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
				    ]					)

				# if just the base model, we should remove "vit" from all keys that start with "vit"
				lowercase__						=				[(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''					) else pair for pair in rename_keys]
		else:
				# layernorm + classification head
				rename_keys.extend(
				    [
				        ('''norm.weight''', '''vit.layernorm.weight'''),
				        ('''norm.bias''', '''vit.layernorm.bias'''),
				        ('''head.weight''', '''classifier.weight'''),
				        ('''head.bias''', '''classifier.bias'''),
				    ]					)
		# fmt: on

		return rename_keys
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=False					):


		"""simple docstring"""




		for i in range(config.num_hidden_layers					):
				if base_model:
						lowercase__						=				''''''
				else:
						lowercase__						=				'''vit.'''
				# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
				lowercase__						=				state_dict.pop(f'blocks.{i}.attn.qkv.weight'					)
				lowercase__						=				state_dict.pop(f'blocks.{i}.attn.qkv.bias'					)
				# next, add query, keys and values (in that order) to the state dict
				lowercase__						=				in_proj_weight[
				    : config.hidden_size, :
				]
				lowercase__						=				in_proj_bias[: config.hidden_size]
				lowercase__						=				in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				lowercase__						=				in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				lowercase__						=				in_proj_weight[
				    -config.hidden_size :, :
				]
				lowercase__						=				in_proj_bias[-config.hidden_size :]
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				['''head.weight''', '''head.bias''']
		for k in ignore_keys:
				state_dict.pop(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				dct.pop(SCREAMING_SNAKE_CASE					)
		lowercase__						=				val
def       _a			(				):


		"""simple docstring"""




		lowercase__						=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowercase__						=				Image.open(requests.get(SCREAMING_SNAKE_CASE						,    stream=SCREAMING_SNAKE_CASE					).raw					)
		return im
@torch.no_grad()
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=False					):


		"""simple docstring"""




		lowercase__						=				BitConfig(
		    global_padding='''same'''						,    layer_type='''bottleneck'''						,    depths=(3, 4, 9)						,    out_features=['''stage3''']						,    embedding_dynamic_padding=SCREAMING_SNAKE_CASE						,    )
		lowercase__						=				ViTHybridConfig(backbone_config=SCREAMING_SNAKE_CASE						,    image_size=3_84						,    num_labels=10_00					)
		lowercase__						=				False

		# load original model from timm
		lowercase__						=				timm.create_model(SCREAMING_SNAKE_CASE						,    pretrained=SCREAMING_SNAKE_CASE					)
		timm_model.eval()

		# load state_dict of original model, remove and rename some keys
		lowercase__						=				timm_model.state_dict()
		if base_model:
				remove_classification_head_(SCREAMING_SNAKE_CASE					)
		lowercase__						=				create_rename_keys(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)
		for src, dest in rename_keys:
				rename_key(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)
		read_in_q_k_v(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)

		lowercase__						=				'''huggingface/label-files'''
		lowercase__						=				'''imagenet-1k-id2label.json'''
		lowercase__						=				json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    repo_type='''dataset'''					)						,    '''r'''					)					)
		lowercase__						=				{int(SCREAMING_SNAKE_CASE					): v for k, v in idalabel.items()}
		lowercase__						=				idalabel
		lowercase__						=				{v: k for k, v in idalabel.items()}

		# load HuggingFace model
		if vit_name[-5:] == "in21k":
				lowercase__						=				ViTHybridModel(SCREAMING_SNAKE_CASE					).eval()
		else:
				lowercase__						=				ViTHybridForImageClassification(SCREAMING_SNAKE_CASE					).eval()
		model.load_state_dict(SCREAMING_SNAKE_CASE					)

		# create image processor
		lowercase__						=				create_transform(**resolve_data_config({}						,    model=SCREAMING_SNAKE_CASE					)					)
		lowercase__						=				transform.transforms

		lowercase__						=				{
		    '''bilinear''': PILImageResampling.BILINEAR,
		    '''bicubic''': PILImageResampling.BICUBIC,
		    '''nearest''': PILImageResampling.NEAREST,
		}

		lowercase__						=				ViTHybridImageProcessor(
		    do_resize=SCREAMING_SNAKE_CASE						,    size={'''shortest_edge''': timm_transforms[0].size}						,    resample=pillow_resamplings[timm_transforms[0].interpolation.value]						,    do_center_crop=SCREAMING_SNAKE_CASE						,    crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}						,    do_normalize=SCREAMING_SNAKE_CASE						,    image_mean=timm_transforms[-1].mean.tolist()						,    image_std=timm_transforms[-1].std.tolist()						,    )

		lowercase__						=				prepare_img()
		lowercase__						=				transform(SCREAMING_SNAKE_CASE					).unsqueeze(0					)
		lowercase__						=				processor(SCREAMING_SNAKE_CASE						,    return_tensors='''pt'''					).pixel_values

		# verify pixel values
		assert torch.allclose(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)

		# verify logits
		with torch.no_grad():
				lowercase__						=				model(SCREAMING_SNAKE_CASE					)
				lowercase__						=				outputs.logits

		print('''Predicted class:'''						,    logits.argmax(-1					).item()					)
		if base_model:
				lowercase__						=				timm_model.forward_features(SCREAMING_SNAKE_CASE					)
				assert timm_pooled_output.shape == outputs.pooler_output.shape
				assert torch.allclose(SCREAMING_SNAKE_CASE						,    outputs.pooler_output						,    atol=1E-3					)
		else:
				lowercase__						=				timm_model(SCREAMING_SNAKE_CASE					)
				assert timm_logits.shape == outputs.logits.shape
				assert torch.allclose(SCREAMING_SNAKE_CASE						,    outputs.logits						,    atol=1E-3					)
		print('''Looks ok!'''					)

		if pytorch_dump_folder_path is not None:
				Path(SCREAMING_SNAKE_CASE					).mkdir(exist_ok=SCREAMING_SNAKE_CASE					)
				print(f'Saving model {vit_name} to {pytorch_dump_folder_path}'					)
				model.save_pretrained(SCREAMING_SNAKE_CASE					)
				print(f'Saving processor to {pytorch_dump_folder_path}'					)
				processor.save_pretrained(SCREAMING_SNAKE_CASE					)

		if push_to_hub:
				print(f'Pushing model and processor to the hub {vit_name}'					)
				model.push_to_hub(f'ybelkada/{vit_name}'					)
				processor.push_to_hub(f'ybelkada/{vit_name}'					)


if __name__ == "__main__":
			lowerCAmelCase	=							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--vit_name',
			    default='vit_base_r50_s16_384',
			    type=str,
			    help='Name of the hybrid ViT timm model you\'d like to convert.',
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
			)
			parser.add_argument(
			    '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
			)

			lowerCAmelCase	=							parser.parse_args()
			convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)






# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
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 	_a							:
							_lowercase     :      CommonSchedulerState

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

							@classmethod
							def 				lowerCamelCase_			(							cls:     Dict   ,						UpperCamelCase_:     CommonSchedulerState   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     jnp.ndarray							)      ->       List[str]:





									"""simple docstring"""







									return cls(common=UpperCamelCase_   ,						init_noise_sigma=UpperCamelCase_   ,						timesteps=UpperCamelCase_							)


@dataclass
class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      DDPMSchedulerState


class 	_a							(      UpperCamelCase__					,     UpperCamelCase__      ):
							_lowercase     :      Tuple  				=			[e.name for e in FlaxKarrasDiffusionSchedulers]

							_lowercase     :      jnp.dtype

							@property
							def 				lowerCamelCase_			(							self:     Optional[Any]							)      ->       Union[str, Any]:





									"""simple docstring"""







									return True

							@register_to_config
							def __init__(							self:     Any   ,						UpperCamelCase_:     int = 1_000   ,						UpperCamelCase_:     float = 0.0001   ,						UpperCamelCase_:     float = 0.02   ,						UpperCamelCase_:     str = "linear"   ,						UpperCamelCase_:     Optional[jnp.ndarray] = None   ,						UpperCamelCase_:     str = "fixed_small"   ,						UpperCamelCase_:     bool = True   ,						UpperCamelCase_:     str = "epsilon"   ,						UpperCamelCase_:     jnp.dtype = jnp.floataa   ,						)      ->       int:





									"""simple docstring"""







									lowercase__						=				dtype

							def 				lowerCamelCase_			(							self:     Optional[int]   ,						UpperCamelCase_:     Optional[CommonSchedulerState] = None							)      ->       DDPMSchedulerState:





									"""simple docstring"""







									if common is None:
											lowercase__						=				CommonSchedulerState.create(self							)

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

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

									return DDPMSchedulerState.create(
									    common=UpperCamelCase_   ,						init_noise_sigma=UpperCamelCase_   ,						timesteps=UpperCamelCase_   ,						)

							def 				lowerCamelCase_			(							self:     Dict   ,						UpperCamelCase_:     DDPMSchedulerState   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     Optional[int] = None							)      ->       jnp.ndarray:





									"""simple docstring"""







									return sample

							def 				lowerCamelCase_			(							self:     Dict   ,						UpperCamelCase_:     DDPMSchedulerState   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     Tuple = ()							)      ->       DDPMSchedulerState:





									"""simple docstring"""







									lowercase__						=				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
									lowercase__						=				(jnp.arange(0   ,						UpperCamelCase_							) * step_ratio).round()[::-1]

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

							def 				lowerCamelCase_			(							self:     Union[str, Any]   ,						UpperCamelCase_:     DDPMSchedulerState   ,						UpperCamelCase_:     Optional[Any]   ,						UpperCamelCase_:     Optional[int]=None   ,						UpperCamelCase_:     str=None							)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				state.common.alphas_cumprod[t]
									lowercase__						=				jnp.where(t > 0   ,						state.common.alphas_cumprod[t - 1]   ,						jnp.array(1.0   ,						dtype=self.dtype							)							)

									# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
									# and sample from it to get previous sample
									# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
									lowercase__						=				(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

									if variance_type is None:
											lowercase__						=				self.config.variance_type

									# hacks - were probably added for training stability
									if variance_type == "fixed_small":
											lowercase__						=				jnp.clip(UpperCamelCase_   ,						a_min=1E-2_0							)
									# for rl-diffuser https://arxiv.org/abs/2205.09991
									elif variance_type == "fixed_small_log":
											lowercase__						=				jnp.log(jnp.clip(UpperCamelCase_   ,						a_min=1E-2_0							)							)
									elif variance_type == "fixed_large":
											lowercase__						=				state.common.betas[t]
									elif variance_type == "fixed_large_log":
											# Glide max_log
											lowercase__						=				jnp.log(state.common.betas[t]							)
									elif variance_type == "learned":
											return predicted_variance
									elif variance_type == "learned_range":
											lowercase__						=				variance
											lowercase__						=				state.common.betas[t]
											lowercase__						=				(predicted_variance + 1) / 2
											lowercase__						=				frac * max_log + (1 - frac) * min_log

									return variance

							def 				lowerCamelCase_			(							self:     Any   ,						UpperCamelCase_:     DDPMSchedulerState   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     Optional[jax.random.KeyArray] = None   ,						UpperCamelCase_:     bool = True   ,						)      ->       Union[FlaxDDPMSchedulerOutput, Tuple]:





									"""simple docstring"""







									lowercase__						=				timestep

									if key is None:
											lowercase__						=				jax.random.PRNGKey(0							)

									if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
											lowercase__					,		lowercase__						=				jnp.split(UpperCamelCase_   ,						sample.shape[1]   ,						axis=1							)
									else:
											lowercase__						=				None

									# 1. compute alphas, betas
									lowercase__						=				state.common.alphas_cumprod[t]
									lowercase__						=				jnp.where(t > 0   ,						state.common.alphas_cumprod[t - 1]   ,						jnp.array(1.0   ,						dtype=self.dtype							)							)
									lowercase__						=				1 - alpha_prod_t
									lowercase__						=				1 - alpha_prod_t_prev

									# 2. compute predicted original sample from predicted noise also called
									# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
									if self.config.prediction_type == "epsilon":
											lowercase__						=				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
									elif self.config.prediction_type == "sample":
											lowercase__						=				model_output
									elif self.config.prediction_type == "v_prediction":
											lowercase__						=				(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
									else:
											raise ValueError(
											    f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '
											    ''' for the FlaxDDPMScheduler.'''							)

									# 3. Clip "predicted x_0"
									if self.config.clip_sample:
											lowercase__						=				jnp.clip(UpperCamelCase_   ,						-1   ,						1							)

									# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
									# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
									lowercase__						=				(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
									lowercase__						=				state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

									# 5. Compute predicted previous sample µ_t
									# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
									lowercase__						=				pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

									# 6. Add noise
									def random_variance():
											lowercase__						=				jax.random.split(UpperCamelCase_   ,						num=1							)
											lowercase__						=				jax.random.normal(UpperCamelCase_   ,						shape=model_output.shape   ,						dtype=self.dtype							)
											return (self._get_variance(UpperCamelCase_   ,						UpperCamelCase_   ,						predicted_variance=UpperCamelCase_							) ** 0.5) * noise

									lowercase__						=				jnp.where(t > 0   ,						random_variance()   ,						jnp.zeros(model_output.shape   ,						dtype=self.dtype							)							)

									lowercase__						=				pred_prev_sample + variance

									if not return_dict:
											return (pred_prev_sample, state)

									return FlaxDDPMSchedulerOutput(prev_sample=UpperCamelCase_   ,						state=UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     int   ,						UpperCamelCase_:     DDPMSchedulerState   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     jnp.ndarray   ,						)      ->       jnp.ndarray:





									"""simple docstring"""







									return add_noise_common(state.common   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     List[str]   ,						UpperCamelCase_:     DDPMSchedulerState   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     jnp.ndarray   ,						)      ->       jnp.ndarray:





									"""simple docstring"""







									return get_velocity_common(state.common   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_							)


							def __len__(							self:     str							)      ->       List[Any]:





									"""simple docstring"""







									return self.config.num_train_timesteps


