





import argparse
import collections
import json
from pathlib import Path

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

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_    =   logging.get_logger(__name__)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Dict:






				"""simple docstring"""


				print('''Loading config file...'''			)

				def flatten_yaml_as_dict(_UpperCamelCase			,    _UpperCamelCase=""			,    _UpperCamelCase="."			):
								snake_case_						:   Any           =  []
								for k, v in d.items():
												snake_case_						:   Optional[int]           =  parent_key + sep + k if parent_key else k
												if isinstance(_UpperCamelCase			,    collections.abc.MutableMapping			):
																items.extend(flatten_yaml_as_dict(_UpperCamelCase			,    _UpperCamelCase			,    sep=_UpperCamelCase			).items()			)
												else:
																items.append((new_key, v)			)
								return dict(_UpperCamelCase			)

				snake_case_						:   List[str]           =  argparse.Namespace()
				with open(_UpperCamelCase			,    '''r'''			) as yaml_file:
								try:
												snake_case_						:   Union[str, Any]           =  yaml.load(_UpperCamelCase			,    Loader=yaml.FullLoader			)

												snake_case_						:   List[str]           =  flatten_yaml_as_dict(_UpperCamelCase			)
												for k, v in flat_cfg.items():
																setattr(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)
								except yaml.YAMLError as exc:
												logger.error('''Error while loading config file: {}. Error message: {}'''.format(_UpperCamelCase			,    str(_UpperCamelCase			)			)			)
				return config






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						List[Any]:






				"""simple docstring"""


				snake_case_						:   List[Any]           =  MobileViTVaConfig()

				snake_case_						:   List[str]           =  False

				# dataset
				if task_name.startswith('''imagenet1k_'''			):
								snake_case_						:   int           =  1_000
								if int(task_name.strip().split('''_'''			)[-1]			) == 384:
												snake_case_						:   Union[str, Any]           =  384
								else:
												snake_case_						:   Optional[Any]           =  256
								snake_case_						:   List[Any]           =  'imagenet-1k-id2label.json'
				elif task_name.startswith('''imagenet21k_to_1k_'''			):
								snake_case_						:   List[str]           =  21_000
								if int(task_name.strip().split('''_'''			)[-1]			) == 384:
												snake_case_						:   Any           =  384
								else:
												snake_case_						:   Optional[int]           =  256
								snake_case_						:   List[str]           =  'imagenet-22k-id2label.json'
				elif task_name.startswith('''ade20k_'''			):
								snake_case_						:   Any           =  151
								snake_case_						:   int           =  512
								snake_case_						:   Any           =  'ade20k-id2label.json'
								snake_case_						:   Dict           =  True
				elif task_name.startswith('''voc_'''			):
								snake_case_						:   Any           =  21
								snake_case_						:   Optional[int]           =  512
								snake_case_						:   Any           =  'pascal-voc-id2label.json'
								snake_case_						:   Dict           =  True

				# orig_config
				snake_case_						:   Tuple           =  load_orig_config_file(_UpperCamelCase			)
				assert getattr(_UpperCamelCase			,    '''model.classification.name'''			,    -1			) == "mobilevit_v2", "Invalid model"
				snake_case_						:   List[str]           =  getattr(_UpperCamelCase			,    '''model.classification.mitv2.width_multiplier'''			,    1.0			)
				assert (
				    getattr(_UpperCamelCase			,    '''model.classification.mitv2.attn_norm_layer'''			,    -1			) == "layer_norm_2d"
				), "Norm layers other than layer_norm_2d is not supported"
				snake_case_						:   str           =  getattr(_UpperCamelCase			,    '''model.classification.activation.name'''			,    '''swish'''			)
				# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

				if is_segmentation_model:
								snake_case_						:   str           =  getattr(_UpperCamelCase			,    '''model.segmentation.output_stride'''			,    16			)
								if "_deeplabv3" in task_name:
												snake_case_						:   Union[str, Any]           =  getattr(_UpperCamelCase			,    '''model.segmentation.deeplabv3.aspp_rates'''			,    [12, 24, 36]			)
												snake_case_						:   Tuple           =  getattr(_UpperCamelCase			,    '''model.segmentation.deeplabv3.aspp_out_channels'''			,    512			)
												snake_case_						:   Tuple           =  getattr(_UpperCamelCase			,    '''model.segmentation.deeplabv3.aspp_dropout'''			,    0.1			)

												 # id2label
				snake_case_						:   Dict           =  'huggingface/label-files'
				snake_case_						:   Optional[Any]           =  json.load(open(hf_hub_download(_UpperCamelCase			,    _UpperCamelCase			,    repo_type='''dataset'''			)			,    '''r'''			)			)
				snake_case_						:   Dict           =  {int(_UpperCamelCase			): v for k, v in idalabel.items()}
				snake_case_						:   int           =  idalabel
				snake_case_						:   Dict           =  {v: k for k, v in idalabel.items()}

				return config






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Dict:






				"""simple docstring"""


				snake_case_						:   Optional[int]           =  dct.pop(_UpperCamelCase			)
				snake_case_						:   Tuple           =  val






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=False			)		->						List[Any]:






				"""simple docstring"""


				if base_model:
								snake_case_						:   int           =  ''
				else:
								snake_case_						:   Any           =  'mobilevitv2.'

				snake_case_						:   Union[str, Any]           =  []
				for k in state_dict.keys():
								if k[:8] == "encoder.":
												snake_case_						:   Optional[Any]           =  k[8:]
								else:
												snake_case_						:   List[Any]           =  k

								if ".block." in k:
												snake_case_						:   Optional[int]           =  k_new.replace('''.block.'''			,    '''.'''			)
								if ".conv." in k:
												snake_case_						:   List[Any]           =  k_new.replace('''.conv.'''			,    '''.convolution.'''			)
								if ".norm." in k:
												snake_case_						:   List[str]           =  k_new.replace('''.norm.'''			,    '''.normalization.'''			)

								if "conv_1." in k:
												snake_case_						:   int           =  k_new.replace('''conv_1.'''			,    f'''{model_prefix}conv_stem.'''			)
								for i in [1, 2]:
												if f'''layer_{i}.''' in k:
																snake_case_						:   Optional[Any]           =  k_new.replace(f'''layer_{i}.'''			,    f'''{model_prefix}encoder.layer.{i-1}.layer.'''			)
								if ".exp_1x1." in k:
												snake_case_						:   int           =  k_new.replace('''.exp_1x1.'''			,    '''.expand_1x1.'''			)
								if ".red_1x1." in k:
												snake_case_						:   List[str]           =  k_new.replace('''.red_1x1.'''			,    '''.reduce_1x1.'''			)

								for i in [3, 4, 5]:
												if f'''layer_{i}.0.''' in k:
																snake_case_						:   int           =  k_new.replace(f'''layer_{i}.0.'''			,    f'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.'''			)
												if f'''layer_{i}.1.local_rep.0.''' in k:
																snake_case_						:   Dict           =  k_new.replace(f'''layer_{i}.1.local_rep.0.'''			,    f'''{model_prefix}encoder.layer.{i-1}.conv_kxk.'''			)
												if f'''layer_{i}.1.local_rep.1.''' in k:
																snake_case_						:   List[Any]           =  k_new.replace(f'''layer_{i}.1.local_rep.1.'''			,    f'''{model_prefix}encoder.layer.{i-1}.conv_1x1.'''			)

								for i in [3, 4, 5]:
												if i == 3:
																snake_case_						:   str           =  [0, 1]
												elif i == 4:
																snake_case_						:   Optional[Any]           =  [0, 1, 2, 3]
												elif i == 5:
																snake_case_						:   Union[str, Any]           =  [0, 1, 2]

												for j in j_in:
																if f'''layer_{i}.1.global_rep.{j}.''' in k:
																				snake_case_						:   Dict           =  k_new.replace(
																				    f'''layer_{i}.1.global_rep.{j}.'''			,    f'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.'''			)
												if f'''layer_{i}.1.global_rep.{j+1}.''' in k:
																snake_case_						:   Tuple           =  k_new.replace(
																    f'''layer_{i}.1.global_rep.{j+1}.'''			,    f'''{model_prefix}encoder.layer.{i-1}.layernorm.'''			)

												if f'''layer_{i}.1.conv_proj.''' in k:
																snake_case_						:   List[str]           =  k_new.replace(f'''layer_{i}.1.conv_proj.'''			,    f'''{model_prefix}encoder.layer.{i-1}.conv_projection.'''			)

								if "pre_norm_attn.0." in k:
												snake_case_						:   List[str]           =  k_new.replace('''pre_norm_attn.0.'''			,    '''layernorm_before.'''			)
								if "pre_norm_attn.1." in k:
												snake_case_						:   List[Any]           =  k_new.replace('''pre_norm_attn.1.'''			,    '''attention.'''			)
								if "pre_norm_ffn.0." in k:
												snake_case_						:   Dict           =  k_new.replace('''pre_norm_ffn.0.'''			,    '''layernorm_after.'''			)
								if "pre_norm_ffn.1." in k:
												snake_case_						:   Union[str, Any]           =  k_new.replace('''pre_norm_ffn.1.'''			,    '''ffn.conv1.'''			)
								if "pre_norm_ffn.3." in k:
												snake_case_						:   Union[str, Any]           =  k_new.replace('''pre_norm_ffn.3.'''			,    '''ffn.conv2.'''			)

								if "classifier.1." in k:
												snake_case_						:   Dict           =  k_new.replace('''classifier.1.'''			,    '''classifier.'''			)

								if "seg_head." in k:
												snake_case_						:   Any           =  k_new.replace('''seg_head.'''			,    '''segmentation_head.'''			)
								if ".aspp_layer." in k:
												snake_case_						:   Union[str, Any]           =  k_new.replace('''.aspp_layer.'''			,    '''.'''			)
								if ".aspp_pool." in k:
												snake_case_						:   List[str]           =  k_new.replace('''.aspp_pool.'''			,    '''.'''			)

								rename_keys.append((k, k_new)			)
				return rename_keys






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[int]:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  []
				for k in state_dict.keys():
								if k.startswith('''seg_head.aux_head.'''			):
												keys_to_ignore.append(_UpperCamelCase			)
				for k in keys_to_ignore:
								state_dict.pop(_UpperCamelCase			,    _UpperCamelCase			)






def 					lowerCamelCase_						(    )		->						Optional[Any]:






				"""simple docstring"""


				snake_case_						:   str           =  'http://images.cocodataset.org/val2017/000000039769.jpg'
				# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
				snake_case_						:   List[Any]           =  Image.open(requests.get(_UpperCamelCase			,    stream=_UpperCamelCase			).raw			)
				return im






@torch.no_grad()
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Optional[int]:






				"""simple docstring"""


				snake_case_						:   List[Any]           =  get_mobilevitva_config(_UpperCamelCase			,    _UpperCamelCase			)

				# load original state_dict
				snake_case_						:   Dict           =  torch.load(_UpperCamelCase			,    map_location='''cpu'''			)

				# load huggingface model
				if task_name.startswith('''ade20k_'''			) or task_name.startswith('''voc_'''			):
								snake_case_						:   List[str]           =  MobileViTVaForSemanticSegmentation(_UpperCamelCase			).eval()
								snake_case_						:   List[Any]           =  False
				else:
								snake_case_						:   List[str]           =  MobileViTVaForImageClassification(_UpperCamelCase			).eval()
								snake_case_						:   Union[str, Any]           =  False

				# remove and rename some keys of load the original model
				snake_case_						:   str           =  checkpoint
				remove_unused_keys(_UpperCamelCase			)
				snake_case_						:   Tuple           =  create_rename_keys(_UpperCamelCase			,    base_model=_UpperCamelCase			)
				for rename_key_src, rename_key_dest in rename_keys:
								rename_key(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)

				# load modified state_dict
				model.load_state_dict(_UpperCamelCase			)

				# Check outputs on an image, prepared by MobileViTImageProcessor
				snake_case_						:   int           =  MobileViTImageProcessor(crop_size=config.image_size			,    size=config.image_size + 32			)
				snake_case_						:   int           =  image_processor(images=prepare_img()			,    return_tensors='''pt'''			)
				snake_case_						:   Any           =  model(**_UpperCamelCase			)

				# verify classification model
				if task_name.startswith('''imagenet'''			):
								snake_case_						:   List[Any]           =  outputs.logits
								snake_case_						:   List[Any]           =  logits.argmax(-1			).item()
								print('''Predicted class:'''			,    model.config.idalabel[predicted_class_idx]			)
								if task_name.startswith('''imagenet1k_256'''			) and config.width_multiplier == 1.0:
												# expected_logits for base variant
												snake_case_						:   int           =  torch.tensor([-1.6336E00, -7.3204E-02, -5.1883E-01]			)
												assert torch.allclose(logits[0, :3]			,    _UpperCamelCase			,    atol=1E-4			)

				Path(_UpperCamelCase			).mkdir(exist_ok=_UpperCamelCase			)
				print(f'''Saving model {task_name} to {pytorch_dump_folder_path}'''			)
				model.save_pretrained(_UpperCamelCase			)
				print(f'''Saving image processor to {pytorch_dump_folder_path}'''			)
				image_processor.save_pretrained(_UpperCamelCase			)


if __name__ == "__main__":
	lowerCAmelCase_    =   argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--task''',
	    default='''imagenet1k_256''',
	    type=str,
	    help=(
	        '''Name of the task for which the MobileViTV2 model you\'d like to convert is trained on . '''
	        '''\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            '''
	    ),
	    choices=[
	        '''imagenet1k_256''',
	        '''imagenet1k_384''',
	        '''imagenet21k_to_1k_256''',
	        '''imagenet21k_to_1k_384''',
	        '''ade20k_deeplabv3''',
	        '''voc_deeplabv3''',
	    ],
	)

	parser.add_argument(
	    '''--orig_checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
	)
	parser.add_argument('''--orig_config_path''', required=True, type=str, help='''Path to the original config file.''')
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
	)

	lowerCAmelCase_    =   parser.parse_args()
	convert_mobilevitva_checkpoint(
	    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
	)







# 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  __lowerCAmelCase	:
			lowerCamelCase_					:					CommonSchedulerState

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






			@classmethod
			def       lowerCamelCase  (cls ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       Dict:


							'''simple docstring'''



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




@dataclass
class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					DDPMSchedulerState




class  __lowerCAmelCase	(      _a,					_a					):
			lowerCamelCase_					:					List[Any]	      =				[e.name for e in FlaxKarrasDiffusionSchedulers]

			lowerCamelCase_					:					jnp.dtype






			@property
			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							return True






			@register_to_config
			def __init__(self ,							__magic_name__ = 1000 ,							__magic_name__ = 0.0_001 ,							__magic_name__ = 0.02 ,							__magic_name__ = "linear" ,							__magic_name__ = None ,							__magic_name__ = "fixed_small" ,							__magic_name__ = True ,							__magic_name__ = "epsilon" ,							__magic_name__ = jnp.floataa ,							)					->       List[str]:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  dtype






			def       lowerCamelCase  (self ,							__magic_name__ = None   )					->       DDPMSchedulerState:


							'''simple docstring'''



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

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

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

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






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = None   )					->       jnp.ndarray:


							'''simple docstring'''



							return sample






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = ()   )					->       DDPMSchedulerState:


							'''simple docstring'''



							snake_case_						:   str           =  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
							snake_case_						:   List[str]           =  (jnp.arange(0 ,							__magic_name__   ) * step_ratio).round()[::-1]

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






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=None ,							__magic_name__=None   )					->       int:


							'''simple docstring'''



							snake_case_						:   Any           =  state.common.alphas_cumprod[t]
							snake_case_						:   Dict           =  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
							snake_case_						:   Tuple           =  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

							if variance_type is None:
											snake_case_						:   Any           =  self.config.variance_type

							# hacks - were probably added for training stability
							if variance_type == "fixed_small":
											snake_case_						:   int           =  jnp.clip(__magic_name__ ,							a_min=1e-20   )
							# for rl-diffuser https://arxiv.org/abs/2205.09991
							elif variance_type == "fixed_small_log":
											snake_case_						:   List[str]           =  jnp.log(jnp.clip(__magic_name__ ,							a_min=1e-20   )   )
							elif variance_type == "fixed_large":
											snake_case_						:   str           =  state.common.betas[t]
							elif variance_type == "fixed_large_log":
											# Glide max_log
											snake_case_						:   int           =  jnp.log(state.common.betas[t]   )
							elif variance_type == "learned":
											return predicted_variance
							elif variance_type == "learned_range":
											snake_case_						:   str           =  variance
											snake_case_						:   Optional[Any]           =  state.common.betas[t]
											snake_case_						:   Optional[Any]           =  (predicted_variance + 1) / 2
											snake_case_						:   Optional[Any]           =  frac * max_log + (1 - frac) * min_log

							return variance






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = None ,							__magic_name__ = True ,							)					->       Union[FlaxDDPMSchedulerOutput, Tuple]:


							'''simple docstring'''



							snake_case_						:   Tuple           =  timestep

							if key is None:
											snake_case_						:   List[Any]           =  jax.random.PRNGKey(0   )

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

							# 1. compute alphas, betas
							snake_case_						:   List[Any]           =  state.common.alphas_cumprod[t]
							snake_case_						:   Union[str, Any]           =  jnp.where(t > 0 ,							state.common.alphas_cumprod[t - 1] ,							jnp.array(1.0 ,							dtype=self.dtype   )   )
							snake_case_						:   List[Any]           =  1 - alpha_prod_t
							snake_case_						:   str           =  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":
											snake_case_						:   int           =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif self.config.prediction_type == "sample":
											snake_case_						:   List[Any]           =  model_output
							elif self.config.prediction_type == "v_prediction":
											snake_case_						:   Optional[Any]           =  (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:
											snake_case_						:   Optional[int]           =  jnp.clip(__magic_name__ ,							-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
							snake_case_						:   str           =  (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
							snake_case_						:   Dict           =  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
							snake_case_						:   Optional[Any]           =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							def random_variance():
											snake_case_						:   List[Any]           =  jax.random.split(__magic_name__ ,							num=1   )
											snake_case_						:   Tuple           =  jax.random.normal(__magic_name__ ,							shape=model_output.shape ,							dtype=self.dtype   )
											return (self._get_variance(__magic_name__ ,							__magic_name__ ,							predicted_variance=__magic_name__   ) ** 0.5) * noise

							snake_case_						:   List[str]           =  jnp.where(t > 0 ,							random_variance() ,							jnp.zeros(model_output.shape ,							dtype=self.dtype   )   )

							snake_case_						:   Any           =  pred_prev_sample + variance

							if not return_dict:
											return (pred_prev_sample, state)

							return FlaxDDPMSchedulerOutput(prev_sample=__magic_name__ ,							state=__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							)					->       jnp.ndarray:


							'''simple docstring'''



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






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							)					->       jnp.ndarray:


							'''simple docstring'''



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






			def __len__(self   )					->       int:


							'''simple docstring'''



							return self.config.num_train_timesteps
