



import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase=False      ,					_UpperCamelCase=False      ,					_UpperCamelCase="data2vec."			):
				_UpperCAmelCase						:  List[Any]     					=	'''backbone.''' if is_semantic else ''''''

				_UpperCAmelCase						:  Optional[Any]     					=	[]
				for i in range(config.num_hidden_layers			):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append(
								    (F'''{prefix}blocks.{i}.norm1.weight''', F'''{hf_prefix}encoder.layer.{i}.layernorm_before.weight''')			)
								rename_keys.append((F'''{prefix}blocks.{i}.norm1.bias''', F'''{hf_prefix}encoder.layer.{i}.layernorm_before.bias''')			)
								rename_keys.append(
								    (F'''{prefix}blocks.{i}.attn.proj.weight''', F'''{hf_prefix}encoder.layer.{i}.attention.output.dense.weight''')			)
								rename_keys.append(
								    (F'''{prefix}blocks.{i}.attn.proj.bias''', F'''{hf_prefix}encoder.layer.{i}.attention.output.dense.bias''')			)
								rename_keys.append(
								    (F'''{prefix}blocks.{i}.norm2.weight''', F'''{hf_prefix}encoder.layer.{i}.layernorm_after.weight''')			)
								rename_keys.append((F'''{prefix}blocks.{i}.norm2.bias''', F'''{hf_prefix}encoder.layer.{i}.layernorm_after.bias''')			)
								rename_keys.append(
								    (F'''{prefix}blocks.{i}.mlp.fc1.weight''', F'''{hf_prefix}encoder.layer.{i}.intermediate.dense.weight''')			)
								rename_keys.append(
								    (F'''{prefix}blocks.{i}.mlp.fc1.bias''', F'''{hf_prefix}encoder.layer.{i}.intermediate.dense.bias''')			)
								rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.weight''', F'''{hf_prefix}encoder.layer.{i}.output.dense.weight''')			)
								rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.bias''', F'''{hf_prefix}encoder.layer.{i}.output.dense.bias''')			)

				# projection layer + position embeddings
				rename_keys.extend(
				    [
				        (F'''{prefix}cls_token''', F'''{hf_prefix}embeddings.cls_token'''),
				        (F'''{prefix}patch_embed.proj.weight''', F'''{hf_prefix}embeddings.patch_embeddings.projection.weight'''),
				        (F'''{prefix}patch_embed.proj.bias''', F'''{hf_prefix}embeddings.patch_embeddings.projection.bias'''),
				    ]			)

				if has_lm_head:
								# mask token + shared relative position bias + layernorm
								rename_keys.extend(
								    [
								        ('''mask_token''', F'''{hf_prefix}embeddings.mask_token'''),
								        (
								            '''rel_pos_bias.relative_position_bias_table''',
								            F'''{hf_prefix}encoder.relative_position_bias.relative_position_bias_table''',
								        ),
								        (
								            '''rel_pos_bias.relative_position_index''',
								            F'''{hf_prefix}encoder.relative_position_bias.relative_position_index''',
								        ),
								        ('''norm.weight''', '''layernorm.weight'''),
								        ('''norm.bias''', '''layernorm.bias'''),
								    ]			)
				elif is_semantic:
								# semantic segmentation classification heads
								rename_keys.extend(
								    [
								        ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
								        ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
								        ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
								        ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
								    ]			)
				else:
								# layernorm + classification head
								rename_keys.extend(
								    [
								        ('''fc_norm.weight''', F'''{hf_prefix}pooler.layernorm.weight'''),
								        ('''fc_norm.bias''', F'''{hf_prefix}pooler.layernorm.bias'''),
								        ('''head.weight''', '''classifier.weight'''),
								        ('''head.bias''', '''classifier.bias'''),
								    ]			)

				return rename_keys


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase=False      ,					_UpperCamelCase=False      ,					_UpperCamelCase="data2vec_vision."			):
				for i in range(config.num_hidden_layers			):
								_UpperCAmelCase						:  int     					=	'''backbone.''' if is_semantic else ''''''
								# queries, keys and values
								_UpperCAmelCase						:  int     					=	state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''			)
								_UpperCAmelCase						:  Tuple     					=	state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''			)
								_UpperCAmelCase						:  str     					=	state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''			)

								_UpperCAmelCase						:  Any     					=	in_proj_weight[
								    : config.hidden_size, :
								]
								_UpperCAmelCase						:  Optional[Any]     					=	q_bias
								_UpperCAmelCase						:  str     					=	in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								_UpperCAmelCase						:  int     					=	in_proj_weight[
								    -config.hidden_size :, :
								]
								_UpperCAmelCase						:  Optional[Any]     					=	v_bias

								# gamma_1 and gamma_2
								# we call them lambda because otherwise they are renamed when using .from_pretrained
								_UpperCAmelCase						:  str     					=	state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''			)
								_UpperCAmelCase						:  Optional[int]     					=	state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''			)

								_UpperCAmelCase						:  Optional[int]     					=	gamma_a
								_UpperCAmelCase						:  str     					=	gamma_a

								# relative_position bias table + index
								if not has_lm_head:
												# each layer has its own relative position bias
												_UpperCAmelCase						:  str     					=	state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_bias_table'''			)
												_UpperCAmelCase						:  Dict     					=	state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_index'''			)

												_UpperCAmelCase						:  int     					=	table
												_UpperCAmelCase						:  Dict     					=	index


def 	_A	(   ):
				_UpperCAmelCase						:  List[Any]     					=	argparse.ArgumentParser(
				    '''Convert Data2VecVision to HF for image classification and pretraining'''      ,					add_help=_UpperCamelCase			)
				parser.add_argument('''--hf_checkpoint_name'''      ,					type=_UpperCamelCase			)
				parser.add_argument('''--input_size'''      ,					default=224      ,					type=_UpperCamelCase      ,					help='''images input size'''			)
				parser.add_argument('''--beit_checkpoint'''      ,					default=''''''      ,					help='''beit checkpoint'''			)

				return parser.parse_args()


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase			):
				def load_state_dict(_UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase=""      ,					_UpperCamelCase="relative_position_index"			):
								_UpperCAmelCase						:  Any     					=	[]
								_UpperCAmelCase						:  Union[str, Any]     					=	[]
								_UpperCAmelCase						:  Union[str, Any]     					=	[]
								# copy state_dict so _load_from_state_dict can modify it
								_UpperCAmelCase						:  List[Any]     					=	getattr(_UpperCamelCase      ,					'''_metadata'''      ,					_UpperCamelCase			)
								_UpperCAmelCase						:  Union[str, Any]     					=	state_dict.copy()
								if metadata is not None:
												_UpperCAmelCase						:  Optional[int]     					=	metadata

								def load(_UpperCamelCase      ,					_UpperCamelCase=""			):
												_UpperCAmelCase						:  Optional[int]     					=	{} if metadata is None else metadata.get(prefix[:-1]      ,					{}			)
												module._load_from_state_dict(
												    _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase			)
												for name, child in module._modules.items():
																if child is not None:
																				load(_UpperCamelCase      ,					prefix + name + '''.'''			)

								load(_UpperCamelCase      ,					prefix=_UpperCamelCase			)

								_UpperCAmelCase						:  int     					=	[]
								_UpperCAmelCase						:  Optional[Any]     					=	[]
								for key in missing_keys:
												_UpperCAmelCase						:  Optional[Any]     					=	True
												for ignore_key in ignore_missing.split('''|'''			):
																if ignore_key in key:
																				_UpperCAmelCase						:  Union[str, Any]     					=	False
																				break
												if keep_flag:
																warn_missing_keys.append(_UpperCamelCase			)
												else:
																ignore_missing_keys.append(_UpperCamelCase			)

								_UpperCAmelCase						:  List[Any]     					=	warn_missing_keys

								if len(_UpperCamelCase			) > 0:
												print(
												    '''Weights of {} not initialized from pretrained model: {}'''.format(
												        model.__class__.__name__      ,					_UpperCamelCase			)			)
								if len(_UpperCamelCase			) > 0:
												print('''Weights from pretrained model not used in {}: {}'''.format(model.__class__.__name__      ,					_UpperCamelCase			)			)
								if len(_UpperCamelCase			) > 0:
												print(
												    '''Ignored weights of {} not initialized from pretrained model: {}'''.format(
												        model.__class__.__name__      ,					_UpperCamelCase			)			)
								if len(_UpperCamelCase			) > 0:
												print('''\n'''.join(_UpperCamelCase			)			)

				_UpperCAmelCase						:  Optional[int]     					=	{
				    '''pretrained''': False,
				    '''use_shared_rel_pos_bias''': True,
				    '''use_abs_pos_emb''': False,
				    '''init_values''': 0.1,
				}

				if is_finetuned:
								model_kwargs.update(
								    {
								        '''num_classes''': 1_000,
								        '''use_mean_pooling''': True,
								        '''init_scale''': 0.001,
								        '''use_rel_pos_bias''': True,
								    }			)

				_UpperCAmelCase						:  List[Any]     					=	create_model(
				    '''beit_large_patch16_224''' if is_large else '''beit_base_patch16_224'''      ,					**_UpperCamelCase      ,					)
				_UpperCAmelCase						:  Union[str, Any]     					=	model.patch_embed.patch_size
				_UpperCAmelCase						:  int     					=	(args.input_size // patch_size[0], args.input_size // patch_size[1])
				_UpperCAmelCase						:  Dict     					=	torch.load(args.beit_checkpoint      ,					map_location='''cpu'''			)

				print(F'''Load ckpt from {args.beit_checkpoint}'''			)
				_UpperCAmelCase						:  Optional[int]     					=	None
				for model_key in ("model", "module"):
								if model_key in checkpoint:
												_UpperCAmelCase						:  List[str]     					=	checkpoint[model_key]
												print(F'''Load state_dict by model_key = {model_key}'''			)
												break

				_UpperCAmelCase						:  Optional[Any]     					=	list(checkpoint_model.keys()			)
				for key in all_keys:
								if "relative_position_index" in key:
												checkpoint_model.pop(_UpperCamelCase			)

								if "relative_position_bias_table" in key:
												_UpperCAmelCase						:  int     					=	checkpoint_model[key]
												_UpperCAmelCase					, _UpperCAmelCase						:  Union[str, Any]     					=	rel_pos_bias.size()
												_UpperCAmelCase					, _UpperCAmelCase						:  List[Any]     					=	model.state_dict()[key].size()
												_UpperCAmelCase						:  Any     					=	model.patch_embed.patch_shape
												if dst_patch_shape[0] != dst_patch_shape[1]:
																raise NotImplementedError()

				load_state_dict(_UpperCamelCase      ,					_UpperCamelCase      ,					prefix=''''''			)

				return model


def 	_A	(   ):
				_UpperCAmelCase						:  List[Any]     					=	get_args()

				_UpperCAmelCase						:  Optional[Any]     					=	'''ft1k''' in args.hf_checkpoint_name
				_UpperCAmelCase						:  Union[str, Any]     					=	'''large''' in args.hf_checkpoint_name

				if is_finetuned:
								# To convert Beit's data2vec_vision to HF you need to copy
								# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
								# into this folder.
								import modeling_finetune  # noqa: F401
				else:
								# To convert Beit's data2vec_vision to HF you need to copy
								# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
								# into this folder
								# IMPORTANT: Note that for now we've only converted the down-stream
								# model and not the full pretrained model. This means for the integration
								# test you need to add a `return x` after the following line:
								# https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
								# to make the integration test pass.
								import modeling_cyclical  # noqa: F401

				# 1. Create model config
				_UpperCAmelCase						:  Tuple     					=	DataaVecVisionConfig()
				if is_finetuned:
								_UpperCAmelCase						:  int     					=	True
								_UpperCAmelCase						:  List[Any]     					=	False
								_UpperCAmelCase						:  str     					=	True
								_UpperCAmelCase						:  Tuple     					=	1_000

								_UpperCAmelCase						:  Union[str, Any]     					=	'''huggingface/label-files'''
								_UpperCAmelCase						:  List[Any]     					=	'''imagenet-1k-id2label.json'''
								_UpperCAmelCase						:  str     					=	json.load(open(hf_hub_download(_UpperCamelCase      ,					_UpperCamelCase      ,					repo_type='''dataset'''			)      ,					'''r'''			)			)
								_UpperCAmelCase						:  Optional[int]     					=	{int(_UpperCamelCase			): v for k, v in idalabel.items()}
								_UpperCAmelCase						:  int     					=	idalabel
								_UpperCAmelCase						:  List[str]     					=	{v: k for k, v in idalabel.items()}
				else:
								_UpperCAmelCase						:  List[Any]     					=	False
								_UpperCAmelCase						:  Optional[Any]     					=	True
								_UpperCAmelCase						:  Any     					=	False

				if is_large:
								_UpperCAmelCase						:  Union[str, Any]     					=	1_024
								_UpperCAmelCase						:  Optional[Any]     					=	4_096
								_UpperCAmelCase						:  str     					=	24
								_UpperCAmelCase						:  Optional[Any]     					=	16

				# 2. Load Beit model
				_UpperCAmelCase						:  List[Any]     					=	load_beit_model(_UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase			)
				orig_model.eval()

				# 3. Forward Beit model
				_UpperCAmelCase						:  str     					=	BeitImageProcessor(size=config.image_size      ,					do_center_crop=_UpperCamelCase			)
				_UpperCAmelCase						:  Optional[int]     					=	Image.open('''../../../../tests/fixtures/tests_samples/COCO/000000039769.png'''			)
				_UpperCAmelCase						:  List[str]     					=	image_processor(images=_UpperCamelCase      ,					return_tensors='''pt'''			)
				_UpperCAmelCase						:  List[str]     					=	encoding['''pixel_values''']

				_UpperCAmelCase						:  Any     					=	(pixel_values,) if is_finetuned else (pixel_values, None)
				with torch.no_grad():
								_UpperCAmelCase						:  Dict     					=	orig_model(*_UpperCamelCase			)

				# 4. Load HF Data2VecVision model
				if is_finetuned:
								_UpperCAmelCase						:  str     					=	DataaVecVisionForImageClassification(_UpperCamelCase			)
								hf_model.eval()
								_UpperCAmelCase						:  str     					=	False
								_UpperCAmelCase						:  int     					=	'''data2vec_vision.'''
				else:
								_UpperCAmelCase						:  int     					=	DataaVecVisionModel(_UpperCamelCase			)
								hf_model.eval()
								_UpperCAmelCase						:  Any     					=	True
								_UpperCAmelCase						:  Tuple     					=	''''''

				_UpperCAmelCase						:  str     					=	create_rename_keys(_UpperCamelCase      ,					hf_prefix=_UpperCamelCase      ,					has_lm_head=_UpperCamelCase			)
				_UpperCAmelCase						:  Tuple     					=	orig_model.state_dict()
				for src, dest in rename_keys:
								_UpperCAmelCase						:  Tuple     					=	state_dict.pop(_UpperCamelCase			)
								_UpperCAmelCase						:  Tuple     					=	val

				read_in_q_k_v(_UpperCamelCase      ,					_UpperCamelCase      ,					hf_prefix=_UpperCamelCase      ,					has_lm_head=_UpperCamelCase			)
				_UpperCAmelCase					, _UpperCAmelCase						:  Dict     					=	hf_model.load_state_dict(_UpperCamelCase      ,					strict=_UpperCamelCase			)
				print('''HF missing'''      ,					_UpperCamelCase			)
				print('''HF unexpected_keys'''      ,					_UpperCamelCase			)

				# 5. Forward HF Data2VecVision model
				with torch.no_grad():
								_UpperCAmelCase						:  Dict     					=	hf_model(_UpperCamelCase			)

				_UpperCAmelCase						:  Any     					=	hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

				# 6. Compare
				_UpperCAmelCase						:  List[str]     					=	torch.max(torch.abs(hf_output - orig_model_output			)			).item()

				print(F'''max_absolute_diff = {max_absolute_diff}'''			)
				_UpperCAmelCase						:  Optional[Any]     					=	torch.allclose(_UpperCamelCase      ,					_UpperCamelCase      ,					atol=1e-3			)
				print('''Do both models output the same tensors?'''      ,					'''🔥''' if success else '''💩'''			)
				if not success:
								raise Exception('''Something went wRoNg'''			)

				# 7. Save
				print(F'''Saving to {args.hf_checkpoint_name}'''			)
				hf_model.save_pretrained(args.hf_checkpoint_name			)
				image_processor.save_pretrained(args.hf_checkpoint_name			)


if __name__ == "__main__":
					main()
					# Run the following to convert checkpoints
					#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
					#          --beit_checkpoint ./pretrained_base.pt \
					#          --hf_checkpoint_name "./data2vec-vision-base"
					#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
					#          --beit_checkpoint ./finetuned_base.pt \
					#          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
					#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
					#          --beit_checkpoint ./pretrained_large.pt \
					#          --hf_checkpoint_name "./data2vec-vision-large"
					#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
					#          --beit_checkpoint ./finetuned_large.pt \
					#          --hf_checkpoint_name "./data2vec-vision-large-ft1k"





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 							lowerCAmelCase_		:
		SCREAMING_SNAKE_CASE_       :			CommonSchedulerState

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

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







		@classmethod
		def 					a_  (    cls  :   List[Any]       ,		UpperCAmelCase_  :   CommonSchedulerState       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   jnp.ndarray					)       ->		Optional[Any]:


						'''simple docstring'''







						return cls(common=UpperCAmelCase_       ,		init_noise_sigma=UpperCAmelCase_       ,		timesteps=UpperCAmelCase_       ,		sigmas=UpperCAmelCase_					)





@dataclass
class 							lowerCAmelCase_		(		lowercase_     ):
		SCREAMING_SNAKE_CASE_       :			LMSDiscreteSchedulerState





class 							lowerCAmelCase_		(		lowercase_							,		lowercase_     ):
		SCREAMING_SNAKE_CASE_       :			Union[str, Any]					       =	[e.name for e in FlaxKarrasDiffusionSchedulers]

		SCREAMING_SNAKE_CASE_       :			jnp.dtype







		@property
		def 					a_  (    self  :   Union[str, Any]					)       ->		List[Any]:


						'''simple docstring'''







						return True







		@register_to_config
		def __init__(    self  :   Optional[Any]       ,		UpperCAmelCase_  :   int = 1000       ,		UpperCAmelCase_  :   float = 0.0_001       ,		UpperCAmelCase_  :   float = 0.02       ,		UpperCAmelCase_  :   str = "linear"       ,		UpperCAmelCase_  :   Optional[jnp.ndarray] = None       ,		UpperCAmelCase_  :   str = "epsilon"       ,		UpperCAmelCase_  :   jnp.dtype = jnp.floataa       ,		)       ->		List[str]:


						'''simple docstring'''







						_UpperCAmelCase						:  str     					=	dtype







		def 					a_  (    self  :   Tuple       ,		UpperCAmelCase_  :   Optional[CommonSchedulerState] = None					)       ->		LMSDiscreteSchedulerState:


						'''simple docstring'''







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

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

						# standard deviation of the initial noise distribution
						_UpperCAmelCase						:  Dict     					=	sigmas.max()

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







		def 					a_  (    self  :   Optional[Any]       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   int					)       ->		jnp.ndarray:


						'''simple docstring'''







						((_UpperCAmelCase)					, )						:  Optional[Any]     					=	jnp.where(state.timesteps == timestep       ,		size=1					)
						_UpperCAmelCase						:  List[str]     					=	step_index[0]

						_UpperCAmelCase						:  List[Any]     					=	state.sigmas[step_index]
						_UpperCAmelCase						:  Union[str, Any]     					=	sample / ((sigma**2 + 1) ** 0.5)
						return sample







		def 					a_  (    self  :   List[Any]       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   int       ,		UpperCAmelCase_  :   str       ,		UpperCAmelCase_  :   Union[str, Any]					)       ->		Union[str, Any]:


						'''simple docstring'''







						def lms_derivative(UpperCAmelCase_  :   Optional[int]					):
										_UpperCAmelCase						:  Optional[Any]     					=	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

						_UpperCAmelCase						:  Optional[Any]     					=	integrate.quad(UpperCAmelCase_       ,		state.sigmas[t]       ,		state.sigmas[t + 1]       ,		epsrel=1E-4					)[0]

						return integrated_coeff







		def 					a_  (    self  :   Dict       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   int       ,		UpperCAmelCase_  :   Tuple = ()					)       ->		LMSDiscreteSchedulerState:


						'''simple docstring'''







						_UpperCAmelCase						:  List[str]     					=	jnp.linspace(self.config.num_train_timesteps - 1       ,		0       ,		UpperCAmelCase_       ,		dtype=self.dtype					)

						_UpperCAmelCase						:  Any     					=	jnp.floor(UpperCAmelCase_					).astype(jnp.intaa					)
						_UpperCAmelCase						:  Optional[Any]     					=	jnp.ceil(UpperCAmelCase_					).astype(jnp.intaa					)

						_UpperCAmelCase						:  str     					=	jnp.mod(UpperCAmelCase_       ,		1.0					)

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

						_UpperCAmelCase						:  int     					=	timesteps.astype(jnp.intaa					)

						# initial running values
						_UpperCAmelCase						:  List[str]     					=	jnp.zeros((0,) + shape       ,		dtype=self.dtype					)

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







		def 					a_  (    self  :   Tuple       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   int       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   int = 4       ,		UpperCAmelCase_  :   bool = True       ,		)       ->		Union[FlaxLMSSchedulerOutput, Tuple]:


						'''simple docstring'''







						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'''					)

						_UpperCAmelCase						:  List[Any]     					=	state.sigmas[timestep]

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
										_UpperCAmelCase						:  Optional[int]     					=	sample - sigma * model_output
						elif self.config.prediction_type == "v_prediction":
										# * c_out + input * c_skip
										_UpperCAmelCase						:  Tuple     					=	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
						_UpperCAmelCase						:  Tuple     					=	(sample - pred_original_sample) / sigma
						_UpperCAmelCase						:  Tuple     					=	state.replace(derivatives=jnp.append(state.derivatives       ,		UpperCAmelCase_					)					)
						if len(state.derivatives					) > order:
										_UpperCAmelCase						:  Union[str, Any]     					=	state.replace(derivatives=jnp.delete(state.derivatives       ,		0					)					)

						# 3. Compute linear multistep coefficients
						_UpperCAmelCase						:  int     					=	min(timestep + 1       ,		UpperCAmelCase_					)
						_UpperCAmelCase						:  Optional[int]     					=	[self.get_lms_coefficient(UpperCAmelCase_       ,		UpperCAmelCase_       ,		UpperCAmelCase_       ,		UpperCAmelCase_					) for curr_order in range(UpperCAmelCase_					)]

						# 4. Compute previous sample based on the derivatives path
						_UpperCAmelCase						:  int     					=	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 					a_  (    self  :   Union[str, Any]       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   jnp.ndarray       ,		)       ->		jnp.ndarray:


						'''simple docstring'''







						_UpperCAmelCase						:  Union[str, Any]     					=	state.sigmas[timesteps].flatten()
						_UpperCAmelCase						:  Union[str, Any]     					=	broadcast_to_shape_from_left(UpperCAmelCase_       ,		noise.shape					)

						_UpperCAmelCase						:  Any     					=	original_samples + noise * sigma

						return noisy_samples







		def __len__(    self  :   Dict					)       ->		Tuple:


						'''simple docstring'''







						return self.config.num_train_timesteps
