


from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers.models.layoutlm.modeling_tf_layoutlm import (
						    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFLayoutLMForMaskedLM,
						    TFLayoutLMForQuestionAnswering,
						    TFLayoutLMForSequenceClassification,
						    TFLayoutLMForTokenClassification,
						    TFLayoutLMModel,
						)







class  snake_case_     :
					def __init__(   self     :Optional[int]    ,__snake_case     :str    ,__snake_case     :Tuple=13    ,__snake_case     :int=7    ,__snake_case     :Optional[int]=True    ,__snake_case     :List[Any]=True    ,__snake_case     :int=True    ,__snake_case     :int=True    ,__snake_case     :int=99    ,__snake_case     :Optional[Any]=32    ,__snake_case     :int=2    ,__snake_case     :List[Any]=4    ,__snake_case     :int=37    ,__snake_case     :Union[str, Any]="gelu"    ,__snake_case     :int=0.1    ,__snake_case     :Tuple=0.1    ,__snake_case     :Tuple=5_12    ,__snake_case     :List[Any]=16    ,__snake_case     :Union[str, Any]=2    ,__snake_case     :Dict=0.02    ,__snake_case     :List[Any]=3    ,__snake_case     :Tuple=4    ,__snake_case     :int=None    ,__snake_case     :Tuple=10_00    ,)      ->					str:
												a__    =  parent
												a__    =  batch_size
												a__    =  seq_length
												a__    =  is_training
												a__    =  use_input_mask
												a__    =  use_token_type_ids
												a__    =  use_labels
												a__    =  vocab_size
												a__    =  hidden_size
												a__    =  num_hidden_layers
												a__    =  num_attention_heads
												a__    =  intermediate_size
												a__    =  hidden_act
												a__    =  hidden_dropout_prob
												a__    =  attention_probs_dropout_prob
												a__    =  max_position_embeddings
												a__    =  type_vocab_size
												a__    =  type_sequence_label_size
												a__    =  initializer_range
												a__    =  num_labels
												a__    =  num_choices
												a__    =  scope
												a__    =  range_bbox
					def 				lowerCamelCase__(   self     :str   )      ->					Dict:
												a__    =  ids_tensor([self.batch_size, self.seq_length]    ,self.vocab_size   )

												# convert bbox to numpy since TF does not support item assignment
												a__    =  ids_tensor([self.batch_size, self.seq_length, 4]    ,self.range_bbox   ).numpy()
												# Ensure that bbox is legal
												for i in range(bbox.shape[0]   ):
																			for j in range(bbox.shape[1]   ):
																										if bbox[i, j, 3] < bbox[i, j, 1]:
																																	a__    =  bbox[i, j, 3]
																																	a__    =  bbox[i, j, 1]
																																	a__    =  t
																										if bbox[i, j, 2] < bbox[i, j, 0]:
																																	a__    =  bbox[i, j, 2]
																																	a__    =  bbox[i, j, 0]
																																	a__    =  t
												a__    =  tf.convert_to_tensor(UpperCamelCase_   )

												a__    =  None
												if self.use_input_mask:
																			a__    =  random_attention_mask([self.batch_size, self.seq_length]   )

												a__    =  None
												if self.use_token_type_ids:
																			a__    =  ids_tensor([self.batch_size, self.seq_length]    ,self.type_vocab_size   )

												a__    =  None
												a__    =  None
												a__    =  None
												if self.use_labels:
																			a__    =  ids_tensor([self.batch_size]    ,self.type_sequence_label_size   )
																			a__    =  ids_tensor([self.batch_size, self.seq_length]    ,self.num_labels   )
																			a__    =  ids_tensor([self.batch_size]    ,self.num_choices   )

												a__    =  LayoutLMConfig(
												    vocab_size=self.vocab_size    ,hidden_size=self.hidden_size    ,num_hidden_layers=self.num_hidden_layers    ,num_attention_heads=self.num_attention_heads    ,intermediate_size=self.intermediate_size    ,hidden_act=self.hidden_act    ,hidden_dropout_prob=self.hidden_dropout_prob    ,attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,max_position_embeddings=self.max_position_embeddings    ,type_vocab_size=self.type_vocab_size    ,initializer_range=self.initializer_range    ,)

												return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
					def 				lowerCamelCase__(   self     :List[str]    ,__snake_case     :Dict    ,__snake_case     :Optional[int]    ,__snake_case     :Optional[int]    ,__snake_case     :List[str]    ,__snake_case     :List[Any]    ,__snake_case     :int    ,__snake_case     :Any    ,__snake_case     :List[Any]   )      ->					Any:
												a__    =  TFLayoutLMModel(config=UpperCamelCase_   )

												a__    =  model(UpperCamelCase_    ,UpperCamelCase_    ,attention_mask=UpperCamelCase_    ,token_type_ids=UpperCamelCase_   )
												a__    =  model(UpperCamelCase_    ,UpperCamelCase_    ,token_type_ids=UpperCamelCase_   )
												a__    =  model(UpperCamelCase_    ,UpperCamelCase_   )
												self.parent.assertEqual(result.last_hidden_state.shape    ,(self.batch_size, self.seq_length, self.hidden_size)   )
												self.parent.assertEqual(result.pooler_output.shape    ,(self.batch_size, self.hidden_size)   )
					def 				lowerCamelCase__(   self     :List[Any]    ,__snake_case     :List[Any]    ,__snake_case     :Optional[int]    ,__snake_case     :Dict    ,__snake_case     :Optional[int]    ,__snake_case     :List[str]    ,__snake_case     :Dict    ,__snake_case     :List[str]    ,__snake_case     :List[str]   )      ->					Optional[Any]:
												a__    =  TFLayoutLMForMaskedLM(config=UpperCamelCase_   )

												a__    =  model(UpperCamelCase_    ,UpperCamelCase_    ,attention_mask=UpperCamelCase_    ,token_type_ids=UpperCamelCase_    ,labels=UpperCamelCase_   )
												self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.seq_length, self.vocab_size)   )
					def 				lowerCamelCase__(   self     :Optional[Any]    ,__snake_case     :Dict    ,__snake_case     :Optional[Any]    ,__snake_case     :Optional[int]    ,__snake_case     :List[str]    ,__snake_case     :Tuple    ,__snake_case     :str    ,__snake_case     :str    ,__snake_case     :Union[str, Any]   )      ->					Dict:
												a__    =  self.num_labels
												a__    =  TFLayoutLMForSequenceClassification(config=UpperCamelCase_   )

												a__    =  model(UpperCamelCase_    ,UpperCamelCase_    ,attention_mask=UpperCamelCase_    ,token_type_ids=UpperCamelCase_   )
												self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.num_labels)   )
					def 				lowerCamelCase__(   self     :Any    ,__snake_case     :str    ,__snake_case     :List[Any]    ,__snake_case     :Optional[Any]    ,__snake_case     :Dict    ,__snake_case     :Union[str, Any]    ,__snake_case     :str    ,__snake_case     :int    ,__snake_case     :List[Any]   )      ->					Optional[int]:
												a__    =  self.num_labels
												a__    =  TFLayoutLMForTokenClassification(config=UpperCamelCase_   )

												a__    =  model(UpperCamelCase_    ,UpperCamelCase_    ,attention_mask=UpperCamelCase_    ,token_type_ids=UpperCamelCase_    ,labels=UpperCamelCase_   )
												self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.seq_length, self.num_labels)   )
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :Optional[Any]    ,__snake_case     :Any    ,__snake_case     :Dict    ,__snake_case     :Tuple    ,__snake_case     :Dict    ,__snake_case     :Optional[int]    ,__snake_case     :Tuple    ,__snake_case     :str   )      ->					Union[str, Any]:
												a__    =  TFLayoutLMForQuestionAnswering(config=UpperCamelCase_   )

												a__    =  model(UpperCamelCase_    ,UpperCamelCase_    ,attention_mask=UpperCamelCase_    ,token_type_ids=UpperCamelCase_   )
												self.parent.assertEqual(result.start_logits.shape    ,(self.batch_size, self.seq_length)   )
												self.parent.assertEqual(result.end_logits.shape    ,(self.batch_size, self.seq_length)   )



					def 				lowerCamelCase__(   self     :List[str]   )      ->					int:
												a__    =  self.prepare_config_and_inputs()
												(
												    a__    
												)    =  config_and_inputs
												a__    =  {
												    'input_ids': input_ids,
												    'bbox': bbox,
												    'token_type_ids': token_type_ids,
												    'attention_mask': input_mask,
												}
												return config, inputs_dict







@require_tf
class  snake_case_     (lowerCamelCase_			,		lowerCamelCase_			,		unittest.TestCase   ):
					UpperCAmelCase__      :					str					   =    (
					    (
					        TFLayoutLMModel,
					        TFLayoutLMForMaskedLM,
					        TFLayoutLMForTokenClassification,
					        TFLayoutLMForSequenceClassification,
					        TFLayoutLMForQuestionAnswering,
					    )
					    if is_tf_available()
					    else ()
					)
					UpperCAmelCase__      :					Union[str, Any]					   =    (
					    {
					        '''feature-extraction''': TFLayoutLMModel,
					        '''fill-mask''': TFLayoutLMForMaskedLM,
					        '''text-classification''': TFLayoutLMForSequenceClassification,
					        '''token-classification''': TFLayoutLMForTokenClassification,
					        '''zero-shot''': TFLayoutLMForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					UpperCAmelCase__      :					Dict					   =    False
					UpperCAmelCase__      :					List[Any]					   =    True
					UpperCAmelCase__      :					Dict					   =    1_0
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					Union[str, Any]:
												a__    =  TFLayoutLMModelTester(self   )
												a__    =  ConfigTester(self    ,config_class=UpperCamelCase_    ,hidden_size=37   )
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					int:
												self.config_tester.run_common_tests()
					def 				lowerCamelCase__(   self     :Any   )      ->					List[Any]:
												a__    =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*UpperCamelCase_   )
					def 				lowerCamelCase__(   self     :List[Any]   )      ->					Union[str, Any]:
												a__    =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase_   )
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					Union[str, Any]:
												a__    =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase_   )
					def 				lowerCamelCase__(   self     :Optional[int]   )      ->					int:
												a__    =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*UpperCamelCase_   )
					def 				lowerCamelCase__(   self     :int   )      ->					int:
												a__    =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*UpperCamelCase_   )
					@slow
					def 				lowerCamelCase__(   self     :str   )      ->					Dict:





												for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			a__    =  TFLayoutLMModel.from_pretrained(UpperCamelCase_   )
																			self.assertIsNotNone(UpperCamelCase_   )



					@unittest.skip('Onnx compliancy broke with TF 2.10'   )
					def 				lowerCamelCase__(   self     :Tuple   )      ->					Optional[Any]:
												pass






def 							__lowercase     (				):
							# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
							# fmt: off
							a__    =  tf.convert_to_tensor([[1_0_1,1_0_1_9,1_0_1_4,1_0_1_6,1_0_3_7,1_2_8_4_9,4_7_4_7,1_0_0_4,1_4_2_4_6,2_2_7_8,5_4_3_9,4_5_2_4,5_0_0_2,2_9_3_0,2_1_9_3,2_9_3_0,4_3_4_1,3_2_0_8,1_0_0_5,1_0_5_5,2_1_7_1,2_8_4_8,1_1_3_0_0,3_5_3_1,1_0_2],[1_0_1,4_0_7_0,4_0_3_4,7_0_2_0,1_0_2_4,3_0_5_8,1_0_1_5,1_0_1_3,2_8_6_1,1_0_1_3,6_0_7_0,1_9_2_7_4,2_7_7_2,6_2_0_5,2_7_8_1_4,1_6_1_4_7,1_6_1_4_7,4_3_4_3,2_0_4_7,1_0_2_8_3,1_0_9_6_9,1_4_3_8_9,1_0_1_2,2_3_3_8,1_0_2]]     )  # noqa: E231
							a__    =  tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]     )  # noqa: E231
							a__    =  tf.convert_to_tensor([[[0,0,0,0],[4_2_3,2_3_7,4_4_0,2_5_1],[4_2_7,2_7_2,4_4_1,2_8_7],[4_1_9,1_1_5,4_3_7,1_2_9],[9_6_1,8_8_5,9_9_2,9_1_2],[2_5_6,3_8,3_3_0,5_8],[2_5_6,3_8,3_3_0,5_8],[3_3_6,4_2,3_5_3,5_7],[3_6_0,3_9,4_0_1,5_6],[3_6_0,3_9,4_0_1,5_6],[4_1_1,3_9,4_7_1,5_9],[4_7_9,4_1,5_2_8,5_9],[5_3_3,3_9,6_3_0,6_0],[6_7,1_1_3,1_3_4,1_3_1],[1_4_1,1_1_5,2_0_9,1_3_2],[6_8,1_4_9,1_3_3,1_6_6],[1_4_1,1_4_9,1_8_7,1_6_4],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[2_9_5,1_4_8,3_4_9,1_6_5],[4_4_1,1_4_9,4_9_2,1_6_6],[4_9_7,1_4_9,5_4_6,1_6_4],[6_4,2_0_1,1_2_5,2_1_8],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]],[[0,0,0,0],[6_6_2,1_5_0,7_5_4,1_6_6],[6_6_5,1_9_9,7_4_2,2_1_1],[5_1_9,2_1_3,5_5_4,2_2_8],[5_1_9,2_1_3,5_5_4,2_2_8],[1_3_4,4_3_3,1_8_7,4_5_4],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[3_1_4,4_6_9,3_7_6,4_8_2],[5_0_4,6_8_4,5_8_2,7_0_6],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[6_1_0,7_4_9,6_5_2,7_6_5],[1_3_0,6_5_9,1_6_8,6_7_2],[1_7_6,6_5_7,2_3_7,6_7_2],[2_3_8,6_5_7,3_1_2,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[7_1_6,3_0_1,8_2_5,3_1_7],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]]]     )  # noqa: E231
							a__    =  tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]     )  # noqa: E231
							# these are sequence labels (i.e. at the token level)
							a__    =  tf.convert_to_tensor([[-1_0_0,1_0,1_0,1_0,9,1,-1_0_0,7,7,-1_0_0,7,7,4,2,5,2,8,8,-1_0_0,-1_0_0,5,0,3,2,-1_0_0],[-1_0_0,1_2,1_2,1_2,-1_0_0,1_2,1_0,-1_0_0,-1_0_0,-1_0_0,-1_0_0,1_0,1_2,9,-1_0_0,-1_0_0,-1_0_0,1_0,1_0,1_0,9,1_2,-1_0_0,1_0,-1_0_0]]     )  # noqa: E231
							# fmt: on

							return input_ids, attention_mask, bbox, token_type_ids, labels







@require_tf
class  snake_case_     (unittest.TestCase   ):
					@slow
					def 				lowerCamelCase__(   self     :Any   )      ->					Any:
												a__    =  TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'   )

												a__    =  prepare_layoutlm_batch_inputs()

												# forward pass
												a__    =  model(input_ids=UpperCamelCase_    ,bbox=UpperCamelCase_    ,attention_mask=UpperCamelCase_    ,token_type_ids=UpperCamelCase_   )

												# test the sequence output on [0, :3, :3]
												a__    =  tf.convert_to_tensor(
												    [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]    ,)

												self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]    ,UpperCamelCase_    ,atol=1E-3   )   )

												# test the pooled output on [1, :3]
												a__    =  tf.convert_to_tensor([-0.65_80, -0.02_14, 0.85_52]   )

												self.assertTrue(np.allclose(outputs.pooler_output[1, :3]    ,UpperCamelCase_    ,atol=1E-3   )   )
					@slow
					def 				lowerCamelCase__(   self     :List[Any]   )      ->					int:
												a__    =  TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'    ,num_labels=2   )

												a__    =  prepare_layoutlm_batch_inputs()

												# forward pass
												a__    =  model(
												    input_ids=UpperCamelCase_    ,bbox=UpperCamelCase_    ,attention_mask=UpperCamelCase_    ,token_type_ids=UpperCamelCase_    ,labels=tf.convert_to_tensor([1, 1]   )    ,)

												# test whether we get a loss as a scalar
												a__    =  outputs.loss
												a__    =  (2,)
												self.assertEqual(loss.shape    ,UpperCamelCase_   )

												# test the shape of the logits
												a__    =  outputs.logits
												a__    =  (2, 2)
												self.assertEqual(logits.shape    ,UpperCamelCase_   )
					@slow
					def 				lowerCamelCase__(   self     :Any   )      ->					Any:
												a__    =  TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'    ,num_labels=13   )

												a__    =  prepare_layoutlm_batch_inputs()

												# forward pass
												a__    =  model(
												    input_ids=UpperCamelCase_    ,bbox=UpperCamelCase_    ,attention_mask=UpperCamelCase_    ,token_type_ids=UpperCamelCase_    ,labels=UpperCamelCase_   )

												# test the shape of the logits
												a__    =  outputs.logits
												a__    =  tf.convert_to_tensor((2, 25, 13)   )
												self.assertEqual(logits.shape    ,UpperCamelCase_   )



					@slow
					def 				lowerCamelCase__(   self     :Optional[int]   )      ->					Tuple:
												a__    =  TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'   )

												a__    =  prepare_layoutlm_batch_inputs()

												# forward pass
												a__    =  model(input_ids=UpperCamelCase_    ,bbox=UpperCamelCase_    ,attention_mask=UpperCamelCase_    ,token_type_ids=UpperCamelCase_   )

												# test the shape of the logits
												a__    =  tf.convert_to_tensor((2, 25)   )
												self.assertEqual(outputs.start_logits.shape    ,UpperCamelCase_   )
												self.assertEqual(outputs.end_logits.shape    ,UpperCamelCase_   )









import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput







class  snake_case_     (lowerCamelCase_			,		lowerCamelCase_   ):
					UpperCAmelCase__      :					Optional[Any]					   =    1
					@register_to_config
					def __init__(   self     :Optional[int]    ,__snake_case     :int = 10_00    ,__snake_case     :Optional[Union[np.ndarray, List[float]]] = None   )      ->					int:
												# set `betas`, `alphas`, `timesteps`
												self.set_timesteps(__snake_case   )

												# standard deviation of the initial noise distribution
												a__    =  1.0

												# For now we only support F-PNDM, i.e. the runge-kutta method
												# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
												# mainly at formula (9), (12), (13) and the Algorithm 2.
												a__    =  4

												# running values
												a__    =  []
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :int    ,__snake_case     :Union[str, torch.device] = None   )      ->					Union[str, Any]:
												a__    =  num_inference_steps
												a__    =  torch.linspace(1    ,0    ,num_inference_steps + 1   )[:-1]
												a__    =  torch.cat([steps, torch.tensor([0.0]   )]   )

												if self.config.trained_betas is not None:
																			a__    =  torch.tensor(self.config.trained_betas    ,dtype=torch.floataa   )
												else:
																			a__    =  torch.sin(steps * math.pi / 2   ) ** 2

												a__    =  (1.0 - self.betas**2) ** 0.5

												a__    =  (torch.atana(self.betas    ,self.alphas   ) / math.pi * 2)[:-1]
												a__    =  timesteps.to(__snake_case   )

												a__    =  []
					def 				lowerCamelCase__(   self     :Any    ,__snake_case     :torch.FloatTensor    ,__snake_case     :int    ,__snake_case     :torch.FloatTensor    ,__snake_case     :bool = True    ,)      ->					Union[SchedulerOutput, Tuple]:
												if self.num_inference_steps is None:
																			raise ValueError(
																			    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'   )

												a__    =  (self.timesteps == timestep).nonzero().item()
												a__    =  timestep_index + 1

												a__    =  sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
												self.ets.append(__snake_case   )

												if len(self.ets   ) == 1:
																			a__    =  self.ets[-1]
												elif len(self.ets   ) == 2:
																			a__    =  (3 * self.ets[-1] - self.ets[-2]) / 2
												elif len(self.ets   ) == 3:
																			a__    =  (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
												else:
																			a__    =  (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

												a__    =  self._get_prev_sample(__snake_case    ,__snake_case    ,__snake_case    ,__snake_case   )

												if not return_dict:
																			return (prev_sample,)

												return SchedulerOutput(prev_sample=__snake_case   )
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :torch.FloatTensor    ,*__snake_case     :int    ,**__snake_case     :Optional[int]   )      ->					torch.FloatTensor:
												return sample
					def 				lowerCamelCase__(   self     :Optional[Any]    ,__snake_case     :List[Any]    ,__snake_case     :Optional[int]    ,__snake_case     :Dict    ,__snake_case     :Any   )      ->					Optional[Any]:
												a__    =  self.alphas[timestep_index]
												a__    =  self.betas[timestep_index]

												a__    =  self.alphas[prev_timestep_index]
												a__    =  self.betas[prev_timestep_index]

												a__    =  (sample - sigma * ets) / max(__snake_case    ,1E-8   )
												a__    =  next_alpha * pred + ets * next_sigma

												return prev_sample



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




