
"""simple docstring"""




import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
						    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
						    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
						    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
						    LayoutLMvaConfig,
						    LayoutLMvaForQuestionAnswering,
						    LayoutLMvaForSequenceClassification,
						    LayoutLMvaForTokenClassification,
						    LayoutLMvaModel,
						)
						from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
						from PIL import Image

						from transformers import LayoutLMvaImageProcessor





class 			snake_case  :

							def __init__(self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_=2							,							SCREAMING_SNAKE_CASE_=3							,							SCREAMING_SNAKE_CASE_=4							,							SCREAMING_SNAKE_CASE_=2							,							SCREAMING_SNAKE_CASE_=7							,							SCREAMING_SNAKE_CASE_=True							,							SCREAMING_SNAKE_CASE_=True							,							SCREAMING_SNAKE_CASE_=True							,							SCREAMING_SNAKE_CASE_=True							,							SCREAMING_SNAKE_CASE_=99							,							SCREAMING_SNAKE_CASE_=36							,							SCREAMING_SNAKE_CASE_=3							,							SCREAMING_SNAKE_CASE_=4							,							SCREAMING_SNAKE_CASE_=37							,							SCREAMING_SNAKE_CASE_="gelu"							,							SCREAMING_SNAKE_CASE_=0.1							,							SCREAMING_SNAKE_CASE_=0.1							,							SCREAMING_SNAKE_CASE_=5_12							,							SCREAMING_SNAKE_CASE_=16							,							SCREAMING_SNAKE_CASE_=2							,							SCREAMING_SNAKE_CASE_=0.02							,							SCREAMING_SNAKE_CASE_=6							,							SCREAMING_SNAKE_CASE_=6							,							SCREAMING_SNAKE_CASE_=3							,							SCREAMING_SNAKE_CASE_=4							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=10_00							,							):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			parent
									SCREAMING_SNAKE_CASE_    			=			batch_size
									SCREAMING_SNAKE_CASE_    			=			num_channels
									SCREAMING_SNAKE_CASE_    			=			image_size
									SCREAMING_SNAKE_CASE_    			=			patch_size
									SCREAMING_SNAKE_CASE_    			=			text_seq_length
									SCREAMING_SNAKE_CASE_    			=			is_training
									SCREAMING_SNAKE_CASE_    			=			use_input_mask
									SCREAMING_SNAKE_CASE_    			=			use_token_type_ids
									SCREAMING_SNAKE_CASE_    			=			use_labels
									SCREAMING_SNAKE_CASE_    			=			vocab_size
									SCREAMING_SNAKE_CASE_    			=			hidden_size
									SCREAMING_SNAKE_CASE_    			=			num_hidden_layers
									SCREAMING_SNAKE_CASE_    			=			num_attention_heads
									SCREAMING_SNAKE_CASE_    			=			intermediate_size
									SCREAMING_SNAKE_CASE_    			=			hidden_act
									SCREAMING_SNAKE_CASE_    			=			hidden_dropout_prob
									SCREAMING_SNAKE_CASE_    			=			attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_    			=			max_position_embeddings
									SCREAMING_SNAKE_CASE_    			=			type_vocab_size
									SCREAMING_SNAKE_CASE_    			=			type_sequence_label_size
									SCREAMING_SNAKE_CASE_    			=			initializer_range
									SCREAMING_SNAKE_CASE_    			=			coordinate_size
									SCREAMING_SNAKE_CASE_    			=			shape_size
									SCREAMING_SNAKE_CASE_    			=			num_labels
									SCREAMING_SNAKE_CASE_    			=			num_choices
									SCREAMING_SNAKE_CASE_    			=			scope
									SCREAMING_SNAKE_CASE_    			=			range_bbox

									# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
									SCREAMING_SNAKE_CASE_    			=			text_seq_length
									SCREAMING_SNAKE_CASE_    			=			(image_size // patch_size) ** 2 + 1
									SCREAMING_SNAKE_CASE_    			=			self.text_seq_length + self.image_seq_length

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			ids_tensor([self.batch_size, self.text_seq_length]							,							self.vocab_size     )

									SCREAMING_SNAKE_CASE_    			=			ids_tensor([self.batch_size, self.text_seq_length, 4]							,							self.range_bbox     )
									# 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]:
															SCREAMING_SNAKE_CASE_    			=			bbox[i, j, 3]
															SCREAMING_SNAKE_CASE_    			=			bbox[i, j, 1]
															SCREAMING_SNAKE_CASE_    			=			t
													if bbox[i, j, 2] < bbox[i, j, 0]:
															SCREAMING_SNAKE_CASE_    			=			bbox[i, j, 2]
															SCREAMING_SNAKE_CASE_    			=			bbox[i, j, 0]
															SCREAMING_SNAKE_CASE_    			=			t

									SCREAMING_SNAKE_CASE_    			=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

									SCREAMING_SNAKE_CASE_    			=			None
									if self.use_input_mask:
											SCREAMING_SNAKE_CASE_    			=			random_attention_mask([self.batch_size, self.text_seq_length]     )

									SCREAMING_SNAKE_CASE_    			=			None
									if self.use_token_type_ids:
											SCREAMING_SNAKE_CASE_    			=			ids_tensor([self.batch_size, self.text_seq_length]							,							self.type_vocab_size     )

									SCREAMING_SNAKE_CASE_    			=			None
									SCREAMING_SNAKE_CASE_    			=			None
									if self.use_labels:
											SCREAMING_SNAKE_CASE_    			=			ids_tensor([self.batch_size]							,							self.type_sequence_label_size     )
											SCREAMING_SNAKE_CASE_    			=			ids_tensor([self.batch_size, self.text_seq_length]							,							self.num_labels     )

									SCREAMING_SNAKE_CASE_    			=			LayoutLMvaConfig(
									    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							,							coordinate_size=self.coordinate_size							,							shape_size=self.shape_size							,							input_size=self.image_size							,							patch_size=self.patch_size							,							)

									return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			LayoutLMvaModel(config=SCREAMING_SNAKE_CASE_     )
									model.to(SCREAMING_SNAKE_CASE_     )
									model.eval()

									# text + image
									SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							pixel_values=SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			model(
									    SCREAMING_SNAKE_CASE_							,							bbox=SCREAMING_SNAKE_CASE_							,							pixel_values=SCREAMING_SNAKE_CASE_							,							attention_mask=SCREAMING_SNAKE_CASE_							,							token_type_ids=SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							bbox=SCREAMING_SNAKE_CASE_							,							pixel_values=SCREAMING_SNAKE_CASE_							,							token_type_ids=SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							bbox=SCREAMING_SNAKE_CASE_							,							pixel_values=SCREAMING_SNAKE_CASE_     )

									self.parent.assertEqual(result.last_hidden_state.shape							,							(self.batch_size, self.seq_length, self.hidden_size)     )

									# text only
									SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_     )
									self.parent.assertEqual(
									    result.last_hidden_state.shape							,							(self.batch_size, self.text_seq_length, self.hidden_size)     )

									# image only
									SCREAMING_SNAKE_CASE_    			=			model(pixel_values=SCREAMING_SNAKE_CASE_     )
									self.parent.assertEqual(
									    result.last_hidden_state.shape							,							(self.batch_size, self.image_seq_length, self.hidden_size)     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.num_labels
									SCREAMING_SNAKE_CASE_    			=			LayoutLMvaForSequenceClassification(SCREAMING_SNAKE_CASE_     )
									model.to(SCREAMING_SNAKE_CASE_     )
									model.eval()
									SCREAMING_SNAKE_CASE_    			=			model(
									    SCREAMING_SNAKE_CASE_							,							bbox=SCREAMING_SNAKE_CASE_							,							pixel_values=SCREAMING_SNAKE_CASE_							,							attention_mask=SCREAMING_SNAKE_CASE_							,							token_type_ids=SCREAMING_SNAKE_CASE_							,							labels=SCREAMING_SNAKE_CASE_							,							)
									self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_labels)     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.num_labels
									SCREAMING_SNAKE_CASE_    			=			LayoutLMvaForTokenClassification(config=SCREAMING_SNAKE_CASE_     )
									model.to(SCREAMING_SNAKE_CASE_     )
									model.eval()
									SCREAMING_SNAKE_CASE_    			=			model(
									    SCREAMING_SNAKE_CASE_							,							bbox=SCREAMING_SNAKE_CASE_							,							pixel_values=SCREAMING_SNAKE_CASE_							,							attention_mask=SCREAMING_SNAKE_CASE_							,							token_type_ids=SCREAMING_SNAKE_CASE_							,							labels=SCREAMING_SNAKE_CASE_							,							)
									self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.text_seq_length, self.num_labels)     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			LayoutLMvaForQuestionAnswering(config=SCREAMING_SNAKE_CASE_     )
									model.to(SCREAMING_SNAKE_CASE_     )
									model.eval()
									SCREAMING_SNAKE_CASE_    			=			model(
									    SCREAMING_SNAKE_CASE_							,							bbox=SCREAMING_SNAKE_CASE_							,							pixel_values=SCREAMING_SNAKE_CASE_							,							attention_mask=SCREAMING_SNAKE_CASE_							,							token_type_ids=SCREAMING_SNAKE_CASE_							,							start_positions=SCREAMING_SNAKE_CASE_							,							end_positions=SCREAMING_SNAKE_CASE_							,							)
									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 _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.prepare_config_and_inputs()
									(
									    (
									    SCREAMING_SNAKE_CASE_
									)			,(
									    SCREAMING_SNAKE_CASE_
									)			,(
									    SCREAMING_SNAKE_CASE_
									)			,(
									    SCREAMING_SNAKE_CASE_
									)			,(
									    SCREAMING_SNAKE_CASE_
									)			,(
									    SCREAMING_SNAKE_CASE_
									)			,(
									    SCREAMING_SNAKE_CASE_
									)			,(
									    SCREAMING_SNAKE_CASE_
									)			,
									)    			=			config_and_inputs
									SCREAMING_SNAKE_CASE_    			=			{
									    '''input_ids''': input_ids,
									    '''bbox''': bbox,
									    '''pixel_values''': pixel_values,
									    '''token_type_ids''': token_type_ids,
									    '''attention_mask''': input_mask,
									}
									return config, inputs_dict







@require_torch
class 			snake_case  (				__lowercase							,      __lowercase							,      unittest.TestCase ):
							UpperCAmelCase__      =   False
							UpperCAmelCase__      =   False
							UpperCAmelCase__      =   False

							UpperCAmelCase__      =   (
							    (
							        LayoutLMvaModel,
							        LayoutLMvaForSequenceClassification,
							        LayoutLMvaForTokenClassification,
							        LayoutLMvaForQuestionAnswering,
							    )
							    if is_torch_available()
							    else ()
							)
							UpperCAmelCase__      =   (
							    {'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
							    if is_torch_available()
							    else {}
							)

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									return True

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			LayoutLMvaModelTester(self     )
									SCREAMING_SNAKE_CASE_    			=			ConfigTester(self							,							config_class=SCREAMING_SNAKE_CASE_							,							hidden_size=37     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_=False     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			copy.deepcopy(SCREAMING_SNAKE_CASE_     )
									if model_class in get_values(SCREAMING_SNAKE_CASE_     ):
											SCREAMING_SNAKE_CASE_    			=			{
											    k: v.unsqueeze(1     ).expand(-1							,							self.model_tester.num_choices							,							-1     ).contiguous()
											    if isinstance(SCREAMING_SNAKE_CASE_							,							torch.Tensor     ) and v.ndim > 1
											    else v
											    for k, v in inputs_dict.items()
											}
									if return_labels:
											if model_class in get_values(SCREAMING_SNAKE_CASE_     ):
													SCREAMING_SNAKE_CASE_    			=			torch.ones(self.model_tester.batch_size							,							dtype=torch.long							,							device=SCREAMING_SNAKE_CASE_     )
											elif model_class in get_values(SCREAMING_SNAKE_CASE_     ):
													SCREAMING_SNAKE_CASE_    			=			torch.zeros(
													    self.model_tester.batch_size							,							dtype=torch.long							,							device=SCREAMING_SNAKE_CASE_     )
													SCREAMING_SNAKE_CASE_    			=			torch.zeros(
													    self.model_tester.batch_size							,							dtype=torch.long							,							device=SCREAMING_SNAKE_CASE_     )
											elif model_class in [
											    *get_values(SCREAMING_SNAKE_CASE_     ),
											]:
													SCREAMING_SNAKE_CASE_    			=			torch.zeros(
													    self.model_tester.batch_size							,							dtype=torch.long							,							device=SCREAMING_SNAKE_CASE_     )
											elif model_class in [
											    *get_values(SCREAMING_SNAKE_CASE_     ),
											]:
													SCREAMING_SNAKE_CASE_    			=			torch.zeros(
													    (self.model_tester.batch_size, self.model_tester.text_seq_length)							,							dtype=torch.long							,							device=SCREAMING_SNAKE_CASE_							,							)

									return inputs_dict

							def _lowercase       (self     ):

									"""simple docstring"""







									self.config_tester.run_common_tests()

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
											SCREAMING_SNAKE_CASE_    			=			type
											self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_     )







							@slow
							def _lowercase       (self     ):

									"""simple docstring"""







									for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											SCREAMING_SNAKE_CASE_    			=			LayoutLMvaModel.from_pretrained(SCREAMING_SNAKE_CASE_     )
											self.assertIsNotNone(SCREAMING_SNAKE_CASE_     )







def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
		return image





@require_torch
class 			snake_case  (				unittest.TestCase ):

							@cached_property
							def _lowercase       (self     ):

									"""simple docstring"""







									return LayoutLMvaImageProcessor(apply_ocr=SCREAMING_SNAKE_CASE_     ) if is_vision_available() else None







							@slow
							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''     ).to(SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			self.default_image_processor
									SCREAMING_SNAKE_CASE_    			=			prepare_img()
									SCREAMING_SNAKE_CASE_    			=			image_processor(images=SCREAMING_SNAKE_CASE_							,							return_tensors='''pt'''     ).pixel_values.to(SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			torch.tensor([[1, 2]]     )
									SCREAMING_SNAKE_CASE_    			=			torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]     ).unsqueeze(0     )

									# forward pass
									SCREAMING_SNAKE_CASE_    			=			model(
									    input_ids=input_ids.to(SCREAMING_SNAKE_CASE_     )							,							bbox=bbox.to(SCREAMING_SNAKE_CASE_     )							,							pixel_values=pixel_values.to(SCREAMING_SNAKE_CASE_     )							,							)

									# verify the logits
									SCREAMING_SNAKE_CASE_    			=			torch.Size((1, 1_99, 7_68)     )
									self.assertEqual(outputs.last_hidden_state.shape							,							SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			torch.tensor(
									    [[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]]     ).to(SCREAMING_SNAKE_CASE_     )

									self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]							,							SCREAMING_SNAKE_CASE_							,							atol=1e-4     )     )

"""simple docstring"""




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   (UnCLIPScheduler,)

							def _lowercase       (self							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			{
									    '''num_train_timesteps''': 10_00,
									    '''variance_type''': '''fixed_small_log''',
									    '''clip_sample''': True,
									    '''clip_sample_range''': 1.0,
									    '''prediction_type''': '''epsilon''',
									}

									config.update(**SCREAMING_SNAKE_CASE_     )
									return config

							def _lowercase       (self     ):

									"""simple docstring"""







									for timesteps in [1, 5, 1_00, 10_00]:
											self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for variance in ["fixed_small_log", "learned_range"]:
											self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for clip_sample_range in [1, 5, 10, 20]:
											self.check_over_configs(clip_sample_range=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for prediction_type in ["epsilon", "sample"]:
											self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for time_step in [0, 5_00, 9_99]:
											for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
													if prev_timestep is not None and prev_timestep >= time_step:
															continue

													self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_							,							prev_timestep=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config(variance_type='''fixed_small_log'''     )
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 1.0_0_0_0e-1_0     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_87     ) - 0.0_54_96_25     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_99     ) - 0.9_99_49_87     )     ) < 1e-5

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config(variance_type='''learned_range'''     )
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			0.5

									assert scheduler._get_variance(1							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -10.1_71_27_90 < 1e-5
									assert scheduler._get_variance(4_87							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -5.7_99_80_52 < 1e-5
									assert scheduler._get_variance(9_99							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -0.0_01_00_11 < 1e-5

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			scheduler.timesteps

									SCREAMING_SNAKE_CASE_    			=			self.dummy_model()
									SCREAMING_SNAKE_CASE_    			=			self.dummy_sample_deter
									SCREAMING_SNAKE_CASE_    			=			torch.manual_seed(0     )

									for i, t in enumerate(SCREAMING_SNAKE_CASE_     ):
											# 1. predict noise residual
											SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

											# 2. predict previous mean of sample x_t-1
											SCREAMING_SNAKE_CASE_    			=			scheduler.step(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							generator=SCREAMING_SNAKE_CASE_     ).prev_sample

											SCREAMING_SNAKE_CASE_    			=			pred_prev_sample

									SCREAMING_SNAKE_CASE_    			=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_     )     )
									SCREAMING_SNAKE_CASE_    			=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_     )     )

									assert abs(result_sum.item() - 2_52.2_68_24_95     ) < 1e-2
									assert abs(result_mean.item() - 0.3_28_47_43     ) < 1e-3

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									scheduler.set_timesteps(25     )

									SCREAMING_SNAKE_CASE_    			=			scheduler.timesteps

									SCREAMING_SNAKE_CASE_    			=			self.dummy_model()
									SCREAMING_SNAKE_CASE_    			=			self.dummy_sample_deter
									SCREAMING_SNAKE_CASE_    			=			torch.manual_seed(0     )

									for i, t in enumerate(SCREAMING_SNAKE_CASE_     ):
											# 1. predict noise residual
											SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

											if i + 1 == timesteps.shape[0]:
													SCREAMING_SNAKE_CASE_    			=			None
											else:
													SCREAMING_SNAKE_CASE_    			=			timesteps[i + 1]

											# 2. predict previous mean of sample x_t-1
											SCREAMING_SNAKE_CASE_    			=			scheduler.step(
											    SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							prev_timestep=SCREAMING_SNAKE_CASE_							,							generator=SCREAMING_SNAKE_CASE_     ).prev_sample

											SCREAMING_SNAKE_CASE_    			=			pred_prev_sample

									SCREAMING_SNAKE_CASE_    			=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_     )     )
									SCREAMING_SNAKE_CASE_    			=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_     )     )

									assert abs(result_sum.item() - 2_58.2_04_49_83     ) < 1e-2
									assert abs(result_mean.item() - 0.3_36_20_38     ) < 1e-3

							def _lowercase       (self     ):

									"""simple docstring"""







									pass







							def _lowercase       (self     ):

									"""simple docstring"""







									pass