



import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
	from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor


class 	__snake_case   :
		def __init__(							self    ,a_    ,a_=13    ,a_=32    ,a_=2    ,a_=3    ,a_=16    ,a_=[1, 2, 1]    ,a_=[2, 2, 4]    ,a_=2    ,a_=2.0    ,a_=True    ,a_=0.0    ,a_=0.0    ,a_=0.1    ,a_="gelu"    ,a_=False    ,a_=True    ,a_=0.02    ,a_=1e-5    ,a_=True    ,a_=None    ,a_=True    ,a_=10    ,a_=8    ,):







						"""simple docstring"""
						lowerCAmelCase__		  =  parent
						lowerCAmelCase__		  =  batch_size
						lowerCAmelCase__		  =  image_size
						lowerCAmelCase__		  =  patch_size
						lowerCAmelCase__		  =  num_channels
						lowerCAmelCase__		  =  embed_dim
						lowerCAmelCase__		  =  depths
						lowerCAmelCase__		  =  num_heads
						lowerCAmelCase__		  =  window_size
						lowerCAmelCase__		  =  mlp_ratio
						lowerCAmelCase__		  =  qkv_bias
						lowerCAmelCase__		  =  hidden_dropout_prob
						lowerCAmelCase__		  =  attention_probs_dropout_prob
						lowerCAmelCase__		  =  drop_path_rate
						lowerCAmelCase__		  =  hidden_act
						lowerCAmelCase__		  =  use_absolute_embeddings
						lowerCAmelCase__		  =  patch_norm
						lowerCAmelCase__		  =  layer_norm_eps
						lowerCAmelCase__		  =  initializer_range
						lowerCAmelCase__		  =  is_training
						lowerCAmelCase__		  =  scope
						lowerCAmelCase__		  =  use_labels
						lowerCAmelCase__		  =  type_sequence_label_size
						lowerCAmelCase__		  =  encoder_stride
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

						lowerCAmelCase__		  =  None
						if self.use_labels:
										lowerCAmelCase__		  =  ids_tensor([self.batch_size]    ,self.type_sequence_label_size						)

						lowerCAmelCase__		  =  self.get_config()

						return config, pixel_values, labels
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return SwinvaConfig(
						    image_size=self.image_size    ,patch_size=self.patch_size    ,num_channels=self.num_channels    ,embed_dim=self.embed_dim    ,depths=self.depths    ,num_heads=self.num_heads    ,window_size=self.window_size    ,mlp_ratio=self.mlp_ratio    ,qkv_bias=self.qkv_bias    ,hidden_dropout_prob=self.hidden_dropout_prob    ,attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,drop_path_rate=self.drop_path_rate    ,hidden_act=self.hidden_act    ,use_absolute_embeddings=self.use_absolute_embeddings    ,path_norm=self.patch_norm    ,layer_norm_eps=self.layer_norm_eps    ,initializer_range=self.initializer_range    ,encoder_stride=self.encoder_stride    ,)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  SwinvaModel(config=SCREAMING_SNAKE_CASE__						)
						model.to(SCREAMING_SNAKE_CASE__						)
						model.eval()
						lowerCAmelCase__		  =  model(SCREAMING_SNAKE_CASE__						)

						lowerCAmelCase__		  =  ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
						lowerCAmelCase__		  =  int(config.embed_dim * 2 ** (len(config.depths						) - 1)						)

						self.parent.assertEqual(result.last_hidden_state.shape    ,(self.batch_size, expected_seq_len, expected_dim)						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  SwinvaForMaskedImageModeling(config=SCREAMING_SNAKE_CASE__						)
						model.to(SCREAMING_SNAKE_CASE__						)
						model.eval()
						lowerCAmelCase__		  =  model(SCREAMING_SNAKE_CASE__						)
						self.parent.assertEqual(
						    result.logits.shape    ,(self.batch_size, self.num_channels, self.image_size, self.image_size)						)

						# test greyscale images
						lowerCAmelCase__		  =  1
						lowerCAmelCase__		  =  SwinvaForMaskedImageModeling(SCREAMING_SNAKE_CASE__						)
						model.to(SCREAMING_SNAKE_CASE__						)
						model.eval()

						lowerCAmelCase__		  =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
						lowerCAmelCase__		  =  model(SCREAMING_SNAKE_CASE__						)
						self.parent.assertEqual(result.logits.shape    ,(self.batch_size, 1, self.image_size, self.image_size)						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.type_sequence_label_size
						lowerCAmelCase__		  =  SwinvaForImageClassification(SCREAMING_SNAKE_CASE__						)
						model.to(SCREAMING_SNAKE_CASE__						)
						model.eval()
						lowerCAmelCase__		  =  model(SCREAMING_SNAKE_CASE__    ,labels=SCREAMING_SNAKE_CASE__						)
						self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.type_sequence_label_size)						)



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.prepare_config_and_inputs()
						lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__		  =  config_and_inputs
						lowerCAmelCase__		  =  {'pixel_values': pixel_values}
						return config, inputs_dict



@require_torch
class 	__snake_case   ( SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, unittest.TestCase ):
		SCREAMING_SNAKE_CASE__								=	(
		    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
		)
		SCREAMING_SNAKE_CASE__								=	(
		    {"feature-extraction": SwinvaModel, "image-classification": SwinvaForImageClassification}
		    if is_torch_available()
		    else {}
		)

		SCREAMING_SNAKE_CASE__								=	False
		SCREAMING_SNAKE_CASE__								=	False
		SCREAMING_SNAKE_CASE__								=	False
		SCREAMING_SNAKE_CASE__								=	False
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  SwinvaModelTester(self						)
						lowerCAmelCase__		  =  ConfigTester(self    ,config_class=SCREAMING_SNAKE_CASE__    ,embed_dim=37						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						self.config_tester.create_and_test_config_to_json_string()
						self.config_tester.create_and_test_config_to_json_file()
						self.config_tester.create_and_test_config_from_and_save_pretrained()
						self.config_tester.create_and_test_config_with_num_labels()
						self.config_tester.check_config_can_be_init_without_params()
						self.config_tester.check_config_arguments_init()
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__						)
		@unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						pass
		@unittest.skip(reason='Swinv2 does not use inputs_embeds'						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						pass
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__  ,   lowerCAmelCase__		  =  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										lowerCAmelCase__		  =  model_class(SCREAMING_SNAKE_CASE__						)
										self.assertIsInstance(model.get_input_embeddings()    ,(nn.Module)						)
										lowerCAmelCase__		  =  model.get_output_embeddings()
										self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE__    ,nn.Linear						)						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__  ,   lowerCAmelCase__		  =  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										lowerCAmelCase__		  =  model_class(SCREAMING_SNAKE_CASE__						)
										lowerCAmelCase__		  =  inspect.signature(model.forward						)
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										lowerCAmelCase__		  =  [*signature.parameters.keys()]

										lowerCAmelCase__		  =  ['pixel_values']
										self.assertListEqual(arg_names[:1]    ,SCREAMING_SNAKE_CASE__						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__  ,   lowerCAmelCase__		  =  self.model_tester.prepare_config_and_inputs_for_common()
						lowerCAmelCase__		  =  True

						for model_class in self.all_model_classes:
										lowerCAmelCase__		  =  True
										lowerCAmelCase__		  =  False
										lowerCAmelCase__		  =  True
										lowerCAmelCase__		  =  model_class(SCREAMING_SNAKE_CASE__						)
										model.to(SCREAMING_SNAKE_CASE__						)
										model.eval()
										with torch.no_grad():
														lowerCAmelCase__		  =  model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__    ,SCREAMING_SNAKE_CASE__						)						)
										lowerCAmelCase__		  =  outputs.attentions
										lowerCAmelCase__		  =  len(self.model_tester.depths						)
										self.assertEqual(len(SCREAMING_SNAKE_CASE__						)    ,SCREAMING_SNAKE_CASE__						)

										# check that output_attentions also work using config
										del inputs_dict["output_attentions"]
										lowerCAmelCase__		  =  True
										lowerCAmelCase__		  =  config.window_size**2
										lowerCAmelCase__		  =  model_class(SCREAMING_SNAKE_CASE__						)
										model.to(SCREAMING_SNAKE_CASE__						)
										model.eval()
										with torch.no_grad():
														lowerCAmelCase__		  =  model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__    ,SCREAMING_SNAKE_CASE__						)						)
										lowerCAmelCase__		  =  outputs.attentions
										self.assertEqual(len(SCREAMING_SNAKE_CASE__						)    ,SCREAMING_SNAKE_CASE__						)

										self.assertListEqual(
										    list(attentions[0].shape[-3:]						)    ,[self.model_tester.num_heads[0], window_size_squared, window_size_squared]    ,)
										lowerCAmelCase__		  =  len(SCREAMING_SNAKE_CASE__						)

										# Check attention is always last and order is fine
										lowerCAmelCase__		  =  True
										lowerCAmelCase__		  =  True
										lowerCAmelCase__		  =  model_class(SCREAMING_SNAKE_CASE__						)
										model.to(SCREAMING_SNAKE_CASE__						)
										model.eval()
										with torch.no_grad():
														lowerCAmelCase__		  =  model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__    ,SCREAMING_SNAKE_CASE__						)						)

										if hasattr(self.model_tester    ,'num_hidden_states_types'						):
														lowerCAmelCase__		  =  self.model_tester.num_hidden_states_types
										else:
														# also another +1 for reshaped_hidden_states
														lowerCAmelCase__		  =  2
										self.assertEqual(out_len + added_hidden_states    ,len(SCREAMING_SNAKE_CASE__						)						)

										lowerCAmelCase__		  =  outputs.attentions

										self.assertEqual(len(SCREAMING_SNAKE_CASE__						)    ,SCREAMING_SNAKE_CASE__						)

										self.assertListEqual(
										    list(self_attentions[0].shape[-3:]						)    ,[self.model_tester.num_heads[0], window_size_squared, window_size_squared]    ,)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  model_class(SCREAMING_SNAKE_CASE__						)
						model.to(SCREAMING_SNAKE_CASE__						)
						model.eval()

						with torch.no_grad():
										lowerCAmelCase__		  =  model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__    ,SCREAMING_SNAKE_CASE__						)						)

						lowerCAmelCase__		  =  outputs.hidden_states

						lowerCAmelCase__		  =  getattr(
						    self.model_tester    ,'expected_num_hidden_layers'    ,len(self.model_tester.depths						) + 1						)
						self.assertEqual(len(SCREAMING_SNAKE_CASE__						)    ,SCREAMING_SNAKE_CASE__						)

						# Swinv2 has a different seq_length
						lowerCAmelCase__		  =  (
						    config.patch_size
						    if isinstance(config.patch_size    ,collections.abc.Iterable						)
						    else (config.patch_size, config.patch_size)
						)

						lowerCAmelCase__		  =  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

						self.assertListEqual(
						    list(hidden_states[0].shape[-2:]						)    ,[num_patches, self.model_tester.embed_dim]    ,)

						lowerCAmelCase__		  =  outputs.reshaped_hidden_states
						self.assertEqual(len(SCREAMING_SNAKE_CASE__						)    ,SCREAMING_SNAKE_CASE__						)

						lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__		  =  reshaped_hidden_states[0].shape
						lowerCAmelCase__		  =  (
						    reshaped_hidden_states[0].view(SCREAMING_SNAKE_CASE__    ,SCREAMING_SNAKE_CASE__    ,height * width						).permute(0    ,2    ,1						)
						)
						self.assertListEqual(
						    list(reshaped_hidden_states.shape[-2:]						)    ,[num_patches, self.model_tester.embed_dim]    ,)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__  ,   lowerCAmelCase__		  =  self.model_tester.prepare_config_and_inputs_for_common()

						lowerCAmelCase__		  =  (
						    self.model_tester.image_size
						    if isinstance(self.model_tester.image_size    ,collections.abc.Iterable						)
						    else (self.model_tester.image_size, self.model_tester.image_size)
						)

						for model_class in self.all_model_classes:
										lowerCAmelCase__		  =  True
										self.check_hidden_states_output(SCREAMING_SNAKE_CASE__    ,SCREAMING_SNAKE_CASE__    ,SCREAMING_SNAKE_CASE__    ,SCREAMING_SNAKE_CASE__						)

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										lowerCAmelCase__		  =  True

										self.check_hidden_states_output(SCREAMING_SNAKE_CASE__    ,SCREAMING_SNAKE_CASE__    ,SCREAMING_SNAKE_CASE__    ,SCREAMING_SNAKE_CASE__						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__  ,   lowerCAmelCase__		  =  self.model_tester.prepare_config_and_inputs_for_common()
						lowerCAmelCase__		  =  3

						lowerCAmelCase__		  =  (
						    self.model_tester.image_size
						    if isinstance(self.model_tester.image_size    ,collections.abc.Iterable						)
						    else (self.model_tester.image_size, self.model_tester.image_size)
						)
						lowerCAmelCase__		  =  (
						    config.patch_size
						    if isinstance(config.patch_size    ,collections.abc.Iterable						)
						    else (config.patch_size, config.patch_size)
						)

						lowerCAmelCase__		  =  image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
						lowerCAmelCase__		  =  image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

						for model_class in self.all_model_classes:
										lowerCAmelCase__		  =  True
										self.check_hidden_states_output(SCREAMING_SNAKE_CASE__    ,SCREAMING_SNAKE_CASE__    ,SCREAMING_SNAKE_CASE__    ,(padded_height, padded_width)						)

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										lowerCAmelCase__		  =  True
										self.check_hidden_states_output(SCREAMING_SNAKE_CASE__    ,SCREAMING_SNAKE_CASE__    ,SCREAMING_SNAKE_CASE__    ,(padded_height, padded_width)						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE__						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__						)
		@slow
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""



						for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase__		  =  SwinvaModel.from_pretrained(SCREAMING_SNAKE_CASE__						)
										self.assertIsNotNone(SCREAMING_SNAKE_CASE__						)



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__  ,   lowerCAmelCase__		  =  self.model_tester.prepare_config_and_inputs_for_common()

						lowerCAmelCase__		  =  _config_zero_init(SCREAMING_SNAKE_CASE__						)
						for model_class in self.all_model_classes:
										lowerCAmelCase__		  =  model_class(config=SCREAMING_SNAKE_CASE__						)
										for name, param in model.named_parameters():
														if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
																		self.assertIn(
																		    ((param.data.mean() * 1e9).round() / 1e9).item()    ,[0.0, 1.0]    ,msg=f'Parameter {name} of model {model_class} seems not properly initialized'    ,)



@require_vision
@require_torch
class 	__snake_case   ( unittest.TestCase ):
		@cached_property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return (
						    AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'						)
						    if is_vision_available()
						    else None
						)



		@slow
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'						).to(
						    SCREAMING_SNAKE_CASE__						)
						lowerCAmelCase__		  =  self.default_image_processor

						lowerCAmelCase__		  =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
						lowerCAmelCase__		  =  image_processor(images=SCREAMING_SNAKE_CASE__    ,return_tensors='pt'						).to(SCREAMING_SNAKE_CASE__						)

						# forward pass
						with torch.no_grad():
										lowerCAmelCase__		  =  model(**SCREAMING_SNAKE_CASE__						)

						# verify the logits
						lowerCAmelCase__		  =  torch.Size((1, 1000)						)
						self.assertEqual(outputs.logits.shape    ,SCREAMING_SNAKE_CASE__						)
						lowerCAmelCase__		  =  torch.tensor([-0.3947, -0.4306, 0.0026]						).to(SCREAMING_SNAKE_CASE__						)
						self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,SCREAMING_SNAKE_CASE__    ,atol=1e-4						)						)


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Tuple       						=(DEISMultistepScheduler,)
 SCREAMING_SNAKE_CASE_				:					List[str]       						=(("num_inference_steps", 25),)

 def      __lowerCAmelCase  (						self			:			Tuple					, **SCREAMING_SNAKE_CASE__			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					{
          'num_train_timesteps': 10_00,
          'beta_start': 0.0001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
          'solver_order': 2,
      }

      config.update(**SCREAMING_SNAKE_CASE__   )
      return config

 def      __lowerCAmelCase  (						self			:			Any					, SCREAMING_SNAKE_CASE__			:			Dict=0					, **SCREAMING_SNAKE_CASE__			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					dict(self.forward_default_kwargs   )
      UpperCamelCase						    =					kwargs.pop('num_inference_steps'					, SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					self.dummy_sample
      UpperCamelCase						    =					0.1 * sample
      UpperCamelCase						    =					[residual + 0.2, residual + 0.15, residual + 0.10]

      for scheduler_class in self.scheduler_classes:
           UpperCamelCase						    =					self.get_scheduler_config(**SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					scheduler_class(**SCREAMING_SNAKE_CASE__   )
           scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )
           # copy over dummy past residuals
           UpperCamelCase						    =					dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(SCREAMING_SNAKE_CASE__   )
                UpperCamelCase						    =					scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE__   )
                new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )
                # copy over dummy past residuals
                UpperCamelCase						    =					dummy_past_residuals[: new_scheduler.config.solver_order]

           UpperCamelCase    ,		UpperCamelCase						    =					sample, sample
           for t in range(SCREAMING_SNAKE_CASE__					, time_step + scheduler.config.solver_order + 1   ):
                UpperCamelCase						    =					scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   ).prev_sample
                UpperCamelCase						    =					new_scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   ).prev_sample

                assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"

 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



      pass

 def      __lowerCAmelCase  (						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=0					, **SCREAMING_SNAKE_CASE__			:			int   ):






      """simple docstring"""



      UpperCamelCase						    =					dict(self.forward_default_kwargs   )
      UpperCamelCase						    =					kwargs.pop('num_inference_steps'					, SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					self.dummy_sample
      UpperCamelCase						    =					0.1 * sample
      UpperCamelCase						    =					[residual + 0.2, residual + 0.15, residual + 0.10]

      for scheduler_class in self.scheduler_classes:
           UpperCamelCase						    =					self.get_scheduler_config()
           UpperCamelCase						    =					scheduler_class(**SCREAMING_SNAKE_CASE__   )
           scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

           # copy over dummy past residuals (must be after setting timesteps)
           UpperCamelCase						    =					dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(SCREAMING_SNAKE_CASE__   )
                UpperCamelCase						    =					scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE__   )
                # copy over dummy past residuals
                new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

                # copy over dummy past residual (must be after setting timesteps)
                UpperCamelCase						    =					dummy_past_residuals[: new_scheduler.config.solver_order]

           UpperCamelCase						    =					scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   ).prev_sample
           UpperCamelCase						    =					new_scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   ).prev_sample

           assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"

 def      __lowerCAmelCase  (						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			List[Any]=None					, **SCREAMING_SNAKE_CASE__			:			Union[str, Any]   ):






      """simple docstring"""



      if scheduler is None:
           UpperCamelCase						    =					self.scheduler_classes[0]
           UpperCamelCase						    =					self.get_scheduler_config(**SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					scheduler_class(**SCREAMING_SNAKE_CASE__   )

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

      UpperCamelCase						    =					10
      UpperCamelCase						    =					self.dummy_model()
      UpperCamelCase						    =					self.dummy_sample_deter
      scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

      for i, t in enumerate(scheduler.timesteps   ):
           UpperCamelCase						    =					model(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ).prev_sample

      return sample

 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					dict(self.forward_default_kwargs   )

      UpperCamelCase						    =					kwargs.pop('num_inference_steps'					, SCREAMING_SNAKE_CASE__   )

      for scheduler_class in self.scheduler_classes:
           UpperCamelCase						    =					self.get_scheduler_config()
           UpperCamelCase						    =					scheduler_class(**SCREAMING_SNAKE_CASE__   )

           UpperCamelCase						    =					self.dummy_sample
           UpperCamelCase						    =					0.1 * sample

           if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE__					, 'set_timesteps'   ):
                scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )
           elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE__					, 'set_timesteps'   ):
                UpperCamelCase						    =					num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           UpperCamelCase						    =					[residual + 0.2, residual + 0.15, residual + 0.10]
           UpperCamelCase						    =					dummy_past_residuals[: scheduler.config.solver_order]

           UpperCamelCase						    =					scheduler.timesteps[5]
           UpperCamelCase						    =					scheduler.timesteps[6]

           UpperCamelCase						    =					scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   ).prev_sample
           UpperCamelCase						    =					scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   ).prev_sample

           self.assertEqual(output_a.shape					, sample.shape   )
           self.assertEqual(output_a.shape					, output_a.shape   )

 def      __lowerCAmelCase  (						self			:			List[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					DEISMultistepScheduler(**self.get_scheduler_config()   )
      UpperCamelCase						    =					self.full_loop(scheduler=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__   )   )

      assert abs(result_mean.item() - 0.23916   ) < 1e-3

      UpperCamelCase						    =					DPMSolverSinglestepScheduler.from_config(scheduler.config   )
      UpperCamelCase						    =					DPMSolverMultistepScheduler.from_config(scheduler.config   )
      UpperCamelCase						    =					UniPCMultistepScheduler.from_config(scheduler.config   )
      UpperCamelCase						    =					DEISMultistepScheduler.from_config(scheduler.config   )

      UpperCamelCase						    =					self.full_loop(scheduler=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__   )   )

      assert abs(result_mean.item() - 0.23916   ) < 1e-3

 def      __lowerCAmelCase  (						self			:			Union[str, Any]   ):






      """simple docstring"""



      for timesteps in [25, 50, 1_00, 9_99, 10_00]:
           self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE__   )
      for order in [1, 2, 3]:
           for solver_type in ["logrho"]:
                for threshold in [0.5, 1.0, 2.0]:
                     for prediction_type in ["epsilon", "sample"]:
                          self.check_over_configs(
                              thresholding=SCREAMING_SNAKE_CASE__					, prediction_type=SCREAMING_SNAKE_CASE__					, sample_max_value=SCREAMING_SNAKE_CASE__					, algorithm_type='deis'					, solver_order=SCREAMING_SNAKE_CASE__					, solver_type=SCREAMING_SNAKE_CASE__					, )

 def      __lowerCAmelCase  (						self			:			List[Any]   ):






      """simple docstring"""



      for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



      for algorithm_type in ["deis"]:
           for solver_type in ["logrho"]:
                for order in [1, 2, 3]:
                     for prediction_type in ["epsilon", "sample"]:
                          self.check_over_configs(
                              solver_order=SCREAMING_SNAKE_CASE__					, solver_type=SCREAMING_SNAKE_CASE__					, prediction_type=SCREAMING_SNAKE_CASE__					, algorithm_type=SCREAMING_SNAKE_CASE__					, )
                          UpperCamelCase						    =					self.full_loop(
                              solver_order=SCREAMING_SNAKE_CASE__					, solver_type=SCREAMING_SNAKE_CASE__					, prediction_type=SCREAMING_SNAKE_CASE__					, algorithm_type=SCREAMING_SNAKE_CASE__					, )
                          assert not torch.isnan(SCREAMING_SNAKE_CASE__   ).any(), "Samples have nan numbers"

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE__   )
      self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



      for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
           self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE__					, time_step=0   )

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.full_loop()
      UpperCamelCase						    =					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__   )   )

      assert abs(result_mean.item() - 0.23916   ) < 1e-3

 def      __lowerCAmelCase  (						self			:			Dict   ):






      """simple docstring"""



      UpperCamelCase						    =					self.full_loop(prediction_type='v_prediction'   )
      UpperCamelCase						    =					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__   )   )

      assert abs(result_mean.item() - 0.091   ) < 1e-3






 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      UpperCamelCase						    =					self.scheduler_classes[0]
      UpperCamelCase						    =					self.get_scheduler_config(thresholding=SCREAMING_SNAKE_CASE__					, dynamic_thresholding_ratio=0   )
      UpperCamelCase						    =					scheduler_class(**SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					10
      UpperCamelCase						    =					self.dummy_model()
      UpperCamelCase						    =					self.dummy_sample_deter.half()
      scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

      for i, t in enumerate(scheduler.timesteps   ):
           UpperCamelCase						    =					model(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ).prev_sample

      assert sample.dtype == torch.floataa


