





import unittest

from transformers import MarkupLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property

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


if is_torch_available():
				import torch

				from transformers import (
				    MarkupLMForQuestionAnswering,
				    MarkupLMForSequenceClassification,
				    MarkupLMForTokenClassification,
				    MarkupLMModel,
				)

# TODO check dependencies
from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer
class 							snake_case_		:







		def __init__(		self      ,  __lowercase      ,  __lowercase=1_3      ,  __lowercase=7      ,  __lowercase=True      ,  __lowercase=True      ,  __lowercase=True      ,  __lowercase=True      ,  __lowercase=9_9      ,  __lowercase=3_2      ,  __lowercase=5      ,  __lowercase=4      ,  __lowercase=3_7      ,  __lowercase="gelu"      ,  __lowercase=0.1      ,  __lowercase=0.1      ,  __lowercase=5_1_2      ,  __lowercase=1_6      ,  __lowercase=2      ,  __lowercase=0.0_2      ,  __lowercase=3      ,  __lowercase=None      ,  __lowercase=2_0      ,  __lowercase=3_0      ,  __lowercase=2      ,  __lowercase=2      ,  __lowercase=1_0      ,  )					-> List[Any]:
				lowerCamelCase :			str    		=parent
				lowerCamelCase :			Any    		=batch_size
				lowerCamelCase :			Tuple    		=seq_length
				lowerCamelCase :			Union[str, Any]    		=is_training
				lowerCamelCase :			List[str]    		=use_input_mask
				lowerCamelCase :			Optional[int]    		=use_token_type_ids
				lowerCamelCase :			List[str]    		=use_labels
				lowerCamelCase :			List[str]    		=vocab_size
				lowerCamelCase :			Tuple    		=hidden_size
				lowerCamelCase :			Dict    		=num_hidden_layers
				lowerCamelCase :			Union[str, Any]    		=num_attention_heads
				lowerCamelCase :			str    		=intermediate_size
				lowerCamelCase :			Any    		=hidden_act
				lowerCamelCase :			Any    		=hidden_dropout_prob
				lowerCamelCase :			int    		=attention_probs_dropout_prob
				lowerCamelCase :			str    		=max_position_embeddings
				lowerCamelCase :			Optional[Any]    		=type_vocab_size
				lowerCamelCase :			Optional[Any]    		=type_sequence_label_size
				lowerCamelCase :			str    		=initializer_range
				lowerCamelCase :			int    		=num_labels
				lowerCamelCase :			Union[str, Any]    		=scope
				lowerCamelCase :			int    		=max_xpath_tag_unit_embeddings
				lowerCamelCase :			Tuple    		=max_xpath_subs_unit_embeddings
				lowerCamelCase :			Optional[int]    		=tag_pad_id
				lowerCamelCase :			List[Any]    		=subs_pad_id
				lowerCamelCase :			Dict    		=max_depth







		def       __lowercase (		self )					-> Dict:
				lowerCamelCase :			Union[str, Any]    		=ids_tensor([self.batch_size, self.seq_length]      ,  self.vocab_size )

				lowerCamelCase :			Optional[Any]    		=ids_tensor(
				    [self.batch_size, self.seq_length, self.max_depth]      ,  self.max_xpath_tag_unit_embeddings )

				lowerCamelCase :			str    		=ids_tensor(
				    [self.batch_size, self.seq_length, self.max_depth]      ,  self.max_xpath_subs_unit_embeddings )

				lowerCamelCase :			Optional[int]    		=None
				if self.use_input_mask:
						lowerCamelCase :			Any    		=ids_tensor([self.batch_size, self.seq_length]      ,  vocab_size=2 )

				lowerCamelCase :			Optional[Any]    		=None
				if self.use_token_type_ids:
						lowerCamelCase :			List[str]    		=ids_tensor([self.batch_size, self.seq_length]      ,  self.type_vocab_size )

				lowerCamelCase :			Any    		=None
				lowerCamelCase :			str    		=None
				if self.use_labels:
						lowerCamelCase :			Dict    		=ids_tensor([self.batch_size]      ,  self.type_sequence_label_size )
						lowerCamelCase :			List[str]    		=ids_tensor([self.batch_size, self.seq_length]      ,  self.num_labels )

				lowerCamelCase :			Optional[int]    		=self.get_config()

				return (
				    config,
				    input_ids,
				    xpath_tags_seq,
				    xpath_subs_seq,
				    token_type_ids,
				    input_mask,
				    sequence_labels,
				    token_labels,
				)







		def       __lowercase (		self )					-> Dict:
				return MarkupLMConfig(
				    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      ,  max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings      ,  max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings      ,  tag_pad_id=self.tag_pad_id      ,  subs_pad_id=self.subs_pad_id      ,  max_depth=self.max_depth      ,  )







		def       __lowercase (		self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  )					-> List[Any]:
				lowerCamelCase :			List[str]    		=MarkupLMModel(config=__UpperCamelCase )
				model.to(__UpperCamelCase )
				model.eval()
				print('''Configs:'''      ,  model.config.tag_pad_id      ,  model.config.subs_pad_id )
				lowerCamelCase :			Union[str, Any]    		=model(__UpperCamelCase      ,  attention_mask=__UpperCamelCase      ,  token_type_ids=__UpperCamelCase )
				lowerCamelCase :			Optional[int]    		=model(__UpperCamelCase      ,  token_type_ids=__UpperCamelCase )
				lowerCamelCase :			List[Any]    		=model(__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       __lowercase (		self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  )					-> int:
				lowerCamelCase :			Union[str, Any]    		=self.num_labels
				lowerCamelCase :			Union[str, Any]    		=MarkupLMForSequenceClassification(__UpperCamelCase )
				model.to(__UpperCamelCase )
				model.eval()
				lowerCamelCase :			Tuple    		=model(
				    __UpperCamelCase      ,  xpath_tags_seq=__UpperCamelCase      ,  xpath_subs_seq=__UpperCamelCase      ,  attention_mask=__UpperCamelCase      ,  token_type_ids=__UpperCamelCase      ,  labels=__UpperCamelCase      ,  )
				self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.num_labels) )







		def       __lowercase (		self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  )					-> Any:
				lowerCamelCase :			List[str]    		=self.num_labels
				lowerCamelCase :			Dict    		=MarkupLMForTokenClassification(config=__UpperCamelCase )
				model.to(__UpperCamelCase )
				model.eval()
				lowerCamelCase :			List[str]    		=model(
				    __UpperCamelCase      ,  xpath_tags_seq=__UpperCamelCase      ,  xpath_subs_seq=__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       __lowercase (		self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  )					-> int:
				lowerCamelCase :			Optional[int]    		=MarkupLMForQuestionAnswering(config=__UpperCamelCase )
				model.to(__UpperCamelCase )
				model.eval()
				lowerCamelCase :			Any    		=model(
				    __UpperCamelCase      ,  xpath_tags_seq=__UpperCamelCase      ,  xpath_subs_seq=__UpperCamelCase      ,  attention_mask=__UpperCamelCase      ,  token_type_ids=__UpperCamelCase      ,  start_positions=__UpperCamelCase      ,  end_positions=__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       __lowercase (		self )					-> Optional[Any]:
				lowerCamelCase :			Any    		=self.prepare_config_and_inputs()
				(
				    (
				    lowerCamelCase					
				)		, (
				    lowerCamelCase					
				)		, (
				    lowerCamelCase					
				)		, (
				    lowerCamelCase					
				)		, (
				    lowerCamelCase					
				)		, (
				    lowerCamelCase					
				)		, (
				    lowerCamelCase					
				)		, (
				    lowerCamelCase					
				)		, 					
				) :			Any    		=config_and_inputs
				lowerCamelCase :			Any    		={
				    '''input_ids''': input_ids,
				    '''xpath_tags_seq''': xpath_tags_seq,
				    '''xpath_subs_seq''': xpath_subs_seq,
				    '''token_type_ids''': token_type_ids,
				    '''attention_mask''': input_mask,
				}
				return config, inputs_dict

@require_torch
class 							snake_case_		(      __SCREAMING_SNAKE_CASE    ,       __SCREAMING_SNAKE_CASE    ,       unittest.TestCase):
		lowerCamelCase     :Tuple							=  (
		    (
		        MarkupLMModel,
		        MarkupLMForSequenceClassification,
		        MarkupLMForTokenClassification,
		        MarkupLMForQuestionAnswering,
		    )
		    if is_torch_available()
		    else None
		)
		lowerCamelCase     :Union[str, Any]							=  (
		    {
		        """feature-extraction""": MarkupLMModel,
		        """question-answering""": MarkupLMForQuestionAnswering,
		        """text-classification""": MarkupLMForSequenceClassification,
		        """token-classification""": MarkupLMForTokenClassification,
		        """zero-shot""": MarkupLMForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)







		def       __lowercase (		self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase )					-> int:
				return True







		def       __lowercase (		self )					-> Dict:
				lowerCamelCase :			List[Any]    		=MarkupLMModelTester(self )
				lowerCamelCase :			Dict    		=ConfigTester(self      ,  config_class=__UpperCamelCase      ,  hidden_size=3_7 )







		def       __lowercase (		self )					-> Dict:
				self.config_tester.run_common_tests()







		def       __lowercase (		self )					-> Optional[Any]:
				lowerCamelCase :			Dict    		=self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__UpperCamelCase )







		def       __lowercase (		self )					-> Union[str, Any]:
				lowerCamelCase :			Optional[int]    		=self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase )







		def       __lowercase (		self )					-> Optional[Any]:
				lowerCamelCase :			List[str]    		=self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase )







		def       __lowercase (		self )					-> Optional[Any]:
				lowerCamelCase :			Union[str, Any]    		=self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase )


def        A__						(							) ->  Union[str, Any]:
		lowerCamelCase :			Union[str, Any]    		='''
    <!DOCTYPE html>
    <html>
    <head>
    <title>Page Title</title>
    </head>
    <body>

    <h1>This is a Heading</h1>
    <p>This is a paragraph.</p>

    </body>
    </html>
    '''

		return html_string



@require_torch
class 							snake_case_		(      unittest.TestCase):







		@cached_property
		def       __lowercase (		self )					-> Optional[Any]:
				lowerCamelCase :			Optional[Any]    		=MarkupLMFeatureExtractor()
				lowerCamelCase :			str    		=MarkupLMTokenizer.from_pretrained('''microsoft/markuplm-base''' )

				return MarkupLMProcessor(__UpperCamelCase      ,  __UpperCamelCase )







		@slow
		def       __lowercase (		self )					-> Optional[Any]:
				lowerCamelCase :			int    		=MarkupLMModel.from_pretrained('''microsoft/markuplm-base''' ).to(__UpperCamelCase )

				lowerCamelCase :			Optional[int]    		=self.default_processor

				lowerCamelCase :			int    		=processor(prepare_html_string()      ,  return_tensors='''pt''' )
				lowerCamelCase :			Dict    		=inputs.to(__UpperCamelCase )

				# forward pass
				with torch.no_grad():
						lowerCamelCase :			str    		=model(**__UpperCamelCase )

				# verify the last hidden states
				lowerCamelCase :			str    		=torch.Size([1, 1_4, 7_6_8] )
				self.assertEqual(outputs.last_hidden_state.shape      ,  __UpperCamelCase )

				lowerCamelCase :			List[str]    		=torch.tensor(
				    [[0.0_6_7_5, -0.0_0_5_2, 0.5_0_0_1], [-0.2_2_8_1, 0.0_8_0_2, 0.2_1_9_2], [-0.0_5_8_3, -0.3_3_1_1, 0.1_1_8_5]] ).to(__UpperCamelCase )

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



import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class 		lowercase_							(							__SCREAMING_SNAKE_CASE					):
 A__    :					List[Any]    =     (DPMSolverMultistepScheduler,)
 A__    :					List[str]    =     (("""num_inference_steps""", 25),)

 def lowerCamelCase_					(   self  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							{
            """num_train_timesteps""": 1_0_0_0,
            """beta_start""": 0.0_001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
            """solver_order""": 2,
            """prediction_type""": """epsilon""",
            """thresholding""": False,
            """sample_max_value""": 1.0,
            """algorithm_type""": """dpmsolver++""",
            """solver_type""": """midpoint""",
            """lower_order_final""": False,
            """lambda_min_clipped""": -float("""inf"""  ),
            """variance_type""": None,
        }

        config.update(**__UpperCamelCase  )
        return config

 def lowerCamelCase_					(   self  ,						__UpperCamelCase=0  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )
        UpperCamelCase_											=							kwargs.pop("""num_inference_steps"""  ,						__UpperCamelCase  )
        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  )
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )
               scheduler.set_timesteps(__UpperCamelCase  )
               # copy over dummy past residuals
               UpperCamelCase_											=							dummy_past_residuals[: scheduler.config.solver_order]

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

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

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

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        pass

 def lowerCamelCase_					(   self  ,						__UpperCamelCase=0  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )
        UpperCamelCase_											=							kwargs.pop("""num_inference_steps"""  ,						__UpperCamelCase  )
        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(**__UpperCamelCase  )
               scheduler.set_timesteps(__UpperCamelCase  )

               # 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(__UpperCamelCase  )
                      UpperCamelCase_											=							scheduler_class.from_pretrained(__UpperCamelCase  )
                      # copy over dummy past residuals
                      new_scheduler.set_timesteps(__UpperCamelCase  )

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

               UpperCamelCase_											=							scheduler.step(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						**__UpperCamelCase  ).prev_sample

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

 def lowerCamelCase_					(   self  ,						__UpperCamelCase=None  ,						**__UpperCamelCase  ):




        """simple docstring"""


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

        UpperCamelCase_											=							1_0
        UpperCamelCase_											=							self.dummy_model()
        UpperCamelCase_											=							self.dummy_sample_deter
        scheduler.set_timesteps(__UpperCamelCase  )

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

        return sample

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )

        UpperCamelCase_											=							kwargs.pop("""num_inference_steps"""  ,						__UpperCamelCase  )

        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.get_scheduler_config()
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

               UpperCamelCase_											=							self.dummy_sample
               UpperCamelCase_											=							0.1 * sample

               if num_inference_steps is not None and hasattr(__UpperCamelCase  ,						"""set_timesteps"""  ):
                      scheduler.set_timesteps(__UpperCamelCase  )
               elif num_inference_steps is not None and not hasattr(__UpperCamelCase  ,						"""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(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							scheduler.step(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						**__UpperCamelCase  ).prev_sample

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

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
               self.check_over_configs(num_train_timesteps=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        self.check_over_configs(thresholding=__UpperCamelCase  )
        for order in [1, 2, 3]:
               for solver_type in ["midpoint", "heun"]:
                      for threshold in [0.5, 1.0, 2.0]:
                             for prediction_type in ["epsilon", "sample"]:
                                    self.check_over_configs(
                                        thresholding=__UpperCamelCase  ,						prediction_type=__UpperCamelCase  ,						sample_max_value=__UpperCamelCase  ,						algorithm_type="""dpmsolver++"""  ,						solver_order=__UpperCamelCase  ,						solver_type=__UpperCamelCase  ,						)

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


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

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
               for solver_type in ["midpoint", "heun"]:
                      for order in [1, 2, 3]:
                             for prediction_type in ["epsilon", "sample"]:
                                    if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                                           if order == 3:
                                                  continue
                                    else:
                                           self.check_over_configs(
                                               solver_order=__UpperCamelCase  ,						solver_type=__UpperCamelCase  ,						prediction_type=__UpperCamelCase  ,						algorithm_type=__UpperCamelCase  ,						)
                                    UpperCamelCase_											=							self.full_loop(
                                        solver_order=__UpperCamelCase  ,						solver_type=__UpperCamelCase  ,						prediction_type=__UpperCamelCase  ,						algorithm_type=__UpperCamelCase  ,						)
                                    assert not torch.isnan(__UpperCamelCase  ).any(), "Samples have nan numbers"

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        self.check_over_configs(lower_order_final=__UpperCamelCase  )
        self.check_over_configs(lower_order_final=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        self.check_over_configs(lambda_min_clipped=-float("""inf"""  )  )
        self.check_over_configs(lambda_min_clipped=-5.1  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        self.check_over_configs(variance_type=__UpperCamelCase  )
        self.check_over_configs(variance_type="""learned_range"""  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
               self.check_over_forward(num_inference_steps=__UpperCamelCase  ,						time_step=0  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.full_loop()
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert abs(result_mean.item() - 0.3_301  ) < 1e-3

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.full_loop(thresholding=__UpperCamelCase  ,						dynamic_thresholding_ratio=0.87  ,						sample_max_value=0.5  )
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert abs(result_mean.item() - 1.1_364  ) < 1e-3

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.full_loop(prediction_type="""v_prediction"""  )
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert abs(result_mean.item() - 0.2_251  ) < 1e-3

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.full_loop(prediction_type="""v_prediction"""  ,						use_karras_sigmas=__UpperCamelCase  )
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert abs(result_mean.item() - 0.2_096  ) < 1e-3

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							DPMSolverMultistepScheduler(**self.get_scheduler_config()  )
        UpperCamelCase_											=							self.full_loop(scheduler=__UpperCamelCase  )
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert abs(result_mean.item() - 0.3_301  ) < 1e-3

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

        UpperCamelCase_											=							self.full_loop(scheduler=__UpperCamelCase  )
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert abs(result_mean.item() - 0.3_301  ) < 1e-3

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.scheduler_classes[0]
        UpperCamelCase_											=							self.get_scheduler_config(thresholding=__UpperCamelCase  ,						dynamic_thresholding_ratio=0  )
        UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

        UpperCamelCase_											=							1_0
        UpperCamelCase_											=							self.dummy_model()
        UpperCamelCase_											=							self.dummy_sample_deter.half()
        scheduler.set_timesteps(__UpperCamelCase  )

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

        assert sample.dtype == torch.floataa

 def lowerCamelCase_					(   self  ,						**__UpperCamelCase  ):




        """simple docstring"""


        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.get_scheduler_config(**__UpperCamelCase  )
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

               scheduler.set_timesteps(scheduler.config.num_train_timesteps  )
               assert len(scheduler.timesteps.unique()  ) == scheduler.num_inference_steps



