






"""simple docstring"""






import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 (
      LiltForQuestionAnswering,
      LiltForSequenceClassification,
      LiltForTokenClassification,
      LiltModel,
  )
  from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST




class 							lowerCAmelCase__				:






      '''simple docstring'''

      def __init__(						self     :  Any						,	lowercase_     :  List[Any]						,	lowercase_     :  List[str]=13						,	lowercase_     :  int=7						,	lowercase_     :  Any=True						,	lowercase_     :  str=True						,	lowercase_     :  List[Any]=True						,	lowercase_     :  List[Any]=True						,	lowercase_     :  Dict=99						,	lowercase_     :  Union[str, Any]=24						,	lowercase_     :  int=2						,	lowercase_     :  List[str]=6						,	lowercase_     :  Any=37						,	lowercase_     :  Dict="gelu"						,	lowercase_     :  List[str]=0.1						,	lowercase_     :  Dict=0.1						,	lowercase_     :  Union[str, Any]=512						,	lowercase_     :  List[str]=16						,	lowercase_     :  Any=2						,	lowercase_     :  Any=0.02						,	lowercase_     :  List[Any]=3						,	lowercase_     :  Optional[int]=None						,	lowercase_     :  str=1000						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		parent
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		batch_size
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		seq_length
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		is_training
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		use_input_mask
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		use_token_type_ids
          SCREAMING_SNAKE_CASE_      :  int    						=		use_labels
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		vocab_size
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		hidden_size
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		num_hidden_layers
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		num_attention_heads
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		intermediate_size
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		hidden_act
          SCREAMING_SNAKE_CASE_      :  int    						=		hidden_dropout_prob
          SCREAMING_SNAKE_CASE_      :  str    						=		attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		max_position_embeddings
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		type_vocab_size
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		type_sequence_label_size
          SCREAMING_SNAKE_CASE_      :  Any    						=		initializer_range
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		num_labels
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		scope
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		range_bbox
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		ids_tensor([self.batch_size, self.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_      :  Optional[int]    						=		bbox[i, j, 3]
                      SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		bbox[i, j, 1]
                      SCREAMING_SNAKE_CASE_      :  str    						=		t
                  if bbox[i, j, 2] < bbox[i, j, 0]:
                      SCREAMING_SNAKE_CASE_      :  List[str]    						=		bbox[i, j, 2]
                      SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		bbox[i, j, 0]
                      SCREAMING_SNAKE_CASE_      :  List[str]    						=		t

          SCREAMING_SNAKE_CASE_      :  Tuple    						=		None
          if self.use_input_mask:
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		ids_tensor([self.batch_size, self.seq_length]						,	vocab_size=2)

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		None
          if self.use_token_type_ids:
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		ids_tensor([self.batch_size, self.seq_length]						,	self.type_vocab_size)

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

          SCREAMING_SNAKE_CASE_      :  Any    						=		self.get_config()

          return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          return LiltConfig(
              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						,	)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]						,	lowercase_     :  Optional[Any]						,	lowercase_     :  Optional[Any]						,	lowercase_     :  str						,	lowercase_     :  Optional[Any]						,	lowercase_     :  int						,	lowercase_     :  Optional[Any]						,	lowercase_     :  int						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		LiltModel(config=lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		model(lowercase_						,	bbox=lowercase_						,	attention_mask=lowercase_						,	token_type_ids=lowercase_)
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		model(lowercase_						,	bbox=lowercase_						,	token_type_ids=lowercase_)
          SCREAMING_SNAKE_CASE_      :  int    						=		model(lowercase_						,	bbox=lowercase_)

          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 _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  Optional[Any]						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  List[Any]						,	lowercase_     :  int						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		self.num_labels
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		LiltForTokenClassification(config=lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		model(
              lowercase_						,	bbox=lowercase_						,	attention_mask=lowercase_						,	token_type_ids=lowercase_						,	labels=lowercase_)
          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.num_labels))
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple						,	lowercase_     :  str						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  Any						,	lowercase_     :  Dict						,	lowercase_     :  List[str]						,	lowercase_     :  List[Any]						,	lowercase_     :  str						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		LiltForQuestionAnswering(config=lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		model(
              lowercase_						,	bbox=lowercase_						,	attention_mask=lowercase_						,	token_type_ids=lowercase_						,	start_positions=lowercase_						,	end_positions=lowercase_						,	)
          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 _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  str    						=		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_
          )						,			
          )      :  List[str]    						=		config_and_inputs
          SCREAMING_SNAKE_CASE_      :  str    						=		{
              '''input_ids''': input_ids,
              '''bbox''': bbox,
              '''token_type_ids''': token_type_ids,
              '''attention_mask''': input_mask,
          }
          return config, inputs_dict




@require_torch
class 							lowerCAmelCase__				(			UpperCAmelCase__				,		UpperCAmelCase__				,		UpperCAmelCase__				,		unittest.TestCase ):






      '''simple docstring'''

      __UpperCamelCase	 =  (
          (
              LiltModel,
              LiltForSequenceClassification,
              LiltForTokenClassification,
              LiltForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      __UpperCamelCase	 =  (
          {
              "feature-extraction": LiltModel,
              "question-answering": LiltForQuestionAnswering,
              "text-classification": LiltForSequenceClassification,
              "token-classification": LiltForTokenClassification,
              "zero-shot": LiltForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      __UpperCamelCase	 =  False
      __UpperCamelCase	 =  False
      def _SCREAMING_SNAKE_CASE (						self     :  Any						,	lowercase_     :  List[str]						,	lowercase_     :  Optional[int]						,	lowercase_     :  Optional[int]						,	lowercase_     :  str						,	lowercase_     :  str):




          '''simple docstring'''


          return True
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Dict    						=		LiltModelTester(self)
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		ConfigTester(self						,	config_class=lowercase_						,	hidden_size=37)
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          self.config_tester.run_common_tests()
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
              SCREAMING_SNAKE_CASE_      :  Dict    						=		type
              self.model_tester.create_and_check_model(*lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*lowercase_)



      @slow
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		LiltModel.from_pretrained(lowercase_)
              self.assertIsNotNone(lowercase_)




@require_torch
@slow
class 							lowerCAmelCase__				(			unittest.TestCase ):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base''').to(lowercase_)

          SCREAMING_SNAKE_CASE_      :  str    						=		torch.tensor([[1, 2]]						,	device=lowercase_)
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]						,	device=lowercase_)

          # forward pass
          with torch.no_grad():
              SCREAMING_SNAKE_CASE_      :  Dict    						=		model(input_ids=lowercase_						,	bbox=lowercase_)

          SCREAMING_SNAKE_CASE_      :  str    						=		torch.Size([1, 2, 768])
          SCREAMING_SNAKE_CASE_      :  Dict    						=		torch.tensor(
              [[-0.06_53, 0.09_50, -0.00_61], [-0.05_45, 0.09_26, -0.03_24]]						,	device=lowercase_						,	)

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










"""simple docstring"""






import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  (PNDMScheduler,)
      __UpperCamelCase	 =  (("num_inference_steps", 5_0),)
      def _SCREAMING_SNAKE_CASE (						self     :  Any						,	**lowercase_     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		{
              '''num_train_timesteps''': 1000,
              '''beta_start''': 0.00_01,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
          }

          config.update(**lowercase_)
          return config
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]						,	lowercase_     :  List[str]=0						,	**lowercase_     :  Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		dict(self.forward_default_kwargs)
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		kwargs.pop('''num_inference_steps'''						,	lowercase_)
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.dummy_sample
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		0.1 * sample
          SCREAMING_SNAKE_CASE_      :  Dict    						=		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

          for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.get_scheduler_config(**lowercase_)
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		scheduler_class(**lowercase_)
              scheduler.set_timesteps(lowercase_)
              # copy over dummy past residuals
              SCREAMING_SNAKE_CASE_      :  Dict    						=		dummy_past_residuals[:]

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(lowercase_)
                  SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		scheduler_class.from_pretrained(lowercase_)
                  new_scheduler.set_timesteps(lowercase_)
                  # copy over dummy past residuals
                  SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		dummy_past_residuals[:]

              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		new_scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample

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

              SCREAMING_SNAKE_CASE_      :  List[str]    						=		scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  Dict    						=		new_scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample

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




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]						,	lowercase_     :  List[str]=0						,	**lowercase_     :  List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		dict(self.forward_default_kwargs)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		kwargs.pop('''num_inference_steps'''						,	lowercase_)
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.dummy_sample
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		0.1 * sample
          SCREAMING_SNAKE_CASE_      :  int    						=		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

          for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_      :  Dict    						=		self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		scheduler_class(**lowercase_)
              scheduler.set_timesteps(lowercase_)

              # copy over dummy past residuals (must be after setting timesteps)
              SCREAMING_SNAKE_CASE_      :  Dict    						=		dummy_past_residuals[:]

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

                  # copy over dummy past residual (must be after setting timesteps)
                  SCREAMING_SNAKE_CASE_      :  Any    						=		dummy_past_residuals[:]

              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		new_scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample

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

              SCREAMING_SNAKE_CASE_      :  List[str]    						=		scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		new_scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample

              assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
      def _SCREAMING_SNAKE_CASE (						self     :  str						,	**lowercase_     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.get_scheduler_config(**lowercase_)
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		scheduler_class(**lowercase_)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		10
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		self.dummy_model()
          SCREAMING_SNAKE_CASE_      :  str    						=		self.dummy_sample_deter
          scheduler.set_timesteps(lowercase_)

          for i, t in enumerate(scheduler.prk_timesteps):
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		model(lowercase_						,	lowercase_)
              SCREAMING_SNAKE_CASE_      :  str    						=		scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_).prev_sample

          for i, t in enumerate(scheduler.plms_timesteps):
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		model(lowercase_						,	lowercase_)
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_).prev_sample

          return sample
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		dict(self.forward_default_kwargs)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		kwargs.pop('''num_inference_steps'''						,	lowercase_)

          for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		scheduler_class(**lowercase_)

              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.dummy_sample
              SCREAMING_SNAKE_CASE_      :  Any    						=		0.1 * sample

              if num_inference_steps is not None and hasattr(lowercase_						,	'''set_timesteps'''):
                  scheduler.set_timesteps(lowercase_)
              elif num_inference_steps is not None and not hasattr(lowercase_						,	'''set_timesteps'''):
                  SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		num_inference_steps

              # copy over dummy past residuals (must be done after set_timesteps)
              SCREAMING_SNAKE_CASE_      :  str    						=		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		dummy_past_residuals[:]

              SCREAMING_SNAKE_CASE_      :  Dict    						=		scheduler.step_prk(lowercase_						,	0						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		scheduler.step_prk(lowercase_						,	1						,	lowercase_						,	**lowercase_).prev_sample

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

              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		scheduler.step_plms(lowercase_						,	0						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  Any    						=		scheduler.step_plms(lowercase_						,	1						,	lowercase_						,	**lowercase_).prev_sample

              self.assertEqual(output_a.shape						,	sample.shape)
              self.assertEqual(output_a.shape						,	output_a.shape)
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          for timesteps in [100, 1000]:
              self.check_over_configs(num_train_timesteps=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          for steps_offset in [0, 1]:
              self.check_over_configs(steps_offset=lowercase_)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.get_scheduler_config(steps_offset=1)
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		scheduler_class(**lowercase_)
          scheduler.set_timesteps(10)
          assert torch.equal(
              scheduler.timesteps						,	torch.LongTensor(
                  [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1])						,	)
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          for beta_start, beta_end in zip([0.00_01, 0.0_01]						,	[0.0_02, 0.02]):
              self.check_over_configs(beta_start=lowercase_						,	beta_end=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  int):




          '''simple docstring'''


          for t in [1, 5, 10]:
              self.check_over_forward(time_step=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          for t, num_inference_steps in zip([1, 5, 10]						,	[10, 50, 100]):
              self.check_over_forward(num_inference_steps=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		27

          for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		self.dummy_sample
              SCREAMING_SNAKE_CASE_      :  str    						=		0.1 * sample

              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		scheduler_class(**lowercase_)

              scheduler.set_timesteps(lowercase_)

              # before power of 3 fix, would error on first step, so we only need to do two
              for i, t in enumerate(scheduler.prk_timesteps[:2]):
                  SCREAMING_SNAKE_CASE_      :  int    						=		scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_).prev_sample
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          with self.assertRaises(lowercase_):
              SCREAMING_SNAKE_CASE_      :  int    						=		self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_      :  Dict    						=		scheduler_class(**lowercase_)

              scheduler.step_plms(self.dummy_sample						,	1						,	self.dummy_sample).prev_sample
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		self.full_loop()
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		torch.sum(torch.abs(lowercase_))
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		torch.mean(torch.abs(lowercase_))

          assert abs(result_sum.item() - 1_98.13_18) < 1e-2
          assert abs(result_mean.item() - 0.25_80) < 1e-3
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  str    						=		self.full_loop(prediction_type='''v_prediction''')
          SCREAMING_SNAKE_CASE_      :  str    						=		torch.sum(torch.abs(lowercase_))
          SCREAMING_SNAKE_CASE_      :  Any    						=		torch.mean(torch.abs(lowercase_))

          assert abs(result_sum.item() - 67.39_86) < 1e-2
          assert abs(result_mean.item() - 0.08_78) < 1e-3
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.full_loop(set_alpha_to_one=lowercase_						,	beta_start=0.01)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		torch.sum(torch.abs(lowercase_))
          SCREAMING_SNAKE_CASE_      :  Any    						=		torch.mean(torch.abs(lowercase_))

          assert abs(result_sum.item() - 2_30.03_99) < 1e-2
          assert abs(result_mean.item() - 0.29_95) < 1e-3



      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self.full_loop(set_alpha_to_one=lowercase_						,	beta_start=0.01)
          SCREAMING_SNAKE_CASE_      :  int    						=		torch.sum(torch.abs(lowercase_))
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		torch.mean(torch.abs(lowercase_))

          assert abs(result_sum.item() - 1_86.94_82) < 1e-2
          assert abs(result_mean.item() - 0.24_34) < 1e-3


