






"""simple docstring"""
import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel



class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	def __init__(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_=13  ,  lowerCAmelCase_=7  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_=False  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_=99  ,  lowerCAmelCase_=32  ,  lowerCAmelCase_=5  ,  lowerCAmelCase_=4  ,  lowerCAmelCase_=37  ,  lowerCAmelCase_="gelu"  ,  lowerCAmelCase_=0.1  ,  lowerCAmelCase_=0.1  ,  lowerCAmelCase_=512  ,  lowerCAmelCase_=16  ,  lowerCAmelCase_=2  ,  lowerCAmelCase_=0.02  ,  lowerCAmelCase_=3  ,  lowerCAmelCase_=4  ,  lowerCAmelCase_=None  ,  ):
		A_		:    Union[str, Any]						     = parent
		A_		:    Optional[Any]						     = batch_size
		A_		:    Tuple						     = seq_length
		A_		:    Union[str, Any]						     = is_training
		A_		:    int						     = use_input_mask
		A_		:    int						     = use_token_type_ids
		A_		:    str						     = use_labels
		A_		:    int						     = vocab_size
		A_		:    int						     = hidden_size
		A_		:    Optional[int]						     = num_hidden_layers
		A_		:    Any						     = num_attention_heads
		A_		:    str						     = intermediate_size
		A_		:    List[Any]						     = hidden_act
		A_		:    Any						     = hidden_dropout_prob
		A_		:    Tuple						     = attention_probs_dropout_prob
		A_		:    List[str]						     = max_position_embeddings
		A_		:    List[str]						     = type_vocab_size
		A_		:    Any						     = type_sequence_label_size
		A_		:    Optional[Any]						     = initializer_range
		A_		:    Any						     = num_labels
		A_		:    Dict						     = num_choices
		A_		:    Union[str, Any]						     = scope





	def  lowerCamelCase(self				):
		A_		:    Optional[Any]						     = ids_tensor([self.batch_size, self.seq_length]  ,  self.vocab_size				)

		A_		:    Optional[Any]						     = None
		if self.use_input_mask:
			A_		:    Tuple						     = random_attention_mask([self.batch_size, self.seq_length]				)

		A_		:    Dict						     = None
		if self.use_token_type_ids:
			A_		:    List[Any]						     = ids_tensor([self.batch_size, self.seq_length]  ,  self.type_vocab_size				)

		A_		:    List[Any]						     = None
		A_		:    List[str]						     = None
		A_		:    Tuple						     = None
		if self.use_labels:
			A_		:    Optional[int]						     = ids_tensor([self.batch_size]  ,  self.type_sequence_label_size				)
			A_		:    Optional[Any]						     = ids_tensor([self.batch_size, self.seq_length]  ,  self.num_labels				)
			A_		:    Optional[int]						     = ids_tensor([self.batch_size]  ,  self.num_choices				)

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

		return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





	def  lowerCamelCase(self				):
		return OpenLlamaConfig(
		    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  ,  is_decoder=lowerCAmelCase_  ,  initializer_range=self.initializer_range  ,  use_stable_embedding=lowerCAmelCase_  ,  )





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    Optional[int]						     = OpenLlamaModel(config=lowerCAmelCase_				)
		model.to(lowerCAmelCase_				)
		model.eval()
		A_		:    Optional[int]						     = model(lowerCAmelCase_  ,  attention_mask=lowerCAmelCase_				)
		A_		:    Optional[int]						     = model(lowerCAmelCase_				)
		self.parent.assertEqual(result.last_hidden_state.shape  ,  (self.batch_size, self.seq_length, self.hidden_size)				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  ):
		A_		:    Dict						     = True
		A_		:    List[Any]						     = OpenLlamaModel(lowerCAmelCase_				)
		model.to(lowerCAmelCase_				)
		model.eval()
		A_		:    Dict						     = model(
		    lowerCAmelCase_  ,  attention_mask=lowerCAmelCase_  ,  encoder_hidden_states=lowerCAmelCase_  ,  encoder_attention_mask=lowerCAmelCase_  ,  )
		A_		:    str						     = model(
		    lowerCAmelCase_  ,  attention_mask=lowerCAmelCase_  ,  encoder_hidden_states=lowerCAmelCase_  ,  )
		A_		:    str						     = model(lowerCAmelCase_  ,  attention_mask=lowerCAmelCase_				)
		self.parent.assertEqual(result.last_hidden_state.shape  ,  (self.batch_size, self.seq_length, self.hidden_size)				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  ):
		A_		:    Optional[Any]						     = OpenLlamaForCausalLM(config=lowerCAmelCase_				)
		model.to(lowerCAmelCase_				)
		model.eval()
		A_		:    Dict						     = model(lowerCAmelCase_  ,  attention_mask=lowerCAmelCase_  ,  labels=lowerCAmelCase_				)
		self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.seq_length, self.vocab_size)				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  ):
		A_		:    Any						     = True
		A_		:    str						     = True
		A_		:    Tuple						     = OpenLlamaForCausalLM(config=lowerCAmelCase_				)
		model.to(lowerCAmelCase_				)
		model.eval()

		# first forward pass
		A_		:    str						     = model(
		    lowerCAmelCase_  ,  attention_mask=lowerCAmelCase_  ,  encoder_hidden_states=lowerCAmelCase_  ,  encoder_attention_mask=lowerCAmelCase_  ,  use_cache=lowerCAmelCase_  ,  )
		A_		:    Optional[Any]						     = outputs.past_key_values

		# create hypothetical multiple next token and extent to next_input_ids
		A_		:    Dict						     = ids_tensor((self.batch_size, 3)  ,  config.vocab_size				)
		A_		:    List[Any]						     = ids_tensor((self.batch_size, 3)  ,  vocab_size=2				)

		# append to next input_ids and
		A_		:    Union[str, Any]						     = torch.cat([input_ids, next_tokens]  ,  dim=-1				)
		A_		:    Any						     = torch.cat([input_mask, next_mask]  ,  dim=-1				)

		A_		:    Union[str, Any]						     = model(
		    lowerCAmelCase_  ,  attention_mask=lowerCAmelCase_  ,  encoder_hidden_states=lowerCAmelCase_  ,  encoder_attention_mask=lowerCAmelCase_  ,  output_hidden_states=lowerCAmelCase_  ,  )["""hidden_states"""][0]
		A_		:    Tuple						     = model(
		    lowerCAmelCase_  ,  attention_mask=lowerCAmelCase_  ,  encoder_hidden_states=lowerCAmelCase_  ,  encoder_attention_mask=lowerCAmelCase_  ,  past_key_values=lowerCAmelCase_  ,  output_hidden_states=lowerCAmelCase_  ,  )["""hidden_states"""][0]

		# select random slice
		A_		:    str						     = ids_tensor((1,)  ,  output_from_past.shape[-1]				).item()
		A_		:    List[str]						     = output_from_no_past[:, -3:, random_slice_idx].detach()
		A_		:    Union[str, Any]						     = output_from_past[:, :, random_slice_idx].detach()

		self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]				)

		# test that outputs are equal for slice
		self.parent.assertTrue(torch.allclose(lowerCAmelCase_  ,  lowerCAmelCase_  ,  atol=1e-3				)				)





	def  lowerCamelCase(self				):
		A_		:    Any						     = self.prepare_config_and_inputs()
		(
		    (
		    A_
		)     ,  (
		    A_
		)     ,  (
		    A_
		)     ,  (
		    A_
		)     ,  (
		    A_
		)     ,  (
		    A_
		)     ,  (
		    A_
		)     ,  
		)		:    int						     = config_and_inputs
		A_		:    int						     = {"""input_ids""": input_ids, """attention_mask""": input_mask}
		return config, inputs_dict






@require_torch
class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE							,							unittest.TestCase    ):





	"""simple docstring"""




	_A							:					Union[str, Any] 			=					(
	    (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
	)
	_A							:					Tuple 			=					(OpenLlamaForCausalLM,) if is_torch_available() else ()
	_A							:					Dict 			=					(
	    {
	        """feature-extraction""": OpenLlamaModel,
	        """text-classification""": OpenLlamaForSequenceClassification,
	        """text-generation""": OpenLlamaForCausalLM,
	        """zero-shot""": OpenLlamaForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	_A							:					Optional[Any] 			=					False
	_A							:					Any 			=					False





	def  lowerCamelCase(self				):
		A_		:    Dict						     = OpenLlamaModelTester(self				)
		A_		:    List[Any]						     = ConfigTester(self  ,  config_class=lowerCAmelCase_  ,  hidden_size=37				)





	def  lowerCamelCase(self				):
		self.config_tester.run_common_tests()





	def  lowerCamelCase(self				):
		A_		:    Dict						     = self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_		:    Optional[int]						     = self.model_tester.prepare_config_and_inputs()
		for type in ["absolute", "relative_key", "relative_key_query"]:
			A_		:    List[str]						     = type
			self.model_tester.create_and_check_model(*lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_     ,  A_		:    List[Any]						     = self.model_tester.prepare_config_and_inputs_for_common()
		A_		:    str						     = 3
		A_		:    Optional[Any]						     = input_dict["""input_ids"""]
		A_		:    Optional[Any]						     = input_ids.ne(1				).to(lowerCAmelCase_				)
		A_		:    Optional[int]						     = ids_tensor([self.model_tester.batch_size]  ,  self.model_tester.type_sequence_label_size				)
		A_		:    str						     = OpenLlamaForSequenceClassification(lowerCAmelCase_				)
		model.to(lowerCAmelCase_				)
		model.eval()
		A_		:    Dict						     = model(lowerCAmelCase_  ,  attention_mask=lowerCAmelCase_  ,  labels=lowerCAmelCase_				)
		self.assertEqual(result.logits.shape  ,  (self.model_tester.batch_size, self.model_tester.num_labels)				)





	def  lowerCamelCase(self				):
		A_     ,  A_		:    Tuple						     = self.model_tester.prepare_config_and_inputs_for_common()
		A_		:    List[str]						     = 3
		A_		:    Optional[Any]						     = """single_label_classification"""
		A_		:    Optional[Any]						     = input_dict["""input_ids"""]
		A_		:    Optional[Any]						     = input_ids.ne(1				).to(lowerCAmelCase_				)
		A_		:    Dict						     = ids_tensor([self.model_tester.batch_size]  ,  self.model_tester.type_sequence_label_size				)
		A_		:    Optional[Any]						     = OpenLlamaForSequenceClassification(lowerCAmelCase_				)
		model.to(lowerCAmelCase_				)
		model.eval()
		A_		:    List[Any]						     = model(lowerCAmelCase_  ,  attention_mask=lowerCAmelCase_  ,  labels=lowerCAmelCase_				)
		self.assertEqual(result.logits.shape  ,  (self.model_tester.batch_size, self.model_tester.num_labels)				)





	def  lowerCamelCase(self				):
		A_     ,  A_		:    Dict						     = self.model_tester.prepare_config_and_inputs_for_common()
		A_		:    Optional[int]						     = 3
		A_		:    Tuple						     = """multi_label_classification"""
		A_		:    Optional[int]						     = input_dict["""input_ids"""]
		A_		:    Any						     = input_ids.ne(1				).to(lowerCAmelCase_				)
		A_		:    List[Any]						     = ids_tensor(
		    [self.model_tester.batch_size, config.num_labels]  ,  self.model_tester.type_sequence_label_size				).to(torch.float				)
		A_		:    Any						     = OpenLlamaForSequenceClassification(lowerCAmelCase_				)
		model.to(lowerCAmelCase_				)
		model.eval()
		A_		:    List[Any]						     = model(lowerCAmelCase_  ,  attention_mask=lowerCAmelCase_  ,  labels=lowerCAmelCase_				)
		self.assertEqual(result.logits.shape  ,  (self.model_tester.batch_size, self.model_tester.num_labels)				)





	@unittest.skip("""Open-Llama buffers include complex numbers, which breaks this test"""				)
	def  lowerCamelCase(self				):
		pass





	@parameterized.expand([("""linear""",), ("""dynamic""",)]				)
	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		A_     ,  A_		:    Union[str, Any]						     = self.model_tester.prepare_config_and_inputs_for_common()
		A_		:    Any						     = ids_tensor([1, 10]  ,  config.vocab_size				)
		A_		:    List[str]						     = ids_tensor([1, int(config.max_position_embeddings * 1.5				)]  ,  config.vocab_size				)

		set_seed(42				)  # Fixed seed at init time so the two models get the same random weights
		A_		:    Optional[Any]						     = OpenLlamaModel(lowerCAmelCase_				)
		original_model.to(lowerCAmelCase_				)
		original_model.eval()
		A_		:    Tuple						     = original_model(lowerCAmelCase_				).last_hidden_state
		A_		:    Optional[Any]						     = original_model(lowerCAmelCase_				).last_hidden_state

		set_seed(42				)  # Fixed seed at init time so the two models get the same random weights
		A_		:    Tuple						     = {"""type""": scaling_type, """factor""": 10.0}
		A_		:    Union[str, Any]						     = OpenLlamaModel(lowerCAmelCase_				)
		scaled_model.to(lowerCAmelCase_				)
		scaled_model.eval()
		A_		:    Union[str, Any]						     = scaled_model(lowerCAmelCase_				).last_hidden_state
		A_		:    Dict						     = scaled_model(lowerCAmelCase_				).last_hidden_state

		# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
		# maximum sequence length, so the outputs for the short input should match.
		if scaling_type == "dynamic":
			self.assertTrue(torch.allclose(lowerCAmelCase_  ,  lowerCAmelCase_  ,  atol=1e-5				)				)
		else:
			self.assertFalse(torch.allclose(lowerCAmelCase_  ,  lowerCAmelCase_  ,  atol=1e-5				)				)

		# The output should be different for long inputs
		self.assertFalse(torch.allclose(lowerCAmelCase_  ,  lowerCAmelCase_  ,  atol=1e-5				)				)









"""simple docstring"""
import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	_A							:					Optional[int] 			=					(UniPCMultistepScheduler,)
	_A							:					Optional[Any] 			=					(("""num_inference_steps""", 25),)





	def  lowerCamelCase(self  ,  **lowerCAmelCase_				):
		A_		:    str						     = {
		    """num_train_timesteps""": 1000,
		    """beta_start""": 0.0001,
		    """beta_end""": 0.02,
		    """beta_schedule""": """linear""",
		    """solver_order""": 2,
		    """solver_type""": """bh2""",
		}

		config.update(**lowerCAmelCase_				)
		return config





	def  lowerCamelCase(self  ,  lowerCAmelCase_=0  ,  **lowerCAmelCase_				):
		A_		:    Tuple						     = dict(self.forward_default_kwargs				)
		A_		:    Any						     = kwargs.pop("""num_inference_steps"""  ,  lowerCAmelCase_				)
		A_		:    str						     = self.dummy_sample
		A_		:    Dict						     = 0.1 * sample
		A_		:    int						     = [residual + 0.2, residual + 0.15, residual + 0.10]

		for scheduler_class in self.scheduler_classes:
			A_		:    List[Any]						     = self.get_scheduler_config(**lowerCAmelCase_				)
			A_		:    Dict						     = scheduler_class(**lowerCAmelCase_				)
			scheduler.set_timesteps(lowerCAmelCase_				)
			# copy over dummy past residuals
			A_		:    Tuple						     = dummy_past_residuals[: scheduler.config.solver_order]

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

			A_     ,  A_		:    Optional[int]						     = sample, sample
			for t in range(lowerCAmelCase_  ,  time_step + scheduler.config.solver_order + 1				):
				A_		:    Any						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				).prev_sample
				A_		:    Dict						     = new_scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				).prev_sample

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





	def  lowerCamelCase(self  ,  lowerCAmelCase_=0  ,  **lowerCAmelCase_				):
		A_		:    Optional[int]						     = dict(self.forward_default_kwargs				)
		A_		:    int						     = kwargs.pop("""num_inference_steps"""  ,  lowerCAmelCase_				)
		A_		:    Optional[Any]						     = self.dummy_sample
		A_		:    List[Any]						     = 0.1 * sample
		A_		:    List[Any]						     = [residual + 0.2, residual + 0.15, residual + 0.10]

		for scheduler_class in self.scheduler_classes:
			A_		:    Dict						     = self.get_scheduler_config()
			A_		:    str						     = scheduler_class(**lowerCAmelCase_				)
			scheduler.set_timesteps(lowerCAmelCase_				)

			# copy over dummy past residuals (must be after setting timesteps)
			A_		:    Union[str, Any]						     = dummy_past_residuals[: scheduler.config.solver_order]

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

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

			A_		:    Tuple						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				).prev_sample
			A_		:    str						     = new_scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				).prev_sample

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





	def  lowerCamelCase(self  ,  lowerCAmelCase_=None  ,  **lowerCAmelCase_				):
		if scheduler is None:
			A_		:    int						     = self.scheduler_classes[0]
			A_		:    List[str]						     = self.get_scheduler_config(**lowerCAmelCase_				)
			A_		:    Any						     = scheduler_class(**lowerCAmelCase_				)

		A_		:    int						     = self.scheduler_classes[0]
		A_		:    str						     = self.get_scheduler_config(**lowerCAmelCase_				)
		A_		:    Union[str, Any]						     = scheduler_class(**lowerCAmelCase_				)

		A_		:    Optional[int]						     = 10
		A_		:    Optional[Any]						     = self.dummy_model()
		A_		:    Optional[Any]						     = self.dummy_sample_deter
		scheduler.set_timesteps(lowerCAmelCase_				)

		for i, t in enumerate(scheduler.timesteps				):
			A_		:    Optional[int]						     = model(lowerCAmelCase_  ,  lowerCAmelCase_				)
			A_		:    Optional[int]						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				).prev_sample

		return sample





	def  lowerCamelCase(self				):
		A_		:    Any						     = dict(self.forward_default_kwargs				)

		A_		:    Any						     = kwargs.pop("""num_inference_steps"""  ,  lowerCAmelCase_				)

		for scheduler_class in self.scheduler_classes:
			A_		:    Dict						     = self.get_scheduler_config()
			A_		:    List[Any]						     = scheduler_class(**lowerCAmelCase_				)

			A_		:    int						     = self.dummy_sample
			A_		:    str						     = 0.1 * sample

			if num_inference_steps is not None and hasattr(lowerCAmelCase_  ,  """set_timesteps"""				):
				scheduler.set_timesteps(lowerCAmelCase_				)
			elif num_inference_steps is not None and not hasattr(lowerCAmelCase_  ,  """set_timesteps"""				):
				A_		:    Tuple						     = num_inference_steps

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

			A_		:    str						     = scheduler.timesteps[5]
			A_		:    Any						     = scheduler.timesteps[6]

			A_		:    Tuple						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				).prev_sample
			A_		:    Dict						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				).prev_sample

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





	def  lowerCamelCase(self				):
		# make sure that iterating over schedulers with same config names gives same results
		# for defaults
		A_		:    Tuple						     = UniPCMultistepScheduler(**self.get_scheduler_config()				)
		A_		:    List[str]						     = self.full_loop(scheduler=lowerCAmelCase_				)
		A_		:    int						     = torch.mean(torch.abs(lowerCAmelCase_				)				)

		assert abs(result_mean.item() - 0.2464				) < 1e-3

		A_		:    List[str]						     = DPMSolverSinglestepScheduler.from_config(scheduler.config				)
		A_		:    List[str]						     = DEISMultistepScheduler.from_config(scheduler.config				)
		A_		:    Optional[int]						     = DPMSolverMultistepScheduler.from_config(scheduler.config				)
		A_		:    str						     = UniPCMultistepScheduler.from_config(scheduler.config				)

		A_		:    Dict						     = self.full_loop(scheduler=lowerCAmelCase_				)
		A_		:    List[Any]						     = torch.mean(torch.abs(lowerCAmelCase_				)				)

		assert abs(result_mean.item() - 0.2464				) < 1e-3





	def  lowerCamelCase(self				):
		for timesteps in [25, 50, 100, 999, 1000]:
			self.check_over_configs(num_train_timesteps=lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		self.check_over_configs(thresholding=lowerCAmelCase_				)
		for order in [1, 2, 3]:
			for solver_type in ["bh1", "bh2"]:
				for threshold in [0.5, 1.0, 2.0]:
					for prediction_type in ["epsilon", "sample"]:
						self.check_over_configs(
						    thresholding=lowerCAmelCase_  ,  prediction_type=lowerCAmelCase_  ,  sample_max_value=lowerCAmelCase_  ,  solver_order=lowerCAmelCase_  ,  solver_type=lowerCAmelCase_  ,  )





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





	def  lowerCamelCase(self				):
		for solver_type in ["bh1", "bh2"]:
			for order in [1, 2, 3]:
				for prediction_type in ["epsilon", "sample"]:
					self.check_over_configs(
					    solver_order=lowerCAmelCase_  ,  solver_type=lowerCAmelCase_  ,  prediction_type=lowerCAmelCase_  ,  )
					A_		:    Union[str, Any]						     = self.full_loop(
					    solver_order=lowerCAmelCase_  ,  solver_type=lowerCAmelCase_  ,  prediction_type=lowerCAmelCase_  ,  )
					assert not torch.isnan(lowerCAmelCase_				).any(), "Samples have nan numbers"





	def  lowerCamelCase(self				):
		self.check_over_configs(lower_order_final=lowerCAmelCase_				)
		self.check_over_configs(lower_order_final=lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
			self.check_over_forward(num_inference_steps=lowerCAmelCase_  ,  time_step=0				)





	def  lowerCamelCase(self				):
		A_		:    Optional[int]						     = self.full_loop()
		A_		:    Optional[int]						     = torch.mean(torch.abs(lowerCAmelCase_				)				)

		assert abs(result_mean.item() - 0.2464				) < 1e-3





	def  lowerCamelCase(self				):
		A_		:    str						     = self.full_loop(prediction_type="""v_prediction"""				)
		A_		:    Optional[Any]						     = torch.mean(torch.abs(lowerCAmelCase_				)				)

		assert abs(result_mean.item() - 0.1014				) < 1e-3





	def  lowerCamelCase(self				):
		A_		:    List[str]						     = self.scheduler_classes[0]
		A_		:    str						     = self.get_scheduler_config(thresholding=lowerCAmelCase_  ,  dynamic_thresholding_ratio=0				)
		A_		:    Union[str, Any]						     = scheduler_class(**lowerCAmelCase_				)

		A_		:    Any						     = 10
		A_		:    int						     = self.dummy_model()
		A_		:    Optional[Any]						     = self.dummy_sample_deter.half()
		scheduler.set_timesteps(lowerCAmelCase_				)

		for i, t in enumerate(scheduler.timesteps				):
			A_		:    Dict						     = model(lowerCAmelCase_  ,  lowerCAmelCase_				)
			A_		:    Tuple						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				).prev_sample

		assert sample.dtype == torch.floataa





	def  lowerCamelCase(self  ,  **lowerCAmelCase_				):
		for scheduler_class in self.scheduler_classes:
			A_		:    Tuple						     = self.get_scheduler_config(**lowerCAmelCase_				)
			A_		:    int						     = scheduler_class(**lowerCAmelCase_				)

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

