
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


snake_case_      :					Optional[int]				      =     logging.get_logger(__name__)

snake_case_      :					Tuple				      =     {
    'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json',
    # See all MBART models at https://huggingface.co/models?filter=mbart
}





class 		__lowerCamelCase     (   _SCREAMING_SNAKE_CASE		):
							lowerCamelCase__:				List[Any]      =							"""mbart"""
							lowerCamelCase__:				List[str]      =							["""past_key_values"""]
							lowerCamelCase__:				Tuple      =							{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



							def __init__(     self	,				__snake_case=5_0_2_6_5	,				__snake_case=1_0_2_4	,				__snake_case=1_2	,				__snake_case=4_0_9_6	,				__snake_case=1_6	,				__snake_case=1_2	,				__snake_case=4_0_9_6	,				__snake_case=1_6	,				__snake_case=0.0	,				__snake_case=0.0	,				__snake_case=True	,				__snake_case=True	,				__snake_case="gelu"	,				__snake_case=1_0_2_4	,				__snake_case=0.1	,				__snake_case=0.0	,				__snake_case=0.0	,				__snake_case=0.02	,				__snake_case=0.0	,				__snake_case=False	,				__snake_case=1	,				__snake_case=0	,				__snake_case=2	,				__snake_case=2	,				**__snake_case	,				)						->     List[Any]:






								"""simple docstring"""



								UpperCAmelCase:					int 						=     vocab_size
								UpperCAmelCase:					List[str] 						=     max_position_embeddings
								UpperCAmelCase:					Dict 						=     d_model
								UpperCAmelCase:					Dict 						=     encoder_ffn_dim
								UpperCAmelCase:					Optional[Any] 						=     encoder_layers
								UpperCAmelCase:					List[str] 						=     encoder_attention_heads
								UpperCAmelCase:					Tuple 						=     decoder_ffn_dim
								UpperCAmelCase:					int 						=     decoder_layers
								UpperCAmelCase:					Dict 						=     decoder_attention_heads
								UpperCAmelCase:					Optional[Any] 						=     dropout
								UpperCAmelCase:					Union[str, Any] 						=     attention_dropout
								UpperCAmelCase:					int 						=     activation_dropout
								UpperCAmelCase:					Dict 						=     activation_function
								UpperCAmelCase:					Union[str, Any] 						=     init_std
								UpperCAmelCase:					List[Any] 						=     encoder_layerdrop
								UpperCAmelCase:					Tuple 						=     decoder_layerdrop
								UpperCAmelCase:					Optional[int] 						=     classifier_dropout
								UpperCAmelCase:					int 						=     use_cache
								UpperCAmelCase:					Any 						=     encoder_layers
								UpperCAmelCase:					Optional[int] 						=     scale_embedding  # scale factor will be sqrt(d_model) if True
								super().__init__(
								    pad_token_id=_SCREAMING_SNAKE_CASE	,				bos_token_id=_SCREAMING_SNAKE_CASE	,				eos_token_id=_SCREAMING_SNAKE_CASE	,				is_encoder_decoder=_SCREAMING_SNAKE_CASE	,				forced_eos_token_id=_SCREAMING_SNAKE_CASE	,				**_SCREAMING_SNAKE_CASE	,				)





class 		__lowerCamelCase     (   _SCREAMING_SNAKE_CASE		):



							@property
							def     A__ (     self							)						->     Union[str, Any]:






								"""simple docstring"""



								if self.task in ["default", "seq2seq-lm"]:
									UpperCAmelCase:					str 						=     OrderedDict(
									    [
									        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
									        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
									    ]							)

									if self.use_past:
										UpperCAmelCase:					str 						=     {0: "batch"}
										UpperCAmelCase:					List[Any] 						=     {0: "batch", 1: "past_decoder_sequence + sequence"}
									else:
										UpperCAmelCase:					Dict 						=     {0: "batch", 1: "decoder_sequence"}
										UpperCAmelCase:					Union[str, Any] 						=     {0: "batch", 1: "decoder_sequence"}

									if self.use_past:
										self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE	,				direction="inputs"							)
								elif self.task == "causal-lm":
									# TODO: figure this case out.
									UpperCAmelCase:					int 						=     OrderedDict(
									    [
									        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
									        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
									    ]							)
									if self.use_past:
										UpperCAmelCase						,   UpperCAmelCase:					List[Any] 						=     self.num_layers
										for i in range(_SCREAMING_SNAKE_CASE							):
											UpperCAmelCase:					Union[str, Any] 						=     {0: "batch", 2: "past_sequence + sequence"}
											UpperCAmelCase:					int 						=     {0: "batch", 2: "past_sequence + sequence"}
								else:
									UpperCAmelCase:					Any 						=     OrderedDict(
									    [
									        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
									        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
									        ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
									        ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
									    ]							)

								return common_inputs



							@property
							def     A__ (     self							)						->     List[str]:






								"""simple docstring"""



								if self.task in ["default", "seq2seq-lm"]:
									UpperCAmelCase:					Optional[int] 						=     super().outputs
								else:
									UpperCAmelCase:					Any 						=     super(_SCREAMING_SNAKE_CASE	,				self							).outputs
									if self.use_past:
										UpperCAmelCase						,   UpperCAmelCase:					int 						=     self.num_layers
										for i in range(_SCREAMING_SNAKE_CASE							):
											UpperCAmelCase:					Dict 						=     {0: "batch", 2: "past_sequence + sequence"}
											UpperCAmelCase:					Optional[int] 						=     {0: "batch", 2: "past_sequence + sequence"}
								return common_outputs



							def     A__ (     self	,				__snake_case	,				__snake_case = -1	,				__snake_case = -1	,				__snake_case = False	,				__snake_case = None	,				)						->     Optional[Any]:






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    _SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE							)

								# Generate decoder inputs
								UpperCAmelCase:					Tuple 						=     seq_length if not self.use_past else 1
								UpperCAmelCase:					Tuple 						=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    _SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE							)
								UpperCAmelCase:					Union[str, Any] 						=     {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
								UpperCAmelCase:					List[str] 						=     dict(**_SCREAMING_SNAKE_CASE	,				**_SCREAMING_SNAKE_CASE							)

								if self.use_past:
									if not is_torch_available():
										raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."							)
									else:
										import torch
									UpperCAmelCase						,   UpperCAmelCase:					Dict 						=     common_inputs["input_ids"].shape
									UpperCAmelCase:					str 						=     common_inputs["decoder_input_ids"].shape[1]
									UpperCAmelCase						,   UpperCAmelCase:					Any 						=     self.num_attention_heads
									UpperCAmelCase:					Optional[Any] 						=     (
									    batch,
									    num_encoder_attention_heads,
									    encoder_seq_length,
									    self._config.hidden_size // num_encoder_attention_heads,
									)
									UpperCAmelCase:					Any 						=     decoder_seq_length + 3
									UpperCAmelCase:					Any 						=     (
									    batch,
									    num_decoder_attention_heads,
									    decoder_past_length,
									    self._config.hidden_size // num_decoder_attention_heads,
									)

									UpperCAmelCase:					Optional[int] 						=     torch.cat(
									    [common_inputs["decoder_attention_mask"], torch.ones(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE							)]	,				dim=1							)

									UpperCAmelCase:					Optional[Any] 						=     []
									# If the number of encoder and decoder layers are present in the model configuration, both are considered
									UpperCAmelCase						,   UpperCAmelCase:					str 						=     self.num_layers
									UpperCAmelCase:					Dict 						=     min(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE							)
									UpperCAmelCase:					Union[str, Any] 						=     max(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE							) - min_num_layers
									UpperCAmelCase:					Union[str, Any] 						=     "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

									for _ in range(_SCREAMING_SNAKE_CASE							):
										common_inputs["past_key_values"].append(
										    (
										        torch.zeros(_SCREAMING_SNAKE_CASE							),
										        torch.zeros(_SCREAMING_SNAKE_CASE							),
										        torch.zeros(_SCREAMING_SNAKE_CASE							),
										        torch.zeros(_SCREAMING_SNAKE_CASE							),
										    )							)
									# TODO: test this.
									UpperCAmelCase:					Union[str, Any] 						=     encoder_shape if remaining_side_name == "encoder" else decoder_shape
									for _ in range(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE							):
										common_inputs["past_key_values"].append((torch.zeros(_SCREAMING_SNAKE_CASE							), torch.zeros(_SCREAMING_SNAKE_CASE							))							)
								return common_inputs



							def     A__ (     self	,				__snake_case	,				__snake_case = -1	,				__snake_case = -1	,				__snake_case = False	,				__snake_case = None	,				)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    _SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE							)

								if self.use_past:
									if not is_torch_available():
										raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."							)
									else:
										import torch
									UpperCAmelCase						,   UpperCAmelCase:					Optional[Any] 						=     common_inputs["input_ids"].shape
									# Not using the same length for past_key_values
									UpperCAmelCase:					Union[str, Any] 						=     seqlen + 2
									UpperCAmelCase						,   UpperCAmelCase:					Dict 						=     self.num_layers
									UpperCAmelCase						,   UpperCAmelCase:					Optional[Any] 						=     self.num_attention_heads
									UpperCAmelCase:					Any 						=     (
									    batch,
									    num_encoder_attention_heads,
									    past_key_values_length,
									    self._config.hidden_size // num_encoder_attention_heads,
									)

									UpperCAmelCase:					Optional[Any] 						=     common_inputs["attention_mask"].dtype
									UpperCAmelCase:					Dict 						=     torch.cat(
									    [common_inputs["attention_mask"], torch.ones(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				dtype=_SCREAMING_SNAKE_CASE							)]	,				dim=1							)
									UpperCAmelCase:					Union[str, Any] 						=     [
									    (torch.zeros(_SCREAMING_SNAKE_CASE							), torch.zeros(_SCREAMING_SNAKE_CASE							)) for _ in range(_SCREAMING_SNAKE_CASE							)
									]
								return common_inputs



							def     A__ (     self	,				__snake_case	,				__snake_case = -1	,				__snake_case = -1	,				__snake_case = False	,				__snake_case = None	,				)						->     Optional[Any]:






								"""simple docstring"""



								UpperCAmelCase:					Dict 						=     compute_effective_axis_dimension(
								    _SCREAMING_SNAKE_CASE	,				fixed_dimension=OnnxConfig.default_fixed_batch	,				num_token_to_add=0							)

								# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
								UpperCAmelCase:					Tuple 						=     tokenizer.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE							)
								UpperCAmelCase:					Dict 						=     compute_effective_axis_dimension(
								    _SCREAMING_SNAKE_CASE	,				fixed_dimension=OnnxConfig.default_fixed_sequence	,				num_token_to_add=_SCREAMING_SNAKE_CASE							)

								# Generate dummy inputs according to compute batch and sequence
								UpperCAmelCase:					Union[str, Any] 						=     [" ".join([tokenizer.unk_token]							) * seq_length] * batch_size
								UpperCAmelCase:					int 						=     dict(tokenizer(_SCREAMING_SNAKE_CASE	,				return_tensors=_SCREAMING_SNAKE_CASE							)							)
								return common_inputs



							def     A__ (     self	,				__snake_case	,				__snake_case = -1	,				__snake_case = -1	,				__snake_case = False	,				__snake_case = None	,				)						->     Tuple:






								"""simple docstring"""



								if self.task in ["default", "seq2seq-lm"]:
									UpperCAmelCase:					Any 						=     self._generate_dummy_inputs_for_default_and_seqaseq_lm(
									    _SCREAMING_SNAKE_CASE	,				batch_size=_SCREAMING_SNAKE_CASE	,				seq_length=_SCREAMING_SNAKE_CASE	,				is_pair=_SCREAMING_SNAKE_CASE	,				framework=_SCREAMING_SNAKE_CASE							)

								elif self.task == "causal-lm":
									UpperCAmelCase:					int 						=     self._generate_dummy_inputs_for_causal_lm(
									    _SCREAMING_SNAKE_CASE	,				batch_size=_SCREAMING_SNAKE_CASE	,				seq_length=_SCREAMING_SNAKE_CASE	,				is_pair=_SCREAMING_SNAKE_CASE	,				framework=_SCREAMING_SNAKE_CASE							)
								else:
									UpperCAmelCase:					Optional[Any] 						=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _SCREAMING_SNAKE_CASE	,				batch_size=_SCREAMING_SNAKE_CASE	,				seq_length=_SCREAMING_SNAKE_CASE	,				is_pair=_SCREAMING_SNAKE_CASE	,				framework=_SCREAMING_SNAKE_CASE							)

								return common_inputs




							def     A__ (     self	,				__snake_case	,				__snake_case	,				__snake_case	,				__snake_case							)						->     Any:






								"""simple docstring"""



								if self.task in ["default", "seq2seq-lm"]:
									UpperCAmelCase:					List[str] 						=     super()._flatten_past_key_values_(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE							)
								else:
									UpperCAmelCase:					List[str] 						=     super(_SCREAMING_SNAKE_CASE	,				self							)._flatten_past_key_values_(
									    _SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE							)




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):
							_lowerCamelCase				: Optional[int]       =					(DPMSolverMultistepScheduler,)
							_lowerCamelCase				: Optional[Any]       =					(("""num_inference_steps""", 25),)





							def   __magic_name__      (							self			,       **_SCREAMING_SNAKE_CASE    ):
								a_     			=   {
								    """num_train_timesteps""": 1000,
								    """beta_start""": 0.0_0_0_1,
								    """beta_end""": 0.0_2,
								    """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(**_SCREAMING_SNAKE_CASE    )
								return config





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE=0			,       **_SCREAMING_SNAKE_CASE    ):
								a_     			=   dict(self.forward_default_kwargs    )
								a_     			=   kwargs.pop("""num_inference_steps"""			,       _SCREAMING_SNAKE_CASE    )
								a_     			=   self.dummy_sample
								a_     			=   0.1 * sample
								a_     			=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

									a_			, a_     			=   sample, sample
									for t in range(_SCREAMING_SNAKE_CASE			,       time_step + scheduler.config.solver_order + 1    ):
										a_     			=   scheduler.step(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE    ).prev_sample
										a_     			=   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   __magic_name__      (							self    ):
								pass





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE=0			,       **_SCREAMING_SNAKE_CASE    ):
								a_     			=   dict(self.forward_default_kwargs    )
								a_     			=   kwargs.pop("""num_inference_steps"""			,       _SCREAMING_SNAKE_CASE    )
								a_     			=   self.dummy_sample
								a_     			=   0.1 * sample
								a_     			=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
									a_     			=   self.get_scheduler_config()
									a_     			=   scheduler_class(**_SCREAMING_SNAKE_CASE    )
									scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

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

									with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(_SCREAMING_SNAKE_CASE    )
										a_     			=   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)
										a_     			=   dummy_past_residuals[: new_scheduler.config.solver_order]

									a_     			=   scheduler.step(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE    ).prev_sample
									a_     			=   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   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE=None			,       **_SCREAMING_SNAKE_CASE    ):
								if scheduler is None:
									a_     			=   self.scheduler_classes[0]
									a_     			=   self.get_scheduler_config(**_SCREAMING_SNAKE_CASE    )
									a_     			=   scheduler_class(**_SCREAMING_SNAKE_CASE    )

								a_     			=   10
								a_     			=   self.dummy_model()
								a_     			=   self.dummy_sample_deter
								scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

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

								return sample





							def   __magic_name__      (							self    ):
								a_     			=   dict(self.forward_default_kwargs    )

								a_     			=   kwargs.pop("""num_inference_steps"""			,       _SCREAMING_SNAKE_CASE    )

								for scheduler_class in self.scheduler_classes:
									a_     			=   self.get_scheduler_config()
									a_     			=   scheduler_class(**_SCREAMING_SNAKE_CASE    )

									a_     			=   self.dummy_sample
									a_     			=   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"""    ):
										a_     			=   num_inference_steps

									# copy over dummy past residuals (must be done after set_timesteps)
									a_     			=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
									a_     			=   dummy_past_residuals[: scheduler.config.solver_order]

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

									a_     			=   scheduler.step(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE    ).prev_sample
									a_     			=   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   __magic_name__      (							self    ):
								for timesteps in [25, 50, 100, 999, 1000]:
									self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE    )
								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=_SCREAMING_SNAKE_CASE			,       prediction_type=_SCREAMING_SNAKE_CASE			,       sample_max_value=_SCREAMING_SNAKE_CASE			,       algorithm_type="""dpmsolver++"""			,       solver_order=_SCREAMING_SNAKE_CASE			,       solver_type=_SCREAMING_SNAKE_CASE			,       )





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





							def   __magic_name__      (							self    ):
								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=_SCREAMING_SNAKE_CASE			,       solver_type=_SCREAMING_SNAKE_CASE			,       prediction_type=_SCREAMING_SNAKE_CASE			,       algorithm_type=_SCREAMING_SNAKE_CASE			,       )
												a_     			=   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   __magic_name__      (							self    ):
								self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE    )
								self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE    )





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





							def   __magic_name__      (							self    ):
								self.check_over_configs(variance_type=_SCREAMING_SNAKE_CASE    )
								self.check_over_configs(variance_type="""learned_range"""    )





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





							def   __magic_name__      (							self    ):
								a_     			=   self.full_loop()
								a_     			=   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

								assert abs(result_mean.item() - 0.3_3_0_1    ) < 1E-3





							def   __magic_name__      (							self    ):
								a_     			=   self.full_loop(thresholding=_SCREAMING_SNAKE_CASE			,       dynamic_thresholding_ratio=0.8_7			,       sample_max_value=0.5    )
								a_     			=   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

								assert abs(result_mean.item() - 1.1_3_6_4    ) < 1E-3





							def   __magic_name__      (							self    ):
								a_     			=   self.full_loop(prediction_type="""v_prediction"""    )
								a_     			=   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

								assert abs(result_mean.item() - 0.2_2_5_1    ) < 1E-3





							def   __magic_name__      (							self    ):
								a_     			=   self.full_loop(prediction_type="""v_prediction"""			,       use_karras_sigmas=_SCREAMING_SNAKE_CASE    )
								a_     			=   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

								assert abs(result_mean.item() - 0.2_0_9_6    ) < 1E-3





							def   __magic_name__      (							self    ):
								# make sure that iterating over schedulers with same config names gives same results
								# for defaults
								a_     			=   DPMSolverMultistepScheduler(**self.get_scheduler_config()    )
								a_     			=   self.full_loop(scheduler=_SCREAMING_SNAKE_CASE    )
								a_     			=   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

								assert abs(result_mean.item() - 0.3_3_0_1    ) < 1E-3

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

								a_     			=   self.full_loop(scheduler=_SCREAMING_SNAKE_CASE    )
								a_     			=   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

								assert abs(result_mean.item() - 0.3_3_0_1    ) < 1E-3





							def   __magic_name__      (							self    ):
								a_     			=   self.scheduler_classes[0]
								a_     			=   self.get_scheduler_config(thresholding=_SCREAMING_SNAKE_CASE			,       dynamic_thresholding_ratio=0    )
								a_     			=   scheduler_class(**_SCREAMING_SNAKE_CASE    )

								a_     			=   10
								a_     			=   self.dummy_model()
								a_     			=   self.dummy_sample_deter.half()
								scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

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

								assert sample.dtype == torch.floataa





							def   __magic_name__      (							self			,       **_SCREAMING_SNAKE_CASE    ):
								for scheduler_class in self.scheduler_classes:
									a_     			=   self.get_scheduler_config(**_SCREAMING_SNAKE_CASE    )
									a_     			=   scheduler_class(**_SCREAMING_SNAKE_CASE    )

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