






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


__A  							=  logging.get_logger(__name__)

__A  							=  {
    "google/bigbird-pegasus-large-arxiv": (
        "https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json"
    ),
    "google/bigbird-pegasus-large-pubmed": (
        "https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json"
    ),
    "google/bigbird-pegasus-large-bigpatent": (
        "https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json"
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}



class 							_A (    UpperCamelCase							):
    """simple docstring"""

    lowerCamelCase  :       Tuple   =   'bigbird_pegasus'
    lowerCamelCase  :       Dict   =   ['past_key_values']
    lowerCamelCase  :       Dict   =   {
        'num_attention_heads': 'encoder_attention_heads',
        'hidden_size': 'd_model',
        'attention_probs_dropout_prob': 'attention_dropout',
    }



    def __init__(							self			:    Any   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=96103   ,    __SCREAMING_SNAKE_CASE			:    List[str]=4096   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=16   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=4096   ,    __SCREAMING_SNAKE_CASE			:    str=16   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=16   ,    __SCREAMING_SNAKE_CASE			:    str=4096   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=16   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=0.0   ,    __SCREAMING_SNAKE_CASE			:    Any=0.0   ,    __SCREAMING_SNAKE_CASE			:    str=True   ,    __SCREAMING_SNAKE_CASE			:    int=True   ,    __SCREAMING_SNAKE_CASE			:    Dict="gelu_new"   ,    __SCREAMING_SNAKE_CASE			:    Dict=1024   ,    __SCREAMING_SNAKE_CASE			:    str=0.1   ,    __SCREAMING_SNAKE_CASE			:    int=0.0   ,    __SCREAMING_SNAKE_CASE			:    Any=0.0   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=0.02   ,    __SCREAMING_SNAKE_CASE			:    Any=2   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=0.0   ,    __SCREAMING_SNAKE_CASE			:    Any=True   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]=0   ,    __SCREAMING_SNAKE_CASE			:    Any=2   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]=1   ,    __SCREAMING_SNAKE_CASE			:    Dict="block_sparse"   ,    __SCREAMING_SNAKE_CASE			:    Dict=64   ,    __SCREAMING_SNAKE_CASE			:    Any=3   ,    __SCREAMING_SNAKE_CASE			:    Dict=False   ,    **__SCREAMING_SNAKE_CASE			:    Tuple   ,    )			->       Dict:
           __UpperCAmelCase    							=vocab_size
           __UpperCAmelCase    							=max_position_embeddings
           __UpperCAmelCase    							=d_model
           __UpperCAmelCase    							=encoder_ffn_dim
           __UpperCAmelCase    							=encoder_layers
           __UpperCAmelCase    							=encoder_attention_heads
           __UpperCAmelCase    							=decoder_ffn_dim
           __UpperCAmelCase    							=decoder_layers
           __UpperCAmelCase    							=decoder_attention_heads
           __UpperCAmelCase    							=dropout
           __UpperCAmelCase    							=attention_dropout
           __UpperCAmelCase    							=activation_dropout
           __UpperCAmelCase    							=activation_function
           __UpperCAmelCase    							=init_std
           __UpperCAmelCase    							=encoder_layerdrop
           __UpperCAmelCase    							=decoder_layerdrop
           __UpperCAmelCase    							=classifier_dropout
           __UpperCAmelCase    							=use_cache
           __UpperCAmelCase    							=encoder_layers
           __UpperCAmelCase    							=scale_embedding  # scale factor will be sqrt(d_model) if True

           # extra config
           __UpperCAmelCase    							=attention_type
           __UpperCAmelCase    							=block_size
           __UpperCAmelCase    							=num_random_blocks
           __UpperCAmelCase    							=use_bias

           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   ,    decoder_start_token_id=__SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE   ,    )




class 							_A (    UpperCamelCase							):
    """simple docstring"""

    @property
    def _a       (							self			:    List[Any]			)			->       Mapping[str, Mapping[int, str]]:
           if self.task in ["default", "seq2seq-lm"]:
                  __UpperCAmelCase    							=OrderedDict(
                      [
                          ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                          ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                      ]			)

                  if self.use_past:
                         __UpperCAmelCase    							={0: """batch"""}
                         __UpperCAmelCase    							={0: """batch""", 1: """past_decoder_sequence + sequence"""}
                  else:
                         __UpperCAmelCase    							={0: """batch""", 1: """decoder_sequence"""}
                         __UpperCAmelCase    							={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    							=OrderedDict(
                      [
                          ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                          ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                      ]			)
                  if self.use_past:
                         __UpperCAmelCase							,   __UpperCAmelCase    							=self.num_layers
                         for i in range(__SCREAMING_SNAKE_CASE			):
                                __UpperCAmelCase    							={0: """batch""", 2: """past_sequence + sequence"""}
                                __UpperCAmelCase    							={0: """batch""", 2: """past_sequence + sequence"""}
           else:
                  __UpperCAmelCase    							=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]			)			->       Mapping[str, Mapping[int, str]]:
           if self.task in ["default", "seq2seq-lm"]:
                  __UpperCAmelCase    							=super().outputs
           else:
                  __UpperCAmelCase    							=super(__SCREAMING_SNAKE_CASE   ,    self			).outputs
                  if self.use_past:
                         __UpperCAmelCase							,   __UpperCAmelCase    							=self.num_layers
                         for i in range(__SCREAMING_SNAKE_CASE			):
                                __UpperCAmelCase    							={0: """batch""", 2: """past_sequence + sequence"""}
                                __UpperCAmelCase    							={0: """batch""", 2: """past_sequence + sequence"""}
           return common_outputs



    def _a       (							self			:    int   ,    __SCREAMING_SNAKE_CASE			:    PreTrainedTokenizer   ,    __SCREAMING_SNAKE_CASE			:    int = -1   ,    __SCREAMING_SNAKE_CASE			:    int = -1   ,    __SCREAMING_SNAKE_CASE			:    bool = False   ,    __SCREAMING_SNAKE_CASE			:    Optional[TensorType] = None   ,    )			->       Mapping[str, Any]:
           __UpperCAmelCase    							=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    							=seq_length if not self.use_past else 1
           __UpperCAmelCase    							=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    							={f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
           __UpperCAmelCase    							=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    							=common_inputs["""input_ids"""].shape
                  __UpperCAmelCase    							=common_inputs["""decoder_input_ids"""].shape[1]
                  __UpperCAmelCase							,   __UpperCAmelCase    							=self.num_attention_heads
                  __UpperCAmelCase    							=(
                      batch,
                      num_encoder_attention_heads,
                      encoder_seq_length,
                      self._config.hidden_size // num_encoder_attention_heads,
                  )
                  __UpperCAmelCase    							=decoder_seq_length + 3
                  __UpperCAmelCase    							=(
                      batch,
                      num_decoder_attention_heads,
                      decoder_past_length,
                      self._config.hidden_size // num_decoder_attention_heads,
                  )

                  __UpperCAmelCase    							=torch.cat(
                      [common_inputs["""decoder_attention_mask"""], torch.ones(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			)]   ,    dim=1			)

                  __UpperCAmelCase    							=[]
                  # If the number of encoder and decoder layers are present in the model configuration, both are considered
                  __UpperCAmelCase							,   __UpperCAmelCase    							=self.num_layers
                  __UpperCAmelCase    							=min(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			)
                  __UpperCAmelCase    							=max(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			) - min_num_layers
                  __UpperCAmelCase    							="""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    							=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			:    Any   ,    __SCREAMING_SNAKE_CASE			:    PreTrainedTokenizer   ,    __SCREAMING_SNAKE_CASE			:    int = -1   ,    __SCREAMING_SNAKE_CASE			:    int = -1   ,    __SCREAMING_SNAKE_CASE			:    bool = False   ,    __SCREAMING_SNAKE_CASE			:    Optional[TensorType] = None   ,    )			->       Mapping[str, Any]:
           __UpperCAmelCase    							=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    							=common_inputs["""input_ids"""].shape
                  # Not using the same length for past_key_values
                  __UpperCAmelCase    							=seqlen + 2
                  __UpperCAmelCase							,   __UpperCAmelCase    							=self.num_layers
                  __UpperCAmelCase							,   __UpperCAmelCase    							=self.num_attention_heads
                  __UpperCAmelCase    							=(
                      batch,
                      num_encoder_attention_heads,
                      past_key_values_length,
                      self._config.hidden_size // num_encoder_attention_heads,
                  )

                  __UpperCAmelCase    							=common_inputs["""attention_mask"""].dtype
                  __UpperCAmelCase    							=torch.cat(
                      [common_inputs["""attention_mask"""], torch.ones(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    dtype=__SCREAMING_SNAKE_CASE			)]   ,    dim=1			)
                  __UpperCAmelCase    							=[
                      (torch.zeros(__SCREAMING_SNAKE_CASE			), torch.zeros(__SCREAMING_SNAKE_CASE			)) for _ in range(__SCREAMING_SNAKE_CASE			)
                  ]
           return common_inputs



    def _a       (							self			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    PreTrainedTokenizer   ,    __SCREAMING_SNAKE_CASE			:    int = -1   ,    __SCREAMING_SNAKE_CASE			:    int = -1   ,    __SCREAMING_SNAKE_CASE			:    bool = False   ,    __SCREAMING_SNAKE_CASE			:    Optional[TensorType] = None   ,    )			->       Mapping[str, Any]:
           # Copied from OnnxConfig.generate_dummy_inputs
           # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
           # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
           __UpperCAmelCase    							=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    							=tokenizer.num_special_tokens_to_add(__SCREAMING_SNAKE_CASE			)
           __UpperCAmelCase    							=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    							=[""" """.join([tokenizer.unk_token]			) * seq_length] * batch_size
           __UpperCAmelCase    							=dict(tokenizer(__SCREAMING_SNAKE_CASE   ,    return_tensors=__SCREAMING_SNAKE_CASE			)			)
           return common_inputs



    def _a       (							self			:    int   ,    __SCREAMING_SNAKE_CASE			:    PreTrainedTokenizer   ,    __SCREAMING_SNAKE_CASE			:    int = -1   ,    __SCREAMING_SNAKE_CASE			:    int = -1   ,    __SCREAMING_SNAKE_CASE			:    bool = False   ,    __SCREAMING_SNAKE_CASE			:    Optional[TensorType] = None   ,    )			->       Mapping[str, Any]:
           if self.task in ["default", "seq2seq-lm"]:
                  __UpperCAmelCase    							=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    							=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    							=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			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    Dict   ,    __SCREAMING_SNAKE_CASE			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    int			)			->       List[str]:
           if self.task in ["default", "seq2seq-lm"]:
                  __UpperCAmelCase    							=super()._flatten_past_key_values_(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			)
           else:
                  __UpperCAmelCase    							=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 							_A (    UpperCamelCase							):
    """simple docstring"""

    lowerCamelCase  :       str   =   (DPMSolverMultistepScheduler,)
    lowerCamelCase  :       List[str]   =   (('num_inference_steps', 25),)



    def _a       (							self			:    Dict   ,    **__SCREAMING_SNAKE_CASE			:    Optional[Any]			)			->       Dict:
           __UpperCAmelCase    							={
               """num_train_timesteps""": 1000,
               """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(**__SCREAMING_SNAKE_CASE			)
           return config



    def _a       (							self			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=0   ,    **__SCREAMING_SNAKE_CASE			:    int			)			->       Dict:
           __UpperCAmelCase    							=dict(self.forward_default_kwargs			)
           __UpperCAmelCase    							=kwargs.pop("""num_inference_steps"""   ,    __SCREAMING_SNAKE_CASE			)
           __UpperCAmelCase    							=self.dummy_sample
           __UpperCAmelCase    							=0.1 * sample
           __UpperCAmelCase    							=[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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



    def _a       (							self			:    Any			)			->       Union[str, Any]:
           pass



    def _a       (							self			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    List[str]=0   ,    **__SCREAMING_SNAKE_CASE			:    Dict			)			->       Optional[Any]:
           __UpperCAmelCase    							=dict(self.forward_default_kwargs			)
           __UpperCAmelCase    							=kwargs.pop("""num_inference_steps"""   ,    __SCREAMING_SNAKE_CASE			)
           __UpperCAmelCase    							=self.dummy_sample
           __UpperCAmelCase    							=0.1 * sample
           __UpperCAmelCase    							=[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                  __UpperCAmelCase    							=self.get_scheduler_config()
                  __UpperCAmelCase    							=scheduler_class(**__SCREAMING_SNAKE_CASE			)
                  scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			)

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

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

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

                  __UpperCAmelCase    							=scheduler.step(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			).prev_sample
                  __UpperCAmelCase    							=new_scheduler.step(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			).prev_sample

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



    def _a       (							self			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]=None   ,    **__SCREAMING_SNAKE_CASE			:    Optional[int]			)			->       Optional[Any]:
           if scheduler is None:
                  __UpperCAmelCase    							=self.scheduler_classes[0]
                  __UpperCAmelCase    							=self.get_scheduler_config(**__SCREAMING_SNAKE_CASE			)
                  __UpperCAmelCase    							=scheduler_class(**__SCREAMING_SNAKE_CASE			)

           __UpperCAmelCase    							=10
           __UpperCAmelCase    							=self.dummy_model()
           __UpperCAmelCase    							=self.dummy_sample_deter
           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			)

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

           return sample



    def _a       (							self			:    Dict			)			->       Optional[int]:
           __UpperCAmelCase    							=dict(self.forward_default_kwargs			)

           __UpperCAmelCase    							=kwargs.pop("""num_inference_steps"""   ,    __SCREAMING_SNAKE_CASE			)

           for scheduler_class in self.scheduler_classes:
                  __UpperCAmelCase    							=self.get_scheduler_config()
                  __UpperCAmelCase    							=scheduler_class(**__SCREAMING_SNAKE_CASE			)

                  __UpperCAmelCase    							=self.dummy_sample
                  __UpperCAmelCase    							=0.1 * sample

                  if num_inference_steps is not None and hasattr(__SCREAMING_SNAKE_CASE   ,    """set_timesteps"""			):
                         scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			)
                  elif num_inference_steps is not None and not hasattr(__SCREAMING_SNAKE_CASE   ,    """set_timesteps"""			):
                         __UpperCAmelCase    							=num_inference_steps

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

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

                  __UpperCAmelCase    							=scheduler.step(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			).prev_sample
                  __UpperCAmelCase    							=scheduler.step(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			).prev_sample

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



    def _a       (							self			:    List[Any]			)			->       Tuple:
           for timesteps in [25, 50, 100, 999, 1000]:
                  self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE			)



    def _a       (							self			:    Union[str, Any]			)			->       int:
           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 _a       (							self			:    Union[str, Any]			)			->       List[str]:
           for prediction_type in ["epsilon", "v_prediction"]:
                  self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE			)



    def _a       (							self			:    Union[str, Any]			)			->       Tuple:
           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   ,    )
                                       __UpperCAmelCase    							=self.full_loop(
                                           solver_order=__SCREAMING_SNAKE_CASE   ,    solver_type=__SCREAMING_SNAKE_CASE   ,    prediction_type=__SCREAMING_SNAKE_CASE   ,    algorithm_type=__SCREAMING_SNAKE_CASE   ,    )
                                       assert not torch.isnan(__SCREAMING_SNAKE_CASE			).any(), "Samples have nan numbers"



    def _a       (							self			:    Dict			)			->       str:
           self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE			)
           self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE			)



    def _a       (							self			:    Union[str, Any]			)			->       Optional[Any]:
           self.check_over_configs(lambda_min_clipped=-float("""inf"""			)			)
           self.check_over_configs(lambda_min_clipped=-5.1			)



    def _a       (							self			:    Any			)			->       List[str]:
           self.check_over_configs(variance_type=__SCREAMING_SNAKE_CASE			)
           self.check_over_configs(variance_type="""learned_range"""			)



    def _a       (							self			:    List[Any]			)			->       int:
           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 _a       (							self			:    Optional[int]			)			->       str:
           __UpperCAmelCase    							=self.full_loop()
           __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)

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



    def _a       (							self			:    Union[str, Any]			)			->       str:
           __UpperCAmelCase    							=self.full_loop(thresholding=__SCREAMING_SNAKE_CASE   ,    dynamic_thresholding_ratio=0.87   ,    sample_max_value=0.5			)
           __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)

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



    def _a       (							self			:    List[Any]			)			->       List[Any]:
           __UpperCAmelCase    							=self.full_loop(prediction_type="""v_prediction"""			)
           __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)

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



    def _a       (							self			:    int			)			->       Union[str, Any]:
           __UpperCAmelCase    							=self.full_loop(prediction_type="""v_prediction"""   ,    use_karras_sigmas=__SCREAMING_SNAKE_CASE			)
           __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)

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



    def _a       (							self			:    int			)			->       Optional[Any]:
           # make sure that iterating over schedulers with same config names gives same results
           # for defaults
           __UpperCAmelCase    							=DPMSolverMultistepScheduler(**self.get_scheduler_config()			)
           __UpperCAmelCase    							=self.full_loop(scheduler=__SCREAMING_SNAKE_CASE			)
           __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)

           assert abs(result_mean.item() - 0.3_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=__SCREAMING_SNAKE_CASE			)
           __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)

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



    def _a       (							self			:    List[Any]			)			->       str:
           __UpperCAmelCase    							=self.scheduler_classes[0]
           __UpperCAmelCase    							=self.get_scheduler_config(thresholding=__SCREAMING_SNAKE_CASE   ,    dynamic_thresholding_ratio=0			)
           __UpperCAmelCase    							=scheduler_class(**__SCREAMING_SNAKE_CASE			)

           __UpperCAmelCase    							=10
           __UpperCAmelCase    							=self.dummy_model()
           __UpperCAmelCase    							=self.dummy_sample_deter.half()
           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			)

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

           assert sample.dtype == torch.floataa



    def _a       (							self			:    Optional[int]   ,    **__SCREAMING_SNAKE_CASE			:    Tuple			)			->       Optional[int]:
           for scheduler_class in self.scheduler_classes:
                  __UpperCAmelCase    							=self.get_scheduler_config(**__SCREAMING_SNAKE_CASE			)
                  __UpperCAmelCase    							=scheduler_class(**__SCREAMING_SNAKE_CASE			)

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




