





import warnings
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


_UpperCAmelCase				:       Dict	   =				logging.get_logger(__name__)

_UpperCAmelCase				:       Any	   =				{
    """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/config.json""",
    # See all BART models at https://huggingface.co/models?filter=bart
}

class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		"""bart"""
 UpperCAmelCase__													=		["""past_key_values"""]
 UpperCAmelCase__													=		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
 def __init__(      self  :      Tuple			,     UpperCAmelCase  :      Optional[int]=50265			,     UpperCAmelCase  :      Optional[int]=1024			,     UpperCAmelCase  :      Optional[int]=12			,     UpperCAmelCase  :      Dict=4096			,     UpperCAmelCase  :      List[str]=16			,     UpperCAmelCase  :      List[Any]=12			,     UpperCAmelCase  :      List[Any]=4096			,     UpperCAmelCase  :      Optional[Any]=16			,     UpperCAmelCase  :      int=0.0			,     UpperCAmelCase  :      Dict=0.0			,     UpperCAmelCase  :      Optional[int]="gelu"			,     UpperCAmelCase  :      Tuple=1024			,     UpperCAmelCase  :      int=0.1			,     UpperCAmelCase  :      Dict=0.0			,     UpperCAmelCase  :      str=0.0			,     UpperCAmelCase  :      Tuple=0.0_2			,     UpperCAmelCase  :      List[str]=0.0			,     UpperCAmelCase  :      Any=False			,     UpperCAmelCase  :      List[str]=True			,     UpperCAmelCase  :      str=3			,     UpperCAmelCase  :      Union[str, Any]=1			,     UpperCAmelCase  :      List[str]=0			,     UpperCAmelCase  :      List[Any]=2			,     UpperCAmelCase  :      Optional[Any]=True			,     UpperCAmelCase  :      Any=2			,     UpperCAmelCase  :      List[str]=2			,     **UpperCAmelCase  :      Dict			,     )			->	Union[str, Any]:
     lowerCamelCase__    :      Union[str, Any]					 =		vocab_size
     lowerCamelCase__    :      Tuple					 =		max_position_embeddings
     lowerCamelCase__    :      Optional[Any]					 =		d_model
     lowerCamelCase__    :      int					 =		encoder_ffn_dim
     lowerCamelCase__    :      int					 =		encoder_layers
     lowerCamelCase__    :      Optional[Any]					 =		encoder_attention_heads
     lowerCamelCase__    :      Dict					 =		decoder_ffn_dim
     lowerCamelCase__    :      Union[str, Any]					 =		decoder_layers
     lowerCamelCase__    :      List[Any]					 =		decoder_attention_heads
     lowerCamelCase__    :      List[str]					 =		dropout
     lowerCamelCase__    :      List[Any]					 =		attention_dropout
     lowerCamelCase__    :      Optional[Any]					 =		activation_dropout
     lowerCamelCase__    :      str					 =		activation_function
     lowerCamelCase__    :      List[str]					 =		init_std
     lowerCamelCase__    :      Optional[Any]					 =		encoder_layerdrop
     lowerCamelCase__    :      Optional[int]					 =		decoder_layerdrop
     lowerCamelCase__    :      int					 =		classifier_dropout
     lowerCamelCase__    :      Union[str, Any]					 =		use_cache
     lowerCamelCase__    :      str					 =		encoder_layers
     lowerCamelCase__    :      str					 =		scale_embedding  # scale factor will be sqrt(d_model) if True

     super().__init__(
         num_labels=UpperCAmelCase			,     pad_token_id=UpperCAmelCase			,     bos_token_id=UpperCAmelCase			,     eos_token_id=UpperCAmelCase			,     is_encoder_decoder=UpperCAmelCase			,     decoder_start_token_id=UpperCAmelCase			,     forced_eos_token_id=UpperCAmelCase			,     **UpperCAmelCase			,     )

     # ensure backward compatibility for BART CNN models
     if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'			,     UpperCAmelCase	):
         lowerCamelCase__    :      Dict					 =		self.bos_token_id
         warnings.warn(
             F"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
             'The config can simply be saved and uploaded again to be fixed.'	)


class    lowerCAmelCase							(					__UpperCamelCase			):
 @property
 def      A_	(      self  :      Dict	)			->	Mapping[str, Mapping[int, str]]:
     if self.task in ["default", "seq2seq-lm"]:
         lowerCamelCase__    :      Any					 =		OrderedDict(
             [
                 ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                 ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
             ]	)

         if self.use_past:
             lowerCamelCase__    :      Tuple					 =		{0: 'batch'}
             lowerCamelCase__    :      Tuple					 =		{0: 'batch', 1: 'past_decoder_sequence + sequence'}
         else:
             lowerCamelCase__    :      Optional[Any]					 =		{0: 'batch', 1: 'decoder_sequence'}
             lowerCamelCase__    :      Dict					 =		{0: 'batch', 1: 'decoder_sequence'}

         if self.use_past:
             self.fill_with_past_key_values_(UpperCAmelCase			,     direction='inputs'	)
     elif self.task == "causal-lm":
         # TODO: figure this case out.
         lowerCamelCase__    :      int					 =		OrderedDict(
             [
                 ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                 ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
             ]	)
         if self.use_past:
             lowerCamelCase__ ,							lowerCamelCase__    :      Optional[Any]					 =		self.num_layers
             for i in range(UpperCAmelCase	):
                 lowerCamelCase__    :      List[Any]					 =		{0: 'batch', 2: 'past_sequence + sequence'}
                 lowerCamelCase__    :      Optional[Any]					 =		{0: 'batch', 2: 'past_sequence + sequence'}
     else:
         lowerCamelCase__    :      Union[str, 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[Any]	)			->	Mapping[str, Mapping[int, str]]:
     if self.task in ["default", "seq2seq-lm"]:
         lowerCamelCase__    :      int					 =		super().outputs
     else:
         lowerCamelCase__    :      Optional[Any]					 =		super(UpperCAmelCase			,     self	).outputs
         if self.use_past:
             lowerCamelCase__ ,							lowerCamelCase__    :      List[str]					 =		self.num_layers
             for i in range(UpperCAmelCase	):
                 lowerCamelCase__    :      Union[str, Any]					 =		{0: 'batch', 2: 'past_sequence + sequence'}
                 lowerCamelCase__    :      List[str]					 =		{0: 'batch', 2: 'past_sequence + sequence'}
     return common_outputs
 def      A_	(      self  :      Optional[Any]			,     UpperCAmelCase  :      PreTrainedTokenizer			,     UpperCAmelCase  :      int = -1			,     UpperCAmelCase  :      int = -1			,     UpperCAmelCase  :      bool = False			,     UpperCAmelCase  :      Optional[TensorType] = None			,     )			->	Mapping[str, Any]:
     lowerCamelCase__    :      List[Any]					 =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase	)

     # Generate decoder inputs
     lowerCamelCase__    :      List[str]					 =		seq_length if not self.use_past else 1
     lowerCamelCase__    :      Optional[int]					 =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase	)
     lowerCamelCase__    :      List[str]					 =		{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
     lowerCamelCase__    :      Optional[int]					 =		dict(**UpperCAmelCase			,     **UpperCAmelCase	)

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

         lowerCamelCase__    :      Dict					 =		torch.cat(
             [common_inputs['decoder_attention_mask'], torch.ones(UpperCAmelCase			,     UpperCAmelCase	)]			,     dim=1	)

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

         for _ in range(UpperCAmelCase	):
             common_inputs["past_key_values"].append(
                 (
                     torch.zeros(UpperCAmelCase	),
                     torch.zeros(UpperCAmelCase	),
                     torch.zeros(UpperCAmelCase	),
                     torch.zeros(UpperCAmelCase	),
                 )	)
         # TODO: test this.
         lowerCamelCase__    :      List[str]					 =		encoder_shape if remaining_side_name == 'encoder' else decoder_shape
         for _ in range(UpperCAmelCase			,     UpperCAmelCase	):
             common_inputs["past_key_values"].append((torch.zeros(UpperCAmelCase	), torch.zeros(UpperCAmelCase	))	)
     return common_inputs
 def      A_	(      self  :      Tuple			,     UpperCAmelCase  :      PreTrainedTokenizer			,     UpperCAmelCase  :      int = -1			,     UpperCAmelCase  :      int = -1			,     UpperCAmelCase  :      bool = False			,     UpperCAmelCase  :      Optional[TensorType] = None			,     )			->	Mapping[str, Any]:
     lowerCamelCase__    :      Any					 =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase	)

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

         lowerCamelCase__    :      Union[str, Any]					 =		common_inputs['attention_mask'].dtype
         lowerCamelCase__    :      int					 =		torch.cat(
             [common_inputs['attention_mask'], torch.ones(UpperCAmelCase			,     UpperCAmelCase			,     dtype=UpperCAmelCase	)]			,     dim=1	)
         lowerCamelCase__    :      Any					 =		[
             (torch.zeros(UpperCAmelCase	), torch.zeros(UpperCAmelCase	)) for _ in range(UpperCAmelCase	)
         ]
     return common_inputs
 def      A_	(      self  :      Optional[int]			,     UpperCAmelCase  :      PreTrainedTokenizer			,     UpperCAmelCase  :      int = -1			,     UpperCAmelCase  :      int = -1			,     UpperCAmelCase  :      bool = False			,     UpperCAmelCase  :      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
     lowerCamelCase__    :      Tuple					 =		compute_effective_axis_dimension(
         UpperCAmelCase			,     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
     lowerCamelCase__    :      List[str]					 =		tokenizer.num_special_tokens_to_add(UpperCAmelCase	)
     lowerCamelCase__    :      List[str]					 =		compute_effective_axis_dimension(
         UpperCAmelCase			,     fixed_dimension=OnnxConfig.default_fixed_sequence			,     num_token_to_add=UpperCAmelCase	)

     # Generate dummy inputs according to compute batch and sequence
     lowerCamelCase__    :      str					 =		[' '.join([tokenizer.unk_token]	) * seq_length] * batch_size
     lowerCamelCase__    :      Dict					 =		dict(tokenizer(UpperCAmelCase			,     return_tensors=UpperCAmelCase	)	)
     return common_inputs
 def      A_	(      self  :      List[Any]			,     UpperCAmelCase  :      PreTrainedTokenizer			,     UpperCAmelCase  :      int = -1			,     UpperCAmelCase  :      int = -1			,     UpperCAmelCase  :      bool = False			,     UpperCAmelCase  :      Optional[TensorType] = None			,     )			->	Mapping[str, Any]:
     if self.task in ["default", "seq2seq-lm"]:
         lowerCamelCase__    :      List[Any]					 =		self._generate_dummy_inputs_for_default_and_seqaseq_lm(
             UpperCAmelCase			,     batch_size=UpperCAmelCase			,     seq_length=UpperCAmelCase			,     is_pair=UpperCAmelCase			,     framework=UpperCAmelCase	)

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

     return common_inputs







 def      A_	(      self  :      List[str]			,     UpperCAmelCase  :      Tuple			,     UpperCAmelCase  :      Any			,     UpperCAmelCase  :      Any			,     UpperCAmelCase  :      int	)			->	List[Any]:
     if self.task in ["default", "seq2seq-lm"]:
         lowerCamelCase__    :      Dict					 =		super()._flatten_past_key_values_(UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase	)
     else:
         lowerCamelCase__    :      str					 =		super(UpperCAmelCase			,     self	)._flatten_past_key_values_(
             UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase	)






"""simple docstring"""






import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = (PNDMScheduler,)
					snake_case__				    = (("num_inference_steps", 50),)







					def 		__lowerCAmelCase						(				self   :	List[str]    ,**lowerCamelCase__   :	str			):
									UpperCAmelCase__             =							{
									    'num_train_timesteps': 1_000,
									    'beta_start': 0.0_0_0_1,
									    'beta_end': 0.0_2,
									    'beta_schedule': 'linear',
									}

									config.update(**lowerCamelCase__			)
									return config







					def 		__lowerCAmelCase						(				self   :	str    ,lowerCamelCase__   :	Optional[Any]=0    ,**lowerCamelCase__   :	List[str]			):
									UpperCAmelCase__             =							dict(self.forward_default_kwargs			)
									UpperCAmelCase__             =							kwargs.pop('num_inference_steps'    ,lowerCamelCase__			)
									UpperCAmelCase__             =							self.dummy_sample
									UpperCAmelCase__             =							0.1 * sample
									UpperCAmelCase__             =							[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

									for scheduler_class in self.scheduler_classes:
													UpperCAmelCase__             =							self.get_scheduler_config(**lowerCamelCase__			)
													UpperCAmelCase__             =							scheduler_class(**lowerCamelCase__			)
													scheduler.set_timesteps(lowerCamelCase__			)
													# copy over dummy past residuals
													UpperCAmelCase__             =							dummy_past_residuals[:]

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

													UpperCAmelCase__             =							scheduler.step_prk(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample
													UpperCAmelCase__             =							new_scheduler.step_prk(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample

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

													UpperCAmelCase__             =							scheduler.step_plms(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample
													UpperCAmelCase__             =							new_scheduler.step_plms(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample

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







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									pass







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	List[str]=0    ,**lowerCamelCase__   :	Tuple			):
									UpperCAmelCase__             =							dict(self.forward_default_kwargs			)
									UpperCAmelCase__             =							kwargs.pop('num_inference_steps'    ,lowerCamelCase__			)
									UpperCAmelCase__             =							self.dummy_sample
									UpperCAmelCase__             =							0.1 * sample
									UpperCAmelCase__             =							[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

									for scheduler_class in self.scheduler_classes:
													UpperCAmelCase__             =							self.get_scheduler_config()
													UpperCAmelCase__             =							scheduler_class(**lowerCamelCase__			)
													scheduler.set_timesteps(lowerCamelCase__			)

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

													with tempfile.TemporaryDirectory() as tmpdirname:
																	scheduler.save_config(lowerCamelCase__			)
																	UpperCAmelCase__             =							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)
																	UpperCAmelCase__             =							dummy_past_residuals[:]

													UpperCAmelCase__             =							scheduler.step_prk(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample
													UpperCAmelCase__             =							new_scheduler.step_prk(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample

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

													UpperCAmelCase__             =							scheduler.step_plms(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample
													UpperCAmelCase__             =							new_scheduler.step_plms(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample

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







					def 		__lowerCAmelCase						(				self   :	List[Any]    ,**lowerCamelCase__   :	int			):
									UpperCAmelCase__             =							self.scheduler_classes[0]
									UpperCAmelCase__             =							self.get_scheduler_config(**lowerCamelCase__			)
									UpperCAmelCase__             =							scheduler_class(**lowerCamelCase__			)

									UpperCAmelCase__             =							10
									UpperCAmelCase__             =							self.dummy_model()
									UpperCAmelCase__             =							self.dummy_sample_deter
									scheduler.set_timesteps(lowerCamelCase__			)

									for i, t in enumerate(scheduler.prk_timesteps			):
													UpperCAmelCase__             =							model(lowerCamelCase__    ,lowerCamelCase__			)
													UpperCAmelCase__             =							scheduler.step_prk(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__			).prev_sample

									for i, t in enumerate(scheduler.plms_timesteps			):
													UpperCAmelCase__             =							model(lowerCamelCase__    ,lowerCamelCase__			)
													UpperCAmelCase__             =							scheduler.step_plms(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__			).prev_sample

									return sample







					def 		__lowerCAmelCase						(				self   :	int			):
									UpperCAmelCase__             =							dict(self.forward_default_kwargs			)

									UpperCAmelCase__             =							kwargs.pop('num_inference_steps'    ,lowerCamelCase__			)

									for scheduler_class in self.scheduler_classes:
													UpperCAmelCase__             =							self.get_scheduler_config()
													UpperCAmelCase__             =							scheduler_class(**lowerCamelCase__			)

													UpperCAmelCase__             =							self.dummy_sample
													UpperCAmelCase__             =							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'			):
																	UpperCAmelCase__             =							num_inference_steps

													# copy over dummy past residuals (must be done after set_timesteps)
													UpperCAmelCase__             =							[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
													UpperCAmelCase__             =							dummy_past_residuals[:]

													UpperCAmelCase__             =							scheduler.step_prk(lowerCamelCase__    ,0    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample
													UpperCAmelCase__             =							scheduler.step_prk(lowerCamelCase__    ,1    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample

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

													UpperCAmelCase__             =							scheduler.step_plms(lowerCamelCase__    ,0    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample
													UpperCAmelCase__             =							scheduler.step_plms(lowerCamelCase__    ,1    ,lowerCamelCase__    ,**lowerCamelCase__			).prev_sample

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







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									for timesteps in [100, 1_000]:
													self.check_over_configs(num_train_timesteps=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									for steps_offset in [0, 1]:
													self.check_over_configs(steps_offset=lowerCamelCase__			)

									UpperCAmelCase__             =							self.scheduler_classes[0]
									UpperCAmelCase__             =							self.get_scheduler_config(steps_offset=1			)
									UpperCAmelCase__             =							scheduler_class(**lowerCamelCase__			)
									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 		__lowerCAmelCase						(				self   :	Dict			):
									for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]    ,[0.0_0_2, 0.0_2]			):
													self.check_over_configs(beta_start=lowerCamelCase__    ,beta_end=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									for schedule in ["linear", "squaredcos_cap_v2"]:
													self.check_over_configs(beta_schedule=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=lowerCamelCase__			)







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







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







					def 		__lowerCAmelCase						(				self   :	int			):
									# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
									UpperCAmelCase__             =							27

									for scheduler_class in self.scheduler_classes:
													UpperCAmelCase__             =							self.dummy_sample
													UpperCAmelCase__             =							0.1 * sample

													UpperCAmelCase__             =							self.get_scheduler_config()
													UpperCAmelCase__             =							scheduler_class(**lowerCamelCase__			)

													scheduler.set_timesteps(lowerCamelCase__			)

													# 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]			):
																	UpperCAmelCase__             =							scheduler.step_prk(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__			).prev_sample







					def 		__lowerCAmelCase						(				self   :	int			):
									with self.assertRaises(lowerCamelCase__			):
													UpperCAmelCase__             =							self.scheduler_classes[0]
													UpperCAmelCase__             =							self.get_scheduler_config()
													UpperCAmelCase__             =							scheduler_class(**lowerCamelCase__			)

													scheduler.step_plms(self.dummy_sample    ,1    ,self.dummy_sample			).prev_sample







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									UpperCAmelCase__             =							self.full_loop()
									UpperCAmelCase__             =							torch.sum(torch.abs(lowerCamelCase__			)			)
									UpperCAmelCase__             =							torch.mean(torch.abs(lowerCamelCase__			)			)

									assert abs(result_sum.item() - 1_9_8.1_3_1_8			) < 1e-2
									assert abs(result_mean.item() - 0.2_5_8_0			) < 1e-3







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									UpperCAmelCase__             =							self.full_loop(prediction_type='v_prediction'			)
									UpperCAmelCase__             =							torch.sum(torch.abs(lowerCamelCase__			)			)
									UpperCAmelCase__             =							torch.mean(torch.abs(lowerCamelCase__			)			)

									assert abs(result_sum.item() - 6_7.3_9_8_6			) < 1e-2
									assert abs(result_mean.item() - 0.0_8_7_8			) < 1e-3







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									# We specify different beta, so that the first alpha is 0.99
									UpperCAmelCase__             =							self.full_loop(set_alpha_to_one=lowerCamelCase__    ,beta_start=0.0_1			)
									UpperCAmelCase__             =							torch.sum(torch.abs(lowerCamelCase__			)			)
									UpperCAmelCase__             =							torch.mean(torch.abs(lowerCamelCase__			)			)

									assert abs(result_sum.item() - 2_3_0.0_3_9_9			) < 1e-2
									assert abs(result_mean.item() - 0.2_9_9_5			) < 1e-3







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									# We specify different beta, so that the first alpha is 0.99
									UpperCAmelCase__             =							self.full_loop(set_alpha_to_one=lowerCamelCase__    ,beta_start=0.0_1			)
									UpperCAmelCase__             =							torch.sum(torch.abs(lowerCamelCase__			)			)
									UpperCAmelCase__             =							torch.mean(torch.abs(lowerCamelCase__			)			)

									assert abs(result_sum.item() - 1_8_6.9_4_8_2			) < 1e-2
									assert abs(result_mean.item() - 0.2_4_3_4			) < 1e-3



