





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class       __lowerCAmelCase					(       _lowercase      ):


	'''simple docstring'''





	_A													=				(DPMSolverSinglestepScheduler,)
	_A													=				(('''num_inference_steps''', 25),)

	def    snake_case__(						self:						List[Any],					**lowerCamelCase_:						str			):
			lowercase__					:      Any      =		{
			    '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',
			    'lambda_min_clipped': -float('inf'			),
			    'variance_type': None,
			}

			config.update(**lowerCamelCase_			)
			return config

	def    snake_case__(						self:						str,					lowerCamelCase_:						Union[str, Any]=0,					**lowerCamelCase_:						Union[str, Any]			):
			lowercase__					:      Dict      =		dict(self.forward_default_kwargs			)
			lowercase__					:      Any      =		kwargs.pop('num_inference_steps',					lowerCamelCase_			)
			lowercase__					:      Optional[int]      =		self.dummy_sample
			lowercase__					:      List[str]      =		0.1 * sample
			lowercase__					:      int      =		[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

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

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

	def    snake_case__(						self:						Tuple			):
			pass

	def    snake_case__(						self:						Union[str, Any],					lowerCamelCase_:						Optional[int]=0,					**lowerCamelCase_:						str			):
			lowercase__					:      int      =		dict(self.forward_default_kwargs			)
			lowercase__					:      str      =		kwargs.pop('num_inference_steps',					lowerCamelCase_			)
			lowercase__					:      Optional[Any]      =		self.dummy_sample
			lowercase__					:      str      =		0.1 * sample
			lowercase__					:      Any      =		[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

			for scheduler_class in self.scheduler_classes:
					lowercase__					:      int      =		self.get_scheduler_config()
					lowercase__					:      Union[str, Any]      =		scheduler_class(**lowerCamelCase_			)
					scheduler.set_timesteps(lowerCamelCase_			)

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

					with tempfile.TemporaryDirectory() as tmpdirname:
							scheduler.save_config(lowerCamelCase_			)
							lowercase__					:      List[str]      =		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)
							lowercase__					:      Optional[Any]      =		dummy_past_residuals[: new_scheduler.config.solver_order]

					lowercase__					:      List[str]      =		scheduler.step(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					**lowerCamelCase_			).prev_sample
					lowercase__					:      str      =		new_scheduler.step(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					**lowerCamelCase_			).prev_sample

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

	def    snake_case__(						self:						str,					lowerCamelCase_:						List[str]=None,					**lowerCamelCase_:						List[str]			):
			if scheduler is None:
					lowercase__					:      Union[str, Any]      =		self.scheduler_classes[0]
					lowercase__					:      Optional[int]      =		self.get_scheduler_config(**lowerCamelCase_			)
					lowercase__					:      Optional[Any]      =		scheduler_class(**lowerCamelCase_			)

			lowercase__					:      int      =		self.scheduler_classes[0]
			lowercase__					:      List[str]      =		self.get_scheduler_config(**lowerCamelCase_			)
			lowercase__					:      Dict      =		scheduler_class(**lowerCamelCase_			)

			lowercase__					:      List[Any]      =		10
			lowercase__					:      Optional[int]      =		self.dummy_model()
			lowercase__					:      Union[str, Any]      =		self.dummy_sample_deter
			scheduler.set_timesteps(lowerCamelCase_			)

			for i, t in enumerate(scheduler.timesteps			):
					lowercase__					:      List[str]      =		model(lowerCamelCase_,					lowerCamelCase_			)
					lowercase__					:      Optional[Any]      =		scheduler.step(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_			).prev_sample

			return sample

	def    snake_case__(						self:						Union[str, Any]			):
			lowercase__					:      str      =		DPMSolverSinglestepScheduler(**self.get_scheduler_config()			)
			lowercase__					:      str      =		50
			lowercase__					:      Any      =		self.dummy_model()
			lowercase__					:      Optional[int]      =		self.dummy_sample_deter
			scheduler.set_timesteps(lowerCamelCase_			)

			# make sure that the first t is uneven
			for i, t in enumerate(scheduler.timesteps[3:]			):
					lowercase__					:      int      =		model(lowerCamelCase_,					lowerCamelCase_			)
					lowercase__					:      Dict      =		scheduler.step(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_			).prev_sample

			lowercase__					:      Any      =		torch.mean(torch.abs(lowerCamelCase_			)			)

			assert abs(result_mean.item() - 0.2_5_7_4			) < 1E-3

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

	def    snake_case__(						self:						List[str]			):
			# make sure that iterating over schedulers with same config names gives same results
			# for defaults
			lowercase__					:      Optional[int]      =		DPMSolverSinglestepScheduler(**self.get_scheduler_config()			)
			lowercase__					:      str      =		self.full_loop(scheduler=lowerCamelCase_			)
			lowercase__					:      str      =		torch.mean(torch.abs(lowerCamelCase_			)			)

			assert abs(result_mean.item() - 0.2_7_9_1			) < 1E-3

			lowercase__					:      Tuple      =		DEISMultistepScheduler.from_config(scheduler.config			)
			lowercase__					:      Optional[Any]      =		DPMSolverMultistepScheduler.from_config(scheduler.config			)
			lowercase__					:      Tuple      =		UniPCMultistepScheduler.from_config(scheduler.config			)
			lowercase__					:      Any      =		DPMSolverSinglestepScheduler.from_config(scheduler.config			)

			lowercase__					:      Tuple      =		self.full_loop(scheduler=lowerCamelCase_			)
			lowercase__					:      Any      =		torch.mean(torch.abs(lowerCamelCase_			)			)

			assert abs(result_mean.item() - 0.2_7_9_1			) < 1E-3

	def    snake_case__(						self:						int			):
			self.check_over_configs(thresholding=lowerCamelCase_			)
			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=lowerCamelCase_,					prediction_type=lowerCamelCase_,					sample_max_value=lowerCamelCase_,					algorithm_type='dpmsolver++',					solver_order=lowerCamelCase_,					solver_type=lowerCamelCase_,					)

	def    snake_case__(						self:						List[str]			):
			for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=lowerCamelCase_			)

	def    snake_case__(						self:						List[str]			):
			for algorithm_type in ["dpmsolver", "dpmsolver++"]:
					for solver_type in ["midpoint", "heun"]:
							for order in [1, 2, 3]:
									for prediction_type in ["epsilon", "sample"]:
											self.check_over_configs(
											    solver_order=lowerCamelCase_,					solver_type=lowerCamelCase_,					prediction_type=lowerCamelCase_,					algorithm_type=lowerCamelCase_,					)
											lowercase__					:      Any      =		self.full_loop(
											    solver_order=lowerCamelCase_,					solver_type=lowerCamelCase_,					prediction_type=lowerCamelCase_,					algorithm_type=lowerCamelCase_,					)
											assert not torch.isnan(lowerCamelCase_			).any(), "Samples have nan numbers"

	def    snake_case__(						self:						int			):
			self.check_over_configs(lower_order_final=lowerCamelCase_			)
			self.check_over_configs(lower_order_final=lowerCamelCase_			)

	def    snake_case__(						self:						Optional[Any]			):
			self.check_over_configs(lambda_min_clipped=-float('inf'			)			)
			self.check_over_configs(lambda_min_clipped=-5.1			)

	def    snake_case__(						self:						Optional[int]			):
			self.check_over_configs(variance_type=lowerCamelCase_			)
			self.check_over_configs(variance_type='learned_range'			)

	def    snake_case__(						self:						Union[str, Any]			):
			for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
					self.check_over_forward(num_inference_steps=lowerCamelCase_,					time_step=0			)

	def    snake_case__(						self:						List[Any]			):
			lowercase__					:      Optional[int]      =		self.full_loop()
			lowercase__					:      Optional[Any]      =		torch.mean(torch.abs(lowerCamelCase_			)			)

			assert abs(result_mean.item() - 0.2_7_9_1			) < 1E-3

	def    snake_case__(						self:						List[Any]			):
			lowercase__					:      Optional[Any]      =		self.full_loop(use_karras_sigmas=lowerCamelCase_			)
			lowercase__					:      Any      =		torch.mean(torch.abs(lowerCamelCase_			)			)

			assert abs(result_mean.item() - 0.2_2_4_8			) < 1E-3

	def    snake_case__(						self:						Optional[int]			):
			lowercase__					:      Tuple      =		self.full_loop(prediction_type='v_prediction'			)
			lowercase__					:      Union[str, Any]      =		torch.mean(torch.abs(lowerCamelCase_			)			)

			assert abs(result_mean.item() - 0.1_4_5_3			) < 1E-3

	def    snake_case__(						self:						str			):
			lowercase__					:      List[str]      =		self.full_loop(prediction_type='v_prediction',					use_karras_sigmas=lowerCamelCase_			)
			lowercase__					:      Dict      =		torch.mean(torch.abs(lowerCamelCase_			)			)

			assert abs(result_mean.item() - 0.0_6_4_9			) < 1E-3





	def    snake_case__(						self:						List[str]			):
			lowercase__					:      Dict      =		self.scheduler_classes[0]
			lowercase__					:      List[str]      =		self.get_scheduler_config(thresholding=lowerCamelCase_,					dynamic_thresholding_ratio=0			)
			lowercase__					:      Union[str, Any]      =		scheduler_class(**lowerCamelCase_			)

			lowercase__					:      Optional[Any]      =		10
			lowercase__					:      Union[str, Any]      =		self.dummy_model()
			lowercase__					:      Union[str, Any]      =		self.dummy_sample_deter.half()
			scheduler.set_timesteps(lowerCamelCase_			)

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

			assert sample.dtype == torch.floataa







from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


a_      :      Optional[int]    =     logging.get_logger(__name__)
class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      ) ->			int:
  a__												= feature_size
  a__												= sampling_rate
  a__												= padding_value

  a__												= kwargs.pop('''padding_side'''		,				'''right'''      )
  a__												= kwargs.pop('''return_attention_mask'''		,				SCREAMING_SNAKE_CASE      )

  super().__init__(**SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = True		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = False		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = None		,				) ->			BatchFeature:
  # If we have a list of dicts, let's convert it in a dict of lists
  # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
  if isinstance(SCREAMING_SNAKE_CASE		,				(list, tuple)      ) and isinstance(processed_features[0]		,				(dict, BatchFeature)      ):
   a__												= {
       key: [example[key] for example in processed_features] for key in processed_features[0].keys()
   }

  # The model's main input name, usually `input_values`, has be passed for padding
  if self.model_input_names[0] not in processed_features:
   raise ValueError(
       '''You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'''
       f" to this method that includes {self.model_input_names[0]}, but you provided"
       f" {list(processed_features.keys()      )}"      )

  a__												= processed_features[self.model_input_names[0]]
  a__												= (
      return_attention_mask if return_attention_mask is not None else self.return_attention_mask
  )

  if len(SCREAMING_SNAKE_CASE      ) == 0:
   if return_attention_mask:
    a__												= []
   return processed_features

  # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
  # and rebuild them afterwards if no return_tensors is specified
  # Note that we lose the specific device the tensor may be on for PyTorch

  a__												= required_input[0]
  if isinstance(SCREAMING_SNAKE_CASE		,				(list, tuple)      ):
   # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
   a__												= 0
   while len(required_input[index]      ) == 0:
    index += 1
   if index < len(SCREAMING_SNAKE_CASE      ):
    a__												= required_input[index][0]

  if return_tensors is None:
   if is_tf_tensor(SCREAMING_SNAKE_CASE      ):
    a__												= '''tf'''
   elif is_torch_tensor(SCREAMING_SNAKE_CASE      ):
    a__												= '''pt'''
   elif isinstance(SCREAMING_SNAKE_CASE		,				(int, float, list, tuple, np.ndarray)      ):
    a__												= '''np'''
   else:
    raise ValueError(
        f"type of {first_element} unknown: {type(SCREAMING_SNAKE_CASE      )}. "
        '''Should be one of a python, numpy, pytorch or tensorflow object.'''      )

  for key, value in processed_features.items():
   if isinstance(value[0]		,				(int, float)      ):
    a__												= to_numpy(SCREAMING_SNAKE_CASE      )
   else:
    a__												= [to_numpy(SCREAMING_SNAKE_CASE      ) for v in value]

        # Convert padding_strategy in PaddingStrategy
  a__												= self._get_padding_strategies(padding=SCREAMING_SNAKE_CASE		,				max_length=SCREAMING_SNAKE_CASE      )

  a__												= processed_features[self.model_input_names[0]]

  a__												= len(SCREAMING_SNAKE_CASE      )
  if not all(len(SCREAMING_SNAKE_CASE      ) == batch_size for v in processed_features.values()      ):
   raise ValueError('''Some items in the output dictionary have a different batch size than others.'''      )

  a__												= []
  for i in range(SCREAMING_SNAKE_CASE      ):
   a__												= {k: v[i] for k, v in processed_features.items()}
   # truncation
   a__												= self._truncate(
       SCREAMING_SNAKE_CASE		,				max_length=SCREAMING_SNAKE_CASE		,				pad_to_multiple_of=SCREAMING_SNAKE_CASE		,				truncation=SCREAMING_SNAKE_CASE		,				)
   truncated_inputs.append(SCREAMING_SNAKE_CASE      )

  if padding_strategy == PaddingStrategy.LONGEST:
   # make sure that `max_length` cannot be longer than the longest truncated length
   a__												= max(len(input_slice[self.model_input_names[0]]      ) for input_slice in truncated_inputs      )
   a__												= PaddingStrategy.MAX_LENGTH

  a__												= {}
  for i in range(SCREAMING_SNAKE_CASE      ):
   # padding
   a__												= self._pad(
       truncated_inputs[i]		,				max_length=SCREAMING_SNAKE_CASE		,				padding_strategy=SCREAMING_SNAKE_CASE		,				pad_to_multiple_of=SCREAMING_SNAKE_CASE		,				return_attention_mask=SCREAMING_SNAKE_CASE		,				)

   for key, value in outputs.items():
    if key not in batch_outputs:
     a__												= []
    if value.dtype is np.dtype(np.floataa      ):
     a__												= value.astype(np.floataa      )
    batch_outputs[key].append(SCREAMING_SNAKE_CASE      )

  return BatchFeature(SCREAMING_SNAKE_CASE		,				tensor_type=SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = PaddingStrategy.DO_NOT_PAD		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = None		,				) ->			dict:
  a__												= processed_features[self.model_input_names[0]]

  if padding_strategy == PaddingStrategy.LONGEST:
   a__												= len(SCREAMING_SNAKE_CASE      )

  if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
   a__												= ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

  a__												= padding_strategy != PaddingStrategy.DO_NOT_PAD and len(SCREAMING_SNAKE_CASE      ) < max_length

  if return_attention_mask and "attention_mask" not in processed_features:
   a__												= np.ones(len(SCREAMING_SNAKE_CASE      )		,				dtype=np.intaa      )

  if needs_to_be_padded:
   a__												= max_length - len(SCREAMING_SNAKE_CASE      )
   if self.padding_side == "right":
    if return_attention_mask:
     a__												= np.pad(
         processed_features['''attention_mask''']		,				(0, difference)      )
    a__												= ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
    a__												= np.pad(
        SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				'''constant'''		,				constant_values=self.padding_value      )
   elif self.padding_side == "left":
    if return_attention_mask:
     a__												= np.pad(
         processed_features['''attention_mask''']		,				(difference, 0)      )
    a__												= ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
    a__												= np.pad(
        SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				'''constant'''		,				constant_values=self.padding_value      )
   else:
    raise ValueError('''Invalid padding strategy:''' + str(self.padding_side      )      )

  return processed_features

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = None		,				) ->			Optional[Any]:
  if not truncation:
   return processed_features
  elif truncation and max_length is None:
   raise ValueError('''When setting ``truncation=True``, make sure that ``max_length`` is defined.'''      )

  a__												= processed_features[self.model_input_names[0]]

  # find `max_length` that fits `pad_to_multiple_of`
  if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
   a__												= ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

  a__												= len(SCREAMING_SNAKE_CASE      ) > max_length

  if needs_to_be_truncated:
   a__												= processed_features[self.model_input_names[0]][:max_length]
   if "attention_mask" in processed_features:
    a__												= processed_features['''attention_mask'''][:max_length]

  return processed_features



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE=False		,				SCREAMING_SNAKE_CASE=None      ) ->			Optional[Any]:

  # Get padding strategy
  if padding is not False:
   if padding is True:
    a__												= PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
   elif not isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ):
    a__												= PaddingStrategy(SCREAMING_SNAKE_CASE      )
   elif isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ):
    a__												= padding
  else:
   a__												= PaddingStrategy.DO_NOT_PAD

  # Set max length if needed
  if max_length is None:
   if padding_strategy == PaddingStrategy.MAX_LENGTH:
    raise ValueError(
        f"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"      )

        # Test if we have a padding value
  if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
   raise ValueError(
       '''Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'''
       ''' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'''      )

  return padding_strategy





