




"""simple docstring"""


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = (DEISMultistepScheduler,)
 lowercase__			 = (("num_inference_steps", 25),)


 def 			UpperCamelCase_       (    self:		Any,				**a_:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  {
       """num_train_timesteps""": 1_000,
       """beta_start""": 0.0_001,
       """beta_end""": 0.02,
       """beta_schedule""": """linear""",
       """solver_order""": 2,
   }

   config.update(**a_		)
   return config


 def 			UpperCamelCase_       (    self:		Union[str, Any],				a_:		int=0,				**a_:		Tuple		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  dict(self.forward_default_kwargs		)
   _snake_case							:		Union[str, Any]								=  kwargs.pop("""num_inference_steps""",				a_		)
   _snake_case							:		List[Any]								=  self.dummy_sample
   _snake_case							:		int								=  0.1 * sample
   _snake_case							:		List[str]								=  [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

     _snake_case		, _snake_case							:		int								=  sample, sample
     for t in range(a_,				time_step + scheduler.config.solver_order + 1		):
       _snake_case							:		Dict								=  scheduler.step(a_,				a_,				a_,				**a_		).prev_sample
       _snake_case							:		Dict								=  new_scheduler.step(a_,				a_,				a_,				**a_		).prev_sample

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


 def 			UpperCamelCase_       (    self:		str		):




   '''simple docstring'''



   pass


 def 			UpperCamelCase_       (    self:		str,				a_:		List[Any]=0,				**a_:		Optional[int]		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  dict(self.forward_default_kwargs		)
   _snake_case							:		Optional[int]								=  kwargs.pop("""num_inference_steps""",				a_		)
   _snake_case							:		Optional[Any]								=  self.dummy_sample
   _snake_case							:		Dict								=  0.1 * sample
   _snake_case							:		List[str]								=  [residual + 0.2, residual + 0.15, residual + 0.10]

   for scheduler_class in self.scheduler_classes:
     _snake_case							:		List[str]								=  self.get_scheduler_config()
     _snake_case							:		Any								=  scheduler_class(**a_		)
     scheduler.set_timesteps(a_		)

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

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

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

     _snake_case							:		Dict								=  scheduler.step(a_,				a_,				a_,				**a_		).prev_sample
     _snake_case							:		int								=  new_scheduler.step(a_,				a_,				a_,				**a_		).prev_sample

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


 def 			UpperCamelCase_       (    self:		List[str],				a_:		str=None,				**a_:		List[Any]		):




   '''simple docstring'''



   if scheduler is None:
     _snake_case							:		Dict								=  self.scheduler_classes[0]
     _snake_case							:		List[str]								=  self.get_scheduler_config(**a_		)
     _snake_case							:		List[Any]								=  scheduler_class(**a_		)

   _snake_case							:		Optional[Any]								=  self.scheduler_classes[0]
   _snake_case							:		List[str]								=  self.get_scheduler_config(**a_		)
   _snake_case							:		Tuple								=  scheduler_class(**a_		)

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

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

   return sample


 def 			UpperCamelCase_       (    self:		Optional[int]		):




   '''simple docstring'''



   _snake_case							:		int								=  dict(self.forward_default_kwargs		)

   _snake_case							:		str								=  kwargs.pop("""num_inference_steps""",				a_		)

   for scheduler_class in self.scheduler_classes:
     _snake_case							:		str								=  self.get_scheduler_config()
     _snake_case							:		Any								=  scheduler_class(**a_		)

     _snake_case							:		Optional[int]								=  self.dummy_sample
     _snake_case							:		Union[str, Any]								=  0.1 * sample

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

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

     _snake_case							:		Optional[int]								=  scheduler.timesteps[5]
     _snake_case							:		Optional[int]								=  scheduler.timesteps[6]

     _snake_case							:		Optional[int]								=  scheduler.step(a_,				a_,				a_,				**a_		).prev_sample
     _snake_case							:		Optional[Any]								=  scheduler.step(a_,				a_,				a_,				**a_		).prev_sample

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


 def 			UpperCamelCase_       (    self:		str		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  DEISMultistepScheduler(**self.get_scheduler_config()		)
   _snake_case							:		Union[str, Any]								=  self.full_loop(scheduler=a_		)
   _snake_case							:		int								=  torch.mean(torch.abs(a_		)		)

   assert abs(result_mean.item() - 0.23_916		) < 1E-3

   _snake_case							:		Union[str, Any]								=  DPMSolverSinglestepScheduler.from_config(scheduler.config		)
   _snake_case							:		str								=  DPMSolverMultistepScheduler.from_config(scheduler.config		)
   _snake_case							:		Dict								=  UniPCMultistepScheduler.from_config(scheduler.config		)
   _snake_case							:		Tuple								=  DEISMultistepScheduler.from_config(scheduler.config		)

   _snake_case							:		List[str]								=  self.full_loop(scheduler=a_		)
   _snake_case							:		List[Any]								=  torch.mean(torch.abs(a_		)		)

   assert abs(result_mean.item() - 0.23_916		) < 1E-3


 def 			UpperCamelCase_       (    self:		Optional[Any]		):




   '''simple docstring'''



   for timesteps in [25, 50, 100, 999, 1_000]:
     self.check_over_configs(num_train_timesteps=a_		)


 def 			UpperCamelCase_       (    self:		Dict		):




   '''simple docstring'''



   self.check_over_configs(thresholding=a_		)
   for order in [1, 2, 3]:
     for solver_type in ["logrho"]:
       for threshold in [0.5, 1.0, 2.0]:
         for prediction_type in ["epsilon", "sample"]:
           self.check_over_configs(
               thresholding=a_,				prediction_type=a_,				sample_max_value=a_,				algorithm_type="""deis""",				solver_order=a_,				solver_type=a_,				)


 def 			UpperCamelCase_       (    self:		Optional[Any]		):




   '''simple docstring'''



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


 def 			UpperCamelCase_       (    self:		Any		):




   '''simple docstring'''



   for algorithm_type in ["deis"]:
     for solver_type in ["logrho"]:
       for order in [1, 2, 3]:
         for prediction_type in ["epsilon", "sample"]:
           self.check_over_configs(
               solver_order=a_,				solver_type=a_,				prediction_type=a_,				algorithm_type=a_,				)
           _snake_case							:		Optional[Any]								=  self.full_loop(
               solver_order=a_,				solver_type=a_,				prediction_type=a_,				algorithm_type=a_,				)
           assert not torch.isnan(a_		).any(), "Samples have nan numbers"


 def 			UpperCamelCase_       (    self:		int		):




   '''simple docstring'''



   self.check_over_configs(lower_order_final=a_		)
   self.check_over_configs(lower_order_final=a_		)


 def 			UpperCamelCase_       (    self:		Dict		):




   '''simple docstring'''



   for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
     self.check_over_forward(num_inference_steps=a_,				time_step=0		)


 def 			UpperCamelCase_       (    self:		Any		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  self.full_loop()
   _snake_case							:		Tuple								=  torch.mean(torch.abs(a_		)		)

   assert abs(result_mean.item() - 0.23_916		) < 1E-3


 def 			UpperCamelCase_       (    self:		Dict		):




   '''simple docstring'''



   _snake_case							:		Dict								=  self.full_loop(prediction_type="""v_prediction"""		)
   _snake_case							:		Optional[Any]								=  torch.mean(torch.abs(a_		)		)

   assert abs(result_mean.item() - 0.091		) < 1E-3


 def 			UpperCamelCase_       (    self:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		int								=  self.scheduler_classes[0]
   _snake_case							:		str								=  self.get_scheduler_config(thresholding=a_,				dynamic_thresholding_ratio=0		)
   _snake_case							:		int								=  scheduler_class(**a_		)

   _snake_case							:		Dict								=  10
   _snake_case							:		Any								=  self.dummy_model()
   _snake_case							:		List[Any]								=  self.dummy_sample_deter.half()
   scheduler.set_timesteps(a_		)

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

   assert sample.dtype == torch.floataa








"""simple docstring"""


from collections.abc import Generator
from math import sin


def  UpperCAmelCase__	(snake_case__							: bytes   ):
  """simple docstring"""

  if len(snake_case__   ) != 32:
    raise ValueError("""Input must be of length 32"""   )

  _snake_case							:		Optional[int]								=  B""""""
  for i in [3, 2, 1, 0]:
    little_endian += string_aa[8 * i : 8 * i + 8]
  return little_endian


def  UpperCAmelCase__	(snake_case__							: int   ):
  """simple docstring"""

  if i < 0:
    raise ValueError("""Input must be non-negative"""   )

  _snake_case							:		Optional[Any]								=  format(snake_case__		,       """08x"""   )[-8:]
  _snake_case							:		Optional[Any]								=  B""""""
  for i in [3, 2, 1, 0]:
    little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("""utf-8"""   )
  return little_endian_hex


def  UpperCAmelCase__	(snake_case__							: bytes   ):
  """simple docstring"""

  _snake_case							:		Union[str, Any]								=  B""""""
  for char in message:
    bit_string += format(snake_case__		,       """08b"""   ).encode("""utf-8"""   )
  _snake_case							:		List[Any]								=  format(len(snake_case__   )		,       """064b"""   ).encode("""utf-8"""   )

  # Pad bit_string to a multiple of 512 chars
  bit_string += b"1"
  while len(snake_case__   ) % 5_12 != 4_48:
    bit_string += b"0"
  bit_string += to_little_endian(start_len[32:]   ) + to_little_endian(start_len[:32]   )

  return bit_string


def  UpperCAmelCase__	(snake_case__							: bytes   ):
  """simple docstring"""

  if len(snake_case__   ) % 5_12 != 0:
    raise ValueError("""Input must have length that's a multiple of 512"""   )

  for pos in range(0		,       len(snake_case__   )		,       5_12   ):
    _snake_case							:		List[str]								=  bit_string[pos : pos + 5_12]
    _snake_case							:		List[str]								=  []
    for i in range(0		,       5_12		,       32   ):
      block_words.append(int(to_little_endian(block[i : i + 32]   )		,       2   )   )
    yield block_words


def  UpperCAmelCase__	(snake_case__							: int   ):
  """simple docstring"""

  if i < 0:
    raise ValueError("""Input must be non-negative"""   )

  _snake_case							:		Optional[int]								=  format(snake_case__		,       """032b"""   )
  _snake_case							:		str								=  """"""
  for c in i_str:
    new_str += "1" if c == "0" else "0"
  return int(snake_case__		,       2   )


def  UpperCAmelCase__	(snake_case__							: int		,       snake_case__							: int   ):
  """simple docstring"""

  return (a + b) % 2**32


def  UpperCAmelCase__	(snake_case__							: int		,       snake_case__							: int   ):
  """simple docstring"""

  if i < 0:
    raise ValueError("""Input must be non-negative"""   )
  if shift < 0:
    raise ValueError("""Shift must be non-negative"""   )
  return ((i << shift) ^ (i >> (32 - shift))) % 2**32


def  UpperCAmelCase__	(snake_case__							: bytes   ):
  """simple docstring"""

  _snake_case							:		Any								=  preprocess(snake_case__   )

  _snake_case							:		Optional[Any]								=  [int(2**32 * abs(sin(i + 1   )   )   ) for i in range(64   )]

  # Starting states
  _snake_case							:		Union[str, Any]								=  0x6745_2301
  _snake_case							:		List[Any]								=  0xEFCD_AB89
  _snake_case							:		Optional[Any]								=  0x98BA_DCFE
  _snake_case							:		Optional[int]								=  0x1032_5476

  _snake_case							:		Tuple								=  [
      7,
      12,
      17,
      22,
      7,
      12,
      17,
      22,
      7,
      12,
      17,
      22,
      7,
      12,
      17,
      22,
      5,
      9,
      14,
      20,
      5,
      9,
      14,
      20,
      5,
      9,
      14,
      20,
      5,
      9,
      14,
      20,
      4,
      11,
      16,
      23,
      4,
      11,
      16,
      23,
      4,
      11,
      16,
      23,
      4,
      11,
      16,
      23,
      6,
      10,
      15,
      21,
      6,
      10,
      15,
      21,
      6,
      10,
      15,
      21,
      6,
      10,
      15,
      21,
  ]

  # Process bit string in chunks, each with 16 32-char words
  for block_words in get_block_words(snake_case__   ):
    _snake_case							:		Tuple								=  aa
    _snake_case							:		str								=  ba
    _snake_case							:		int								=  ca
    _snake_case							:		Dict								=  da

    # Hash current chunk
    for i in range(64   ):
      if i <= 15:
        # f = (b & c) | (not_32(b) & d)     # Alternate definition for f
        _snake_case							:		int								=  d ^ (b & (c ^ d))
        _snake_case							:		Dict								=  i
      elif i <= 31:
        # f = (d & b) | (not_32(d) & c)     # Alternate definition for f
        _snake_case							:		Tuple								=  c ^ (d & (b ^ c))
        _snake_case							:		int								=  (5 * i + 1) % 16
      elif i <= 47:
        _snake_case							:		List[Any]								=  b ^ c ^ d
        _snake_case							:		Union[str, Any]								=  (3 * i + 5) % 16
      else:
        _snake_case							:		Tuple								=  c ^ (b | not_aa(snake_case__   ))
        _snake_case							:		Optional[int]								=  (7 * i) % 16
      _snake_case							:		Dict								=  (f + a + added_consts[i] + block_words[g]) % 2**32
      _snake_case							:		List[str]								=  d
      _snake_case							:		List[Any]								=  c
      _snake_case							:		str								=  b
      _snake_case							:		List[str]								=  sum_aa(snake_case__		,       left_rotate_aa(snake_case__		,       shift_amounts[i]   )   )

    # Add hashed chunk to running total
    _snake_case							:		Union[str, Any]								=  sum_aa(snake_case__		,       snake_case__   )
    _snake_case							:		str								=  sum_aa(snake_case__		,       snake_case__   )
    _snake_case							:		Any								=  sum_aa(snake_case__		,       snake_case__   )
    _snake_case							:		List[str]								=  sum_aa(snake_case__		,       snake_case__   )

  _snake_case							:		Any								=  reformat_hex(snake_case__   ) + reformat_hex(snake_case__   ) + reformat_hex(snake_case__   ) + reformat_hex(snake_case__   )
  return digest


if __name__ == "__main__":
     import doctest

     doctest.testmod()


