






"""simple docstring"""




from collections.abc import Generator
from math import sin






def        lowerCAmelCase (      UpperCamelCase_:							bytes					)       ->							str:







  '''simple docstring'''







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

  _a							=	b""
  for i in [3, 2, 1, 0]:
    little_endian += string_aa[8 * i : 8 * i + 8]
  return little_endian






def        lowerCAmelCase (      UpperCamelCase_:							int					)       ->							Any:







  '''simple docstring'''







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

  _a							=	format(A__     , "08x"					)[-8:]
  _a							=	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        lowerCAmelCase (      UpperCamelCase_:							bytes					)       ->							Optional[int]:







  '''simple docstring'''







  _a							=	b""
  for char in message:
    bit_string += format(A__     , "08b"					).encode("utf-8"					)
  _a							=	format(len(A__					)     , "064b"					).encode("utf-8"					)

  # Pad bit_string to a multiple of 512 chars
  bit_string += b"1"
  while len(A__					) % 512 != 448:
    bit_string += b"0"
  bit_string += to_little_endian(start_len[32:]					) + to_little_endian(start_len[:32]					)

  return bit_string






def        lowerCAmelCase (      UpperCamelCase_:							bytes					)       ->							Optional[int]:







  '''simple docstring'''







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

  for pos in range(0     , len(A__					)     , 512					):
    _a							=	bit_string[pos : pos + 512]
    _a							=	[]
    for i in range(0     , 512     , 32					):
      block_words.append(int(to_little_endian(block[i : i + 32]					)     , 2					)					)
    yield block_words






def        lowerCAmelCase (      UpperCamelCase_:							int					)       ->							Dict:







  '''simple docstring'''







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

  _a							=	format(A__     , "032b"					)
  _a							=	""
  for c in i_str:
    new_str += "1" if c == "0" else "0"
  return int(A__     , 2					)






def        lowerCAmelCase (      UpperCamelCase_:							int     , UpperCamelCase_:							int					)       ->							int:







  '''simple docstring'''







  return (a + b) % 2**32






def        lowerCAmelCase (      UpperCamelCase_:							int     , UpperCamelCase_:							int					)       ->							Optional[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        lowerCAmelCase (      UpperCamelCase_:							bytes					)       ->							Optional[int]:







  '''simple docstring'''







  _a							=	preprocess(A__					)

  _a							=	[int(2**32 * abs(sin(i + 1					)					)					) for i in range(64					)]

  # Starting states
  _a							=	0X67452301
  _a							=	0Xefcdab89
  _a							=	0X98badcfe
  _a							=	0X10325476

  _a							=	[
      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(A__					):
    _a							=	aa
    _a							=	ba
    _a							=	ca
    _a							=	da

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

    # Add hashed chunk to running total
    _a							=	sum_aa(A__     , A__					)
    _a							=	sum_aa(A__     , A__					)
    _a							=	sum_aa(A__     , A__					)
    _a							=	sum_aa(A__     , A__					)

  _a							=	reformat_hex(A__					) + reformat_hex(A__					) + reformat_hex(A__					) + reformat_hex(A__					)
  return digest


if __name__ == "__main__":
     import doctest

     doctest.testmod()










"""simple docstring"""




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class    lowercase_    (_UpperCAmelCase     ):
      A__	:		Union[str, Any]       			=				(PNDMScheduler,)
      A__	:		Optional[int]       			=				(('''num_inference_steps''', 50),)






      def 			lowerCamelCase__    (    self     ,     **a_	)	->Optional[Any]:







        '''simple docstring'''


        _a							=	{
            "num_train_timesteps": 1_0_0_0,
            "beta_start": 0.0_001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
        }

        config.update(**a_	)
        return config






      def 			lowerCamelCase__    (    self     ,     a_=0     ,     **a_	)	->Tuple:







        '''simple docstring'''


        _a							=	dict(self.forward_default_kwargs	)
        _a							=	kwargs.pop("num_inference_steps"     ,     a_	)
        _a							=	self.dummy_sample
        _a							=	0.1 * sample
        _a							=	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

          _a							=	scheduler.step_prk(a_     ,     a_     ,     a_     ,     **a_	).prev_sample
          _a							=	new_scheduler.step_prk(a_     ,     a_     ,     a_     ,     **a_	).prev_sample

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

          _a							=	scheduler.step_plms(a_     ,     a_     ,     a_     ,     **a_	).prev_sample
          _a							=	new_scheduler.step_plms(a_     ,     a_     ,     a_     ,     **a_	).prev_sample

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






      def 			lowerCamelCase__    (    self	)	->Dict:







        '''simple docstring'''


        pass






      def 			lowerCamelCase__    (    self     ,     a_=0     ,     **a_	)	->Tuple:







        '''simple docstring'''


        _a							=	dict(self.forward_default_kwargs	)
        _a							=	kwargs.pop("num_inference_steps"     ,     a_	)
        _a							=	self.dummy_sample
        _a							=	0.1 * sample
        _a							=	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

          with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(a_	)
            _a							=	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)
            _a							=	dummy_past_residuals[:]

          _a							=	scheduler.step_prk(a_     ,     a_     ,     a_     ,     **a_	).prev_sample
          _a							=	new_scheduler.step_prk(a_     ,     a_     ,     a_     ,     **a_	).prev_sample

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

          _a							=	scheduler.step_plms(a_     ,     a_     ,     a_     ,     **a_	).prev_sample
          _a							=	new_scheduler.step_plms(a_     ,     a_     ,     a_     ,     **a_	).prev_sample

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






      def 			lowerCamelCase__    (    self     ,     **a_	)	->Optional[int]:







        '''simple docstring'''


        _a							=	self.scheduler_classes[0]
        _a							=	self.get_scheduler_config(**a_	)
        _a							=	scheduler_class(**a_	)

        _a							=	1_0
        _a							=	self.dummy_model()
        _a							=	self.dummy_sample_deter
        scheduler.set_timesteps(a_	)

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

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

        return sample






      def 			lowerCamelCase__    (    self	)	->Dict:







        '''simple docstring'''


        _a							=	dict(self.forward_default_kwargs	)

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

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

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

          # copy over dummy past residuals (must be done after set_timesteps)
          _a							=	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
          _a							=	dummy_past_residuals[:]

          _a							=	scheduler.step_prk(a_     ,     0     ,     a_     ,     **a_	).prev_sample
          _a							=	scheduler.step_prk(a_     ,     1     ,     a_     ,     **a_	).prev_sample

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

          _a							=	scheduler.step_plms(a_     ,     0     ,     a_     ,     **a_	).prev_sample
          _a							=	scheduler.step_plms(a_     ,     1     ,     a_     ,     **a_	).prev_sample

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






      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


        for timesteps in [1_0_0, 1_0_0_0]:
          self.check_over_configs(num_train_timesteps=a_	)






      def 			lowerCamelCase__    (    self	)	->Optional[Any]:







        '''simple docstring'''


        for steps_offset in [0, 1]:
          self.check_over_configs(steps_offset=a_	)

        _a							=	self.scheduler_classes[0]
        _a							=	self.get_scheduler_config(steps_offset=1	)
        _a							=	scheduler_class(**a_	)
        scheduler.set_timesteps(1_0	)
        assert torch.equal(
            scheduler.timesteps     ,     torch.LongTensor(
                [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]	)     ,     )






      def 			lowerCamelCase__    (    self	)	->Tuple:







        '''simple docstring'''


        for beta_start, beta_end in zip([0.0_001, 0.001]     ,     [0.002, 0.02]	):
          self.check_over_configs(beta_start=a_     ,     beta_end=a_	)






      def 			lowerCamelCase__    (    self	)	->Dict:







        '''simple docstring'''


        for schedule in ["linear", "squaredcos_cap_v2"]:
          self.check_over_configs(beta_schedule=a_	)






      def 			lowerCamelCase__    (    self	)	->str:







        '''simple docstring'''


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






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        for t in [1, 5, 1_0]:
          self.check_over_forward(time_step=a_	)






      def 			lowerCamelCase__    (    self	)	->List[Any]:







        '''simple docstring'''


        for t, num_inference_steps in zip([1, 5, 1_0]     ,     [1_0, 5_0, 1_0_0]	):
          self.check_over_forward(num_inference_steps=a_	)






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







        '''simple docstring'''


        _a							=	2_7

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

          _a							=	self.get_scheduler_config()
          _a							=	scheduler_class(**a_	)

          scheduler.set_timesteps(a_	)

          # 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]	):
            _a							=	scheduler.step_prk(a_     ,     a_     ,     a_	).prev_sample






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        with self.assertRaises(a_	):
          _a							=	self.scheduler_classes[0]
          _a							=	self.get_scheduler_config()
          _a							=	scheduler_class(**a_	)

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






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        _a							=	self.full_loop()
        _a							=	torch.sum(torch.abs(a_	)	)
        _a							=	torch.mean(torch.abs(a_	)	)

        assert abs(result_sum.item() - 198.1_318	) < 1E-2
        assert abs(result_mean.item() - 0.2_580	) < 1E-3






      def 			lowerCamelCase__    (    self	)	->Optional[Any]:







        '''simple docstring'''


        _a							=	self.full_loop(prediction_type="v_prediction"	)
        _a							=	torch.sum(torch.abs(a_	)	)
        _a							=	torch.mean(torch.abs(a_	)	)

        assert abs(result_sum.item() - 67.3_986	) < 1E-2
        assert abs(result_mean.item() - 0.0_878	) < 1E-3






      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


        _a							=	self.full_loop(set_alpha_to_one=a_     ,     beta_start=0.01	)
        _a							=	torch.sum(torch.abs(a_	)	)
        _a							=	torch.mean(torch.abs(a_	)	)

        assert abs(result_sum.item() - 230.0_399	) < 1E-2
        assert abs(result_mean.item() - 0.2_995	) < 1E-3






      def 			lowerCamelCase__    (    self	)	->str:







        '''simple docstring'''


        _a							=	self.full_loop(set_alpha_to_one=a_     ,     beta_start=0.01	)
        _a							=	torch.sum(torch.abs(a_	)	)
        _a							=	torch.mean(torch.abs(a_	)	)

        assert abs(result_sum.item() - 186.9_482	) < 1E-2
        assert abs(result_mean.item() - 0.2_434	) < 1E-3


