
"""simple docstring"""




from collections.abc import Generator
from math import sin


def     __magic_name__      ( __snake_case							:			bytes    )					->							bytes:
       if len(__snake_case    ) != 32:
              raise ValueError("Input must be of length 32"    )

       lowercase       :							List[Any]									=				B""
       for i in [3, 2, 1, 0]:
              little_endian += string_aa[8 * i : 8 * i + 8]
       return little_endian


def     __magic_name__      ( __snake_case							:			int    )					->							bytes:
       if i < 0:
              raise ValueError("Input must be non-negative"    )

       lowercase       :							Dict									=				format(__snake_case	,					"08x"    )[-8:]
       lowercase       :							Union[str, 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     __magic_name__      ( __snake_case							:			bytes    )					->							bytes:
       lowercase       :							Tuple									=				B""
       for char in message:
              bit_string += format(__snake_case	,					"08b"    ).encode("utf-8"    )
       lowercase       :							List[str]									=				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    ) % 512 != 448:
              bit_string += b"0"
       bit_string += to_little_endian(start_len[32:]    ) + to_little_endian(start_len[:32]    )

       return bit_string


def     __magic_name__      ( __snake_case							:			bytes    )					->							Generator[list[int], None, None]:
       if len(__snake_case    ) % 512 != 0:
              raise ValueError("Input must have length that's a multiple of 512"    )

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


def     __magic_name__      ( __snake_case							:			int    )					->							int:
       if i < 0:
              raise ValueError("Input must be non-negative"    )

       lowercase       :							Union[str, Any]									=				format(__snake_case	,					"032b"    )
       lowercase       :							List[Any]									=				""
       for c in i_str:
              new_str += "1" if c == "0" else "0"
       return int(__snake_case	,					2    )


def     __magic_name__      ( __snake_case							:			int	,					__snake_case							:			int    )					->							int:
       return (a + b) % 2**32


def     __magic_name__      ( __snake_case							:			int	,					__snake_case							:			int    )					->							int:
       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     __magic_name__      ( __snake_case							:			bytes    )					->							bytes:
       lowercase       :							Dict									=				preprocess(__snake_case    )

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

       # Starting states
       lowercase       :							str									=				0X6745_2301
       lowercase       :							Optional[int]									=				0Xefcd_ab89
       lowercase       :							Dict									=				0X98ba_dcfe
       lowercase       :							Tuple									=				0X1032_5476

       lowercase       :							Dict									=				[
           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    ):
              lowercase       :							Tuple									=				aa
              lowercase       :							Any									=				ba
              lowercase       :							Optional[int]									=				ca
              lowercase       :							Tuple									=				da

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

              # Add hashed chunk to running total
              lowercase       :							Tuple									=				sum_aa(__snake_case	,					__snake_case    )
              lowercase       :							List[str]									=				sum_aa(__snake_case	,					__snake_case    )
              lowercase       :							str									=				sum_aa(__snake_case	,					__snake_case    )
              lowercase       :							Any									=				sum_aa(__snake_case	,					__snake_case    )

       lowercase       :							Dict									=				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()




"""simple docstring"""




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class 						a__		( a_		):
   __lowerCAmelCase				=							(DDPMScheduler,)
   def      __magic_name__     (     self      ,       **_a							):
          lowercase       :							Dict									=				{
              "num_train_timesteps": 1_000,
              "beta_start": 0.0_0_0_1,
              "beta_end": 0.0_2,
              "beta_schedule": "linear",
              "variance_type": "fixed_small",
              "clip_sample": True,
          }

          config.update(**_a							)
          return config
   def      __magic_name__     (     self							):
          for timesteps in [1, 5, 100, 1_000]:
                 self.check_over_configs(num_train_timesteps=_a							)
   def      __magic_name__     (     self							):
          for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]      ,       [0.0_0_2, 0.0_2, 0.2, 2]							):
                 self.check_over_configs(beta_start=_a      ,       beta_end=_a							)
   def      __magic_name__     (     self							):
          for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=_a							)
   def      __magic_name__     (     self							):
          for variance in ["fixed_small", "fixed_large", "other"]:
                 self.check_over_configs(variance_type=_a							)
   def      __magic_name__     (     self							):
          for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=_a							)
   def      __magic_name__     (     self							):
          self.check_over_configs(thresholding=_a							)
          for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "sample", "v_prediction"]:
                        self.check_over_configs(
                            thresholding=_a      ,       prediction_type=_a      ,       sample_max_value=_a      ,       )
   def      __magic_name__     (     self							):
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
                 self.check_over_configs(prediction_type=_a							)
   def      __magic_name__     (     self							):
          for t in [0, 500, 999]:
                 self.check_over_forward(time_step=_a							)
   def      __magic_name__     (     self							):
          lowercase       :							Union[str, Any]									=				self.scheduler_classes[0]
          lowercase       :							Any									=				self.get_scheduler_config()
          lowercase       :							Dict									=				scheduler_class(**_a							)

          assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(487							) - 0.0_0_9_7_9							)							) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(999							) - 0.0_2							)							) < 1E-5
   def      __magic_name__     (     self							):
          lowercase       :							Optional[int]									=				self.scheduler_classes[0]
          lowercase       :							Tuple									=				self.get_scheduler_config()
          lowercase       :							Dict									=				scheduler_class(**_a							)

          lowercase       :							Dict									=				len(_a							)

          lowercase       :							str									=				self.dummy_model()
          lowercase       :							Optional[int]									=				self.dummy_sample_deter
          lowercase       :							List[Any]									=				torch.manual_seed(0							)

          for t in reversed(range(_a							)							):
                 # 1. predict noise residual
                 lowercase       :							Optional[Any]									=				model(_a      ,       _a							)

                 # 2. predict previous mean of sample x_t-1
                 lowercase       :							str									=				scheduler.step(_a      ,       _a      ,       _a      ,       generator=_a							).prev_sample

                 # if t > 0:
                 #     noise = self.dummy_sample_deter
                 #     variance = scheduler.get_variance(t) ** (0.5) * noise
                 #
                 # sample = pred_prev_sample + variance
                 lowercase       :							List[str]									=				pred_prev_sample

          lowercase       :							Dict									=				torch.sum(torch.abs(_a							)							)
          lowercase       :							List[Any]									=				torch.mean(torch.abs(_a							)							)

          assert abs(result_sum.item() - 2_5_8.9_6_0_6							) < 1E-2
          assert abs(result_mean.item() - 0.3_3_7_2							) < 1E-3
   def      __magic_name__     (     self							):
          lowercase       :							Optional[int]									=				self.scheduler_classes[0]
          lowercase       :							Any									=				self.get_scheduler_config(prediction_type="v_prediction"							)
          lowercase       :							int									=				scheduler_class(**_a							)

          lowercase       :							str									=				len(_a							)

          lowercase       :							Optional[int]									=				self.dummy_model()
          lowercase       :							List[str]									=				self.dummy_sample_deter
          lowercase       :							Optional[Any]									=				torch.manual_seed(0							)

          for t in reversed(range(_a							)							):
                 # 1. predict noise residual
                 lowercase       :							Union[str, Any]									=				model(_a      ,       _a							)

                 # 2. predict previous mean of sample x_t-1
                 lowercase       :							Optional[Any]									=				scheduler.step(_a      ,       _a      ,       _a      ,       generator=_a							).prev_sample

                 # if t > 0:
                 #     noise = self.dummy_sample_deter
                 #     variance = scheduler.get_variance(t) ** (0.5) * noise
                 #
                 # sample = pred_prev_sample + variance
                 lowercase       :							Dict									=				pred_prev_sample

          lowercase       :							str									=				torch.sum(torch.abs(_a							)							)
          lowercase       :							Tuple									=				torch.mean(torch.abs(_a							)							)

          assert abs(result_sum.item() - 2_0_2.0_2_9_6							) < 1E-2
          assert abs(result_mean.item() - 0.2_6_3_1							) < 1E-3
   def      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				self.scheduler_classes[0]
          lowercase       :							Tuple									=				self.get_scheduler_config()
          lowercase       :							Tuple									=				scheduler_class(**_a							)

          lowercase       :							List[str]									=				[100, 87, 50, 1, 0]

          scheduler.set_timesteps(timesteps=_a							)

          lowercase       :							Union[str, Any]									=				scheduler.timesteps

          for i, timestep in enumerate(_a							):
                 if i == len(_a							) - 1:
                        lowercase       :							Any									=				-1
                 else:
                        lowercase       :							Union[str, Any]									=				timesteps[i + 1]

                 lowercase       :							Optional[int]									=				scheduler.previous_timestep(_a							)
                 lowercase       :							Union[str, Any]									=				prev_t.item()

                 self.assertEqual(_a      ,       _a							)
   def      __magic_name__     (     self							):
          lowercase       :							str									=				self.scheduler_classes[0]
          lowercase       :							List[str]									=				self.get_scheduler_config()
          lowercase       :							List[Any]									=				scheduler_class(**_a							)

          lowercase       :							Optional[int]									=				[100, 87, 50, 51, 0]

          with self.assertRaises(_a      ,       msg="`custom_timesteps` must be in descending order."							):
                 scheduler.set_timesteps(timesteps=_a							)
   def      __magic_name__     (     self							):
          lowercase       :							Dict									=				self.scheduler_classes[0]
          lowercase       :							Union[str, Any]									=				self.get_scheduler_config()
          lowercase       :							Any									=				scheduler_class(**_a							)

          lowercase       :							int									=				[100, 87, 50, 1, 0]
          lowercase       :							Any									=				len(_a							)

          with self.assertRaises(_a      ,       msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."							):
                 scheduler.set_timesteps(num_inference_steps=_a      ,       timesteps=_a							)

   def      __magic_name__     (     self							):
          lowercase       :							str									=				self.scheduler_classes[0]
          lowercase       :							Tuple									=				self.get_scheduler_config()
          lowercase       :							Optional[int]									=				scheduler_class(**_a							)

          lowercase       :							List[str]									=				[scheduler.config.num_train_timesteps]

          with self.assertRaises(
              _a      ,       msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"      ,       ):
                 scheduler.set_timesteps(timesteps=_a							)


