






import argparse
import struct
import unittest


class 			SCREAMING_SNAKE_CASE      :
     '''simple docstring'''


     def __init__(					self							:						int       ,			UpperCAmelCase_							:						bytes   ):
       SCREAMING_SNAKE_CASE							:   Dict						    =     data

       # Initialize hash values
       SCREAMING_SNAKE_CASE							:   List[str]						    =     [
           0X6A_09_E6_67,
           0XBB_67_AE_85,
           0X3C_6E_F3_72,
           0XA5_4F_F5_3A,
           0X51_0E_52_7F,
           0X9B_05_68_8C,
           0X1F_83_D9_AB,
           0X5B_E0_CD_19,
       ]

       # Initialize round constants
       SCREAMING_SNAKE_CASE							:   List[str]						    =     [
           0X42_8A_2F_98,
           0X71_37_44_91,
           0XB5_C0_FB_CF,
           0XE9_B5_DB_A5,
           0X39_56_C2_5B,
           0X59_F1_11_F1,
           0X92_3F_82_A4,
           0XAB_1C_5E_D5,
           0XD8_07_AA_98,
           0X12_83_5B_01,
           0X24_31_85_BE,
           0X55_0C_7D_C3,
           0X72_BE_5D_74,
           0X80_DE_B1_FE,
           0X9B_DC_06_A7,
           0XC1_9B_F1_74,
           0XE4_9B_69_C1,
           0XEF_BE_47_86,
           0X0F_C1_9D_C6,
           0X24_0C_A1_CC,
           0X2D_E9_2C_6F,
           0X4A_74_84_AA,
           0X5C_B0_A9_DC,
           0X76_F9_88_DA,
           0X98_3E_51_52,
           0XA8_31_C6_6D,
           0XB0_03_27_C8,
           0XBF_59_7F_C7,
           0XC6_E0_0B_F3,
           0XD5_A7_91_47,
           0X06_CA_63_51,
           0X14_29_29_67,
           0X27_B7_0A_85,
           0X2E_1B_21_38,
           0X4D_2C_6D_FC,
           0X53_38_0D_13,
           0X65_0A_73_54,
           0X76_6A_0A_BB,
           0X81_C2_C9_2E,
           0X92_72_2C_85,
           0XA2_BF_E8_A1,
           0XA8_1A_66_4B,
           0XC2_4B_8B_70,
           0XC7_6C_51_A3,
           0XD1_92_E8_19,
           0XD6_99_06_24,
           0XF4_0E_35_85,
           0X10_6A_A0_70,
           0X19_A4_C1_16,
           0X1E_37_6C_08,
           0X27_48_77_4C,
           0X34_B0_BC_B5,
           0X39_1C_0C_B3,
           0X4E_D8_AA_4A,
           0X5B_9C_CA_4F,
           0X68_2E_6F_F3,
           0X74_8F_82_EE,
           0X78_A5_63_6F,
           0X84_C8_78_14,
           0X8C_C7_02_08,
           0X90_BE_FF_FA,
           0XA4_50_6C_EB,
           0XBE_F9_A3_F7,
           0XC6_71_78_F2,
       ]

       SCREAMING_SNAKE_CASE							:   Tuple						    =     self.preprocessing(self.data   )
       self.final_hash()


     @staticmethod
     def 					_A							(					UpperCAmelCase_							:						bytes   ):
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     b"\x80" + (b"\x00" * (63 - (len(UpperCAmelCase_   ) + 8) % 64))
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     struct.pack(">Q"       ,			(len(UpperCAmelCase_   ) * 8)   )
       return data + padding + big_endian_integer


     def 					_A							(					self							:						Optional[Any]   ):
       # Convert into blocks of 64 bytes
       SCREAMING_SNAKE_CASE							:   int						    =     [
           self.preprocessed_data[x : x + 64]
           for x in range(0       ,			len(self.preprocessed_data   )       ,			64   )
       ]

       for block in self.blocks:
         # Convert the given block into a list of 4 byte integers
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     list(struct.unpack(">16L"       ,			UpperCAmelCase_   )   )
         # add 48 0-ed integers
         words += [0] * 48

         SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   int						    =     self.hashes

         for index in range(0       ,			64   ):
           if index > 15:
             # modify the zero-ed indexes at the end of the array
             SCREAMING_SNAKE_CASE							:   Optional[int]						    =     (
                 self.ror(words[index - 15]       ,			7   )
                 ^ self.ror(words[index - 15]       ,			18   )
                 ^ (words[index - 15] >> 3)
             )
             SCREAMING_SNAKE_CASE							:   int						    =     (
                 self.ror(words[index - 2]       ,			17   )
                 ^ self.ror(words[index - 2]       ,			19   )
                 ^ (words[index - 2] >> 10)
             )

             SCREAMING_SNAKE_CASE							:   List[str]						    =     (
                 words[index - 16] + sa + words[index - 7] + sa
             ) % 0X1_00_00_00_00

           # Compression
           SCREAMING_SNAKE_CASE							:   Any						    =     self.ror(UpperCAmelCase_       ,			6   ) ^ self.ror(UpperCAmelCase_       ,			11   ) ^ self.ror(UpperCAmelCase_       ,			25   )
           SCREAMING_SNAKE_CASE							:   Any						    =     (e & f) ^ ((~e & 0XFF_FF_FF_FF) & g)
           SCREAMING_SNAKE_CASE							:   Dict						    =     (
               h + sa + ch + self.round_constants[index] + words[index]
           ) % 0X1_00_00_00_00
           SCREAMING_SNAKE_CASE							:   List[Any]						    =     self.ror(UpperCAmelCase_       ,			2   ) ^ self.ror(UpperCAmelCase_       ,			13   ) ^ self.ror(UpperCAmelCase_       ,			22   )
           SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     (a & b) ^ (a & c) ^ (b & c)
           SCREAMING_SNAKE_CASE							:   Optional[int]						    =     (sa + maj) % 0X1_00_00_00_00

           SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Any						    =     (
               g,
               f,
               e,
               ((d + tempa) % 0X1_00_00_00_00),
               c,
               b,
               a,
               ((tempa + tempa) % 0X1_00_00_00_00),
           )

         SCREAMING_SNAKE_CASE							:   List[str]						    =     [a, b, c, d, e, f, g, h]

         # Modify final values
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     [
             ((element + mutated_hash_values[index]) % 0X1_00_00_00_00)
             for index, element in enumerate(self.hashes   )
         ]

       SCREAMING_SNAKE_CASE							:   Any						    =     "".join([hex(UpperCAmelCase_   )[2:].zfill(8   ) for value in self.hashes]   )






     def 					_A							(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						int       ,			UpperCAmelCase_							:						int   ):
       return 0XFF_FF_FF_FF & (value << (32 - rotations)) | (value >> rotations)





class 			SCREAMING_SNAKE_CASE      (      unittest.TestCase       ):
     '''simple docstring'''


     def 					_A							(					self							:						Optional[Any]   ):
       import hashlib

       SCREAMING_SNAKE_CASE							:   int						    =     bytes("Test String"       ,			"utf-8"   )
       self.assertEqual(SHAaaa(UpperCAmelCase_   ).hash       ,			hashlib.shaaaa(UpperCAmelCase_   ).hexdigest()   )





def 	lowerCamelCase__      (  ):

  """simple docstring"""






  import doctest

  doctest.testmod()

  SCREAMING_SNAKE_CASE							:   int						    =     argparse.ArgumentParser()
  parser.add_argument(
      "-s"    ,  "--string"    ,  dest="input_string"    ,  default="Hello World!! Welcome to Cryptography"    ,  help="Hash the string"    ,  )
  parser.add_argument(
      "-f"    ,  "--file"    ,  dest="input_file"    ,  help="Hash contents of a file"					)

  SCREAMING_SNAKE_CASE							:   Tuple						    =     parser.parse_args()

  SCREAMING_SNAKE_CASE							:   str						    =     args.input_string

  # hash input should be a bytestring
  if args.input_file:
    with open(args.input_file    ,  "rb"					) as f:
      SCREAMING_SNAKE_CASE							:   int						    =     f.read()
  else:
    SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     bytes(lowercase    ,  "utf-8"					)

  print(SHAaaa(lowercase					).hash					)


if __name__ == "__main__":
   main()








import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 							lowerCAmelCase__   ( __lowercase						):
						UpperCamelCase_				:			int						=		(KDPMaDiscreteScheduler,)
						UpperCamelCase_				:			Optional[int]						=		10

						def 			A_					(      self							,				**a					)       ->   int:


								'''simple docstring'''

								_UpperCamelCase         =						{
								    """num_train_timesteps""": 11_00,
								    """beta_start""": 0.0001,
								    """beta_end""": 0.02,
								    """beta_schedule""": """linear""",
								}

								config.update(**a					)
								return config

						def 			A_					(      self					)       ->   List[str]:


								'''simple docstring'''

								for timesteps in [10, 50, 1_00, 10_00]:
										self.check_over_configs(num_train_timesteps=a					)

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


								'''simple docstring'''

								for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]							,				[0.0002, 0.002, 0.02]					):
										self.check_over_configs(beta_start=a							,				beta_end=a					)

						def 			A_					(      self					)       ->   Any:


								'''simple docstring'''

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

						def 			A_					(      self					)       ->   Optional[int]:


								'''simple docstring'''

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

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


								'''simple docstring'''

								_UpperCamelCase         =						self.scheduler_classes[0]
								_UpperCamelCase         =						self.get_scheduler_config(prediction_type="""v_prediction"""					)
								_UpperCamelCase         =						scheduler_class(**a					)

								scheduler.set_timesteps(self.num_inference_steps					)

								_UpperCamelCase         =						self.dummy_model()
								_UpperCamelCase         =						self.dummy_sample_deter * scheduler.init_noise_sigma
								_UpperCamelCase         =						sample.to(a					)

								for i, t in enumerate(scheduler.timesteps					):
										_UpperCamelCase         =						scheduler.scale_model_input(a							,				a					)

										_UpperCamelCase         =						model(a							,				a					)

										_UpperCamelCase         =						scheduler.step(a							,				a							,				a					)
										_UpperCamelCase         =						output.prev_sample

								_UpperCamelCase         =						torch.sum(torch.abs(a					)					)
								_UpperCamelCase         =						torch.mean(torch.abs(a					)					)

								if torch_device in ["cpu", "mps"]:
										assert abs(result_sum.item() - 4.6934e-07					) < 1e-2
										assert abs(result_mean.item() - 6.1112e-10					) < 1e-3
								else:
										# CUDA
										assert abs(result_sum.item() - 4.693_4286_5017_0972e-07					) < 1e-2
										assert abs(result_mean.item() - 0.0002					) < 1e-3

						def 			A_					(      self					)       ->   str:


								'''simple docstring'''

								if torch_device == "mps":
										return
								_UpperCamelCase         =						self.scheduler_classes[0]
								_UpperCamelCase         =						self.get_scheduler_config()
								_UpperCamelCase         =						scheduler_class(**a					)

								scheduler.set_timesteps(self.num_inference_steps					)

								_UpperCamelCase         =						self.dummy_model()
								_UpperCamelCase         =						self.dummy_sample_deter * scheduler.init_noise_sigma
								_UpperCamelCase         =						sample.to(a					)

								for i, t in enumerate(scheduler.timesteps					):
										_UpperCamelCase         =						scheduler.scale_model_input(a							,				a					)

										_UpperCamelCase         =						model(a							,				a					)

										_UpperCamelCase         =						scheduler.step(a							,				a							,				a					)
										_UpperCamelCase         =						output.prev_sample

								_UpperCamelCase         =						torch.sum(torch.abs(a					)					)
								_UpperCamelCase         =						torch.mean(torch.abs(a					)					)

								if torch_device in ["cpu", "mps"]:
										assert abs(result_sum.item() - 20.4125					) < 1e-2
										assert abs(result_mean.item() - 0.0266					) < 1e-3
								else:
										# CUDA
										assert abs(result_sum.item() - 20.4125					) < 1e-2
										assert abs(result_mean.item() - 0.0266					) < 1e-3







						def 			A_					(      self					)       ->   str:


								'''simple docstring'''

								if torch_device == "mps":
										return
								_UpperCamelCase         =						self.scheduler_classes[0]
								_UpperCamelCase         =						self.get_scheduler_config()
								_UpperCamelCase         =						scheduler_class(**a					)

								scheduler.set_timesteps(self.num_inference_steps							,				device=a					)

								_UpperCamelCase         =						self.dummy_model()
								_UpperCamelCase         =						self.dummy_sample_deter.to(a					) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
										_UpperCamelCase         =						scheduler.scale_model_input(a							,				a					)

										_UpperCamelCase         =						model(a							,				a					)

										_UpperCamelCase         =						scheduler.step(a							,				a							,				a					)
										_UpperCamelCase         =						output.prev_sample

								_UpperCamelCase         =						torch.sum(torch.abs(a					)					)
								_UpperCamelCase         =						torch.mean(torch.abs(a					)					)

								if str(a					).startswith("""cpu"""					):
										# The following sum varies between 148 and 156 on mps. Why?
										assert abs(result_sum.item() - 20.4125					) < 1e-2
										assert abs(result_mean.item() - 0.0266					) < 1e-3
								else:
										# CUDA
										assert abs(result_sum.item() - 20.4125					) < 1e-2
										assert abs(result_mean.item() - 0.0266					) < 1e-3



