






'''simple docstring'''




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class       __lowercase					(SCREAMING_SNAKE_CASE__    ):
	_lowerCamelCase              =  (DEISMultistepScheduler,)
	_lowerCamelCase              =  (('''num_inference_steps''', 25),)

	def      __UpperCamelCase							(      self   :						Optional[int]  ,					**UpperCAmelCase_   :						int):
			UpperCamelCase__				:					Optional[Any]					=					{
			    "num_train_timesteps": 1_000,
			    "beta_start": 0.00_01,
			    "beta_end": 0.02,
			    "beta_schedule": "linear",
			    "solver_order": 2,
			}

			config.update(**snake_case__)
			return config

	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						Tuple=0  ,					**UpperCAmelCase_   :						Tuple):
			UpperCamelCase__				:					Tuple					=					dict(self.forward_default_kwargs)
			UpperCamelCase__				:					str					=					kwargs.pop('num_inference_steps'  ,					snake_case__)
			UpperCamelCase__				:					Optional[Any]					=					self.dummy_sample
			UpperCamelCase__				:					Tuple					=					0.1 * sample
			UpperCamelCase__				:					str					=					[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

					UpperCamelCase__				:					Optional[Any]					=					sample, sample
					for t in range(snake_case__  ,					time_step + scheduler.config.solver_order + 1):
							UpperCamelCase__				:					str					=					scheduler.step(snake_case__  ,					snake_case__  ,					snake_case__  ,					**snake_case__).prev_sample
							UpperCamelCase__				:					Union[str, Any]					=					new_scheduler.step(snake_case__  ,					snake_case__  ,					snake_case__  ,					**snake_case__).prev_sample

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

	def      __UpperCamelCase							(      self   :						str):
			pass

	def      __UpperCamelCase							(      self   :						Optional[Any]  ,					UpperCAmelCase_   :						List[Any]=0  ,					**UpperCAmelCase_   :						List[str]):
			UpperCamelCase__				:					Dict					=					dict(self.forward_default_kwargs)
			UpperCamelCase__				:					Dict					=					kwargs.pop('num_inference_steps'  ,					snake_case__)
			UpperCamelCase__				:					str					=					self.dummy_sample
			UpperCamelCase__				:					Tuple					=					0.1 * sample
			UpperCamelCase__				:					Optional[Any]					=					[residual + 0.2, residual + 0.15, residual + 0.10]

			for scheduler_class in self.scheduler_classes:
					UpperCamelCase__				:					List[Any]					=					self.get_scheduler_config()
					UpperCamelCase__				:					Dict					=					scheduler_class(**snake_case__)
					scheduler.set_timesteps(snake_case__)

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

					with tempfile.TemporaryDirectory() as tmpdirname:
							scheduler.save_config(snake_case__)
							UpperCamelCase__				:					Tuple					=					scheduler_class.from_pretrained(snake_case__)
							# copy over dummy past residuals
							new_scheduler.set_timesteps(snake_case__)

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

					UpperCamelCase__				:					Union[str, Any]					=					scheduler.step(snake_case__  ,					snake_case__  ,					snake_case__  ,					**snake_case__).prev_sample
					UpperCamelCase__				:					Union[str, Any]					=					new_scheduler.step(snake_case__  ,					snake_case__  ,					snake_case__  ,					**snake_case__).prev_sample

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

	def      __UpperCamelCase							(      self   :						List[str]  ,					UpperCAmelCase_   :						Union[str, Any]=None  ,					**UpperCAmelCase_   :						Union[str, Any]):
			if scheduler is None:
					UpperCamelCase__				:					str					=					self.scheduler_classes[0]
					UpperCamelCase__				:					int					=					self.get_scheduler_config(**snake_case__)
					UpperCamelCase__				:					Dict					=					scheduler_class(**snake_case__)

			UpperCamelCase__				:					int					=					self.scheduler_classes[0]
			UpperCamelCase__				:					List[Any]					=					self.get_scheduler_config(**snake_case__)
			UpperCamelCase__				:					List[Any]					=					scheduler_class(**snake_case__)

			UpperCamelCase__				:					Any					=					10
			UpperCamelCase__				:					Any					=					self.dummy_model()
			UpperCamelCase__				:					int					=					self.dummy_sample_deter
			scheduler.set_timesteps(snake_case__)

			for i, t in enumerate(scheduler.timesteps):
					UpperCamelCase__				:					List[str]					=					model(snake_case__  ,					snake_case__)
					UpperCamelCase__				:					int					=					scheduler.step(snake_case__  ,					snake_case__  ,					snake_case__).prev_sample

			return sample

	def      __UpperCamelCase							(      self   :						str):
			UpperCamelCase__				:					int					=					dict(self.forward_default_kwargs)

			UpperCamelCase__				:					int					=					kwargs.pop('num_inference_steps'  ,					snake_case__)

			for scheduler_class in self.scheduler_classes:
					UpperCamelCase__				:					Dict					=					self.get_scheduler_config()
					UpperCamelCase__				:					Optional[int]					=					scheduler_class(**snake_case__)

					UpperCamelCase__				:					Any					=					self.dummy_sample
					UpperCamelCase__				:					List[str]					=					0.1 * sample

					if num_inference_steps is not None and hasattr(snake_case__  ,					'set_timesteps'):
							scheduler.set_timesteps(snake_case__)
					elif num_inference_steps is not None and not hasattr(snake_case__  ,					'set_timesteps'):
							UpperCamelCase__				:					Any					=					num_inference_steps

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

					UpperCamelCase__				:					int					=					scheduler.timesteps[5]
					UpperCamelCase__				:					int					=					scheduler.timesteps[6]

					UpperCamelCase__				:					Union[str, Any]					=					scheduler.step(snake_case__  ,					snake_case__  ,					snake_case__  ,					**snake_case__).prev_sample
					UpperCamelCase__				:					Dict					=					scheduler.step(snake_case__  ,					snake_case__  ,					snake_case__  ,					**snake_case__).prev_sample

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

	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			UpperCamelCase__				:					Union[str, Any]					=					DEISMultistepScheduler(**self.get_scheduler_config())
			UpperCamelCase__				:					Any					=					self.full_loop(scheduler=snake_case__)
			UpperCamelCase__				:					Dict					=					torch.mean(torch.abs(snake_case__))

			assert abs(result_mean.item() - 0.2_39_16) < 1e-3

			UpperCamelCase__				:					Tuple					=					DPMSolverSinglestepScheduler.from_config(scheduler.config)
			UpperCamelCase__				:					List[str]					=					DPMSolverMultistepScheduler.from_config(scheduler.config)
			UpperCamelCase__				:					Optional[int]					=					UniPCMultistepScheduler.from_config(scheduler.config)
			UpperCamelCase__				:					Optional[int]					=					DEISMultistepScheduler.from_config(scheduler.config)

			UpperCamelCase__				:					Dict					=					self.full_loop(scheduler=snake_case__)
			UpperCamelCase__				:					List[Any]					=					torch.mean(torch.abs(snake_case__))

			assert abs(result_mean.item() - 0.2_39_16) < 1e-3

	def      __UpperCamelCase							(      self   :						List[str]):
			for timesteps in [25, 50, 100, 999, 1_000]:
					self.check_over_configs(num_train_timesteps=snake_case__)

	def      __UpperCamelCase							(      self   :						List[Any]):
			self.check_over_configs(thresholding=snake_case__)
			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=snake_case__  ,					prediction_type=snake_case__  ,					sample_max_value=snake_case__  ,					algorithm_type='deis'  ,					solver_order=snake_case__  ,					solver_type=snake_case__  ,					)

	def      __UpperCamelCase							(      self   :						Any):
			for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=snake_case__)

	def      __UpperCamelCase							(      self   :						str):
			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=snake_case__  ,					solver_type=snake_case__  ,					prediction_type=snake_case__  ,					algorithm_type=snake_case__  ,					)
											UpperCamelCase__				:					int					=					self.full_loop(
											    solver_order=snake_case__  ,					solver_type=snake_case__  ,					prediction_type=snake_case__  ,					algorithm_type=snake_case__  ,					)
											assert not torch.isnan(snake_case__).any(), "Samples have nan numbers"

	def      __UpperCamelCase							(      self   :						Any):
			self.check_over_configs(lower_order_final=snake_case__)
			self.check_over_configs(lower_order_final=snake_case__)

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

	def      __UpperCamelCase							(      self   :						List[str]):
			UpperCamelCase__				:					str					=					self.full_loop()
			UpperCamelCase__				:					Union[str, Any]					=					torch.mean(torch.abs(snake_case__))

			assert abs(result_mean.item() - 0.2_39_16) < 1e-3

	def      __UpperCamelCase							(      self   :						str):
			UpperCamelCase__				:					Tuple					=					self.full_loop(prediction_type='v_prediction')
			UpperCamelCase__				:					List[Any]					=					torch.mean(torch.abs(snake_case__))

			assert abs(result_mean.item() - 0.0_91) < 1e-3





	def      __UpperCamelCase							(      self   :						str):
			UpperCamelCase__				:					str					=					self.scheduler_classes[0]
			UpperCamelCase__				:					Tuple					=					self.get_scheduler_config(thresholding=snake_case__  ,					dynamic_thresholding_ratio=0)
			UpperCamelCase__				:					Optional[int]					=					scheduler_class(**snake_case__)

			UpperCamelCase__				:					Tuple					=					10
			UpperCamelCase__				:					Dict					=					self.dummy_model()
			UpperCamelCase__				:					Optional[Any]					=					self.dummy_sample_deter.half()
			scheduler.set_timesteps(snake_case__)

			for i, t in enumerate(scheduler.timesteps):
					UpperCamelCase__				:					Union[str, Any]					=					model(snake_case__  ,					snake_case__)
					UpperCamelCase__				:					Tuple					=					scheduler.step(snake_case__  ,					snake_case__  ,					snake_case__).prev_sample

			assert sample.dtype == torch.floataa












'''simple docstring'''




import argparse
import struct
import unittest







class       __lowercase					:

	def __init__(      self   :						Tuple  ,					UpperCAmelCase_   :						bytes):
			UpperCamelCase__				:					Dict					=					data

			# Initialize hash values
			UpperCamelCase__				:					Any					=					[
			    0X6A_09E_667,
			    0XBB_67A_E85,
			    0X3C_6EF_372,
			    0XA5_4FF_53A,
			    0X51_0E5_27F,
			    0X9B_056_88C,
			    0X1F_83D_9AB,
			    0X5B_E0C_D19,
			]

			# Initialize round constants
			UpperCamelCase__				:					List[Any]					=					[
			    0X42_8A2_F98,
			    0X71_374_491,
			    0XB5_C0F_BCF,
			    0XE9_B5D_BA5,
			    0X39_56C_25B,
			    0X59_F11_1F1,
			    0X92_3F8_2A4,
			    0XAB_1C5_ED5,
			    0XD8_07A_A98,
			    0X12_835_B01,
			    0X24_318_5BE,
			    0X55_0C7_DC3,
			    0X72_BE5_D74,
			    0X80_DEB_1FE,
			    0X9B_DC0_6A7,
			    0XC1_9BF_174,
			    0XE4_9B6_9C1,
			    0XEF_BE4_786,
			    0X0F_C19_DC6,
			    0X24_0CA_1CC,
			    0X2D_E92_C6F,
			    0X4A_748_4AA,
			    0X5C_B0A_9DC,
			    0X76_F98_8DA,
			    0X98_3E5_152,
			    0XA8_31C_66D,
			    0XB0_032_7C8,
			    0XBF_597_FC7,
			    0XC6_E00_BF3,
			    0XD5_A79_147,
			    0X06_CA6_351,
			    0X14_292_967,
			    0X27_B70_A85,
			    0X2E_1B2_138,
			    0X4D_2C6_DFC,
			    0X53_380_D13,
			    0X65_0A7_354,
			    0X76_6A0_ABB,
			    0X81_C2C_92E,
			    0X92_722_C85,
			    0XA2_BFE_8A1,
			    0XA8_1A6_64B,
			    0XC2_4B8_B70,
			    0XC7_6C5_1A3,
			    0XD1_92E_819,
			    0XD6_990_624,
			    0XF4_0E3_585,
			    0X10_6AA_070,
			    0X19_A4C_116,
			    0X1E_376_C08,
			    0X27_487_74C,
			    0X34_B0B_CB5,
			    0X39_1C0_CB3,
			    0X4E_D8A_A4A,
			    0X5B_9CC_A4F,
			    0X68_2E6_FF3,
			    0X74_8F8_2EE,
			    0X78_A56_36F,
			    0X84_C87_814,
			    0X8C_C70_208,
			    0X90_BEF_FFA,
			    0XA4_506_CEB,
			    0XBE_F9A_3F7,
			    0XC6_717_8F2,
			]

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

	@staticmethod
	def      __UpperCamelCase							(      UpperCAmelCase_   :						bytes):
			UpperCamelCase__				:					List[Any]					=					B'\x80' + (B'\x00' * (63 - (len(UpperCAmelCase_) + 8) % 64))
			UpperCamelCase__				:					List[Any]					=					struct.pack('>Q'  ,					(len(UpperCAmelCase_) * 8))
			return data + padding + big_endian_integer

	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			# Convert into blocks of 64 bytes
			UpperCamelCase__				:					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
					UpperCamelCase__				:					Tuple					=					list(struct.unpack('>16L'  ,					UpperCAmelCase_))
					# add 48 0-ed integers
					words += [0] * 48

					UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					str					=					self.hashes

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

									UpperCamelCase__				:					int					=					(
									    words[index - 16] + sa + words[index - 7] + sa
									) % 0X100_000_000

							# Compression
							UpperCamelCase__				:					Optional[Any]					=					self.ror(UpperCAmelCase_  ,					6) ^ self.ror(UpperCAmelCase_  ,					11) ^ self.ror(UpperCAmelCase_  ,					25)
							UpperCamelCase__				:					List[str]					=					(e & f) ^ ((~e & 0XFF_FFF_FFF) & g)
							UpperCamelCase__				:					List[Any]					=					(
							    h + sa + ch + self.round_constants[index] + words[index]
							) % 0X100_000_000
							UpperCamelCase__				:					List[str]					=					self.ror(UpperCAmelCase_  ,					2) ^ self.ror(UpperCAmelCase_  ,					13) ^ self.ror(UpperCAmelCase_  ,					22)
							UpperCamelCase__				:					Dict					=					(a & b) ^ (a & c) ^ (b & c)
							UpperCamelCase__				:					List[str]					=					(sa + maj) % 0X100_000_000

							UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					Tuple					=					(
							    g,
							    f,
							    e,
							    ((d + tempa) % 0X100_000_000),
							    c,
							    b,
							    a,
							    ((tempa + tempa) % 0X100_000_000),
							)

					UpperCamelCase__				:					List[Any]					=					[a, b, c, d, e, f, g, h]

					# Modify final values
					UpperCamelCase__				:					Optional[Any]					=					[
					    ((element + mutated_hash_values[index]) % 0X100_000_000)
					    for index, element in enumerate(self.hashes)
					]

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





	def      __UpperCamelCase							(      self   :						int  ,					UpperCAmelCase_   :						int  ,					UpperCAmelCase_   :						int):
			return 0XFF_FFF_FFF & (value << (32 - rotations)) | (value >> rotations)







class       __lowercase					(unittest.TestCase    ):

	def      __UpperCamelCase							(      self   :						int):
			import hashlib

			UpperCamelCase__				:					str					=					bytes('Test String'  ,					'utf-8')
			self.assertEqual(SHAaaa(UpperCAmelCase_).hash  ,					hashlib.shaaaa(UpperCAmelCase_).hexdigest())







def       __UpperCAmelCase						(  )							->     None:
		import doctest

		doctest.testmod()

		UpperCamelCase__				:					Union[str, Any]					=					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')

		UpperCamelCase__				:					List[str]					=					parser.parse_args()

		UpperCamelCase__				:					str					=					args.input_string

		# hash input should be a bytestring
		if args.input_file:
				with open(args.input_file     ,      'rb') as f:
						UpperCamelCase__				:					Any					=					f.read()
		else:
				UpperCamelCase__				:					List[Any]					=					bytes(lowerCamelCase_     ,      'utf-8')

		print(SHAaaa(lowerCamelCase_).hash)


if __name__ == "__main__":
		main()




