
import argparse
import struct
import unittest




class    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



	def __init__(  self	:    Dict			,  lowercase	:    bytes	):


				'''simple docstring'''


				_snake_case		=			data

				# Initialize hash values
				_snake_case		=			[
				    0x6A09E667,
				    0xBB67AE85,
				    0x3C6EF372,
				    0xA54FF53A,
				    0x510E527F,
				    0x9B05688C,
				    0x1F83D9AB,
				    0x5BE0CD19,
				]

				# Initialize round constants
				_snake_case		=			[
				    0x428A2F98,
				    0x71374491,
				    0xB5C0FBCF,
				    0xE9B5DBA5,
				    0x3956C25B,
				    0x59F111F1,
				    0x923F82A4,
				    0xAB1C5ED5,
				    0xD807AA98,
				    0x12835B01,
				    0x243185BE,
				    0x550C7DC3,
				    0x72BE5D74,
				    0x80DEB1FE,
				    0x9BDC06A7,
				    0xC19BF174,
				    0xE49B69C1,
				    0xEFBE4786,
				    0x0FC19DC6,
				    0x240CA1CC,
				    0x2DE92C6F,
				    0x4A7484AA,
				    0x5CB0A9DC,
				    0x76F988DA,
				    0x983E5152,
				    0xA831C66D,
				    0xB00327C8,
				    0xBF597FC7,
				    0xC6E00BF3,
				    0xD5A79147,
				    0x06CA6351,
				    0x14292967,
				    0x27B70A85,
				    0x2E1B2138,
				    0x4D2C6DFC,
				    0x53380D13,
				    0x650A7354,
				    0x766A0ABB,
				    0x81C2C92E,
				    0x92722C85,
				    0xA2BFE8A1,
				    0xA81A664B,
				    0xC24B8B70,
				    0xC76C51A3,
				    0xD192E819,
				    0xD6990624,
				    0xF40E3585,
				    0x106AA070,
				    0x19A4C116,
				    0x1E376C08,
				    0x2748774C,
				    0x34B0BCB5,
				    0x391C0CB3,
				    0x4ED8AA4A,
				    0x5B9CCA4F,
				    0x682E6FF3,
				    0x748F82EE,
				    0x78A5636F,
				    0x84C87814,
				    0x8CC70208,
				    0x90BEFFFA,
				    0xA4506CEB,
				    0xBEF9A3F7,
				    0xC67178F2,
				]

				_snake_case		=			self.preprocessing(self.data	)
				self.final_hash()



	@staticmethod
	def  A							(  lowercase	:    bytes	):


				'''simple docstring'''


				_snake_case		=			B'\x80' + (B'\x00' * (63 - (len(lowercase	) + 8) % 64))
				_snake_case		=			struct.pack('>Q'			,  (len(lowercase	) * 8)	)
				return data + padding + big_endian_integer



	def  A							(  self	:    str	):


				'''simple docstring'''


				_snake_case		=			[
				    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
							_snake_case		=			list(struct.unpack('>16L'			,  lowercase	)	)
							# add 48 0-ed integers
							words += [0] * 48

							_snake_case	,					_snake_case	,					_snake_case	,					_snake_case	,					_snake_case	,					_snake_case	,					_snake_case	,					_snake_case		=			self.hashes

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

													_snake_case		=			(
													    words[index - 16] + sa + words[index - 7] + sa
													) % 0x100000000

										# Compression
										_snake_case		=			self.ror(lowercase			,  6	) ^ self.ror(lowercase			,  11	) ^ self.ror(lowercase			,  25	)
										_snake_case		=			(e & f) ^ ((~e & 0xFFFFFFFF) & g)
										_snake_case		=			(
										    h + sa + ch + self.round_constants[index] + words[index]
										) % 0x100000000
										_snake_case		=			self.ror(lowercase			,  2	) ^ self.ror(lowercase			,  13	) ^ self.ror(lowercase			,  22	)
										_snake_case		=			(a & b) ^ (a & c) ^ (b & c)
										_snake_case		=			(sa + maj) % 0x100000000

										_snake_case	,					_snake_case	,					_snake_case	,					_snake_case	,					_snake_case	,					_snake_case	,					_snake_case	,					_snake_case		=			(
										    g,
										    f,
										    e,
										    ((d + tempa) % 0x100000000),
										    c,
										    b,
										    a,
										    ((tempa + tempa) % 0x100000000),
										)

							_snake_case		=			[a, b, c, d, e, f, g, h]

							# Modify final values
							_snake_case		=			[
							    ((element + mutated_hash_values[index]) % 0x100000000)
							    for index, element in enumerate(self.hashes	)
							]

				_snake_case		=			''.join([hex(lowercase	)[2:].zfill(8	) for value in self.hashes]	)





	def  A							(  self	:    List[str]			,  lowercase	:    int			,  lowercase	:    int	):


				'''simple docstring'''


				return 0xFFFFFFFF & (value << (32 - rotations)) | (value >> rotations)




class    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				import hashlib

				_snake_case		=			bytes('Test String'			,  'utf-8'	)
				self.assertEqual(SHAaaa(lowercase	).hash			,  hashlib.shaaaa(lowercase	).hexdigest()	)






def        a_		(       ) ->  None:
			import doctest

			doctest.testmod()

			_snake_case		=			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' )

			_snake_case		=			parser.parse_args()

			_snake_case		=			args.input_string

			# hash input should be a bytestring
			if args.input_file:
						with open(args.input_file       ,					'rb' ) as f:
									_snake_case		=			f.read()
			else:
						_snake_case		=			bytes(__lowercase       ,					'utf-8' )

			print(SHAaaa(__lowercase ).hash )


if __name__ == "__main__":
			main()

from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class    SCREAMING_SNAKE_CASE__     (	nn.Module					):





	'''simple docstring'''



	def __init__(  self	:    Optional[int]			,  lowercase	:    int = 16			,  lowercase	:    int = 88			,  lowercase	:    Optional[int] = None			,  lowercase	:    int = 1			,  lowercase	:    float = 0.0			,  lowercase	:    int = 32			,  lowercase	:    Optional[int] = None			,  lowercase	:    bool = False			,  lowercase	:    Optional[int] = None			,  lowercase	:    Optional[int] = None			,  lowercase	:    str = "geglu"			,  lowercase	:    Optional[int] = None			,  ):


				'''simple docstring'''


				super().__init__()
				_snake_case		=			nn.ModuleList(
				    [
				        TransformeraDModel(
				            num_attention_heads=lowercase			,  attention_head_dim=lowercase			,  in_channels=lowercase			,  num_layers=lowercase			,  dropout=lowercase			,  norm_num_groups=lowercase			,  cross_attention_dim=lowercase			,  attention_bias=lowercase			,  sample_size=lowercase			,  num_vector_embeds=lowercase			,  activation_fn=lowercase			,  num_embeds_ada_norm=lowercase			,  )
				        for _ in range(2	)
				    ]	)

				# Variables that can be set by a pipeline:

				# The ratio of transformer1 to transformer2's output states to be combined during inference
				_snake_case		=			0.5

				# The shape of `encoder_hidden_states` is expected to be
				# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
				_snake_case		=			[77, 257]

				# Which transformer to use to encode which condition.
				# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
				_snake_case		=			[1, 0]





	def  A							(  self	:    Optional[int]			,  lowercase	:    Optional[int]			,  lowercase	:    List[Any]			,  lowercase	:    List[str]=None			,  lowercase	:    Tuple=None			,  lowercase	:    Dict=None			,  lowercase	:    bool = True			,  ):


				'''simple docstring'''


				_snake_case		=			hidden_states

				_snake_case		=			[]
				_snake_case		=			0
				# attention_mask is not used yet
				for i in range(2	):
							# for each of the two transformers, pass the corresponding condition tokens
							_snake_case		=			encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
							_snake_case		=			self.transformer_index_for_condition[i]
							_snake_case		=			self.transformers[transformer_index](
							    lowercase			,  encoder_hidden_states=lowercase			,  timestep=lowercase			,  cross_attention_kwargs=lowercase			,  return_dict=lowercase			,  )[0]
							encoded_states.append(encoded_state - input_states	)
							tokens_start += self.condition_lengths[i]

				_snake_case		=			encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
				_snake_case		=			output_states + input_states

				if not return_dict:
							return (output_states,)

				return TransformeraDModelOutput(sample=lowercase	)