import tensorflow as tf

from ...tf_utils import shape_list






class   __lowercase       (     tf.keras.layers.Layer     ):

						def __init__(						self					:   Union[str, Any]				,				__lowerCamelCase					:   List[str]				,				__lowerCamelCase					:   Dict				,				__lowerCamelCase					:   List[Any]				,				__lowerCamelCase					:   List[str]				,				__lowerCamelCase					:   Optional[int]=1				,				__lowerCamelCase					:   Any=False				,				**__lowerCamelCase					:   Optional[int] )				->		Any:


									'''simple docstring'''

									super().__init__(**__lowerCamelCase )

									lowercase       		=		vocab_size
									lowercase       		=		d_embed
									lowercase       		=		d_proj

									lowercase       		=		cutoffs + [vocab_size]
									lowercase       		=		[0] + self.cutoffs
									lowercase       		=		div_val

									lowercase       		=		self.cutoffs[0]
									lowercase       		=		len(self.cutoffs ) - 1
									lowercase       		=		self.shortlist_size + self.n_clusters
									lowercase       		=		keep_order

									lowercase       		=		[]
									lowercase       		=		[]

						def 							__a		(						self					:   Optional[int]				,				__lowerCamelCase					:   Optional[int] )				->		Dict:


									'''simple docstring'''

									if self.n_clusters > 0:
												lowercase       		=		self.add_weight(
												    shape=(self.n_clusters, self.d_embed)				,				initializer='''zeros'''				,				trainable=__lowerCamelCase				,				name='''cluster_weight''' )
												lowercase       		=		self.add_weight(
												    shape=(self.n_clusters,)				,				initializer='''zeros'''				,				trainable=__lowerCamelCase				,				name='''cluster_bias''' )

									if self.div_val == 1:
												for i in range(len(self.cutoffs ) ):
															if self.d_proj != self.d_embed:
																		lowercase       		=		self.add_weight(
																		    shape=(self.d_embed, self.d_proj)				,				initializer='''zeros'''				,				trainable=__lowerCamelCase				,				name=f'out_projs_._{i}'				,				)
																		self.out_projs.append(__lowerCamelCase )
															else:
																		self.out_projs.append(__lowerCamelCase )
															lowercase       		=		self.add_weight(
															    shape=(self.vocab_size, self.d_embed)				,				initializer='''zeros'''				,				trainable=__lowerCamelCase				,				name=f'out_layers_._{i}_._weight'				,				)
															lowercase       		=		self.add_weight(
															    shape=(self.vocab_size,)				,				initializer='''zeros'''				,				trainable=__lowerCamelCase				,				name=f'out_layers_._{i}_._bias'				,				)
															self.out_layers.append((weight, bias) )
									else:
												for i in range(len(self.cutoffs ) ):
															lowercase		,lowercase       		=		self.cutoff_ends[i], self.cutoff_ends[i + 1]
															lowercase       		=		self.d_embed // (self.div_val**i)

															lowercase       		=		self.add_weight(
															    shape=(d_emb_i, self.d_proj)				,				initializer='''zeros'''				,				trainable=__lowerCamelCase				,				name=f'out_projs_._{i}' )
															self.out_projs.append(__lowerCamelCase )
															lowercase       		=		self.add_weight(
															    shape=(r_idx - l_idx, d_emb_i)				,				initializer='''zeros'''				,				trainable=__lowerCamelCase				,				name=f'out_layers_._{i}_._weight'				,				)
															lowercase       		=		self.add_weight(
															    shape=(r_idx - l_idx,)				,				initializer='''zeros'''				,				trainable=__lowerCamelCase				,				name=f'out_layers_._{i}_._bias'				,				)
															self.out_layers.append((weight, bias) )
									super().build(__lowerCamelCase )

						@staticmethod
						def 							__a		(						__lowerCamelCase					:   Dict				,				__lowerCamelCase					:   Tuple				,				__lowerCamelCase					:   int				,				__lowerCamelCase					:   Dict=None )				->		Optional[int]:


									'''simple docstring'''

									lowercase       		=		x
									if proj is not None:
												lowercase       		=		tf.einsum('''ibd,ed->ibe'''				,				__lowerCamelCase				,				__lowerCamelCase )
									return tf.einsum('''ibd,nd->ibn'''				,				__lowerCamelCase				,				__lowerCamelCase ) + b

						@staticmethod
						def 							__a		(						__lowerCamelCase					:   Any				,				__lowerCamelCase					:   int )				->		Optional[int]:


									'''simple docstring'''

									lowercase       		=		shape_list(__lowerCamelCase )
									lowercase       		=		tf.range(lp_size[0]				,				dtype=target.dtype )
									lowercase       		=		tf.stack([r, target]				,				1 )
									return tf.gather_nd(__lowerCamelCase				,				__lowerCamelCase )



						def 							__a		(						self					:   Union[str, Any]				,				__lowerCamelCase					:   Any				,				__lowerCamelCase					:   Optional[int]				,				__lowerCamelCase					:   Any=True				,				__lowerCamelCase					:   List[Any]=False )				->		int:


									'''simple docstring'''

									lowercase       		=		0
									if self.n_clusters == 0:
												lowercase       		=		self._logit(__lowerCamelCase				,				self.out_layers[0][0]				,				self.out_layers[0][1]				,				self.out_projs[0] )
												if target is not None:
															lowercase       		=		tf.nn.sparse_softmax_cross_entropy_with_logits(labels=__lowerCamelCase				,				logits=__lowerCamelCase )
												lowercase       		=		tf.nn.log_softmax(__lowerCamelCase				,				axis=-1 )
									else:
												lowercase       		=		shape_list(__lowerCamelCase )
												lowercase       		=		[]
												lowercase       		=		tf.zeros(hidden_sizes[:2] )
												for i in range(len(self.cutoffs ) ):
															lowercase		,lowercase       		=		self.cutoff_ends[i], self.cutoff_ends[i + 1]
															if target is not None:
																		lowercase       		=		(target >= l_idx) & (target < r_idx)
																		lowercase       		=		tf.where(__lowerCamelCase )
																		lowercase       		=		tf.boolean_mask(__lowerCamelCase				,				__lowerCamelCase ) - l_idx

															if self.div_val == 1:
																		lowercase       		=		self.out_layers[0][0][l_idx:r_idx]
																		lowercase       		=		self.out_layers[0][1][l_idx:r_idx]
															else:
																		lowercase       		=		self.out_layers[i][0]
																		lowercase       		=		self.out_layers[i][1]

															if i == 0:
																		lowercase       		=		tf.concat([cur_W, self.cluster_weight]				,				0 )
																		lowercase       		=		tf.concat([cur_b, self.cluster_bias]				,				0 )

																		lowercase       		=		self._logit(__lowerCamelCase				,				__lowerCamelCase				,				__lowerCamelCase				,				self.out_projs[0] )
																		lowercase       		=		tf.nn.log_softmax(__lowerCamelCase )
																		out.append(head_logprob[..., : self.cutoffs[0]] )
																		if target is not None:
																					lowercase       		=		tf.boolean_mask(__lowerCamelCase				,				__lowerCamelCase )
																					lowercase       		=		self._gather_logprob(__lowerCamelCase				,				__lowerCamelCase )
															else:
																		lowercase       		=		self._logit(__lowerCamelCase				,				__lowerCamelCase				,				__lowerCamelCase				,				self.out_projs[i] )
																		lowercase       		=		tf.nn.log_softmax(__lowerCamelCase )
																		lowercase       		=		self.cutoffs[0] + i - 1  # No probability for the head cluster
																		lowercase       		=		head_logprob[..., cluster_prob_idx, None] + tail_logprob
																		out.append(__lowerCamelCase )
																		if target is not None:
																					lowercase       		=		tf.boolean_mask(__lowerCamelCase				,				__lowerCamelCase )
																					lowercase       		=		tf.boolean_mask(__lowerCamelCase				,				__lowerCamelCase )
																					lowercase       		=		self._gather_logprob(__lowerCamelCase				,				__lowerCamelCase )
																					cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
															if target is not None:
																		loss += tf.scatter_nd(__lowerCamelCase				,				-cur_logprob				,				shape_list(__lowerCamelCase ) )
												lowercase       		=		tf.concat(__lowerCamelCase				,				axis=-1 )

									if target is not None:
												if return_mean:
															lowercase       		=		tf.reduce_mean(__lowerCamelCase )
												# Add the training-time loss value to the layer using `self.add_loss()`.
												self.add_loss(__lowerCamelCase )

												# Log the loss as a metric (we could log arbitrary metrics,
												# including different metrics for training and inference.
												self.add_metric(__lowerCamelCase				,				name=self.name				,				aggregation='''mean''' if return_mean else '''''' )

									return out








import argparse
import struct
import unittest







class 					lowercase							:




				def __init__(   self ,  snake_case			):
											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				(   snake_case			):
											snake_case_     							=       b'\x80' + (b'\x00' * (63 - (len(snake_case			) + 8) % 64))
											snake_case_     							=       struct.pack('>Q' ,  (len(snake_case			) * 8)			)
											return data + padding + big_endian_integer




				def        a				(   self			):
											# Convert into blocks of 64 bytes
											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' ,  snake_case			)			)
																		# 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(snake_case ,  6			) ^ self.ror(snake_case ,  11			) ^ self.ror(snake_case ,  25			)
																									snake_case_     							=       (e & f) ^ ((~e & 0xFFFFFFFF) & g)
																									snake_case_     							=       (
																									    h + sa + ch + self.round_constants[index] + words[index]
																									) % 0x100000000
																									snake_case_     							=       self.ror(snake_case ,  2			) ^ self.ror(snake_case ,  13			) ^ self.ror(snake_case ,  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(snake_case			)[2:].zfill(8			) for value in self.hashes]			)




				def        a				(   self ,  snake_case ,  snake_case			):
											return 0xFFFFFFFF & (value << (32 - rotations)) | (value >> rotations)







class 					lowercase							(			unittest.TestCase     ):




				def        a				(   self			):
											import hashlib

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





def    __lowerCamelCase      (					):







							'''simple docstring'''






							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(UpperCamelCase__  ,				'utf-8' )

							print(SHAaaa(UpperCamelCase__ ).hash )


if __name__ == "__main__":
					main()




