





"""simple docstring"""






import argparse
import struct
import unittest



class 					__UpperCAmelCase							:
				def __init__(		self    :			Dict	, a_    :			bytes    )	->	None:



							'''simple docstring'''



							a__  :	int		 =			data

							# Initialize hash values
							a__  :	Union[str, Any]		 =			[
							    0x6a09e667,
							    0xbb67ae85,
							    0x3c6ef372,
							    0xa54ff53a,
							    0x510e527f,
							    0x9b05688c,
							    0x1f83d9ab,
							    0x5be0cd19,
							]

							# Initialize round constants
							a__  :	Any		 =			[
							    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,
							]

							a__  :	Dict		 =			self.preprocessing(self.data    )
							self.final_hash()
				@staticmethod
				def       UpperCAmelCase      (		a_    :			bytes    )	->	bytes:



							'''simple docstring'''



							a__  :	Any		 =			B"\x80" + (B"\x00" * (63 - (len(a_    ) + 8) % 64))
							a__  :	Any		 =			struct.pack(">Q"	, (len(a_    ) * 8)    )
							return data + padding + big_endian_integer
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	None:



							'''simple docstring'''



							a__  :	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
										a__  :	List[Any]		 =			list(struct.unpack(">16L"	, a_    )    )
										# add 48 0-ed integers
										words += [0] * 48

										a__ ,	a__ ,	a__ ,	a__ ,	a__ ,	a__ ,	a__ ,	a__  :	Tuple		 =			self.hashes

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

																a__  :	Optional[Any]		 =			(
																    words[index - 16] + sa + words[index - 7] + sa
																) % 0x100000000

													# Compression
													a__  :	Any		 =			self.ror(a_	, 6    ) ^ self.ror(a_	, 11    ) ^ self.ror(a_	, 25    )
													a__  :	Dict		 =			(e & f) ^ ((~e & 0xffffffff) & g)
													a__  :	int		 =			(
													    h + sa + ch + self.round_constants[index] + words[index]
													) % 0x100000000
													a__  :	Dict		 =			self.ror(a_	, 2    ) ^ self.ror(a_	, 13    ) ^ self.ror(a_	, 22    )
													a__  :	List[str]		 =			(a & b) ^ (a & c) ^ (b & c)
													a__  :	Optional[Any]		 =			(sa + maj) % 0x100000000

													a__ ,	a__ ,	a__ ,	a__ ,	a__ ,	a__ ,	a__ ,	a__  :	List[Any]		 =			(
													    g,
													    f,
													    e,
													    ((d + tempa) % 0x100000000),
													    c,
													    b,
													    a,
													    ((tempa + tempa) % 0x100000000),
													)

										a__  :	Optional[Any]		 =			[a, b, c, d, e, f, g, h]

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

							a__  :	int		 =			"".join([hex(a_    )[2:].zfill(8    ) for value in self.hashes]    )






				def       UpperCAmelCase      (		self    :			int	, a_    :			int	, a_    :			int    )	->	int:



							'''simple docstring'''



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



class 					__UpperCAmelCase							(						unittest.TestCase  ):
				def       UpperCAmelCase      (		self    :			int    )	->	None:



							'''simple docstring'''



							import hashlib

							a__  :	Tuple		 =			bytes("Test String"	, "utf-8"    )
							self.assertEqual(SHAaaa(a_    ).hash	, hashlib.shaaaa(a_    ).hexdigest()    )






def      lowercase__ (					)    ->			None:



			'''simple docstring'''
			import doctest

			doctest.testmod()

			a__  :	Optional[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"			)

			a__  :	Optional[int]		 =			parser.parse_args()

			a__  :	int		 =			args.input_string

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

			print(SHAaaa(lowerCAmelCase__			).hash			)


if __name__ == "__main__":
		main()






"""simple docstring"""






from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


__UpperCAmelCase						=			logging.get_logger(__name__)






def      lowercase__ (					lowerCAmelCase__   :			Union[tf.Tensor, np.ndarray]			)    ->			List[int]:



			'''simple docstring'''
			if isinstance(lowerCAmelCase__							,			np.ndarray			):
						return list(tensor.shape			)

			a__  :	Optional[int]		 =			tf.shape(lowerCAmelCase__			)

			if tensor.shape == tf.TensorShape(lowerCAmelCase__			):
						return dynamic

			a__  :	Union[str, Any]		 =			tensor.shape.as_list()

			return [dynamic[i] if s is None else s for i, s in enumerate(lowerCAmelCase__			)]






def      lowercase__ (					lowerCAmelCase__   :			tf.Tensor							,			lowerCAmelCase__   :			Optional[int] = None							,			lowerCAmelCase__   :			Optional[str] = None			)    ->			tf.Tensor:



			'''simple docstring'''
			return tf.nn.softmax(logits=logits + 1E-9							,			axis=lowerCAmelCase__							,			name=lowerCAmelCase__			)






def      lowercase__ (					lowerCAmelCase__   :			List[Any]							,			lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			Any							,			lowerCAmelCase__   :			Union[str, Any]=1E-5							,			lowerCAmelCase__   :			str=-1			)    ->			int:



			'''simple docstring'''
			# This is a very simplified functional layernorm, designed to duplicate
			# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
			# models in Transformers.

			if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(lowerCAmelCase__							,			lowerCAmelCase__			):
						raise NotImplementedError("Only 1D weight and bias tensors are supported for now, with only a single axis."			)

			# Get mean and variance on the axis to be normalized
			a__ ,	a__  :	Dict		 =			tf.nn.moments(lowerCAmelCase__							,			axes=[axis]							,			keepdims=lowerCAmelCase__			)

			if axis != -1:
						# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
						# on every dimension except axis
						a__  :	Dict		 =			[1] * inputs.shape.rank
						a__  :	Dict		 =			shape_list(lowerCAmelCase__			)[axis]
						a__  :	Any		 =			tf.reshape(lowerCAmelCase__							,			lowerCAmelCase__			)
						a__  :	List[Any]		 =			tf.reshape(lowerCAmelCase__							,			lowerCAmelCase__			)

			# Compute layer normalization using the batch_normalization
			# function.
			a__  :	List[str]		 =			tf.nn.batch_normalization(
			    lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			offset=lowerCAmelCase__							,			scale=lowerCAmelCase__							,			variance_epsilon=lowerCAmelCase__							,			)
			return outputs






def      lowercase__ (					lowerCAmelCase__   :			Optional[int]							,			lowerCAmelCase__   :			Any=0							,			lowerCAmelCase__   :			List[str]=-1			)    ->			List[str]:



			'''simple docstring'''
			# Replicates the behavior of torch.flatten in TF

			# If end_dim or start_dim is negative, count them from the end
			if end_dim < 0:
						end_dim += input.shape.rank
			if start_dim < 0:
						start_dim += input.shape.rank

			if start_dim == end_dim:
						return input

			a__  :	Optional[int]		 =			tf.shape(lowerCAmelCase__			)
			a__  :	str		 =			tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]			)
			a__  :	Union[str, Any]		 =			tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]							,			axis=0			)
			return tf.reshape(lowerCAmelCase__							,			lowerCAmelCase__			)






def      lowercase__ (					lowerCAmelCase__   :			tf.Tensor			)    ->			tf.Tensor:



			'''simple docstring'''
			if not isinstance(lowerCAmelCase__							,			tf.Tensor			):
						a__  :	Optional[Any]		 =			tf.convert_to_tensor(lowerCAmelCase__			)  # Catches stray NumPy inputs
			if encoder_attention_mask.shape.rank == 3:
						a__  :	Tuple		 =			encoder_attention_mask[:, None, :, :]
			if encoder_attention_mask.shape.rank == 2:
						a__  :	int		 =			encoder_attention_mask[:, None, None, :]
			# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
			# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
			# /transformer/transformer_layers.py#L270
			# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
			# encoder_extended_attention_mask.transpose(-1, -2))
			a__  :	int		 =			(
			    tf.cast(1							,			encoder_attention_mask.dtype			) - encoder_extended_attention_mask
			) * encoder_extended_attention_mask.dtype.min

			return encoder_extended_attention_mask






def      lowercase__ (					lowerCAmelCase__   :			tf.Tensor							,			lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			str = "input_ids"			)    ->			None:



			'''simple docstring'''
			tf.debugging.assert_less(
			    lowerCAmelCase__							,			tf.cast(lowerCAmelCase__							,			dtype=tensor.dtype			)							,			message=(
			        F"The maximum value of {tensor_name} ({tf.math.reduce_max(lowerCAmelCase__			)}) must be smaller than the embedding "
			        F"layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."
			    )							,			)






def      lowercase__ (					lowerCAmelCase__   :			Dict							,			lowerCAmelCase__   :			str							,			lowerCAmelCase__   :			Dict			)    ->			Tuple:



			'''simple docstring'''
			a__  :	Optional[Any]		 =			6_4_5_1_2
			# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
			# because in that case even chunking the array would not make the saving
			# possible.
			a__  :	Optional[Any]		 =			[x for x in data if len(lowerCAmelCase__			) > HDF5_OBJECT_HEADER_LIMIT]

			# Expecting this to never be true.
			if bad_attributes:
						raise RuntimeError(
						    "The following attributes cannot be saved to HDF5 file because "
						    F"they are larger than {HDF5_OBJECT_HEADER_LIMIT} "
						    F"bytes: {bad_attributes}"			)

			a__  :	List[str]		 =			np.asarray(lowerCAmelCase__			)

			a__  :	List[str]		 =			1
			a__  :	str		 =			np.array_split(lowerCAmelCase__							,			lowerCAmelCase__			)

			# This will never loop forever thanks to the test above.
			while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data			):
						num_chunks += 1
						a__  :	str		 =			np.array_split(lowerCAmelCase__							,			lowerCAmelCase__			)

			if num_chunks > 1:
						for chunk_id, chunk_data in enumerate(lowerCAmelCase__			):
									a__  :	List[Any]		 =			chunk_data
			else:
						a__  :	List[str]		 =			data






def      lowercase__ (					lowerCAmelCase__   :			List[Any]							,			lowerCAmelCase__   :			Tuple			)    ->			Tuple:



			'''simple docstring'''
			if name in group.attrs:
						a__  :	str		 =			[n.decode("utf8"			) if hasattr(lowerCAmelCase__							,			"decode"			) else n for n in group.attrs[name]]
			else:
						a__  :	str		 =			[]
						a__  :	Any		 =			0
						while "%s%d" % (name, chunk_id) in group.attrs:
									data.extend(
									    [n.decode("utf8"			) if hasattr(lowerCAmelCase__							,			"decode"			) else n for n in group.attrs["%s%d" % (name, chunk_id)]]			)
									chunk_id += 1
			return data






def      lowercase__ (					lowerCAmelCase__   :			Tuple			)    ->			List[Any]:



			'''simple docstring'''

			def _expand_single_ad_tensor(lowerCAmelCase__   :			List[str]			):
						if isinstance(lowerCAmelCase__							,			tf.Tensor			) and t.shape.rank == 1:
									return tf.expand_dims(lowerCAmelCase__							,			axis=-1			)
						return t

			return tf.nest.map_structure(_expand_single_ad_tensor							,			lowerCAmelCase__			)