
import argparse
import struct
import unittest



class __lowerCAmelCase				:




			"""simple docstring"""







			def __init__(				self						:					Dict       ,   _snake_case						:					Dict	)      -> None:







								"""simple docstring"""
								A_				 =						data

								# Initialize hash values
								A_				 =						[
								    0X6a09_e667,
								    0Xbb67_ae85,
								    0X3c6e_f372,
								    0Xa54f_f53a,
								    0X510e_527f,
								    0X9b05_688c,
								    0X1f83_d9ab,
								    0X5be0_cd19,
								]

								# Initialize round constants
								A_				 =						[
								    0X428a_2f98,
								    0X7137_4491,
								    0Xb5c0_fbcf,
								    0Xe9b5_dba5,
								    0X3956_c25b,
								    0X59f1_11f1,
								    0X923f_82a4,
								    0Xab1c_5ed5,
								    0Xd807_aa98,
								    0X1283_5b01,
								    0X2431_85be,
								    0X550c_7dc3,
								    0X72be_5d74,
								    0X80de_b1fe,
								    0X9bdc_06a7,
								    0Xc19b_f174,
								    0Xe49b_69c1,
								    0Xefbe_4786,
								    0X0fc1_9dc6,
								    0X240c_a1cc,
								    0X2de9_2c6f,
								    0X4a74_84aa,
								    0X5cb0_a9dc,
								    0X76f9_88da,
								    0X983e_5152,
								    0Xa831_c66d,
								    0Xb003_27c8,
								    0Xbf59_7fc7,
								    0Xc6e0_0bf3,
								    0Xd5a7_9147,
								    0X06ca_6351,
								    0X1429_2967,
								    0X27b7_0a85,
								    0X2e1b_2138,
								    0X4d2c_6dfc,
								    0X5338_0d13,
								    0X650a_7354,
								    0X766a_0abb,
								    0X81c2_c92e,
								    0X9272_2c85,
								    0Xa2bf_e8a1,
								    0Xa81a_664b,
								    0Xc24b_8b70,
								    0Xc76c_51a3,
								    0Xd192_e819,
								    0Xd699_0624,
								    0Xf40e_3585,
								    0X106a_a070,
								    0X19a4_c116,
								    0X1e37_6c08,
								    0X2748_774c,
								    0X34b0_bcb5,
								    0X391c_0cb3,
								    0X4ed8_aa4a,
								    0X5b9c_ca4f,
								    0X682e_6ff3,
								    0X748f_82ee,
								    0X78a5_636f,
								    0X84c8_7814,
								    0X8cc7_0208,
								    0X90be_fffa,
								    0Xa450_6ceb,
								    0Xbef9_a3f7,
								    0Xc671_78f2,
								]

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




			@staticmethod
			def      lowerCamelCase__     (				_snake_case						:					Union[str, Any]	)      -> bytes:







								"""simple docstring"""
								A_				 =						b"\x80" + (b"\x00" * (63 - (len(lowerCAmelCase_	) + 8) % 64))
								A_				 =						struct.pack(">Q"       ,   (len(lowerCAmelCase_	) * 8)	)
								return data + padding + big_endian_integer




			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> None:







								"""simple docstring"""
								A_				 =						[
								    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(struct.unpack(">16L"       ,   lowerCAmelCase_	)	)
													# add 48 0-ed integers
													words += [0] * 48

													A_ ,    A_ ,    A_ ,    A_ ,    A_ ,    A_ ,    A_ ,    A_				 =						self.hashes

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

																							A_				 =						(
																							    words[index - 16] + sa + words[index - 7] + sa
																							) % 0X1_0000_0000

																		# Compression
																		A_				 =						self.ror(lowerCAmelCase_       ,   6	) ^ self.ror(lowerCAmelCase_       ,   11	) ^ self.ror(lowerCAmelCase_       ,   25	)
																		A_				 =						(e & f) ^ ((~e & 0Xffff_ffff) & g)
																		A_				 =						(
																		    h + sa + ch + self.round_constants[index] + words[index]
																		) % 0X1_0000_0000
																		A_				 =						self.ror(lowerCAmelCase_       ,   2	) ^ self.ror(lowerCAmelCase_       ,   13	) ^ self.ror(lowerCAmelCase_       ,   22	)
																		A_				 =						(a & b) ^ (a & c) ^ (b & c)
																		A_				 =						(sa + maj) % 0X1_0000_0000

																		A_ ,    A_ ,    A_ ,    A_ ,    A_ ,    A_ ,    A_ ,    A_				 =						(
																		    g,
																		    f,
																		    e,
																		    ((d + tempa) % 0X1_0000_0000),
																		    c,
																		    b,
																		    a,
																		    ((tempa + tempa) % 0X1_0000_0000),
																		)

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

													# Modify final values
													A_				 =						[
													    ((element + mutated_hash_values[index]) % 0X1_0000_0000)
													    for index, element in enumerate(self.hashes	)
													]

								A_				 =						"".join([hex(lowerCAmelCase_	)[2:].zfill(8	) for value in self.hashes]	)






			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					Dict       ,   _snake_case						:					str	)      -> int:







								"""simple docstring"""
								return 0Xffff_ffff & (value << (32 - rotations)) | (value >> rotations)





class __lowerCAmelCase				(					unittest.TestCase      ):




			"""simple docstring"""







			def      lowerCamelCase__     (				self						:					List[Any]	)      -> None:







								"""simple docstring"""
								import hashlib

								A_				 =						bytes("Test String"       ,   "utf-8"	)
								self.assertEqual(SHAaaa(lowerCAmelCase_	).hash       ,   hashlib.shaaaa(lowerCAmelCase_	).hexdigest()	)





def 							A_     ():

					'''simple docstring'''





					import doctest

					doctest.testmod()

					A_				 =						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_				 =						parser.parse_args()

					A_				 =						args.input_string

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

					print(SHAaaa(lowercase__				).hash				)


if __name__ == "__main__":
		main()






"""simple docstring"""





import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
		import torch

if is_datasets_available():
		from datasets import load_dataset

UpperCamelCase_					:						Union[str, Any]				= random.Random()
def 							A_     (__a				,   __a=1.0				,   __a=None				,   __a=None				):

					'''simple docstring'''





					if rng is None:
										A_				 =						global_rng

					A_				 =						[]
					for batch_idx in range(shape[0]				):
										values.append([]				)
										for _ in range(shape[1]				):
															values[-1].append(rng.random() * scale				)

					return values



class __lowerCAmelCase				(					unittest.TestCase      ):




			"""simple docstring"""







			def __init__(				self						:					Optional[Any]       ,   _snake_case						:					str       ,   _snake_case						:					Union[str, Any]=7       ,   _snake_case						:					Any=400       ,   _snake_case						:					Tuple=2_000       ,   _snake_case						:					str=2_048       ,   _snake_case						:					List[Any]=128       ,   _snake_case						:					List[Any]=1       ,   _snake_case						:					Union[str, Any]=512       ,   _snake_case						:					List[str]=30       ,   _snake_case						:					List[str]=44_100       ,   )      -> Optional[int]:







								"""simple docstring"""
								A_				 =						parent
								A_				 =						batch_size
								A_				 =						min_seq_length
								A_				 =						max_seq_length
								A_				 =						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								A_				 =						spectrogram_length
								A_				 =						feature_size
								A_				 =						num_audio_channels
								A_				 =						hop_length
								A_				 =						chunk_length
								A_				 =						sampling_rate




			def      lowerCamelCase__     (				self						:					Any	)      -> Optional[int]:







								"""simple docstring"""
								return {
								    "spectrogram_length": self.spectrogram_length,
								    "feature_size": self.feature_size,
								    "num_audio_channels": self.num_audio_channels,
								    "hop_length": self.hop_length,
								    "chunk_length": self.chunk_length,
								    "sampling_rate": self.sampling_rate,
								}






			def      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					str=False       ,   _snake_case						:					Optional[Any]=False	)      -> List[str]:







								"""simple docstring"""
								def _flatten(_snake_case						:					List[Any]	):
													return list(itertools.chain(*_snake_case	)	)

								if equal_length:
													A_				 =						[floats_list((self.max_seq_length, self.feature_size)	) for _ in range(self.batch_size	)]
								else:
													# make sure that inputs increase in size
													A_				 =						[
													    floats_list((x, self.feature_size)	)
													    for x in range(self.min_seq_length       ,   self.max_seq_length       ,   self.seq_length_diff	)
													]
								if numpify:
													A_				 =						[np.asarray(_snake_case	) for x in speech_inputs]
								return speech_inputs





@require_torch
@require_torchaudio
class __lowerCAmelCase				(					_lowercase     ,				unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				TvltFeatureExtractor




			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						TvltFeatureExtractionTester(self	)




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						self.feature_extraction_class(**self.feat_extract_dict	)
								self.assertTrue(hasattr(_snake_case       ,   "spectrogram_length"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "feature_size"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "num_audio_channels"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "hop_length"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "chunk_length"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "sampling_rate"	)	)




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						self.feature_extraction_class(**self.feat_extract_dict	)

								with tempfile.TemporaryDirectory() as tmpdirname:
													A_				 =						feat_extract_first.save_pretrained(_snake_case	)[0]
													check_json_file_has_correct_format(_snake_case	)
													A_				 =						self.feature_extraction_class.from_pretrained(_snake_case	)

								A_				 =						feat_extract_first.to_dict()
								A_				 =						feat_extract_second.to_dict()
								A_				 =						dict_first.pop("mel_filters"	)
								A_				 =						dict_second.pop("mel_filters"	)
								self.assertTrue(np.allclose(_snake_case       ,   _snake_case	)	)
								self.assertEqual(_snake_case       ,   _snake_case	)




			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Tuple:







								"""simple docstring"""
								A_				 =						self.feature_extraction_class(**self.feat_extract_dict	)

								with tempfile.TemporaryDirectory() as tmpdirname:
													A_				 =						os.path.join(_snake_case       ,   "feat_extract.json"	)
													feat_extract_first.to_json_file(_snake_case	)
													A_				 =						self.feature_extraction_class.from_json_file(_snake_case	)

								A_				 =						feat_extract_first.to_dict()
								A_				 =						feat_extract_second.to_dict()
								A_				 =						dict_first.pop("mel_filters"	)
								A_				 =						dict_second.pop("mel_filters"	)
								self.assertTrue(np.allclose(_snake_case       ,   _snake_case	)	)
								self.assertEqual(_snake_case       ,   _snake_case	)




			def      lowerCamelCase__     (				self						:					int	)      -> List[Any]:







								"""simple docstring"""
								# Initialize feature_extractor
								A_				 =						self.feature_extraction_class(**self.feat_extract_dict	)

								# create three inputs of length 800, 1000, and 1200
								A_				 =						[floats_list((1, x)	)[0] for x in range(800       ,   1_400       ,   200	)]
								A_				 =						[np.asarray(_snake_case	) for speech_input in speech_inputs]

								# Test not batched input
								A_				 =						feature_extractor(np_speech_inputs[0]       ,   return_tensors="np"       ,   sampling_rate=44_100	).audio_values

								self.assertTrue(encoded_audios.ndim == 4	)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size	)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length	)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels	)

								# Test batched
								A_				 =						feature_extractor(_snake_case       ,   return_tensors="np"       ,   sampling_rate=44_100	).audio_values

								self.assertTrue(encoded_audios.ndim == 4	)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size	)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length	)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels	)

								# Test audio masking
								A_				 =						feature_extractor(
								    _snake_case       ,   return_tensors="np"       ,   sampling_rate=44_100       ,   mask_audio=_snake_case	).audio_values

								self.assertTrue(encoded_audios.ndim == 4	)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size	)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length	)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels	)

								# Test 2-D numpy arrays are batched.
								A_				 =						[floats_list((1, x)	)[0] for x in (800, 800, 800)]
								A_				 =						np.asarray(_snake_case	)
								A_				 =						feature_extractor(_snake_case       ,   return_tensors="np"       ,   sampling_rate=44_100	).audio_values
								self.assertTrue(encoded_audios.ndim == 4	)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size	)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length	)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels	)




			def      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					int	)      -> List[str]:







								"""simple docstring"""
								A_				 =						load_dataset("hf-internal-testing/librispeech_asr_dummy"       ,   "clean"       ,   split="validation"	)
								# automatic decoding with librispeech
								A_				 =						ds.sort("id"	).select(range(_snake_case	)	)[:num_samples]["audio"]

								return [x["array"] for x in speech_samples]






			def      lowerCamelCase__     (				self						:					List[str]	)      -> int:







								"""simple docstring"""
								A_				 =						self._load_datasamples(1	)
								A_				 =						TvltFeatureExtractor()
								A_				 =						feature_extractor(_snake_case       ,   return_tensors="pt"	).audio_values

								self.assertEquals(audio_values.shape       ,   (1, 1, 192, 128)	)

								A_				 =						torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]	)
								self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]       ,   _snake_case       ,   atol=1e-4	)	)




