

import argparse
import struct
import unittest



class 					_a		:


					'''simple docstring'''






					def __init__(				self					, A__    ):
						A__							:					Optional[int]       =	data

						# Initialize hash values
						A__							:					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
						A__							:					Optional[int]       =	[
						    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,
						]

						A__							:					List[Any]       =	self.preprocessing(self.data    )
						self.final_hash()






					@staticmethod
					def 						__A    (				A__    ):
						A__							:					Optional[Any]       =	b"""\x80""" + (b"""\x00""" * (63 - (len(A__    ) + 8) % 64))
						A__							:					List[str]       =	struct.pack(""">Q"""					, (len(A__    ) * 8)    )
						return data + padding + big_endian_integer






					def 						__A    (				self    ):
						# Convert into blocks of 64 bytes
						A__							:					str       =	[
						    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__							:					Optional[int]       =	list(struct.unpack(""">16L"""					, A__    )    )
							# add 48 0-ed integers
							words += [0] * 48

							A__     ,     A__     ,     A__     ,     A__     ,     A__     ,     A__     ,     A__     ,     A__							:					Any       =	self.hashes

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

									A__							:					List[str]       =	(
									    words[index - 16] + sa + words[index - 7] + sa
									) % 0x100_000_000

								# Compression
								A__							:					Optional[int]       =	self.ror(A__					, 6    ) ^ self.ror(A__					, 11    ) ^ self.ror(A__					, 25    )
								A__							:					List[Any]       =	(e & f) ^ ((~e & 0xFF_FFF_FFF) & g)
								A__							:					List[Any]       =	(
								    h + sa + ch + self.round_constants[index] + words[index]
								) % 0x100_000_000
								A__							:					str       =	self.ror(A__					, 2    ) ^ self.ror(A__					, 13    ) ^ self.ror(A__					, 22    )
								A__							:					str       =	(a & b) ^ (a & c) ^ (b & c)
								A__							:					Dict       =	(sa + maj) % 0x100_000_000

								A__     ,     A__     ,     A__     ,     A__     ,     A__     ,     A__     ,     A__     ,     A__							:					Optional[int]       =	(
								    g,
								    f,
								    e,
								    ((d + tempa) % 0x100_000_000),
								    c,
								    b,
								    a,
								    ((tempa + tempa) % 0x100_000_000),
								)

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

							# Modify final values
							A__							:					int       =	[
							    ((element + mutated_hash_values[index]) % 0x100_000_000)
							    for index, element in enumerate(self.hashes    )
							]

						A__							:					List[Any]       =	"""""".join([hex(A__    )[2:].zfill(8    ) for value in self.hashes]    )






					def 						__A    (				self					, A__					, A__    ):
						return 0xFF_FFF_FFF & (value << (32 - rotations)) | (value >> rotations)



class 					_a		(unittest.TestCase       ):


					'''simple docstring'''






					def 						__A    (				self    ):
						import hashlib

						A__							:					Optional[Any]       =	bytes("""Test String"""					, """utf-8"""    )
						self.assertEqual(SHAaaa(A__    ).hash					, hashlib.shaaaa(A__    ).hexdigest()    )



def 		UpperCamelCase      ()  ->   None:
	import doctest

	doctest.testmod()

	A__							:					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"""      )

	A__							:					Dict       =	parser.parse_args()

	A__							:					List[str]       =	args.input_string

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

	print(SHAaaa(lowercase_      ).hash      )


if __name__ == "__main__":
	main()






import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
	from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments



@require_torch
class 					_a		(unittest.TestCase       ):


					'''simple docstring'''






					def 						__A    (				self					, A__    ):
						for model_result in results.values():
							for batch_size, sequence_length in zip(model_result["""bs"""]					, model_result["""ss"""]    ):
								A__							:					str       =	model_result["""result"""][batch_size][sequence_length]
								self.assertIsNotNone(A__    )






					def 						__A    (				self    ):
						A__							:					Dict       =	"""sshleifer/tiny-gpt2"""
						A__							:					Tuple       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					int       =	PyTorchBenchmark(A__    )
						A__							:					List[Any]       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result    )
						self.check_results_dict_not_empty(results.memory_inference_result    )






					def 						__A    (				self    ):
						A__							:					Dict       =	"""sgugger/tiny-distilbert-classification"""
						A__							:					Dict       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, only_pretrain_model=A__					, )
						A__							:					str       =	PyTorchBenchmark(A__    )
						A__							:					List[str]       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result    )
						self.check_results_dict_not_empty(results.memory_inference_result    )






					def 						__A    (				self    ):
						A__							:					Any       =	"""sshleifer/tiny-gpt2"""
						A__							:					List[Any]       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, torchscript=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					Tuple       =	PyTorchBenchmark(A__    )
						A__							:					str       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result    )
						self.check_results_dict_not_empty(results.memory_inference_result    )






					@unittest.skipIf(torch_device == """cpu"""					, """Cant do half precision"""    )
					def 						__A    (				self    ):
						A__							:					Optional[Any]       =	"""sshleifer/tiny-gpt2"""
						A__							:					Optional[Any]       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, fpaa=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					str       =	PyTorchBenchmark(A__    )
						A__							:					Any       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result    )
						self.check_results_dict_not_empty(results.memory_inference_result    )






					def 						__A    (				self    ):
						A__							:					Optional[Any]       =	"""sshleifer/tiny-gpt2"""
						A__							:					Tuple       =	AutoConfig.from_pretrained(A__    )
						# set architectures equal to `None`
						A__							:					List[Any]       =	None
						A__							:					str       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					List[str]       =	PyTorchBenchmark(A__					, configs=[config]    )
						A__							:					Any       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result    )
						self.check_results_dict_not_empty(results.memory_inference_result    )






					def 						__A    (				self    ):
						A__							:					Optional[int]       =	"""sshleifer/tiny-gpt2"""
						A__							:					Optional[int]       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					Any       =	PyTorchBenchmark(A__    )
						A__							:					Dict       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_train_result    )
						self.check_results_dict_not_empty(results.memory_train_result    )






					@unittest.skipIf(torch_device == """cpu"""					, """Can't do half precision"""    )
					def 						__A    (				self    ):
						A__							:					Optional[int]       =	"""sshleifer/tiny-gpt2"""
						A__							:					List[str]       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, fpaa=A__					, multi_process=A__					, )
						A__							:					Dict       =	PyTorchBenchmark(A__    )
						A__							:					Optional[int]       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_train_result    )
						self.check_results_dict_not_empty(results.memory_train_result    )






					def 						__A    (				self    ):
						A__							:					int       =	"""sshleifer/tiny-gpt2"""
						A__							:					Optional[int]       =	AutoConfig.from_pretrained(A__    )
						A__							:					str       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					int       =	PyTorchBenchmark(A__					, configs=[config]    )
						A__							:					Tuple       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result    )
						self.check_results_dict_not_empty(results.memory_inference_result    )






					def 						__A    (				self    ):
						A__							:					List[str]       =	"""sshleifer/tinier_bart"""
						A__							:					List[str]       =	AutoConfig.from_pretrained(A__    )
						A__							:					List[str]       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					Union[str, Any]       =	PyTorchBenchmark(A__					, configs=[config]    )
						A__							:					str       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result    )
						self.check_results_dict_not_empty(results.memory_inference_result    )






					def 						__A    (				self    ):
						A__							:					Optional[int]       =	"""sshleifer/tiny-gpt2"""
						A__							:					Union[str, Any]       =	AutoConfig.from_pretrained(A__    )
						A__							:					Tuple       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					int       =	PyTorchBenchmark(A__					, configs=[config]    )
						A__							:					Tuple       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_train_result    )
						self.check_results_dict_not_empty(results.memory_train_result    )






					def 						__A    (				self    ):
						A__							:					Dict       =	"""sshleifer/tinier_bart"""
						A__							:					int       =	AutoConfig.from_pretrained(A__    )
						A__							:					Union[str, Any]       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					List[Any]       =	PyTorchBenchmark(A__					, configs=[config]    )
						A__							:					Optional[Any]       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_train_result    )
						self.check_results_dict_not_empty(results.memory_train_result    )






					def 						__A    (				self    ):
						A__							:					int       =	"""sshleifer/tiny-gpt2"""
						with tempfile.TemporaryDirectory() as tmp_dir:
							A__							:					Dict       =	PyTorchBenchmarkArguments(
							    models=[MODEL_ID]					, training=A__					, inference=A__					, save_to_csv=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, inference_time_csv_file=os.path.join(A__					, """inf_time.csv"""    )					, train_memory_csv_file=os.path.join(A__					, """train_mem.csv"""    )					, inference_memory_csv_file=os.path.join(A__					, """inf_mem.csv"""    )					, train_time_csv_file=os.path.join(A__					, """train_time.csv"""    )					, env_info_csv_file=os.path.join(A__					, """env.csv"""    )					, multi_process=A__					, )
							A__							:					Optional[Any]       =	PyTorchBenchmark(A__    )
							benchmark.run()
							self.assertTrue(Path(os.path.join(A__					, """inf_time.csv"""    )    ).exists()    )
							self.assertTrue(Path(os.path.join(A__					, """train_time.csv"""    )    ).exists()    )
							self.assertTrue(Path(os.path.join(A__					, """inf_mem.csv"""    )    ).exists()    )
							self.assertTrue(Path(os.path.join(A__					, """train_mem.csv"""    )    ).exists()    )
							self.assertTrue(Path(os.path.join(A__					, """env.csv"""    )    ).exists()    )






					def 						__A    (				self    ):
						A__							:					Optional[int]       =	"""sshleifer/tiny-gpt2"""

						def _check_summary_is_not_empty(A__    ):
							self.assertTrue(hasattr(A__					, """sequential"""    )    )
							self.assertTrue(hasattr(A__					, """cumulative"""    )    )
							self.assertTrue(hasattr(A__					, """current"""    )    )
							self.assertTrue(hasattr(A__					, """total"""    )    )

						with tempfile.TemporaryDirectory() as tmp_dir:
							A__							:					Dict       =	PyTorchBenchmarkArguments(
							    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, log_filename=os.path.join(A__					, """log.txt"""    )					, log_print=A__					, trace_memory_line_by_line=A__					, multi_process=A__					, )
							A__							:					Dict       =	PyTorchBenchmark(A__    )
							A__							:					str       =	benchmark.run()
							_check_summary_is_not_empty(result.inference_summary    )
							_check_summary_is_not_empty(result.train_summary    )
							self.assertTrue(Path(os.path.join(A__					, """log.txt"""    )    ).exists()    )



