



import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
			import tensorflow as tf

			from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments

@require_tf
class   UpperCAmelCase_    (  unittest.TestCase			):

						'''simple docstring'''




						def 			_A      (       self ,				_A						):







													'''simple docstring'''

													for model_result in results.values():
																				for batch_size, sequence_length in zip(model_result['bs'] ,				model_result['ss']						):
																											__SCREAMING_SNAKE_CASE										=   model_result['result'][batch_size][sequence_length]
																											self.assertIsNotNone(_UpperCamelCase						)






						def 			_A      (       self						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   'sshleifer/tiny-gpt2'
													__SCREAMING_SNAKE_CASE										=   TensorFlowBenchmarkArguments(
													    models=[MODEL_ID] ,				training=_UpperCamelCase ,				inference=_UpperCamelCase ,				sequence_lengths=[8] ,				batch_sizes=[1] ,				eager_mode=_UpperCamelCase ,				multi_process=_UpperCamelCase ,				)
													__SCREAMING_SNAKE_CASE										=   TensorFlowBenchmark(_UpperCamelCase						)
													__SCREAMING_SNAKE_CASE										=   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						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   'sgugger/tiny-distilbert-classification'
													__SCREAMING_SNAKE_CASE										=   TensorFlowBenchmarkArguments(
													    models=[MODEL_ID] ,				training=_UpperCamelCase ,				inference=_UpperCamelCase ,				sequence_lengths=[8] ,				batch_sizes=[1] ,				multi_process=_UpperCamelCase ,				only_pretrain_model=_UpperCamelCase ,				)
													__SCREAMING_SNAKE_CASE										=   TensorFlowBenchmark(_UpperCamelCase						)
													__SCREAMING_SNAKE_CASE										=   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						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   'sshleifer/tiny-gpt2'
													__SCREAMING_SNAKE_CASE										=   TensorFlowBenchmarkArguments(
													    models=[MODEL_ID] ,				training=_UpperCamelCase ,				inference=_UpperCamelCase ,				sequence_lengths=[8] ,				batch_sizes=[1] ,				multi_process=_UpperCamelCase ,				)
													__SCREAMING_SNAKE_CASE										=   TensorFlowBenchmark(_UpperCamelCase						)
													__SCREAMING_SNAKE_CASE										=   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						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   'sshleifer/tiny-gpt2'
													__SCREAMING_SNAKE_CASE										=   AutoConfig.from_pretrained(_UpperCamelCase						)
													__SCREAMING_SNAKE_CASE										=   TensorFlowBenchmarkArguments(
													    models=[MODEL_ID] ,				training=_UpperCamelCase ,				inference=_UpperCamelCase ,				sequence_lengths=[8] ,				batch_sizes=[1] ,				eager_mode=_UpperCamelCase ,				multi_process=_UpperCamelCase ,				)
													__SCREAMING_SNAKE_CASE										=   TensorFlowBenchmark(_UpperCamelCase ,				[config]						)
													__SCREAMING_SNAKE_CASE										=   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						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   'sshleifer/tiny-gpt2'
													__SCREAMING_SNAKE_CASE										=   AutoConfig.from_pretrained(_UpperCamelCase						)
													__SCREAMING_SNAKE_CASE										=   TensorFlowBenchmarkArguments(
													    models=[MODEL_ID] ,				training=_UpperCamelCase ,				inference=_UpperCamelCase ,				sequence_lengths=[8] ,				batch_sizes=[1] ,				multi_process=_UpperCamelCase ,				)
													__SCREAMING_SNAKE_CASE										=   TensorFlowBenchmark(_UpperCamelCase ,				[config]						)
													__SCREAMING_SNAKE_CASE										=   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						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   'sshleifer/tiny-gpt2'
													__SCREAMING_SNAKE_CASE										=   TensorFlowBenchmarkArguments(
													    models=[MODEL_ID] ,				training=_UpperCamelCase ,				inference=_UpperCamelCase ,				sequence_lengths=[8] ,				batch_sizes=[1] ,				multi_process=_UpperCamelCase ,				)
													__SCREAMING_SNAKE_CASE										=   TensorFlowBenchmark(_UpperCamelCase						)
													__SCREAMING_SNAKE_CASE										=   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						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   'sshleifer/tiny-gpt2'
													__SCREAMING_SNAKE_CASE										=   AutoConfig.from_pretrained(_UpperCamelCase						)
													__SCREAMING_SNAKE_CASE										=   TensorFlowBenchmarkArguments(
													    models=[MODEL_ID] ,				training=_UpperCamelCase ,				inference=_UpperCamelCase ,				sequence_lengths=[8] ,				batch_sizes=[1] ,				multi_process=_UpperCamelCase ,				)
													__SCREAMING_SNAKE_CASE										=   TensorFlowBenchmark(_UpperCamelCase ,				[config]						)
													__SCREAMING_SNAKE_CASE										=   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						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   'patrickvonplaten/t5-tiny-random'
													__SCREAMING_SNAKE_CASE										=   AutoConfig.from_pretrained(_UpperCamelCase						)
													__SCREAMING_SNAKE_CASE										=   TensorFlowBenchmarkArguments(
													    models=[MODEL_ID] ,				training=_UpperCamelCase ,				inference=_UpperCamelCase ,				sequence_lengths=[8] ,				batch_sizes=[1] ,				multi_process=_UpperCamelCase ,				)
													__SCREAMING_SNAKE_CASE										=   TensorFlowBenchmark(_UpperCamelCase ,				configs=[config]						)
													__SCREAMING_SNAKE_CASE										=   benchmark.run()
													self.check_results_dict_not_empty(results.time_inference_result						)
													self.check_results_dict_not_empty(results.memory_inference_result						)






						@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU'						)						) == 0 ,				'Cannot do xla on CPU.'						)
						def 			_A      (       self						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   'sshleifer/tiny-gpt2'
													__SCREAMING_SNAKE_CASE										=   TensorFlowBenchmarkArguments(
													    models=[MODEL_ID] ,				training=_UpperCamelCase ,				inference=_UpperCamelCase ,				sequence_lengths=[8] ,				batch_sizes=[1] ,				use_xla=_UpperCamelCase ,				multi_process=_UpperCamelCase ,				)
													__SCREAMING_SNAKE_CASE										=   TensorFlowBenchmark(_UpperCamelCase						)
													__SCREAMING_SNAKE_CASE										=   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						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   'sshleifer/tiny-gpt2'
													with tempfile.TemporaryDirectory() as tmp_dir:
																				__SCREAMING_SNAKE_CASE										=   TensorFlowBenchmarkArguments(
																				    models=[MODEL_ID] ,				inference=_UpperCamelCase ,				save_to_csv=_UpperCamelCase ,				sequence_lengths=[8] ,				batch_sizes=[1] ,				inference_time_csv_file=os.path.join(_UpperCamelCase ,				'inf_time.csv'						) ,				inference_memory_csv_file=os.path.join(_UpperCamelCase ,				'inf_mem.csv'						) ,				env_info_csv_file=os.path.join(_UpperCamelCase ,				'env.csv'						) ,				multi_process=_UpperCamelCase ,				)
																				__SCREAMING_SNAKE_CASE										=   TensorFlowBenchmark(_UpperCamelCase						)
																				benchmark.run()
																				self.assertTrue(Path(os.path.join(_UpperCamelCase ,				'inf_time.csv'						)						).exists()						)
																				self.assertTrue(Path(os.path.join(_UpperCamelCase ,				'inf_mem.csv'						)						).exists()						)
																				self.assertTrue(Path(os.path.join(_UpperCamelCase ,				'env.csv'						)						).exists()						)






						def 			_A      (       self						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   'sshleifer/tiny-gpt2'

													def _check_summary_is_not_empty(_A						):
																				self.assertTrue(hasattr(_UpperCamelCase ,				'sequential'						)						)
																				self.assertTrue(hasattr(_UpperCamelCase ,				'cumulative'						)						)
																				self.assertTrue(hasattr(_UpperCamelCase ,				'current'						)						)
																				self.assertTrue(hasattr(_UpperCamelCase ,				'total'						)						)

													with tempfile.TemporaryDirectory() as tmp_dir:
																				__SCREAMING_SNAKE_CASE										=   TensorFlowBenchmarkArguments(
																				    models=[MODEL_ID] ,				inference=_UpperCamelCase ,				sequence_lengths=[8] ,				batch_sizes=[1] ,				log_filename=os.path.join(_UpperCamelCase ,				'log.txt'						) ,				log_print=_UpperCamelCase ,				trace_memory_line_by_line=_UpperCamelCase ,				eager_mode=_UpperCamelCase ,				multi_process=_UpperCamelCase ,				)
																				__SCREAMING_SNAKE_CASE										=   TensorFlowBenchmark(_UpperCamelCase						)
																				__SCREAMING_SNAKE_CASE										=   benchmark.run()
																				_check_summary_is_not_empty(result.inference_summary						)
																				self.assertTrue(Path(os.path.join(_UpperCamelCase ,				'log.txt'						)						).exists()						)








import argparse
import struct
import unittest





class       _lowerCAmelCase   :




				def __init__(   self							,							_UpperCamelCase	)							-> None:
							lowerCAmelCase_         =      data

							# Initialize hash values
							lowerCAmelCase_         =      [
							    0x6_A_0_9_E_6_6_7,
							    0xB_B_6_7_A_E_8_5,
							    0x3_C_6_E_F_3_7_2,
							    0xA_5_4_F_F_5_3_A,
							    0x5_1_0_E_5_2_7_F,
							    0x9_B_0_5_6_8_8_C,
							    0x1_F_8_3_D_9_A_B,
							    0x5_B_E_0_C_D_1_9,
							]

							# Initialize round constants
							lowerCAmelCase_         =      [
							    0x4_2_8_A_2_F_9_8,
							    0x7_1_3_7_4_4_9_1,
							    0xB_5_C_0_F_B_C_F,
							    0xE_9_B_5_D_B_A_5,
							    0x3_9_5_6_C_2_5_B,
							    0x5_9_F_1_1_1_F_1,
							    0x9_2_3_F_8_2_A_4,
							    0xA_B_1_C_5_E_D_5,
							    0xD_8_0_7_A_A_9_8,
							    0x1_2_8_3_5_B_0_1,
							    0x2_4_3_1_8_5_B_E,
							    0x5_5_0_C_7_D_C_3,
							    0x7_2_B_E_5_D_7_4,
							    0x8_0_D_E_B_1_F_E,
							    0x9_B_D_C_0_6_A_7,
							    0xC_1_9_B_F_1_7_4,
							    0xE_4_9_B_6_9_C_1,
							    0xE_F_B_E_4_7_8_6,
							    0x0_F_C_1_9_D_C_6,
							    0x2_4_0_C_A_1_C_C,
							    0x2_D_E_9_2_C_6_F,
							    0x4_A_7_4_8_4_A_A,
							    0x5_C_B_0_A_9_D_C,
							    0x7_6_F_9_8_8_D_A,
							    0x9_8_3_E_5_1_5_2,
							    0xA_8_3_1_C_6_6_D,
							    0xB_0_0_3_2_7_C_8,
							    0xB_F_5_9_7_F_C_7,
							    0xC_6_E_0_0_B_F_3,
							    0xD_5_A_7_9_1_4_7,
							    0x0_6_C_A_6_3_5_1,
							    0x1_4_2_9_2_9_6_7,
							    0x2_7_B_7_0_A_8_5,
							    0x2_E_1_B_2_1_3_8,
							    0x4_D_2_C_6_D_F_C,
							    0x5_3_3_8_0_D_1_3,
							    0x6_5_0_A_7_3_5_4,
							    0x7_6_6_A_0_A_B_B,
							    0x8_1_C_2_C_9_2_E,
							    0x9_2_7_2_2_C_8_5,
							    0xA_2_B_F_E_8_A_1,
							    0xA_8_1_A_6_6_4_B,
							    0xC_2_4_B_8_B_7_0,
							    0xC_7_6_C_5_1_A_3,
							    0xD_1_9_2_E_8_1_9,
							    0xD_6_9_9_0_6_2_4,
							    0xF_4_0_E_3_5_8_5,
							    0x1_0_6_A_A_0_7_0,
							    0x1_9_A_4_C_1_1_6,
							    0x1_E_3_7_6_C_0_8,
							    0x2_7_4_8_7_7_4_C,
							    0x3_4_B_0_B_C_B_5,
							    0x3_9_1_C_0_C_B_3,
							    0x4_E_D_8_A_A_4_A,
							    0x5_B_9_C_C_A_4_F,
							    0x6_8_2_E_6_F_F_3,
							    0x7_4_8_F_8_2_E_E,
							    0x7_8_A_5_6_3_6_F,
							    0x8_4_C_8_7_8_1_4,
							    0x8_C_C_7_0_2_0_8,
							    0x9_0_B_E_F_F_F_A,
							    0xA_4_5_0_6_C_E_B,
							    0xB_E_F_9_A_3_F_7,
							    0xC_6_7_1_7_8_F_2,
							]

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




				@staticmethod
				def 	__a		(   _UpperCamelCase	)							-> bytes:
							lowerCAmelCase_         =      B"\x80" + (B"\x00" * (63 - (len(_UpperCamelCase	) + 8) % 64))
							lowerCAmelCase_         =      struct.pack(">Q"							,							(len(_UpperCamelCase	) * 8)	)
							return data + padding + big_endian_integer




				def 	__a		(   self	)							-> None:
							# Convert into blocks of 64 bytes
							lowerCAmelCase_         =      [
							    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
										lowerCAmelCase_         =      list(struct.unpack(">16L"							,							_UpperCamelCase	)	)
										# add 48 0-ed integers
										words += [0] * 48

										lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      self.hashes

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

																lowerCAmelCase_         =      (
																    words[index - 16] + sa + words[index - 7] + sa
																) % 0x1_0_0_0_0_0_0_0_0

													# Compression
													lowerCAmelCase_         =      self.ror(_UpperCamelCase							,							6	) ^ self.ror(_UpperCamelCase							,							11	) ^ self.ror(_UpperCamelCase							,							25	)
													lowerCAmelCase_         =      (e & f) ^ ((~e & 0xF_F_F_F_F_F_F_F) & g)
													lowerCAmelCase_         =      (
													    h + sa + ch + self.round_constants[index] + words[index]
													) % 0x1_0_0_0_0_0_0_0_0
													lowerCAmelCase_         =      self.ror(_UpperCamelCase							,							2	) ^ self.ror(_UpperCamelCase							,							13	) ^ self.ror(_UpperCamelCase							,							22	)
													lowerCAmelCase_         =      (a & b) ^ (a & c) ^ (b & c)
													lowerCAmelCase_         =      (sa + maj) % 0x1_0_0_0_0_0_0_0_0

													lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      (
													    g,
													    f,
													    e,
													    ((d + tempa) % 0x1_0_0_0_0_0_0_0_0),
													    c,
													    b,
													    a,
													    ((tempa + tempa) % 0x1_0_0_0_0_0_0_0_0),
													)

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

										# Modify final values
										lowerCAmelCase_         =      [
										    ((element + mutated_hash_values[index]) % 0x1_0_0_0_0_0_0_0_0)
										    for index, element in enumerate(self.hashes	)
										]

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




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase	)							-> int:
							return 0xF_F_F_F_F_F_F_F & (value << (32 - rotations)) | (value >> rotations)





class       _lowerCAmelCase   (      unittest.TestCase			):




				def 	__a		(   self	)							-> None:
							import hashlib

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






def   lowerCamelCase__							(							):




			"""simple docstring"""


			import doctest

			doctest.testmod()

			lowerCAmelCase_         =      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"							)

			lowerCAmelCase_         =      parser.parse_args()

			lowerCAmelCase_         =      args.input_string

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

			print(SHAaaa(__lowerCAmelCase							).hash							)


if __name__ == "__main__":
			main()



