import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
						import torch_xla.core.xla_model as xm
						import torch_xla.debug.metrics as met






class   __lowercase       (     lowercase_     ):

						def __init__(						self					:   List[Any]				,				*__lowerCamelCase					:   List[Any]				,				__lowerCamelCase					:   Dict=None				,				__lowerCamelCase					:   Dict=None				,				**__lowerCamelCase					:   Optional[int] )				->		str:


									'''simple docstring'''

									super().__init__(*__lowerCamelCase				,				**__lowerCamelCase )
									lowercase       		=		eval_examples
									lowercase       		=		post_process_function

						def 							__a		(						self					:   Optional[int]				,				__lowerCamelCase					:   Any = None				,				__lowerCamelCase					:   Tuple=None				,				__lowerCamelCase					:   List[Any] = None				,				__lowerCamelCase					:   Union[str, Any] = "eval"				,				**__lowerCamelCase					:   int				,				)				->		Any:


									'''simple docstring'''

									lowercase       		=		gen_kwargs.copy()
									lowercase       		=		(
									    gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length''' ) is not None else self.args.generation_max_length
									)
									lowercase       		=		(
									    gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams''' ) is not None else self.args.generation_num_beams
									)
									lowercase       		=		gen_kwargs

									lowercase       		=		self.eval_dataset if eval_dataset is None else eval_dataset
									lowercase       		=		self.get_eval_dataloader(__lowerCamelCase )
									lowercase       		=		self.eval_examples if eval_examples is None else eval_examples

									# Temporarily disable metric computation, we will do it in the loop here.
									lowercase       		=		self.compute_metrics
									lowercase       		=		None
									lowercase       		=		time.time()
									lowercase       		=		self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
									try:
												lowercase       		=		eval_loop(
												    __lowerCamelCase				,				description='''Evaluation'''				,				prediction_loss_only=True if compute_metrics is None else None				,				ignore_keys=__lowerCamelCase				,				metric_key_prefix=__lowerCamelCase				,				)
									finally:
												lowercase       		=		compute_metrics
									lowercase       		=		self.args.eval_batch_size * self.args.world_size
									if f'{metric_key_prefix}_jit_compilation_time' in output.metrics:
												start_time += output.metrics[f'{metric_key_prefix}_jit_compilation_time']
									output.metrics.update(
									    speed_metrics(
									        __lowerCamelCase				,				__lowerCamelCase				,				num_samples=output.num_samples				,				num_steps=math.ceil(output.num_samples / total_batch_size )				,				) )

									if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
												# Only the main node write the results by default
												lowercase       		=		self.post_process_function(__lowerCamelCase				,				__lowerCamelCase				,				__lowerCamelCase )
												lowercase       		=		self.compute_metrics(__lowerCamelCase )

												# Prefix all keys with metric_key_prefix + '_'
												for key in list(metrics.keys() ):
															if not key.startswith(f'{metric_key_prefix}_' ):
																		lowercase       		=		metrics.pop(__lowerCamelCase )

												metrics.update(output.metrics )
									else:
												lowercase       		=		output.metrics

									if self.args.should_log:
												# Only the main node log the results by default
												self.log(__lowerCamelCase )

									if self.args.tpu_metrics_debug or self.args.debug:
												# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
												xm.master_print(met.metrics_report() )

									lowercase       		=		self.callback_handler.on_evaluate(self.args				,				self.state				,				self.control				,				__lowerCamelCase )
									return metrics



						def 							__a		(						self					:   List[Any]				,				__lowerCamelCase					:   Optional[Any]				,				__lowerCamelCase					:   Any				,				__lowerCamelCase					:   Optional[int]=None				,				__lowerCamelCase					:   List[str] = "test"				,				**__lowerCamelCase					:   Tuple )				->		int:


									'''simple docstring'''

									lowercase       		=		gen_kwargs.copy()

									lowercase       		=		self.get_test_dataloader(__lowerCamelCase )

									# Temporarily disable metric computation, we will do it in the loop here.
									lowercase       		=		self.compute_metrics
									lowercase       		=		None
									lowercase       		=		time.time()
									lowercase       		=		self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
									try:
												lowercase       		=		eval_loop(
												    __lowerCamelCase				,				description='''Prediction'''				,				prediction_loss_only=True if compute_metrics is None else None				,				ignore_keys=__lowerCamelCase				,				metric_key_prefix=__lowerCamelCase				,				)
									finally:
												lowercase       		=		compute_metrics

									lowercase       		=		self.args.eval_batch_size * self.args.world_size
									if f'{metric_key_prefix}_jit_compilation_time' in output.metrics:
												start_time += output.metrics[f'{metric_key_prefix}_jit_compilation_time']
									output.metrics.update(
									    speed_metrics(
									        __lowerCamelCase				,				__lowerCamelCase				,				num_samples=output.num_samples				,				num_steps=math.ceil(output.num_samples / total_batch_size )				,				) )
									if self.post_process_function is None or self.compute_metrics is None:
												return output

									lowercase       		=		self.post_process_function(__lowerCamelCase				,				__lowerCamelCase				,				__lowerCamelCase				,				'''predict''' )
									lowercase       		=		self.compute_metrics(__lowerCamelCase )

									# Prefix all keys with metric_key_prefix + '_'
									for key in list(metrics.keys() ):
												if not key.startswith(f'{metric_key_prefix}_' ):
															lowercase       		=		metrics.pop(__lowerCamelCase )
									metrics.update(output.metrics )
									return PredictionOutput(predictions=predictions.predictions				,				label_ids=predictions.label_ids				,				metrics=__lowerCamelCase )








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()




