

'''simple docstring'''

import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
							from sagemaker import Session, TrainingJobAnalytics
							from sagemaker.huggingface import HuggingFace






@pytest.mark.skipif(
    literal_eval(os.getenv("""TEST_SAGEMAKER""",						"""False"""		)		) is not True,						reason="""Skipping test because should only be run when releasing minor transformers version""",						)
@pytest.mark.usefixtures("""sm_env"""		)
@parameterized_class(
    [
        {
            """framework""": """pytorch""",
            """script""": """run_glue_model_parallelism.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 1600, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_glue.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 1600, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
    ]		)
class    snake_case     (      unittest.TestCase		):






				"""simple docstring"""



				def   a__					(    self       )     ->							Tuple:
											if self.framework == "pytorch":
																		subprocess.run(
																		    f"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split(),     encoding='utf-8',     check=_lowercase,     )
											assert hasattr(self,     'env'       )



				def   a__					(    self,     _lowercase       )     ->							Optional[int]:
											# configuration for running training on smdistributed Model Parallel
											SCREAMING_SNAKE_CASE_				=				{
											    'enabled': True,
											    'processes_per_host': 8,
											}
											SCREAMING_SNAKE_CASE_				=				{
											    'enabled': True,
											    'parameters': {
											        'microbatches': 4,
											        'placement_strategy': 'spread',
											        'pipeline': 'interleaved',
											        'optimize': 'speed',
											        'partitions': 4,
											        'ddp': True,
											    },
											}

											SCREAMING_SNAKE_CASE_				=				{'smdistributed': {'modelparallel': smp_options}, 'mpi': mpi_options}

											SCREAMING_SNAKE_CASE_				=				'trainer' if self.script == 'run_glue.py' else 'smtrainer'
											# creates estimator
											return HuggingFace(
											    entry_point=self.script,     source_dir=self.env.test_path,     role=self.env.role,     image_uri=self.env.image_uri,     base_job_name=f"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}""",     instance_count=_lowercase,     instance_type=self.instance_type,     debugger_hook_config=_lowercase,     hyperparameters={
											        **self.env.hyperparameters,
											        'model_name_or_path': self.model_name_or_path,
											        'max_steps': 500,
											    },     metric_definitions=self.env.metric_definitions,     distribution=_lowercase,     py_version='py36',     )



				def   a__					(    self,     _lowercase       )     ->							Dict:
											TrainingJobAnalytics(_lowercase       ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""       )





				@parameterized.expand([(1,)]       )
				def   a__					(    self,     _lowercase       )     ->							Optional[int]:
											# create estimator
											SCREAMING_SNAKE_CASE_				=				self.create_estimator(_lowercase       )

											# run training
											estimator.fit()

											# result dataframe
											SCREAMING_SNAKE_CASE_				=				TrainingJobAnalytics(estimator.latest_training_job.name       ).dataframe()

											# extract kpis
											SCREAMING_SNAKE_CASE_				=				list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']       )
											SCREAMING_SNAKE_CASE_				=				list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']       )
											# get train time from SageMaker job, this includes starting, preprocessing, stopping
											SCREAMING_SNAKE_CASE_				=				(
											    Session().describe_training_job(estimator.latest_training_job.name       ).get('TrainingTimeInSeconds',     999999       )
											)

											# assert kpis
											assert train_runtime <= self.results["train_runtime"]
											assert all(t >= self.results['eval_accuracy'] for t in eval_accuracy       )
											assert all(t <= self.results['eval_loss'] for t in eval_loss       )

											# dump tests result into json file to share in PR
											with open(f"""{estimator.latest_training_job.name}.json""",     'w'       ) as outfile:
																		json.dump({'train_time': train_runtime, 'eval_accuracy': eval_accuracy, 'eval_loss': eval_loss},     _lowercase       )








'''simple docstring'''

import argparse
import hashlib  # hashlib is only used inside the Test class
import struct






class    snake_case     :






				"""simple docstring"""



				def __init__(    self,     _lowercase       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				data
											SCREAMING_SNAKE_CASE_				=				[0x6_7_4_5_2_3_0_1, 0xe_f_c_d_a_b_8_9, 0x9_8_b_a_d_c_f_e, 0x1_0_3_2_5_4_7_6, 0xc_3_d_2_e_1_f_0]



				@staticmethod
				def   a__					(    _lowercase,     _lowercase       )     ->							List[str]:
											return ((n << b) | (n >> (32 - b))) & 0xf_f_f_f_f_f_f_f



				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				=				B'\x80' + B'\x00' * (63 - (len(self.data       ) + 8) % 64)
											SCREAMING_SNAKE_CASE_				=				self.data + padding + struct.pack('>Q',     8 * len(self.data       )       )
											return padded_data



				def   a__					(    self       )     ->							Optional[Any]:
											return [
											    self.padded_data[i : i + 64] for i in range(0,     len(self.padded_data       ),     64       )
											]



				def   a__					(    self,     _lowercase       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				list(struct.unpack('>16L',     _lowercase       )       ) + [0] * 64
											for i in range(16,     80       ):
																		SCREAMING_SNAKE_CASE_				=				self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]),     1       )
											return w





				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				=				self.padding()
											SCREAMING_SNAKE_CASE_				=				self.split_blocks()
											for block in self.blocks:
																		SCREAMING_SNAKE_CASE_				=				self.expand_block(_lowercase       )
																		SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self.h
																		for i in range(0,     80       ):
																									if 0 <= i < 20:
																																SCREAMING_SNAKE_CASE_				=				(b & c) | ((~b) & d)
																																SCREAMING_SNAKE_CASE_				=				0x5_a_8_2_7_9_9_9
																									elif 20 <= i < 40:
																																SCREAMING_SNAKE_CASE_				=				b ^ c ^ d
																																SCREAMING_SNAKE_CASE_				=				0x6_e_d_9_e_b_a_1
																									elif 40 <= i < 60:
																																SCREAMING_SNAKE_CASE_				=				(b & c) | (b & d) | (c & d)
																																SCREAMING_SNAKE_CASE_				=				0x8_f_1_b_b_c_d_c
																									elif 60 <= i < 80:
																																SCREAMING_SNAKE_CASE_				=				b ^ c ^ d
																																SCREAMING_SNAKE_CASE_				=				0xc_a_6_2_c_1_d_6
																									SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				(
																									    self.rotate(_lowercase,     5       ) + f + e + k + expanded_block[i] & 0xf_f_f_f_f_f_f_f,
																									    a,
																									    self.rotate(_lowercase,     30       ),
																									    c,
																									    d,
																									)
																		SCREAMING_SNAKE_CASE_				=				(
																		    self.h[0] + a & 0xf_f_f_f_f_f_f_f,
																		    self.h[1] + b & 0xf_f_f_f_f_f_f_f,
																		    self.h[2] + c & 0xf_f_f_f_f_f_f_f,
																		    self.h[3] + d & 0xf_f_f_f_f_f_f_f,
																		    self.h[4] + e & 0xf_f_f_f_f_f_f_f,
																		)
											return ("{:08x}" * 5).format(*self.h       )




def 							_UpperCamelCase				(   )      -> Optional[int]:
							SCREAMING_SNAKE_CASE_				=				B'Test String'
							assert SHAaHash(lowerCAmelCase__   ).final_hash() == hashlib.shaa(lowerCAmelCase__   ).hexdigest()  # noqa: S324




def 							_UpperCamelCase				(   )      -> Any:
							SCREAMING_SNAKE_CASE_				=				argparse.ArgumentParser(description='Process some strings or files'   )
							parser.add_argument(
							    '--string'		,dest='input_string'		,default='Hello World!! Welcome to Cryptography'		,help='Hash the string'		,)
							parser.add_argument('--file'		,dest='input_file'		,help='Hash contents of a file'   )
							SCREAMING_SNAKE_CASE_				=				parser.parse_args()
							SCREAMING_SNAKE_CASE_				=				args.input_string
							# In any case hash input should be a bytestring
							if args.input_file:
														with open(args.input_file		,'rb'   ) as f:
																					SCREAMING_SNAKE_CASE_				=				f.read()
							else:
														SCREAMING_SNAKE_CASE_				=				bytes(lowerCAmelCase__		,'utf-8'   )
							print(SHAaHash(lowerCAmelCase__   ).final_hash()   )


if __name__ == "__main__":
							main()
							import doctest

							doctest.testmod()





