





from collections.abc import Generator
from math import sin







def 					lowerCAmelCase__(		lowercase				:			str )       ->			bytes:

  if len(UpperCamelCase__ ) != 32:
    raise ValueError("Input must be of length 32" )

  __snake_case      : Any						=				B""
  for i in [3, 2, 1, 0]:
    little_endian += string_aa[8 * i : 8 * i + 8]
  return little_endian







def 					lowerCAmelCase__(		lowercase				:			str )       ->			bytes:

  if i < 0:
    raise ValueError("Input must be non-negative" )

  __snake_case      : Any						=				format(UpperCamelCase__      ,   "08x" )[-8:]
  __snake_case      : List[str]						=				B""
  for i in [3, 2, 1, 0]:
    little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("utf-8" )
  return little_endian_hex







def 					lowerCAmelCase__(		lowercase				:			str )       ->			bytes:
  __snake_case      : List[Any]						=				B""
  for char in message:
    bit_string += format(UpperCamelCase__      ,   "08b" ).encode("utf-8" )
  __snake_case      : List[str]						=				format(len(UpperCamelCase__ )      ,   "064b" ).encode("utf-8" )

  # Pad bit_string to a multiple of 512 chars
  bit_string += b"1"
  while len(UpperCamelCase__ ) % 512 != 448:
    bit_string += b"0"
  bit_string += to_little_endian(start_len[32:] ) + to_little_endian(start_len[:32] )

  return bit_string







def 					lowerCAmelCase__(		lowercase				:			Union[str, Any] )       ->			Generator[list[int], None, None]:

  if len(UpperCamelCase__ ) % 512 != 0:
    raise ValueError("Input must have length that\'s a multiple of 512" )

  for pos in range(0      ,   len(UpperCamelCase__ )      ,   512 ):
    __snake_case      : int						=				bit_string[pos : pos + 512]
    __snake_case      : Tuple						=				[]
    for i in range(0      ,   512      ,   32 ):
      block_words.append(int(to_little_endian(block[i : i + 32] )      ,   2 ) )
    yield block_words







def 					lowerCAmelCase__(		lowercase				:			Optional[Any] )       ->			int:

  if i < 0:
    raise ValueError("Input must be non-negative" )

  __snake_case      : Any						=				format(UpperCamelCase__      ,   "032b" )
  __snake_case      : List[str]						=				""
  for c in i_str:
    new_str += "1" if c == "0" else "0"
  return int(UpperCamelCase__      ,   2 )







def 					lowerCAmelCase__(		lowercase				:			Any      ,   lowercase				:			Dict )       ->			int:
  return (a + b) % 2**32







def 					lowerCAmelCase__(		lowercase				:			Union[str, Any]      ,   lowercase				:			List[str] )       ->			int:

  if i < 0:
    raise ValueError("Input must be non-negative" )
  if shift < 0:
    raise ValueError("Shift must be non-negative" )
  return ((i << shift) ^ (i >> (32 - shift))) % 2**32







def 					lowerCAmelCase__(		lowercase				:			Optional[int] )       ->			bytes:
  __snake_case      : Union[str, Any]						=				preprocess(UpperCamelCase__ )

  __snake_case      : Optional[int]						=				[int(2**32 * abs(sin(i + 1 ) ) ) for i in range(64 )]

  # Starting states
  __snake_case      : Union[str, Any]						=				0X67_452_301
  __snake_case      : List[Any]						=				0Xef_cda_b89
  __snake_case      : Optional[int]						=				0X98_bad_cfe
  __snake_case      : List[Any]						=				0X10_325_476

  __snake_case      : List[str]						=				[
      7,
      12,
      17,
      22,
      7,
      12,
      17,
      22,
      7,
      12,
      17,
      22,
      7,
      12,
      17,
      22,
      5,
      9,
      14,
      20,
      5,
      9,
      14,
      20,
      5,
      9,
      14,
      20,
      5,
      9,
      14,
      20,
      4,
      11,
      16,
      23,
      4,
      11,
      16,
      23,
      4,
      11,
      16,
      23,
      4,
      11,
      16,
      23,
      6,
      10,
      15,
      21,
      6,
      10,
      15,
      21,
      6,
      10,
      15,
      21,
      6,
      10,
      15,
      21,
  ]

  # Process bit string in chunks, each with 16 32-char words
  for block_words in get_block_words(UpperCamelCase__ ):
    __snake_case      : Tuple						=				aa
    __snake_case      : str						=				ba
    __snake_case      : Dict						=				ca
    __snake_case      : Any						=				da

    # Hash current chunk
    for i in range(64 ):
      if i <= 15:
        # f = (b & c) | (not_32(b) & d)     # Alternate definition for f
        __snake_case      : Optional[int]						=				d ^ (b & (c ^ d))
        __snake_case      : str						=				i
      elif i <= 31:
        # f = (d & b) | (not_32(d) & c)     # Alternate definition for f
        __snake_case      : Optional[int]						=				c ^ (d & (b ^ c))
        __snake_case      : Dict						=				(5 * i + 1) % 16
      elif i <= 47:
        __snake_case      : Tuple						=				b ^ c ^ d
        __snake_case      : Optional[int]						=				(3 * i + 5) % 16
      else:
        __snake_case      : str						=				c ^ (b | not_aa(UpperCamelCase__ ))
        __snake_case      : Union[str, Any]						=				(7 * i) % 16
      __snake_case      : Any						=				(f + a + added_consts[i] + block_words[g]) % 2**32
      __snake_case      : Any						=				d
      __snake_case      : int						=				c
      __snake_case      : int						=				b
      __snake_case      : List[Any]						=				sum_aa(UpperCamelCase__      ,   left_rotate_aa(UpperCamelCase__      ,   shift_amounts[i] ) )

    # Add hashed chunk to running total
    __snake_case      : Dict						=				sum_aa(UpperCamelCase__      ,   UpperCamelCase__ )
    __snake_case      : Optional[int]						=				sum_aa(UpperCamelCase__      ,   UpperCamelCase__ )
    __snake_case      : Optional[int]						=				sum_aa(UpperCamelCase__      ,   UpperCamelCase__ )
    __snake_case      : Optional[Any]						=				sum_aa(UpperCamelCase__      ,   UpperCamelCase__ )

  __snake_case      : Optional[int]						=				reformat_hex(UpperCamelCase__ ) + reformat_hex(UpperCamelCase__ ) + reformat_hex(UpperCamelCase__ ) + reformat_hex(UpperCamelCase__ )
  return digest


if __name__ == "__main__":
     import doctest

     doctest.testmod()








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

import pytest
from parameterized import 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.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 6_5_0, '''eval_accuracy''': 0.6, '''eval_loss''': 0.9},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]			)
class 	A_       (unittest.TestCase			):
	def      _lowercase				(    self		):






				'''simple docstring'''







				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=_A							,					)
				assert hasattr(self							,					'''env'''		)
	def      _lowercase				(    self							,					_A=1		):






				'''simple docstring'''







				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}-single"""							,					instance_count=_A							,					instance_type=self.instance_type							,					debugger_hook_config=_A							,					hyperparameters={**self.env.hyperparameters, '''model_name_or_path''': self.model_name_or_path}							,					metric_definitions=self.env.metric_definitions							,					py_version='''py36'''							,					)
	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







				TrainingJobAnalytics(_A		).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""		)







	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.create_estimator()

				# run training
				estimator.fit()

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

				# extract kpis
				UpperCAmelCase   		=				list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']		)
				UpperCAmelCase   		=				list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']		)
				# get train time from SageMaker job, this includes starting, preprocessing, stopping
				UpperCAmelCase   		=				(
				    Session().describe_training_job(estimator.latest_training_job.name		).get('''TrainingTimeInSeconds'''							,					9_9_9_9_9_9		)
				)

				# 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}							,					_A		)



