from __future__ import annotations
def    A			(					_lowercase	,						_lowercase = None	,						_lowercase = None	,						_lowercase = False	,						)     ->   Tuple:
						SCREAMING_SNAKE_CASE							:					List[str]           =							cipher_alphabet or [chr(UpperCAmelCase__		) for i in range(97	,						123		)]

						# If the argument is None or the user provided an empty dictionary
						if not frequencies_dict:
												# Frequencies of letters in the english language (how much they show up)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							{
												    '''a''': 0.0_8497,
												    '''b''': 0.0_1492,
												    '''c''': 0.0_2202,
												    '''d''': 0.0_4253,
												    '''e''': 0.1_1162,
												    '''f''': 0.0_2228,
												    '''g''': 0.0_2015,
												    '''h''': 0.0_6094,
												    '''i''': 0.0_7546,
												    '''j''': 0.0_0153,
												    '''k''': 0.0_1292,
												    '''l''': 0.0_4025,
												    '''m''': 0.0_2406,
												    '''n''': 0.0_6749,
												    '''o''': 0.0_7507,
												    '''p''': 0.0_1929,
												    '''q''': 0.0_0095,
												    '''r''': 0.0_7587,
												    '''s''': 0.0_6327,
												    '''t''': 0.0_9356,
												    '''u''': 0.0_2758,
												    '''v''': 0.0_0978,
												    '''w''': 0.0_2560,
												    '''x''': 0.0_0150,
												    '''y''': 0.0_1994,
												    '''z''': 0.0_0077,
												}
						else:
												# Custom frequencies dictionary
												SCREAMING_SNAKE_CASE							:					Any           =							frequencies_dict

						if not case_sensitive:
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							ciphertext.lower()

						# Chi squared statistic values
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							{}

						# cycle through all of the shifts
						for shift in range(len(UpperCAmelCase__		)		):
												SCREAMING_SNAKE_CASE							:					Dict           =							''''''

												# decrypt the message with the shift
												for letter in ciphertext:
																		try:
																								# Try to index the letter in the alphabet
																								SCREAMING_SNAKE_CASE							:					Dict           =							(alphabet_letters.index(letter.lower()		) - shift) % len(
																								    UpperCAmelCase__		)
																								decrypted_with_shift += (
																								    alphabet_letters[new_key].upper()
																								    if case_sensitive and letter.isupper()
																								    else alphabet_letters[new_key]
																								)
																		except ValueError:
																								# Append the character if it isn't in the alphabet
																								decrypted_with_shift += letter

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							0.0

												# Loop through each letter in the decoded message with the shift
												for letter in decrypted_with_shift:
																		if case_sensitive:
																								SCREAMING_SNAKE_CASE							:					str           =							letter.lower()
																								if letter in frequencies:
																														# Get the amount of times the letter occurs in the message
																														SCREAMING_SNAKE_CASE							:					List[Any]           =							decrypted_with_shift.lower().count(UpperCAmelCase__		)

																														# Get the excepcted amount of times the letter should appear based
																														# on letter frequencies
																														SCREAMING_SNAKE_CASE							:					List[str]           =							frequencies[letter] * occurrences

																														# Complete the chi squared statistic formula
																														SCREAMING_SNAKE_CASE							:					Tuple           =							((occurrences - expected) ** 2) / expected

																														# Add the margin of error to the total chi squared statistic
																														chi_squared_statistic += chi_letter_value
																		else:
																								if letter.lower() in frequencies:
																														# Get the amount of times the letter occurs in the message
																														SCREAMING_SNAKE_CASE							:					Optional[Any]           =							decrypted_with_shift.count(UpperCAmelCase__		)

																														# Get the excepcted amount of times the letter should appear based
																														# on letter frequencies
																														SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							frequencies[letter] * occurrences

																														# Complete the chi squared statistic formula
																														SCREAMING_SNAKE_CASE							:					Optional[Any]           =							((occurrences - expected) ** 2) / expected

																														# Add the margin of error to the total chi squared statistic
																														chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							(
												    chi_squared_statistic,
												    decrypted_with_shift,
												)

						# Get the most likely cipher by finding the cipher with the smallest chi squared
						# statistic
						def chi_squared_statistic_values_sorting_key(_lowercase		) -> tuple[float, str]:
												return chi_squared_statistic_values[key]

						SCREAMING_SNAKE_CASE							:					Tuple           =							min(
						    UpperCAmelCase__	,						key=UpperCAmelCase__	,						)

						# Get all the data from the most likely cipher (key, decoded message)
						(
						    (
						    SCREAMING_SNAKE_CASE	
						)       ,						(
						    SCREAMING_SNAKE_CASE	
						)       ,							
						)							:					Optional[int]           =							chi_squared_statistic_values[most_likely_cipher]

						# Return the data on the most likely shift
						return (
						    most_likely_cipher,
						    most_likely_cipher_chi_squared_value,
						    decoded_most_likely_cipher,
						)






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.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 650, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_ddp.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf_dist.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.6, """eval_loss""": 0.7},
        },
    ])
class       lowercase__		(     unittest.TestCase):


						def  __A ( self			:       Any		):





												'''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=UpperCamelCase__					,	)
												assert hasattr(self					,	'''env'''		)


						def  __A ( self			:       str					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							f"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
												# distributed data settings
												SCREAMING_SNAKE_CASE							:					Any           =							{'''smdistributed''': {'''dataparallel''': {'''enabled''': True}}} if self.script != '''run_ddp.py''' else None

												# 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=UpperCamelCase__					,	instance_count=UpperCamelCase__					,	instance_type=self.instance_type					,	debugger_hook_config=UpperCamelCase__					,	hyperparameters={**self.env.distributed_hyperparameters, '''model_name_or_path''': self.model_name_or_path}					,	metric_definitions=self.env.metric_definitions					,	distribution=UpperCamelCase__					,	py_version='''py36'''					,	)


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       List[str]		):





												'''simple docstring'''





												TrainingJobAnalytics(UpperCamelCase__		).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""		)



						@parameterized.expand([(2,)]		)
						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       Optional[int]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							self.create_estimator(UpperCamelCase__		)

												# run training
												estimator.fit()

												# result dataframe
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							TrainingJobAnalytics(estimator.latest_training_job.name		).dataframe()

												# extract kpis
												SCREAMING_SNAKE_CASE							:					List[str]           =							list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']		)
												SCREAMING_SNAKE_CASE							:					Dict           =							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							:					List[Any]           =							(
												    Session().describe_training_job(estimator.latest_training_job.name		).get('''TrainingTimeInSeconds'''					,	99_9999		)
												)

												# 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}					,	UpperCamelCase__		)





