





"""simple docstring"""
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 					_UpperCAmelCase   (	unittest.TestCase ):


					def 	lowerCamelCase       (					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=__UpperCamelCase     ,       )
												assert hasattr(self     ,       "env"		)


					def 	lowerCamelCase       (					self			:Any     ,       __UpperCamelCase			:Any=1		):
												# 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}-single"     ,       instance_count=__UpperCamelCase     ,       instance_type=self.instance_type     ,       debugger_hook_config=__UpperCamelCase     ,       hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}     ,       metric_definitions=self.env.metric_definitions     ,       py_version="py36"     ,       )


					def 	lowerCamelCase       (					self			:Union[str, Any]     ,       __UpperCamelCase			:Optional[Any]		):
												TrainingJobAnalytics(__UpperCamelCase		).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv"		)


					def 	lowerCamelCase       (					self			:Optional[int]		):
												# create estimator
												A		=  self.create_estimator()

												# run training
												estimator.fit()

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

												# extract kpis
												A		=  list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]		)
												A		=  list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]		)
												# get train time from SageMaker job, this includes starting, preprocessing, stopping
												A		=  (
												    Session().describe_training_job(estimator.latest_training_job.name		).get("TrainingTimeInSeconds"     ,       99_99_99		)
												)

												# 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		)









"""simple docstring"""
import math
import sys






def 					A__				(			UpperCamelCase       ):
							A		=  ""
							try:
														with open(UpperCamelCase					,  "rb"       ) as binary_file:
																					A		=  binary_file.read()
														for dat in data:
																					A		=  F"{dat:08b}"
																					result += curr_byte
														return result
							except OSError:
														print("File not accessible"       )
														sys.exit()






def 					A__				(			UpperCamelCase       ):
							A		=  {"0": "0", "1": "1"}
							A,	A		=  "", ""
							A		=  len(UpperCamelCase       )

							for i in range(len(UpperCamelCase       )       ):
														curr_string += data_bits[i]
														if curr_string not in lexicon:
																					continue

														A		=  lexicon[curr_string]
														result += last_match_id
														A		=  last_match_id + "0"

														if math.loga(UpperCamelCase       ).is_integer():
																					A		=  {}
																					for curr_key in list(UpperCamelCase       ):
																												A		=  lexicon.pop(UpperCamelCase       )
																					A		=  new_lex

														A		=  last_match_id + "1"
														index += 1
														A		=  ""
							return result






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  8
							try:
														with open(UpperCamelCase					,  "wb"       ) as opened_file:
																					A		=  [
																					    to_write[i : i + byte_length]
																					    for i in range(0					,  len(UpperCamelCase       )					,  UpperCamelCase       )
																					]

																					if len(result_byte_array[-1]       ) % byte_length == 0:
																												result_byte_array.append("10000000"       )
																					else:
																												result_byte_array[-1] += "1" + "0" * (
																												    byte_length - len(result_byte_array[-1]       ) - 1
																												)

																					for elem in result_byte_array[:-1]:
																												opened_file.write(int(UpperCamelCase					,  2       ).to_bytes(1					,  byteorder="big"       )       )
							except OSError:
														print("File not accessible"       )
														sys.exit()






def 					A__				(			UpperCamelCase       ):
							A		=  0
							for letter in data_bits:
														if letter == "1":
																					break
														counter += 1

							A		=  data_bits[counter:]
							A		=  data_bits[counter + 1 :]
							return data_bits






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  read_file_binary(UpperCamelCase       )
							A		=  remove_prefix(UpperCamelCase       )
							A		=  decompress_data(UpperCamelCase       )
							write_file_binary(UpperCamelCase					,  UpperCamelCase       )


if __name__ == "__main__":
		compress(sys.argv[1], sys.argv[2])


