
'''simple docstring'''

from __future__ import annotations

from collections.abc import Sequence
from typing import Literal







def 	A  (   UpperCamelCase_       :  str    ,						UpperCamelCase_       :  str   )       ->							str | Literal[False]:







			'''simple docstring'''



			lowerCAmelCase__										=				list(UpperCamelCase_   )
			lowerCAmelCase__										=				list(UpperCamelCase_   )
			lowerCAmelCase__										=				0
			for i in range(len(UpperCamelCase_   )   ):
						if lista[i] != lista[i]:
									count += 1
									lowerCAmelCase__										=				"_"
			if count > 1:
						return False
			else:
						return "".join(UpperCamelCase_   )







def 	A  (   UpperCamelCase_       :  list[str]   )       ->							list[str]:







			'''simple docstring'''



			lowerCAmelCase__										=				[]
			while True:
						lowerCAmelCase__										=				["$"] * len(UpperCamelCase_   )
						lowerCAmelCase__										=				[]
						for i in range(len(UpperCamelCase_   )   ):
									for j in range(i + 1    ,						len(UpperCamelCase_   )   ):
												lowerCAmelCase__										=				compare_string(binary[i]    ,						binary[j]   )
												if k is False:
															lowerCAmelCase__										=				"*"
															lowerCAmelCase__										=				"*"
															temp.append("X"   )
						for i in range(len(UpperCamelCase_   )   ):
									if checka[i] == "$":
												pi.append(binary[i]   )
						if len(UpperCamelCase_   ) == 0:
									return pi
						lowerCAmelCase__										=				list(set(UpperCamelCase_   )   )







def 	A  (   UpperCamelCase_       :  int    ,						UpperCamelCase_       :  Sequence[float]   )       ->							list[str]:







			'''simple docstring'''



			lowerCAmelCase__										=				[]
			for minterm in minterms:
						lowerCAmelCase__										=				""
						for _ in range(UpperCamelCase_   ):
									lowerCAmelCase__										=				str(minterm % 2   ) + string
									minterm //= 2
						temp.append(UpperCamelCase_   )
			return temp







def 	A  (   UpperCamelCase_       :  str    ,						UpperCamelCase_       :  str    ,						UpperCamelCase_       :  int   )       ->							bool:







			'''simple docstring'''



			lowerCAmelCase__										=				list(UpperCamelCase_   )
			lowerCAmelCase__										=				list(UpperCamelCase_   )
			lowerCAmelCase__										=				0
			for i in range(len(UpperCamelCase_   )   ):
						if lista[i] != lista[i]:
									count_n += 1
			return count_n == count







def 	A  (   UpperCamelCase_       :  list[list[int]]    ,						UpperCamelCase_       :  list[str]   )       ->							list[str]:







			'''simple docstring'''



			lowerCAmelCase__										=				[]
			lowerCAmelCase__										=				[0] * len(UpperCamelCase_   )
			for i in range(len(chart[0]   )   ):
						lowerCAmelCase__										=				0
						lowerCAmelCase__										=				-1
						for j in range(len(UpperCamelCase_   )   ):
									if chart[j][i] == 1:
												count += 1
												lowerCAmelCase__										=				j
						if count == 1:
									lowerCAmelCase__										=				1
			for i in range(len(UpperCamelCase_   )   ):
						if select[i] == 1:
									for j in range(len(chart[0]   )   ):
												if chart[i][j] == 1:
															for k in range(len(UpperCamelCase_   )   ):
																		lowerCAmelCase__										=				0
									temp.append(prime_implicants[i]   )
			while True:
						lowerCAmelCase__										=				0
						lowerCAmelCase__										=				-1
						lowerCAmelCase__										=				0
						for i in range(len(UpperCamelCase_   )   ):
									lowerCAmelCase__										=				chart[i].count(1   )
									if count_n > max_n:
												lowerCAmelCase__										=				count_n
												lowerCAmelCase__										=				i

						if max_n == 0:
									return temp

						temp.append(prime_implicants[rem]   )

						for i in range(len(chart[0]   )   ):
									if chart[rem][i] == 1:
												for j in range(len(UpperCamelCase_   )   ):
															lowerCAmelCase__										=				0







def 	A  (   UpperCamelCase_       :  list[str]    ,						UpperCamelCase_       :  list[str]   )       ->							list[list[int]]:







			'''simple docstring'''



			lowerCAmelCase__										=				[[0 for x in range(len(UpperCamelCase_   )   )] for x in range(len(UpperCamelCase_   )   )]
			for i in range(len(UpperCamelCase_   )   ):
						lowerCAmelCase__										=				prime_implicants[i].count("_"   )
						for j in range(len(UpperCamelCase_   )   ):
									if is_for_table(prime_implicants[i]    ,						binary[j]    ,						UpperCamelCase_   ):
												lowerCAmelCase__										=				1

			return chart







def 	A  (   )       ->							None:







			'''simple docstring'''



			lowerCAmelCase__										=				int(input("Enter the no. of variables\n"   )   )
			lowerCAmelCase__										=				[
			    float(UpperCamelCase_   )
			    for x in input(
			        "Enter the decimal representation of Minterms 'Spaces Separated'\n"   ).split()
			]
			lowerCAmelCase__										=				decimal_to_binary(UpperCamelCase_    ,						UpperCamelCase_   )

			lowerCAmelCase__										=				check(UpperCamelCase_   )
			print("Prime Implicants are:"   )
			print(UpperCamelCase_   )
			lowerCAmelCase__										=				prime_implicant_chart(UpperCamelCase_    ,						UpperCamelCase_   )

			lowerCAmelCase__										=				selection(UpperCamelCase_    ,						UpperCamelCase_   )
			print("Essential Prime Implicants are:"   )
			print(UpperCamelCase_   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()












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""": 650, """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""": 600, """eval_accuracy""": 0.3, """eval_loss""": 0.9},
        },
    ]	)
class       __A							(   unittest.TestCase	):




      """simple docstring"""







      def 					__snake_case      (		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 					__snake_case      (		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 					__snake_case      (		self   ,				a__):



             """simple docstring"""





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



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Dict						=		self.create_estimator()

             # run training
             estimator.fit()

             # result dataframe
             _lowerCamelCase				:  Tuple						=		TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

             # extract kpis
             _lowerCamelCase				:  Tuple						=		list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value'''])
             _lowerCamelCase				:  Dict						=		list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value'''])
             # get train time from SageMaker job, this includes starting, preprocessing, stopping
             _lowerCamelCase				:  Optional[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}   ,				a__)

