


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': 16_00, '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': 16_00, 'eval_accuracy': 0.3, 'eval_loss': 1.2},
        },
    ]				)
class 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       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 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:Tuple   ):
       # configuration for running training on smdistributed Model Parallel
       snake_case__						= {
           '''enabled''': True,
           '''processes_per_host''': 8,
       }
       snake_case__						= {
           '''enabled''': True,
           '''parameters''': {
               '''microbatches''': 4,
               '''placement_strategy''': '''spread''',
               '''pipeline''': '''interleaved''',
               '''optimize''': '''speed''',
               '''partitions''': 4,
               '''ddp''': True,
           },
       }

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

       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=_a     ,		instance_type=self.instance_type     ,		debugger_hook_config=_a     ,		hyperparameters={
               **self.env.hyperparameters,
               '''model_name_or_path''': self.model_name_or_path,
               '''max_steps''': 5_00,
           }     ,		metric_definitions=self.env.metric_definitions     ,		distribution=_a     ,		py_version='''py36'''     ,		)

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]     ,		_a:int   ):
       TrainingJobAnalytics(_a   ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""   )







     @parameterized.expand([(1,)]   )
     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:Optional[Any]   ):
       # create estimator
       snake_case__						= self.create_estimator(_a   )

       # run training
       estimator.fit()

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

       # extract kpis
       snake_case__						= list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']   )
       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
       snake_case__						= (
           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}     ,		_a   )




"""simple docstring"""





from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase

def     __SCREAMING_SNAKE_CASE    ( A_					):
	lowerCAmelCase__							,lowerCAmelCase__      :		Dict    							=      analyze_text(A_					)
	lowerCAmelCase__      :		Any    							=      list(''' ''' + ascii_lowercase					)
	# what is our total sum of probabilities.
	lowerCAmelCase__      :		Optional[Any]    							=      sum(single_char_strings.values()					)

	# one length string
	lowerCAmelCase__      :		int    							=      0
	# for each alpha we go in our dict and if it is in it we calculate entropy
	for ch in my_alphas:
		if ch in single_char_strings:
			lowerCAmelCase__      :		Union[str, Any]    							=      single_char_strings[ch]
			lowerCAmelCase__      :		Dict    							=      my_str / all_sum
			my_fir_sum += prob * math.loga(A_					)  # entropy formula.

    # print entropy
	print(f'{round(-1 * my_fir_sum					):.1f}'					)

	# two len string
	lowerCAmelCase__      :		List[Any]    							=      sum(two_char_strings.values()					)
	lowerCAmelCase__      :		Optional[int]    							=      0
	# for each alpha (two in size) calculate entropy.
	for cha in my_alphas:
		for cha in my_alphas:
			lowerCAmelCase__      :		int    							=      cha + cha
			if sequence in two_char_strings:
				lowerCAmelCase__      :		Optional[Any]    							=      two_char_strings[sequence]
				lowerCAmelCase__      :		int    							=      int(A_					) / all_sum
				my_sec_sum += prob * math.loga(A_					)

    # print second entropy
	print(f'{round(-1 * my_sec_sum					):.1f}'					)

	# print the difference between them
	print(f'{round((-1 * my_sec_sum) - (-1 * my_fir_sum)					):.1f}'					)

def     __SCREAMING_SNAKE_CASE    ( A_					):
	lowerCAmelCase__      :		List[str]    							=      Counter()  # type: ignore
	lowerCAmelCase__      :		Optional[int]    							=      Counter()  # type: ignore
	single_char_strings[text[-1]] += 1

	# first case when we have space at start.
	two_char_strings[" " + text[0]] += 1
	for i in range(0		,	len(A_					) - 1					):
		single_char_strings[text[i]] += 1
		two_char_strings[text[i : i + 2]] += 1
	return single_char_strings, two_char_strings

def     __SCREAMING_SNAKE_CASE    ( ):
	import doctest

	doctest.testmod()
	# text = (
	#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
	#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
	#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
	#     "beloved evident through no service elderly is. Blind there if every no so "
	#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
	#     "of message cottage windows do besides against uncivil.  Delightful "
	#     "unreserved impossible few estimating men favourable see entreaties. She "
	#     "propriety immediate was improving. He or entrance humoured likewise "
	#     "moderate. Much nor game son say feel. Fat make met can must form into "
	#     "gate. Me we offending prevailed discovery. "
	# )

	# calculate_prob(text)


if __name__ == "__main__":
							main()




