
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': 1_600, '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': 1_600, 'eval_accuracy': 0.3, 'eval_loss': 1.2},
        },
    ]							)
class  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	def 						_lowercase  (       self  :			str       )       ->     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=SCREAMING_SNAKE_CASE_       ,							)
					assert hasattr(self       ,							'''env'''       )
	def 						_lowercase  (       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       )       ->     Optional[int]:
					# configuration for running training on smdistributed Model Parallel
					lowercase_       =					{
					    '''enabled''': True,
					    '''processes_per_host''': 8,
					}
					lowercase_       =					{
					    '''enabled''': True,
					    '''parameters''': {
					        '''microbatches''': 4,
					        '''placement_strategy''': '''spread''',
					        '''pipeline''': '''interleaved''',
					        '''optimize''': '''speed''',
					        '''partitions''': 4,
					        '''ddp''': True,
					    },
					}

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

					lowercase_       =					'''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=SCREAMING_SNAKE_CASE_       ,							instance_type=self.instance_type       ,							debugger_hook_config=SCREAMING_SNAKE_CASE_       ,							hyperparameters={
					        **self.env.hyperparameters,
					        '''model_name_or_path''': self.model_name_or_path,
					        '''max_steps''': 5_0_0,
					    }       ,							metric_definitions=self.env.metric_definitions       ,							distribution=SCREAMING_SNAKE_CASE_       ,							py_version='''py36'''       ,							)
	def 						_lowercase  (       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			Tuple       )       ->     List[str]:
					TrainingJobAnalytics(SCREAMING_SNAKE_CASE_       ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''       )




	@parameterized.expand([(1,)]       )
	def 						_lowercase  (       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			Any       )       ->     Any:
					# create estimator
					lowercase_       =					self.create_estimator(SCREAMING_SNAKE_CASE_       )

					# run training
					estimator.fit()

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

					# extract kpis
					lowercase_       =					list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']       )
					lowercase_       =					list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']       )
					# get train time from SageMaker job, this includes starting, preprocessing, stopping
					lowercase_       =					(
					    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}       ,							SCREAMING_SNAKE_CASE_       )






import argparse
import os
import re


__a  			=					'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__a  			=					re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
__a  			=					re.compile(r'\s*\(\s*"(\S[^"]+)"')




def 				a						(							snake_case__:					str						,   snake_case__:					bool = False					):


				'''simple docstring'''
				with open(snake_case__						,   '''r'''						,   encoding='''utf-8'''					) as f:
								lowercase_       =					f.read()

				lowercase_       =					content.split('''\n'''					)
				lowercase_       =					[]
				lowercase_       =					0
				while line_idx < len(snake_case__					):
								if _re_intro_mapping.search(lines[line_idx]					) is not None:
												lowercase_       =					len(re.search(r'''^(\s*)\S'''						,   lines[line_idx]					).groups()[0]					) + 8
												# Start of a new mapping!
												while not lines[line_idx].startswith(''' ''' * indent + '''('''					):
																new_lines.append(lines[line_idx]					)
																line_idx += 1

												lowercase_       =					[]
												while lines[line_idx].strip() != "]":
																# Blocks either fit in one line or not
																if lines[line_idx].strip() == "(":
																				lowercase_       =					line_idx
																				while not lines[line_idx].startswith(''' ''' * indent + ''')'''					):
																								line_idx += 1
																				blocks.append('''\n'''.join(lines[start_idx : line_idx + 1]					)					)
																else:
																				blocks.append(lines[line_idx]					)
																line_idx += 1

												# Sort blocks by their identifiers
												lowercase_       =					sorted(snake_case__						,   key=lambda snake_case__					: _re_identifier.search(snake_case__					).groups()[0]					)
												new_lines += blocks
								else:
												new_lines.append(lines[line_idx]					)
												line_idx += 1

				if overwrite:
								with open(snake_case__						,   '''w'''						,   encoding='''utf-8'''					) as f:
												f.write('''\n'''.join(snake_case__					)					)
				elif "\n".join(snake_case__					) != content:
								return True




def 				a						(							snake_case__:					bool = False					):


				'''simple docstring'''
				lowercase_       =					[os.path.join(snake_case__						,   snake_case__					) for f in os.listdir(snake_case__					) if f.endswith('''.py'''					)]
				lowercase_       =					[sort_auto_mapping(snake_case__						,   overwrite=snake_case__					) for fname in fnames]

				if not overwrite and any(snake_case__					):
								lowercase_       =					[f for f, d in zip(snake_case__						,   snake_case__					) if d]
								raise ValueError(
								    F'''The following files have auto mappings that need sorting: {', '.join(snake_case__					)}. Run `make style` to fix'''
								    ''' this.'''					)


if __name__ == "__main__":
						__a  			=					argparse.ArgumentParser()
						parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
						__a  			=					parser.parse_args()

						sort_all_auto_mappings(not args.check_only)




