





import copy
import re



class 							_UpperCamelCase					:
							_UpperCamelCase				:					Any         =   '''hp'''
							_UpperCamelCase				:					Optional[int]         =   {}
							_UpperCamelCase				:					Union[str, Any]         =   None

							@classmethod
							def lowercase (		cls: Optional[int]					,   _SCREAMING_SNAKE_CASE: List[Any]					,   _SCREAMING_SNAKE_CASE: List[Any]		)       -> List[Any]:




														"""simple docstring"""


														UpperCamelCase_          =  prefix
														UpperCamelCase_          =  defaults
														cls.build_naming_info()

							@staticmethod
							def lowercase (		_SCREAMING_SNAKE_CASE: str					,   _SCREAMING_SNAKE_CASE: List[str]		)       -> Union[str, Any]:




														"""simple docstring"""


														if len(lowerCamelCase_		) == 0:
																					return ""
														UpperCamelCase_          =  None
														if any(char.isdigit() for char in word		):
																					raise Exception(f'''Parameters should not contain numbers: \'{word}\' contains a number'''		)
														if word in info["short_word"]:
																					return info["short_word"][word]
														for prefix_len in range(1					,   len(lowerCamelCase_		) + 1		):
																					UpperCamelCase_          =  word[:prefix_len]
																					if prefix in info["reverse_short_word"]:
																												continue
																					else:
																												UpperCamelCase_          =  prefix
																												break

														if short_word is None:
																					# Paranoid fallback
																					def int_to_alphabetic(_SCREAMING_SNAKE_CASE: str		):
																												UpperCamelCase_          =  """"""
																												while integer != 0:
																																			UpperCamelCase_          =  chr(ord("A"		) + integer % 10		) + s
																																			integer //= 10
																												return s

																					UpperCamelCase_          =  0
																					while True:
																												UpperCamelCase_          =  word + """#""" + int_to_alphabetic(lowerCamelCase_		)
																												if sword in info["reverse_short_word"]:
																																			continue
																												else:
																																			UpperCamelCase_          =  sword
																																			break

														UpperCamelCase_          =  short_word
														UpperCamelCase_          =  word
														return short_word

							@staticmethod
							def lowercase (		_SCREAMING_SNAKE_CASE: int					,   _SCREAMING_SNAKE_CASE: str		)       -> Optional[int]:




														"""simple docstring"""


														UpperCamelCase_          =  param_name.split("_"		)

														UpperCamelCase_          =  [TrialShortNamer.shortname_for_word(lowerCamelCase_					,   lowerCamelCase_		) for word in words]

														# We try to create a separatorless short name, but if there is a collision we have to fallback
														# to a separated short name
														UpperCamelCase_          =  ["""""", """_"""]

														for separator in separators:
																					UpperCamelCase_          =  separator.join(lowerCamelCase_		)
																					if shortname not in info["reverse_short_param"]:
																												UpperCamelCase_          =  shortname
																												UpperCamelCase_          =  param_name
																												return shortname

														return param_name

							@staticmethod
							def lowercase (		_SCREAMING_SNAKE_CASE: List[str]					,   _SCREAMING_SNAKE_CASE: Union[str, Any]		)       -> int:




														"""simple docstring"""


														UpperCamelCase_          =  TrialShortNamer.shortname_for_key(lowerCamelCase_					,   lowerCamelCase_		)
														UpperCamelCase_          =  short_name
														UpperCamelCase_          =  param_name

							@classmethod
							def lowercase (		cls: Optional[int]		)       -> Optional[Any]:




														"""simple docstring"""


														if cls.NAMING_INFO is not None:
																					return

														UpperCamelCase_          =  {
														    """short_word""": {},
														    """reverse_short_word""": {},
														    """short_param""": {},
														    """reverse_short_param""": {},
														}

														UpperCamelCase_          =  list(cls.DEFAULTS.keys()		)

														for k in field_keys:
																					cls.add_new_param_name(lowerCamelCase_					,   lowerCamelCase_		)

														UpperCamelCase_          =  info

							@classmethod
							def lowercase (		cls: Dict					,   _SCREAMING_SNAKE_CASE: int		)       -> List[Any]:




														"""simple docstring"""


														cls.build_naming_info()
														assert cls.PREFIX is not None
														UpperCamelCase_          =  [copy.copy(cls.PREFIX		)]

														for k, v in params.items():
																					if k not in cls.DEFAULTS:
																												raise Exception(f'''You should provide a default value for the param name {k} with value {v}'''		)
																					if v == cls.DEFAULTS[k]:
																												# The default value is not added to the name
																												continue

																					UpperCamelCase_          =  cls.NAMING_INFO["""short_param"""][k]

																					if isinstance(lowerCamelCase_					,   lowerCamelCase_		):
																												UpperCamelCase_          =  1 if v else 0

																					UpperCamelCase_          =  """""" if isinstance(lowerCamelCase_					,   (int, float)		) else """-"""
																					UpperCamelCase_          =  f'''{key}{sep}{v}'''
																					name.append(lowerCamelCase_		)

														return "_".join(lowerCamelCase_		)



							@classmethod
							def lowercase (		cls: Optional[int]					,   _SCREAMING_SNAKE_CASE: Any		)       -> List[str]:




														"""simple docstring"""


														UpperCamelCase_          =  repr[len(cls.PREFIX		) + 1 :]
														if repr == "":
																					UpperCamelCase_          =  []
														else:
																					UpperCamelCase_          =  repr.split("_"		)

														UpperCamelCase_          =  {}

														for value in values:
																					if "-" in value:
																												UpperCamelCase_          =  value.split("-"		)
																					else:
																												UpperCamelCase_          =  re.sub("[0-9.]"					,   ""					,   lowerCamelCase_		)
																												UpperCamelCase_          =  float(re.sub("[^0-9.]"					,   ""					,   lowerCamelCase_		)		)

																					UpperCamelCase_          =  cls.NAMING_INFO["""reverse_short_param"""][p_k]

																					UpperCamelCase_          =  p_v

														for k in cls.DEFAULTS:
																					if k not in parameters:
																												UpperCamelCase_          =  cls.DEFAULTS[k]

														return parameters








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''': 6_5_0, '''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''': 6_0_0, '''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''': 6_0_0, '''eval_accuracy''': 0.6, '''eval_loss''': 0.7},
        },
    ]     )
class 							_UpperCamelCase					(	unittest.TestCase     ):

       def lowercase (		self: Union[str, Any]		)       -> List[str]:




              """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=_SCREAMING_SNAKE_CASE					,   )
              assert hasattr(self					,   "env"		)

       def lowercase (		self: List[str]					,   _SCREAMING_SNAKE_CASE: Dict		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  f'''{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'''
              # distributed data settings
              UpperCamelCase_          =  {"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=_SCREAMING_SNAKE_CASE					,   instance_count=_SCREAMING_SNAKE_CASE					,   instance_type=self.instance_type					,   debugger_hook_config=_SCREAMING_SNAKE_CASE					,   hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}					,   metric_definitions=self.env.metric_definitions					,   distribution=_SCREAMING_SNAKE_CASE					,   py_version="py36"					,   )

       def lowercase (		self: List[str]					,   _SCREAMING_SNAKE_CASE: Any		)       -> List[Any]:




              """simple docstring"""


              TrainingJobAnalytics(_SCREAMING_SNAKE_CASE		).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''		)



       @parameterized.expand([(2,)]		)
       def lowercase (		self: str					,   _SCREAMING_SNAKE_CASE: Tuple		)       -> Optional[Any]:




              """simple docstring"""


              UpperCamelCase_          =  self.create_estimator(_SCREAMING_SNAKE_CASE		)

              # run training
              estimator.fit()

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

              # extract kpis
              UpperCamelCase_          =  list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]		)
              UpperCamelCase_          =  list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]		)
              # get train time from SageMaker job, this includes starting, preprocessing, stopping
              UpperCamelCase_          =  (
                  Session().describe_training_job(estimator.latest_training_job.name		).get("TrainingTimeInSeconds"					,   999999		)
              )

              # 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		)

