




"""simple docstring"""






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_50, """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_00, """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_00, """eval_accuracy""": 0.6, """eval_loss""": 0.7},
        },
    ]		)
class lowerCAmelCase__			(  unittest.TestCase		):






			'''simple docstring'''



			def       A_					(						self						):
							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=lowercase						,   )
							assert hasattr(self						,   'env'						)
			def       A_					(						self						,   lowercase						):
							_lowerCamelCase  :    Union[str, Any]					=					F'''{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}'''
							# distributed data settings
							_lowerCamelCase  :    Optional[Any]					=					{'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=lowercase						,   instance_count=lowercase						,   instance_type=self.instance_type						,   debugger_hook_config=lowercase						,   hyperparameters={**self.env.distributed_hyperparameters, 'model_name_or_path': self.model_name_or_path}						,   metric_definitions=self.env.metric_definitions						,   distribution=lowercase						,   py_version='py36'						,   )
			def       A_					(						self						,   lowercase						):
							TrainingJobAnalytics(lowercase						).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''						)






			@parameterized.expand([(2,)]						)
			def       A_					(						self						,   lowercase						):
							# create estimator
							_lowerCamelCase  :    Union[str, Any]					=					self.create_estimator(lowercase						)

							# run training
							estimator.fit()

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

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





"""simple docstring"""






# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		{}
lowercase__  		=		{}
lowercase__  		=		{}







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__ = None							,					):
				_lowerCamelCase  :    str					=					aliases if aliases is not None else []
				if format_type in _FORMAT_TYPES:
								logger.warning(
								    f'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''	)
				_lowerCamelCase  :    str					=					formatter_cls
				for alias in set(aliases + [format_type]	):
								if alias in _FORMAT_TYPES_ALIASES:
												logger.warning(
												    f'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''	)
								_lowerCamelCase  :    Dict					=					format_type







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__ = None	):
				_lowerCamelCase  :    Dict					=					aliases if aliases is not None else []
				for alias in set(aliases + [format_type]	):
								_lowerCamelCase  :    Optional[Any]					=					unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=["""python"""])
_register_formatter(ArrowFormatter, """arrow""", aliases=["""pa""", """pyarrow"""])
_register_formatter(NumpyFormatter, """numpy""", aliases=["""np"""])
_register_formatter(PandasFormatter, """pandas""", aliases=["""pd"""])
_register_formatter(CustomFormatter, """custom""")

if config.TORCH_AVAILABLE:
			from .torch_formatter import TorchFormatter

			_register_formatter(TorchFormatter, """torch""", aliases=["""pt""", """pytorch"""])
else:
			lowercase__  		=		ValueError("""PyTorch needs to be installed to be able to return PyTorch tensors.""")
			_register_unavailable_formatter(_torch_error, """torch""", aliases=["""pt""", """pytorch"""])

if config.TF_AVAILABLE:
			from .tf_formatter import TFFormatter

			_register_formatter(TFFormatter, """tensorflow""", aliases=["""tf"""])
else:
			lowercase__  		=		ValueError("""Tensorflow needs to be installed to be able to return Tensorflow tensors.""")
			_register_unavailable_formatter(_tf_error, """tensorflow""", aliases=["""tf"""])

if config.JAX_AVAILABLE:
			from .jax_formatter import JaxFormatter

			_register_formatter(JaxFormatter, """jax""", aliases=[])
else:
			lowercase__  		=		ValueError("""JAX needs to be installed to be able to return JAX arrays.""")
			_register_unavailable_formatter(_jax_error, """jax""", aliases=[])







def   _snake_case    (       lowercase__	):
				if format_type in _FORMAT_TYPES_ALIASES:
								return _FORMAT_TYPES_ALIASES[format_type]
				else:
								return format_type







def   _snake_case    (       lowercase__							,					**lowercase__	):
				_lowerCamelCase  :    List[str]					=					get_format_type_from_alias(lowercase__	)
				if format_type in _FORMAT_TYPES:
								return _FORMAT_TYPES[format_type](**lowercase__	)
				if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
								raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
				else:
								raise ValueError(
								    f'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None	)}, but got \'{format_type}\''''	)