



"""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_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 				A_      (					unittest.TestCase    ):



						"""simple docstring"""






						def      UpperCAmelCase__					( self			:List[Any] )    ->      List[Any]:
												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      UpperCAmelCase__					( self			:Union[str, Any]       ,  lowercase_			:Dict )    ->      Any:
												# configuration for running training on smdistributed Model Parallel
												UpperCAmelCase					=			{
												    'enabled': True,
												    'processes_per_host': 8,
												}
												UpperCAmelCase					=			{
												    'enabled': True,
												    'parameters': {
												        'microbatches': 4,
												        'placement_strategy': 'spread',
												        'pipeline': 'interleaved',
												        'optimize': 'speed',
												        'partitions': 4,
												        'ddp': True,
												    },
												}

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

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






						def      UpperCAmelCase__					( self			:Tuple       ,  lowercase_			:Tuple )    ->      Union[str, Any]:
												TrainingJobAnalytics(lowercase_ ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv""" )






						@parameterized.expand([(1,)] )
						def      UpperCAmelCase__					( self			:Tuple       ,  lowercase_			:Optional[Any] )    ->      int:
												# create estimator
												UpperCAmelCase					=			self.create_estimator(lowercase_ )

												# 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'       ,  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}       ,  lowercase_ )










"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_    					=	{
    """configuration_rembert""": ["""REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RemBertConfig""", """RemBertOnnxConfig"""]
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	["""RemBertTokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	["""RemBertTokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	[
				    """REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """RemBertForCausalLM""",
				    """RemBertForMaskedLM""",
				    """RemBertForMultipleChoice""",
				    """RemBertForQuestionAnswering""",
				    """RemBertForSequenceClassification""",
				    """RemBertForTokenClassification""",
				    """RemBertLayer""",
				    """RemBertModel""",
				    """RemBertPreTrainedModel""",
				    """load_tf_weights_in_rembert""",
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	[
				    """TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFRemBertForCausalLM""",
				    """TFRemBertForMaskedLM""",
				    """TFRemBertForMultipleChoice""",
				    """TFRemBertForQuestionAnswering""",
				    """TFRemBertForSequenceClassification""",
				    """TFRemBertForTokenClassification""",
				    """TFRemBertLayer""",
				    """TFRemBertModel""",
				    """TFRemBertPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_rembert import RemBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_rembert_fast import RemBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_rembert import (
								    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RemBertForCausalLM,
								    RemBertForMaskedLM,
								    RemBertForMultipleChoice,
								    RemBertForQuestionAnswering,
								    RemBertForSequenceClassification,
								    RemBertForTokenClassification,
								    RemBertLayer,
								    RemBertModel,
								    RemBertPreTrainedModel,
								    load_tf_weights_in_rembert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_rembert import (
								    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRemBertForCausalLM,
								    TFRemBertForMaskedLM,
								    TFRemBertForMultipleChoice,
								    TFRemBertForQuestionAnswering,
								    TFRemBertForSequenceClassification,
								    TFRemBertForTokenClassification,
								    TFRemBertLayer,
								    TFRemBertModel,
								    TFRemBertPreTrainedModel,
								)


else:
				import sys

				snake_case_    					=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





