



import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import 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.g4dn.xlarge""",
            """results""": {"""train_runtime""": 6_5_0, """eval_accuracy""": 0.6, """eval_loss""": 0.9},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 0.9},
        },
    ]      )
class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					def 			snake_case					(							self :     Union[str, 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=SCREAMING_SNAKE_CASE				,       )
									assert hasattr(self				,       "env"					)







					def 			snake_case					(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     str=1					):
									# 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}-single"""				,       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}				,       metric_definitions=self.env.metric_definitions				,       py_version="py36"				,       )







					def 			snake_case					(							self :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     str					):
									TrainingJobAnalytics(SCREAMING_SNAKE_CASE					).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""					)







					def 			snake_case					(							self :     str					):
									# create estimator
									lowercase__					:   Optional[int]													=					self.create_estimator()

									# run training
									estimator.fit()

									# result dataframe
									lowercase__					:   Union[str, Any]													=					TrainingJobAnalytics(estimator.latest_training_job.name					).dataframe()

									# extract kpis
									lowercase__					:   str													=					list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]					)
									lowercase__					:   Tuple													=					list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]					)
									# get train time from SageMaker job, this includes starting, preprocessing, stopping
									lowercase__					:   Any													=					(
									    Session().describe_training_job(estimator.latest_training_job.name					).get("TrainingTimeInSeconds"				,       999_999					)
									)

									# 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					)









from typing import TYPE_CHECKING

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


lowerCAmelCase__					  =  {
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  ['''DebertaTokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  [
				    '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''DebertaForMaskedLM''',
				    '''DebertaForQuestionAnswering''',
				    '''DebertaForSequenceClassification''',
				    '''DebertaForTokenClassification''',
				    '''DebertaModel''',
				    '''DebertaPreTrainedModel''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  [
				    '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFDebertaForMaskedLM''',
				    '''TFDebertaForQuestionAnswering''',
				    '''TFDebertaForSequenceClassification''',
				    '''TFDebertaForTokenClassification''',
				    '''TFDebertaModel''',
				    '''TFDebertaPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
				from .tokenization_deberta import DebertaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_deberta_fast import DebertaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deberta import (
								    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DebertaForMaskedLM,
								    DebertaForQuestionAnswering,
								    DebertaForSequenceClassification,
								    DebertaForTokenClassification,
								    DebertaModel,
								    DebertaPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_deberta import (
								    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFDebertaForMaskedLM,
								    TFDebertaForQuestionAnswering,
								    TFDebertaForSequenceClassification,
								    TFDebertaForTokenClassification,
								    TFDebertaModel,
								    TFDebertaPreTrainedModel,
								)


else:
				import sys

				lowerCAmelCase__					  =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




