



"""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_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      ):

			"""simple docstring"""


			def   snake_case__	(      self		)      ->	Optional[int]:
				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						,	SCREAMING_SNAKE_CASE__		)      ->	int:
				A__											=					f"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
				# distributed data settings
				A__											=					{"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   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	Union[str, Any]:
				TrainingJobAnalytics(SCREAMING_SNAKE_CASE__		).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""		)







			@parameterized.expand([(2,)]		)
			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	Optional[Any]:
				# create estimator
				A__											=					self.create_estimator(SCREAMING_SNAKE_CASE__		)

				# run training
				estimator.fit()

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

				# extract kpis
				A__											=					list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]		)
				A__											=					list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]		)
				# get train time from SageMaker job, this includes starting, preprocessing, stopping
				A__											=					(
				    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__		)









"""simple docstring"""







from typing import TYPE_CHECKING

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


UpperCamelCase     =							{
    """configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PerceiverConfig""", """PerceiverOnnxConfig"""],
    """tokenization_perceiver""": ["""PerceiverTokenizer"""],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase     =							["""PerceiverFeatureExtractor"""]
			UpperCamelCase     =							["""PerceiverImageProcessor"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase     =							[
			    """PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """PerceiverForImageClassificationConvProcessing""",
			    """PerceiverForImageClassificationFourier""",
			    """PerceiverForImageClassificationLearned""",
			    """PerceiverForMaskedLM""",
			    """PerceiverForMultimodalAutoencoding""",
			    """PerceiverForOpticalFlow""",
			    """PerceiverForSequenceClassification""",
			    """PerceiverLayer""",
			    """PerceiverModel""",
			    """PerceiverPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
			from .tokenization_perceiver import PerceiverTokenizer

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_perceiver import PerceiverFeatureExtractor
						from .image_processing_perceiver import PerceiverImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_perceiver import (
						    PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    PerceiverForImageClassificationConvProcessing,
						    PerceiverForImageClassificationFourier,
						    PerceiverForImageClassificationLearned,
						    PerceiverForMaskedLM,
						    PerceiverForMultimodalAutoencoding,
						    PerceiverForOpticalFlow,
						    PerceiverForSequenceClassification,
						    PerceiverLayer,
						    PerceiverModel,
						    PerceiverPreTrainedModel,
						)

else:
			import sys

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




