"""simple docstring"""




from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class       a_      (     _snake_case			):
					UpperCamelCase_ :	Optional[int]			=    ["image_processor", "tokenizer"]
					UpperCamelCase_ :	List[str]			=    "AutoImageProcessor"
					UpperCamelCase_ :	Optional[int]			=    "AutoTokenizer"




					def __init__(	self						:     Tuple  ,						snake_case__						:     Tuple  ,						snake_case__						:     str     ):
							super().__init__(lowerCAmelCase__  ,						lowerCAmelCase__     )
							lowerCAmelCase__        =   self.image_processor




					def __call__(	self						:     List[Any]  ,						snake_case__						:     int=None  ,						snake_case__						:     Any=None  ,						snake_case__						:     Optional[Any]=None  ,						**snake_case__						:     int     ):

							if text is None and images is None:
									raise ValueError("""You have to specify either text or images. Both cannot be none."""     )

							if text is not None:
									lowerCAmelCase__        =   self.tokenizer(lowerCAmelCase__  ,						return_tensors=lowerCAmelCase__  ,						**lowerCAmelCase__     )

							if images is not None:
									lowerCAmelCase__        =   self.image_processor(lowerCAmelCase__  ,						return_tensors=lowerCAmelCase__  ,						**lowerCAmelCase__     )

							if text is not None and images is not None:
									lowerCAmelCase__        =   image_features.pixel_values
									return encoding
							elif text is not None:
									return encoding
							else:
									return BatchEncoding(data=dict(**lowerCAmelCase__     )  ,						tensor_type=lowerCAmelCase__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						*snake_case__						:     int  ,						**snake_case__						:     List[str]     ):
							return self.tokenizer.batch_decode(*lowerCAmelCase__  ,						**lowerCAmelCase__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						*snake_case__						:     Tuple  ,						**snake_case__						:     Optional[Any]     ):
							return self.tokenizer.decode(*lowerCAmelCase__  ,						**lowerCAmelCase__     )




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							return ["input_ids", "attention_mask", "pixel_values"]





"""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": 1600, "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": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]			)
class       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     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=snake_case__  ,						)
							assert hasattr(self  ,						"""env"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     Optional[Any]     ):
							# configuration for running training on smdistributed Model Parallel
							lowerCAmelCase__        =   {
							    """enabled""": True,
							    """processes_per_host""": 8,
							}
							lowerCAmelCase__        =   {
							    """enabled""": True,
							    """parameters""": {
							        """microbatches""": 4,
							        """placement_strategy""": """spread""",
							        """pipeline""": """interleaved""",
							        """optimize""": """speed""",
							        """partitions""": 4,
							        """ddp""": True,
							    },
							}

							lowerCAmelCase__        =   {"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

							lowerCAmelCase__        =   """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=snake_case__  ,						instance_type=self.instance_type  ,						debugger_hook_config=snake_case__  ,						hyperparameters={
							        **self.env.hyperparameters,
							        """model_name_or_path""": self.model_name_or_path,
							        """max_steps""": 500,
							    }  ,						metric_definitions=self.env.metric_definitions  ,						distribution=snake_case__  ,						py_version="""py36"""  ,						)




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     str     ):
							TrainingJobAnalytics(snake_case__     ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""     )




					@parameterized.expand([(1,)]     )
					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						snake_case__						:     List[str]     ):
							# create estimator
							lowerCAmelCase__        =   self.create_estimator(snake_case__     )

							# run training
							estimator.fit()

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

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




