"""simple docstring"""




import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
__lowerCAmelCase    :							str													=						logging.getLogger()

__lowerCAmelCase    :							Tuple													=						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)

class       a_      (     __UpperCamelCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]  ,						snake_case__						:     Any     ):
							os.makedirs(snake_case__  ,						exist_ok=snake_case__     )
							lowerCAmelCase__        =   {"""source""": """What is love ?""", """target""": """life"""}
							lowerCAmelCase__        =   {"""train""": 12, """val""": 2, """test""": 2}
							for split in ["train", "test", "val"]:
									for field in ["source", "target"]:
											lowerCAmelCase__        =   """\n""".join([contents[field]] * n_lines[split]     )
											with open(os.path.join(snake_case__  ,						F"""{split}.{field}"""     )  ,						"""w"""     ) as f:
													f.write(snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     int  ,						snake_case__						:     str = "pytorch"     ):
							lowerCAmelCase__        =   self.get_auto_remove_tmp_dir()
							lowerCAmelCase__        =   os.path.join(snake_case__  ,						"""output"""     )
							lowerCAmelCase__        =   os.path.join(snake_case__  ,						"""data"""     )
							self._create_dummy_data(data_dir=snake_case__     )

							lowerCAmelCase__        =   F"""
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            """.split()

							if gpus > 0:
									testargs.append(F"""--gpus={gpus}"""     )
									if is_apex_available():
											testargs.append("""--fp16"""     )
							else:
									testargs.append("""--gpus=0"""     )
									testargs.append("""--distributed_backend=ddp_cpu"""     )
									testargs.append("""--num_processes=2"""     )

							lowerCAmelCase__        =   [sys.executable, str(Path(finetune_rag.__file__     ).resolve()     )] + testargs
							execute_subprocess_async(snake_case__  ,						env=self.get_env()     )

							lowerCAmelCase__        =   os.path.join(snake_case__  ,						"""metrics.json"""     )
							with open(snake_case__     ) as f:
									lowerCAmelCase__        =   json.load(snake_case__     )
							return result




					@require_torch_gpu
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   self._run_finetune(gpus=1     )
							self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]  ,						0.2     )




					@require_torch_multi_gpu
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__        =   self._run_finetune(gpus=2     )
							self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]  ,						0.2     )




					@require_torch_gpu
					@require_ray
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							lowerCAmelCase__        =   self._run_finetune(gpus=1  ,						distributed_retriever="""ray"""     )
							self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]  ,						0.2     )




					@require_torch_multi_gpu
					@require_ray
					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							lowerCAmelCase__        =   self._run_finetune(gpus=1  ,						distributed_retriever="""ray"""     )
							self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]  ,						0.2     )





"""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__     )




