



"""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": 1_6_0_0, "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": 1_6_0_0, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]				)
class 			snake_case_						(						unittest.TestCase				):






							def snake_case_  (   self      ):
									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=a_			,     )
									assert hasattr(self			,     "env"      )






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

									a_     :      Optional[Any] 						=     {"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

									a_     :      Dict 						=     "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=a_			,     instance_type=self.instance_type			,     debugger_hook_config=a_			,     hyperparameters={
									        **self.env.hyperparameters,
									        "model_name_or_path": self.model_name_or_path,
									        "max_steps": 5_0_0,
									    }			,     metric_definitions=self.env.metric_definitions			,     distribution=a_			,     py_version="py36"			,     )






							def snake_case_  (   self			,     a_      ):
									TrainingJobAnalytics(a_      ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""      )






							@parameterized.expand([(1,)]      )
							def snake_case_  (   self			,     a_      ):
									# create estimator
									a_     :      Optional[Any] 						=     self.create_estimator(a_      )

									# run training
									estimator.fit()

									# result dataframe
									a_     :      Tuple 						=     TrainingJobAnalytics(estimator.latest_training_job.name      ).dataframe()

									# extract kpis
									a_     :      int 						=     list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]      )
									a_     :      Tuple 						=     list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]      )
									# get train time from SageMaker job, this includes starting, preprocessing, stopping
									a_     :      Dict 						=     (
									    Session().describe_training_job(estimator.latest_training_job.name      ).get("TrainingTimeInSeconds"			,     9_9_9_9_9_9      )
									)

									# 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}			,     a_      )




"""simple docstring"""
from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_				= {
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

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

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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= [
					    """LlamaForCausalLM""",
					    """LlamaModel""",
					    """LlamaPreTrainedModel""",
					    """LlamaForSequenceClassification""",
					]


if TYPE_CHECKING:
					from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_llama import LlamaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_llama_fast import LlamaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
					import sys

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