






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': 650, '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': 600, 'eval_accuracy': 0.3, 'eval_loss': 0.9},
        },
    ]					)
class  lowerCAmelCase_       (				unittest.TestCase					):




							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     :   Any ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=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     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   Optional[int]	):
											TrainingJobAnalytics(SCREAMING_SNAKE_CASE_	).export_csv(f'{self.env.test_path}/{job_name}_metrics.csv'	)




							def 						__snake_case					(   self     :   str	):
											# create estimator
											lowerCAmelCase__      			=						self.create_estimator()

											# 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''' ,							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_torch_available


_UpperCAmelCase			:		int   				=    {
    "configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
    "processing_git": ["GitProcessor"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Dict   				=    [
			    "GIT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "GitForCausalLM",
			    "GitModel",
			    "GitPreTrainedModel",
			    "GitVisionModel",
			]

if TYPE_CHECKING:
			from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
			from .processing_git import GitProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_git import (
						    GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GitForCausalLM,
						    GitModel,
						    GitPreTrainedModel,
						    GitVisionModel,
						)

else:
			import sys

			_UpperCAmelCase			:		Optional[int]   				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





