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      lowerCamelCase_					(       unittest.TestCase			):






    '''simple docstring'''






    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        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       UpperCamelCase__       (     self			,			__lowercase)	->     Union[str, Any]:
        __UpperCamelCase					:Tuple        =		f"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
        # distributed data settings
        __UpperCamelCase					:int        =		{'''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=__A			,			instance_count=__A			,			instance_type=self.instance_type			,			debugger_hook_config=__A			,			hyperparameters={**self.env.distributed_hyperparameters, '''model_name_or_path''': self.model_name_or_path}			,			metric_definitions=self.env.metric_definitions			,			distribution=__A			,			py_version='''py36'''			,			)







    def       UpperCamelCase__       (     self			,			__lowercase)	->     List[Any]:
        TrainingJobAnalytics(__A).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv""")







    @parameterized.expand([(2,)])
    def       UpperCamelCase__       (     self			,			__lowercase)	->     Union[str, Any]:
        # create estimator
        __UpperCamelCase					:List[Any]        =		self.create_estimator(__A)

        # run training
        estimator.fit()

        # result dataframe
        __UpperCamelCase					:Any        =		TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

        # extract kpis
        __UpperCamelCase					:Union[str, Any]        =		list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value'''])
        __UpperCamelCase					:List[Any]        =		list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value'''])
        # get train time from SageMaker job, this includes starting, preprocessing, stopping
        __UpperCamelCase					:List[str]        =		(
            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}			,			__A)






import os

import pytest

from transformers.dynamic_module_utils import get_imports


__lowercase										= '''
import os
'''

__lowercase										= '''
def foo():
    import os
    return False
'''

__lowercase										= '''
def foo():
    def bar():
        if True:
            import os
        return False
    return bar()
'''

__lowercase										= '''
import os

try:
    import bar
except ImportError:
    raise ValueError()
'''

__lowercase										= '''
import os

def foo():
    try:
        import bar
    except ImportError:
        raise ValueError()
'''

__lowercase										= '''
import os

try:
    import bar
except (ImportError, AttributeError):
    raise ValueError()
'''

__lowercase										= '''
import os

try:
    import bar
except ImportError as e:
    raise ValueError()
'''

__lowercase										= '''
import os

try:
    import bar
except:
    raise ValueError()
'''

__lowercase										= '''
import os

try:
    import bar
    import baz
except ImportError:
    raise ValueError()
'''

__lowercase										= '''
import os

try:
    import bar
    import baz
except ImportError:
    x = 1
    raise ValueError()
'''

__lowercase										= [
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]



@pytest.mark.parametrize('''case'''  ,      SCREAMING_SNAKE_CASE     )
def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:List[Any]        =		os.path.join(SCREAMING_SNAKE_CASE  ,      '''test_file.py'''     )
    with open(SCREAMING_SNAKE_CASE  ,      '''w'''     ) as _tmp_file:
        _tmp_file.write(SCREAMING_SNAKE_CASE     )

    __UpperCamelCase					:Dict        =		get_imports(SCREAMING_SNAKE_CASE     )
    assert parsed_imports == ["os"]





