


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 					_lowerCamelCase (	self				:   List[str]	):





       '''simple docstring'''




       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 					_lowerCamelCase (	self				:   Optional[Any]       ,   __snake_case				:   Dict	):





       '''simple docstring'''
       UpperCAmelCase_							:      str						=   f'''{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}'''
       # distributed data settings
       UpperCAmelCase_							:      Dict						=   {"""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 					_lowerCamelCase (	self				:   Tuple       ,   __snake_case				:   Tuple	):





       '''simple docstring'''
       TrainingJobAnalytics(_a	).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''	)





    @parameterized.expand([(2,)]	)
    def 					_lowerCamelCase (	self				:   Any       ,   __snake_case				:   str	):





       '''simple docstring'''
       UpperCAmelCase_							:      List[Any]						=   self.create_estimator(_a	)

       # run training
       estimator.fit()

       # result dataframe
       UpperCAmelCase_							:      Optional[int]						=   TrainingJobAnalytics(estimator.latest_training_job.name	).dataframe()

       # extract kpis
       UpperCAmelCase_							:      Optional[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	)



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase				:  str      					=							{
    'configuration_x_clip': [
        'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XCLIPConfig',
        'XCLIPTextConfig',
        'XCLIPVisionConfig',
    ],
    'processing_x_clip': ['XCLIPProcessor'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase				:  Any      					=							[
           'XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
           'XCLIPModel',
           'XCLIPPreTrainedModel',
           'XCLIPTextModel',
           'XCLIPVisionModel',
       ]

if TYPE_CHECKING:
       from .configuration_x_clip import (
           XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
           XCLIPConfig,
           XCLIPTextConfig,
           XCLIPVisionConfig,
       )
       from .processing_x_clip import XCLIPProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_x_clip import (
                  XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XCLIPModel,
                  XCLIPPreTrainedModel,
                  XCLIPTextModel,
                  XCLIPVisionModel,
              )

else:
       import sys

       __UpperCamelCase				:  List[str]      					=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)