


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 snake_case__			(    unittest.TestCase					):




       def     UpperCAmelCase__    ( self				:    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=snake_case__    ,				)
              assert hasattr(self    ,				'env'			)




       def     UpperCAmelCase__    ( self				:    Optional[int]    ,				_lowerCamelCase				:    Tuple			):
              snake_case__ :       str	   =							{
                  "enabled": True,
                  "processes_per_host": 8,
              }
              snake_case__ :       int	   =							{
                  "enabled": True,
                  "parameters": {
                      "microbatches": 4,
                      "placement_strategy": "spread",
                      "pipeline": "interleaved",
                      "optimize": "speed",
                      "partitions": 4,
                      "ddp": True,
                  },
              }

              snake_case__ :       int	   =							{"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

              snake_case__ :       Tuple	   =							"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': 5_0_0,
                  }    ,				metric_definitions=self.env.metric_definitions    ,				distribution=snake_case__    ,				py_version='py36'    ,				)




       def     UpperCAmelCase__    ( self				:    int    ,				_lowerCamelCase				:    str			):
              TrainingJobAnalytics(snake_case__			).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''			)




       @parameterized.expand([(1,)]			)
       def     UpperCAmelCase__    ( self				:    Dict    ,				_lowerCamelCase				:    List[str]			):
              snake_case__ :       Tuple	   =							self.create_estimator(snake_case__			)

              # run training
              estimator.fit()

              # result dataframe
              snake_case__ :       str	   =							TrainingJobAnalytics(estimator.latest_training_job.name			).dataframe()

              # extract kpis
              snake_case__ :       Optional[Any]	   =							list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']			)
              snake_case__ :       Optional[int]	   =							list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']			)
              # get train time from SageMaker job, this includes starting, preprocessing, stopping
              snake_case__ :       Optional[Any]	   =							(
                  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}    ,				snake_case__			)













"""simple docstring"""



from typing import TYPE_CHECKING

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


_lowerCAmelCase	:	Tuple				=      {
    """configuration_lxmert""": ["""LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LxmertConfig"""],
    """tokenization_lxmert""": ["""LxmertTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase	:	int				=      ["""LxmertTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase	:	Tuple				=      [
     """LxmertEncoder""",
     """LxmertForPreTraining""",
     """LxmertForQuestionAnswering""",
     """LxmertModel""",
     """LxmertPreTrainedModel""",
     """LxmertVisualFeatureEncoder""",
     """LxmertXLayer""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase	:	Optional[int]				=      [
     """TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFLxmertForPreTraining""",
     """TFLxmertMainLayer""",
     """TFLxmertModel""",
     """TFLxmertPreTrainedModel""",
     """TFLxmertVisualFeatureEncoder""",
 ]


if TYPE_CHECKING:
 from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
 from .tokenization_lxmert import LxmertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_lxmert_fast import LxmertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_lxmert import (
      LxmertEncoder,
      LxmertForPreTraining,
      LxmertForQuestionAnswering,
      LxmertModel,
      LxmertPreTrainedModel,
      LxmertVisualFeatureEncoder,
      LxmertXLayer,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_lxmert import (
      TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLxmertForPreTraining,
      TFLxmertMainLayer,
      TFLxmertModel,
      TFLxmertPreTrainedModel,
      TFLxmertVisualFeatureEncoder,
  )

else:
 import sys

 _lowerCAmelCase	:	Any				=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


