

"""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": 16_00, "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": 16_00, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]						)
class 	__UpperCamelCase    (    unittest.TestCase						):

  def __lowerCamelCase		(		self      ):
      '''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    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    {
          'enabled': True,
          'processes_per_host': 8,
      }
      _lowerCAmelCase       :   Dict  =    {
          'enabled': True,
          'parameters': {
              'microbatches': 4,
              'placement_strategy': 'spread',
              'pipeline': 'interleaved',
              'optimize': 'speed',
              'partitions': 4,
              'ddp': True,
          },
      }

      _lowerCAmelCase       :   List[str]  =    {'smdistributed': {'modelparallel': smp_options}, 'mpi': mpi_options}

      _lowerCAmelCase       :   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': 500,
          }    ,metric_definitions=self.env.metric_definitions    ,distribution=_A    ,py_version='py36'    ,)

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      TrainingJobAnalytics(_A      ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""      )







  @parameterized.expand([(1,)]      )
  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    self.create_estimator(_A      )

      # run training
      estimator.fit()

      # result dataframe
      _lowerCAmelCase       :   Union[str, Any]  =    TrainingJobAnalytics(estimator.latest_training_job.name      ).dataframe()

      # extract kpis
      _lowerCAmelCase       :   str  =    list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']      )
      _lowerCAmelCase       :   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
      _lowerCAmelCase       :   List[str]  =    (
          Session().describe_training_job(estimator.latest_training_job.name      ).get('TrainingTimeInSeconds'    ,99_9999      )
      )

      # 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"""






# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
 import jax
 import jaxlib

_lowerCAmelCase						=		get_logger()

_lowerCAmelCase						=		None







class 	__UpperCamelCase    (    TensorFormatter[Mapping, "jax.Array", Mapping]						):

  def __init__(		self    ,_A=None    ,_A=None    ,**_A      ):
      '''simple docstring'''

      super().__init__(features=_A      )
      import jax
      from jaxlib.xla_client import Device

      if isinstance(_A    ,_A      ):
          raise ValueError(
              F"""Expected {device} to be a `str` not {type(_A      )}, as `jaxlib.xla_extension.Device` """
              'is not serializable neither with `pickle` nor with `dill`. Instead you can surround '
              'the device with `str()` to get its string identifier that will be internally mapped '
              'to the actual `jaxlib.xla_extension.Device`.'      )
      _lowerCAmelCase       :   int  =    device if isinstance(_A    ,_A      ) else str(jax.devices()[0]      )
      # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
      # with `pickle` nor with `dill`, so we need to use a global variable instead
      global DEVICE_MAPPING
      if DEVICE_MAPPING is None:
          _lowerCAmelCase       :   Any  =    self._map_devices_to_str()
      if self.device not in list(DEVICE_MAPPING.keys()      ):
          logger.warning(
              F"""Device with string identifier {self.device} not listed among the available """
              F"""devices: {list(DEVICE_MAPPING.keys()      )}, so falling back to the default """
              F"""device: {str(jax.devices()[0]      )}."""      )
          _lowerCAmelCase       :   List[str]  =    str(jax.devices()[0]      )
      _lowerCAmelCase       :   int  =    jnp_array_kwargs

  @staticmethod
  def __lowerCamelCase		(		):
      '''simple docstring'''

      import jax

      return {str(_A      ): device for device in jax.devices()}

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      import jax
      import jax.numpy as jnp

      if isinstance(_A    ,_A      ) and column:
          if all(
              isinstance(_A    ,jax.Array      ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column      ):
              return jnp.stack(_A    ,axis=0      )
      return column

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      import jax
      import jax.numpy as jnp

      if isinstance(_A    ,(str, bytes, type(_A      ))      ):
          return value
      elif isinstance(_A    ,(np.character, np.ndarray)      ) and np.issubdtype(value.dtype    ,np.character      ):
          return value.tolist()

      _lowerCAmelCase       :   Optional[Any]  =    {}

      if isinstance(_A    ,(np.number, np.ndarray)      ) and np.issubdtype(value.dtype    ,np.integer      ):
          # the default int precision depends on the jax config
          # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
          if jax.config.jax_enable_xaa:
              _lowerCAmelCase       :   List[str]  =    {'dtype': jnp.intaa}
          else:
              _lowerCAmelCase       :   Tuple  =    {'dtype': jnp.intaa}
      elif isinstance(_A    ,(np.number, np.ndarray)      ) and np.issubdtype(value.dtype    ,np.floating      ):
          _lowerCAmelCase       :   Any  =    {'dtype': jnp.floataa}
      elif config.PIL_AVAILABLE and "PIL" in sys.modules:
          import PIL.Image

          if isinstance(_A    ,PIL.Image.Image      ):
              _lowerCAmelCase       :   int  =    np.asarray(_A      )

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
      global DEVICE_MAPPING
      if DEVICE_MAPPING is None:
          _lowerCAmelCase       :   Optional[Any]  =    self._map_devices_to_str()

      with jax.default_device(DEVICE_MAPPING[self.device]      ):
          # calling jnp.array on a np.ndarray does copy the data
          # see https://github.com/google/jax/issues/4486
          return jnp.array(_A    ,**{**default_dtype, **self.jnp_array_kwargs}      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      import jax

      # support for torch, tf, jax etc.
      if config.TORCH_AVAILABLE and "torch" in sys.modules:
          import torch

          if isinstance(_A    ,torch.Tensor      ):
              return self._tensorize(data_struct.detach().cpu().numpy()[()]      )
      if hasattr(_A    ,'__array__'      ) and not isinstance(_A    ,jax.Array      ):
          _lowerCAmelCase       :   Optional[Any]  =    data_struct.__array__()
      # support for nested types like struct of list of struct
      if isinstance(_A    ,np.ndarray      ):
          if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
              return self._consolidate([self.recursive_tensorize(_A      ) for substruct in data_struct]      )
      elif isinstance(_A    ,(list, tuple)      ):
          return self._consolidate([self.recursive_tensorize(_A      ) for substruct in data_struct]      )
      return self._tensorize(_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      return map_nested(self._recursive_tensorize    ,_A    ,map_list=_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    self.numpy_arrow_extractor().extract_row(_A      )
      _lowerCAmelCase       :   int  =    self.python_features_decoder.decode_row(_A      )
      return self.recursive_tensorize(_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Dict  =    self.numpy_arrow_extractor().extract_column(_A      )
      _lowerCAmelCase       :   List[Any]  =    self.python_features_decoder.decode_column(_A    ,pa_table.column_names[0]      )
      _lowerCAmelCase       :   Optional[Any]  =    self.recursive_tensorize(_A      )
      _lowerCAmelCase       :   Optional[Any]  =    self._consolidate(_A      )
      return column







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    self.numpy_arrow_extractor().extract_batch(_A      )
      _lowerCAmelCase       :   Any  =    self.python_features_decoder.decode_batch(_A      )
      _lowerCAmelCase       :   str  =    self.recursive_tensorize(_A      )
      for column_name in batch:
          _lowerCAmelCase       :   Optional[Any]  =    self._consolidate(batch[column_name]      )
      return batch





