import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows


def      a__ (		__UpperCamelCase				):  # picklable for multiprocessing
      return i + 1


@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def      a__ (		):
      with parallel_backend("spark"				):
            assert ParallelBackendConfig.backend_name == "spark"

      SCREAMING_SNAKE_CASE_          =  [1, 2, 3]
      with pytest.raises(__UpperCamelCase				):
            with parallel_backend("unsupported backend"				):
                  map_nested(__UpperCamelCase     ,      __UpperCamelCase     ,      num_proc=2				)

      with pytest.raises(__UpperCamelCase				):
            with parallel_backend("unsupported backend"				):
                  map_nested(__UpperCamelCase     ,      __UpperCamelCase     ,      num_proc=-1				)





@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("num_proc"     ,      [2, -1]				)
def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  [1, 2]
      SCREAMING_SNAKE_CASE_          =  {"a": 1, "b": 2}
      SCREAMING_SNAKE_CASE_          =  {"a": [1, 2], "b": [3, 4]}
      SCREAMING_SNAKE_CASE_          =  {"a": {"1": 1}, "b": 2}
      SCREAMING_SNAKE_CASE_          =  {"a": 1, "b": 2, "c": 3, "d": 4}
      SCREAMING_SNAKE_CASE_          =  [2, 3]
      SCREAMING_SNAKE_CASE_          =  {"a": 2, "b": 3}
      SCREAMING_SNAKE_CASE_          =  {"a": [2, 3], "b": [4, 5]}
      SCREAMING_SNAKE_CASE_          =  {"a": {"1": 2}, "b": 3}
      SCREAMING_SNAKE_CASE_          =  {"a": 2, "b": 3, "c": 4, "d": 5}

      with parallel_backend("spark"				):
            assert map_nested(__UpperCamelCase     ,      __UpperCamelCase     ,      num_proc=__UpperCamelCase				) == expected_map_nested_sa
            assert map_nested(__UpperCamelCase     ,      __UpperCamelCase     ,      num_proc=__UpperCamelCase				) == expected_map_nested_sa
            assert map_nested(__UpperCamelCase     ,      __UpperCamelCase     ,      num_proc=__UpperCamelCase				) == expected_map_nested_sa
            assert map_nested(__UpperCamelCase     ,      __UpperCamelCase     ,      num_proc=__UpperCamelCase				) == expected_map_nested_sa
            assert map_nested(__UpperCamelCase     ,      __UpperCamelCase     ,      num_proc=__UpperCamelCase				) == expected_map_nested_sa



from typing import TYPE_CHECKING

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


A     :  int					     =       {
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  str					     =       ["RobertaTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       [
          "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaForCausalLM",
          "RobertaForMaskedLM",
          "RobertaForMultipleChoice",
          "RobertaForQuestionAnswering",
          "RobertaForSequenceClassification",
          "RobertaForTokenClassification",
          "RobertaModel",
          "RobertaPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Any					     =       [
          "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaForCausalLM",
          "TFRobertaForMaskedLM",
          "TFRobertaForMultipleChoice",
          "TFRobertaForQuestionAnswering",
          "TFRobertaForSequenceClassification",
          "TFRobertaForTokenClassification",
          "TFRobertaMainLayer",
          "TFRobertaModel",
          "TFRobertaPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  int					     =       [
          "FlaxRobertaForCausalLM",
          "FlaxRobertaForMaskedLM",
          "FlaxRobertaForMultipleChoice",
          "FlaxRobertaForQuestionAnswering",
          "FlaxRobertaForSequenceClassification",
          "FlaxRobertaForTokenClassification",
          "FlaxRobertaModel",
          "FlaxRobertaPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

      A     :  Tuple					     =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


