


"""simple docstring"""







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
     from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


lowerCAmelCase__				    =       logging.get_logger(__name__)

lowerCAmelCase__				    =       {
    '''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json''',
    '''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json''',
    '''microsoft/deberta-v2-xlarge-mnli''': (
        '''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'''
    ),
    '''microsoft/deberta-v2-xxlarge-mnli''': (
        '''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'''
    ),
}


class 				SCREAMING_SNAKE_CASE__    (		__SCREAMING_SNAKE_CASE    ):
       """simple docstring"""


       a  :    Any       ="deberta-v2"

       def __init__(						self		,					snake_case__=128_100		,					snake_case__=1_536		,					snake_case__=24		,					snake_case__=24		,					snake_case__=6_144		,					snake_case__="gelu"		,					snake_case__=0.1		,					snake_case__=0.1		,					snake_case__=512		,					snake_case__=0		,					snake_case__=0.02		,					snake_case__=1e-7		,					snake_case__=False		,					snake_case__=-1		,					snake_case__=0		,					snake_case__=True		,					snake_case__=None		,					snake_case__=0		,					snake_case__="gelu"		,					**snake_case__		,					):






         """simple docstring"""





         super().__init__(**UpperCamelCase__						)

         lowerCAmelCase					:					List[Any]								=							hidden_size
         lowerCAmelCase					:					List[Any]								=							num_hidden_layers
         lowerCAmelCase					:					Dict								=							num_attention_heads
         lowerCAmelCase					:					Optional[Any]								=							intermediate_size
         lowerCAmelCase					:					str								=							hidden_act
         lowerCAmelCase					:					List[str]								=							hidden_dropout_prob
         lowerCAmelCase					:					Optional[Any]								=							attention_probs_dropout_prob
         lowerCAmelCase					:					Any								=							max_position_embeddings
         lowerCAmelCase					:					List[str]								=							type_vocab_size
         lowerCAmelCase					:					Union[str, Any]								=							initializer_range
         lowerCAmelCase					:					str								=							relative_attention
         lowerCAmelCase					:					Optional[int]								=							max_relative_positions
         lowerCAmelCase					:					List[str]								=							pad_token_id
         lowerCAmelCase					:					Dict								=							position_biased_input

         # Backwards compatibility
         if type(UpperCamelCase__						) == str:
           lowerCAmelCase					:					int								=							[x.strip() for x in pos_att_type.lower().split("|"						)]

         lowerCAmelCase					:					Dict								=							pos_att_type
         lowerCAmelCase					:					Optional[int]								=							vocab_size
         lowerCAmelCase					:					Optional[int]								=							layer_norm_eps

         lowerCAmelCase					:					Union[str, Any]								=							kwargs.get("pooler_hidden_size"		,					UpperCamelCase__						)
         lowerCAmelCase					:					Union[str, Any]								=							pooler_dropout
         lowerCAmelCase					:					Optional[Any]								=							pooler_hidden_act


class 				SCREAMING_SNAKE_CASE__    (		__SCREAMING_SNAKE_CASE    ):
       """simple docstring"""


       @property
       def 	lowercase__							(						self						):






         """simple docstring"""





         if self.task == "multiple-choice":
           lowerCAmelCase					:					int								=							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
           lowerCAmelCase					:					str								=							{0: '''batch''', 1: '''sequence'''}
         if self._config.type_vocab_size > 0:
           return OrderedDict(
               [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)]						)
         else:
           return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)]						)

       @property
       def 	lowercase__							(						self						):






         """simple docstring"""





         return 12







       def 	lowercase__							(						self		,					snake_case__		,					snake_case__ = -1		,					snake_case__ = -1		,					snake_case__ = -1		,					snake_case__ = False		,					snake_case__ = None		,					snake_case__ = 3		,					snake_case__ = 40		,					snake_case__ = 40		,					snake_case__ = None		,					):






         """simple docstring"""





         lowerCAmelCase					:					int								=							super().generate_dummy_inputs(preprocessor=UpperCamelCase__		,					framework=UpperCamelCase__						)
         if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
           del dummy_inputs["token_type_ids"]
         return dummy_inputs









"""simple docstring"""







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__      (    SCREAMING_SNAKE_CASE   :   str    ):  # picklable for multiprocessing


  '''simple docstring'''







  return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def  a__      (    ):


  '''simple docstring'''







  with parallel_backend("spark"    ):
    assert ParallelBackendConfig.backend_name == "spark"

  lowerCAmelCase					:					List[str]								=							[1, 2, 3]
  with pytest.raises(SCREAMING_SNAKE_CASE    ):
    with parallel_backend("unsupported backend"    ):
      map_nested(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				num_proc=2    )

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





@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("num_proc"    ,				[2, -1]    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Tuple    ):


  '''simple docstring'''







  lowerCAmelCase					:					Tuple								=							[1, 2]
  lowerCAmelCase					:					int								=							{"a": 1, "b": 2}
  lowerCAmelCase					:					List[str]								=							{"a": [1, 2], "b": [3, 4]}
  lowerCAmelCase					:					Dict								=							{"a": {"1": 1}, "b": 2}
  lowerCAmelCase					:					Tuple								=							{"a": 1, "b": 2, "c": 3, "d": 4}
  lowerCAmelCase					:					Any								=							[2, 3]
  lowerCAmelCase					:					Any								=							{"a": 2, "b": 3}
  lowerCAmelCase					:					Optional[int]								=							{"a": [2, 3], "b": [4, 5]}
  lowerCAmelCase					:					Optional[int]								=							{"a": {"1": 2}, "b": 3}
  lowerCAmelCase					:					str								=							{"a": 2, "b": 3, "c": 4, "d": 5}

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





