




'''simple docstring'''



import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text
def 			SCREAMING_SNAKE_CASE_			(							__A    :			List[str]  )			->			Union[str, Any]:
   _SCREAMING_SNAKE_CASE	=				[]
   for line in lines:
      _SCREAMING_SNAKE_CASE	=				re.sub(R"#.*"	,					""	,					_a  )  # remove comments
      if line:
         filtered_lines.append(_a  )
   _SCREAMING_SNAKE_CASE	=				"""\n""".join(_a  )

   # Make a hash from all this code
   _SCREAMING_SNAKE_CASE	=				full_str.encode("utf-8"  )
   return shaaaa(_a  ).hexdigest()


# get importable module names and hash for caching
lowerCamelCase_			=						{
    'csv': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    'json': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    'pandas': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    'parquet': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    'arrow': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    'text': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    'imagefolder': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    'audiofolder': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
lowerCamelCase_			=						{
    '.csv': ('csv', {}),
    '.tsv': ('csv', {'sep': '\t'}),
    '.json': ('json', {}),
    '.jsonl': ('json', {}),
    '.parquet': ('parquet', {}),
    '.arrow': ('arrow', {}),
    '.txt': ('text', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
lowerCamelCase_			=						{'imagefolder', 'audiofolder'}

# Used to filter data files based on extensions given a module name
lowerCamelCase_			=						{}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
      _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append('.zip')






_MODULE_TO_EXTENSIONS["audiofolder"].append('.zip')







'''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 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						'''deberta-v2'''



   def __init__(   self :     str  ,				__lowerCamelCase :     Union[str, Any]=1_2_8_1_0_0  ,				__lowerCamelCase :     Optional[int]=1_5_3_6  ,				__lowerCamelCase :     Optional[int]=2_4  ,				__lowerCamelCase :     Optional[int]=2_4  ,				__lowerCamelCase :     Tuple=6_1_4_4  ,				__lowerCamelCase :     List[str]="gelu"  ,				__lowerCamelCase :     int=0.1  ,				__lowerCamelCase :     Optional[Any]=0.1  ,				__lowerCamelCase :     Union[str, Any]=5_1_2  ,				__lowerCamelCase :     Optional[Any]=0  ,				__lowerCamelCase :     str=0.0_2  ,				__lowerCamelCase :     int=1e-7  ,				__lowerCamelCase :     Any=False  ,				__lowerCamelCase :     Any=-1  ,				__lowerCamelCase :     Tuple=0  ,				__lowerCamelCase :     str=True  ,				__lowerCamelCase :     List[Any]=None  ,				__lowerCamelCase :     Optional[int]=0  ,				__lowerCamelCase :     Any="gelu"  ,				**__lowerCamelCase :     Union[str, Any]  ,				):

      """simple docstring"""
      super().__init__(**__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				hidden_size
      _SCREAMING_SNAKE_CASE	=				num_hidden_layers
      _SCREAMING_SNAKE_CASE	=				num_attention_heads
      _SCREAMING_SNAKE_CASE	=				intermediate_size
      _SCREAMING_SNAKE_CASE	=				hidden_act
      _SCREAMING_SNAKE_CASE	=				hidden_dropout_prob
      _SCREAMING_SNAKE_CASE	=				attention_probs_dropout_prob
      _SCREAMING_SNAKE_CASE	=				max_position_embeddings
      _SCREAMING_SNAKE_CASE	=				type_vocab_size
      _SCREAMING_SNAKE_CASE	=				initializer_range
      _SCREAMING_SNAKE_CASE	=				relative_attention
      _SCREAMING_SNAKE_CASE	=				max_relative_positions
      _SCREAMING_SNAKE_CASE	=				pad_token_id
      _SCREAMING_SNAKE_CASE	=				position_biased_input

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

      _SCREAMING_SNAKE_CASE	=				pos_att_type
      _SCREAMING_SNAKE_CASE	=				vocab_size
      _SCREAMING_SNAKE_CASE	=				layer_norm_eps

      _SCREAMING_SNAKE_CASE	=				kwargs.get("pooler_hidden_size"  ,				__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				pooler_dropout
      _SCREAMING_SNAKE_CASE	=				pooler_hidden_act







class 		lowercase_   (			A       ):


   """simple docstring"""

   @property
   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      if self.task == "multiple-choice":
         _SCREAMING_SNAKE_CASE	=				{0: "batch", 1: "choice", 2: "sequence"}
      else:
         _SCREAMING_SNAKE_CASE	=				{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 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      return 1_2



   def 							lowerCAmelCase_      (   self :     List[str]  ,				__lowerCamelCase :     Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]  ,				__lowerCamelCase :     int = -1  ,				__lowerCamelCase :     int = -1  ,				__lowerCamelCase :     int = -1  ,				__lowerCamelCase :     bool = False  ,				__lowerCamelCase :     Optional["TensorType"] = None  ,				__lowerCamelCase :     int = 3  ,				__lowerCamelCase :     int = 4_0  ,				__lowerCamelCase :     int = 4_0  ,				__lowerCamelCase :     "PreTrainedTokenizerBase" = None  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				super().generate_dummy_inputs(preprocessor=__lowerCamelCase  ,				framework=__lowerCamelCase   )
      if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
         del dummy_inputs["token_type_ids"]
      return dummy_inputs

