




import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast



@dataclass
class       a					(       datasets.BuilderConfig      ):

     '''simple docstring'''





     lowerCAmelCase    :	Optional[datasets.Features]       		=  None



class       a					(       datasets.ArrowBasedBuilder      ):

     '''simple docstring'''





     lowerCAmelCase    :	Optional[int]       		=  PandasConfig
     def   lowerCamelCase_       (     self       :      int    ):
         return datasets.DatasetInfo(features=self.config.features    )
     def   lowerCamelCase_       (     self       :      List[str] ,   __snake_case       :      List[str]    ):
         if not self.config.data_files:
             raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}'    )
         UpperCAmelCase_										=					dl_manager.download_and_extract(self.config.data_files    )
         if isinstance(__snake_case ,   (str, list, tuple)    ):
             UpperCAmelCase_										=					data_files
             if isinstance(__snake_case ,   __snake_case    ):
                 UpperCAmelCase_										=					[files]
             # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
             UpperCAmelCase_										=					[dl_manager.iter_files(__snake_case    ) for file in files]
             return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,   gen_kwargs={'''files''': files}    )]
         UpperCAmelCase_										=					[]
         for split_name, files in data_files.items():
             if isinstance(__snake_case ,   __snake_case    ):
                 UpperCAmelCase_										=					[files]
             # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
             UpperCAmelCase_										=					[dl_manager.iter_files(__snake_case    ) for file in files]
             splits.append(datasets.SplitGenerator(name=__snake_case ,   gen_kwargs={'''files''': files}    )    )
         return splits
     def   lowerCamelCase_       (     self       :      Dict ,   __snake_case       :      pa.Table    ):
         if self.config.features is not None:
             # more expensive cast to support nested features with keys in a different order
             # allows str <-> int/float or str to Audio for example
             UpperCAmelCase_										=					table_cast(__snake_case ,   self.config.features.arrow_schema    )
         return pa_table




     def   lowerCamelCase_       (     self       :      Union[str, Any] ,   __snake_case       :      List[Any]    ):
         for i, file in enumerate(itertools.chain.from_iterable(__snake_case    )    ):
             with open(__snake_case ,   '''rb'''    ) as f:
                 UpperCAmelCase_										=					pa.Table.from_pandas(pd.read_pickle(__snake_case    )    )
                 yield i, self._cast_table(__snake_case    )











from collections import OrderedDict
from typing import Mapping

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


_lowerCamelCase      			=						logging.get_logger(__name__)

_lowerCamelCase      			=						{
    'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}



class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	Optional[Any]       		=  'data2vec-text'
     def __init__(     self       :      Optional[Any] ,   __snake_case       :      Optional[int]=3_05_22 ,   __snake_case       :      List[str]=7_68 ,   __snake_case       :      Tuple=12 ,   __snake_case       :      int=12 ,   __snake_case       :      Union[str, Any]=30_72 ,   __snake_case       :      List[Any]="gelu" ,   __snake_case       :      Any=0.1 ,   __snake_case       :      Union[str, Any]=0.1 ,   __snake_case       :      Tuple=5_12 ,   __snake_case       :      str=2 ,   __snake_case       :      str=0.02 ,   __snake_case       :      List[Any]=1E-12 ,   __snake_case       :      Any=1 ,   __snake_case       :      List[Any]=0 ,   __snake_case       :      Dict=2 ,   __snake_case       :      Any="absolute" ,   __snake_case       :      Union[str, Any]=True ,   __snake_case       :      Any=None ,   **__snake_case       :      List[Any] ,   ):
         super().__init__(pad_token_id=__snake_case ,   bos_token_id=__snake_case ,   eos_token_id=__snake_case ,   **__snake_case    )

         UpperCAmelCase_										=					vocab_size
         UpperCAmelCase_										=					hidden_size
         UpperCAmelCase_										=					num_hidden_layers
         UpperCAmelCase_										=					num_attention_heads
         UpperCAmelCase_										=					hidden_act
         UpperCAmelCase_										=					intermediate_size
         UpperCAmelCase_										=					hidden_dropout_prob
         UpperCAmelCase_										=					attention_probs_dropout_prob
         UpperCAmelCase_										=					max_position_embeddings
         UpperCAmelCase_										=					type_vocab_size
         UpperCAmelCase_										=					initializer_range
         UpperCAmelCase_										=					layer_norm_eps
         UpperCAmelCase_										=					position_embedding_type
         UpperCAmelCase_										=					use_cache
         UpperCAmelCase_										=					classifier_dropout



class       a					(       _A      ):

     '''simple docstring'''





     @property
     def   lowerCamelCase_       (     self       :      str    ):
         if self.task == "multiple-choice":
             UpperCAmelCase_										=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
             UpperCAmelCase_										=					{0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
             ]    )





