


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        lowerCamelCase__	(							datasets.BuilderConfig      ):






			"""simple docstring"""






			_UpperCamelCase		:					Optional[datasets.Features]    =		None







class        lowerCamelCase__	(							datasets.ArrowBasedBuilder      ):






			"""simple docstring"""






			_UpperCamelCase		:					Optional[Any]    =		PandasConfig




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return datasets.DatasetInfo(features=self.config.features     )




			def 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


						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 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


						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 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


						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


__UpperCamelCase    		=       logging.get_logger(__name__)

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







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					int    =		'data2vec-text'




			def __init__(							self      ,						snake_case=30522      ,						snake_case=768      ,						snake_case=12      ,						snake_case=12      ,						snake_case=3072      ,						snake_case="gelu"      ,						snake_case=0.1      ,						snake_case=0.1      ,						snake_case=512      ,						snake_case=2      ,						snake_case=0.02      ,						snake_case=1E-1_2      ,						snake_case=1      ,						snake_case=0      ,						snake_case=2      ,						snake_case="absolute"      ,						snake_case=True      ,						snake_case=None      ,						**snake_case      ,						):

						'''simple docstring'''


						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        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						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),
						    ]     )


