
"""simple docstring"""



import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


UpperCAmelCase__          = datasets.utils.logging.get_logger(__name__)

@dataclass
class       lowerCAmelCase__				(						datasets.BuilderConfig	):
     __a						=					10000
     __a						=					None
     __a						=					None






class       lowerCAmelCase__				(						datasets.ArrowBasedBuilder	):
     __a						=					ParquetConfig







     def      lowercase	(							self			:			int			):
       return datasets.DatasetInfo(features=self.config.features			)







     def      lowercase	(							self			:			Any    ,      _lowerCamelCase			:			Union[str, Any]			):
       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}'''			)
       _snake_case					     =       dl_manager.download_and_extract(self.config.data_files			)
       if isinstance(_lowerCamelCase    ,      (str, list, tuple)			):
         _snake_case					     =       data_files
         if isinstance(_lowerCamelCase    ,      _lowerCamelCase			):
           _snake_case					     =       [files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         _snake_case					     =       [dl_manager.iter_files(_lowerCamelCase			) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN    ,      gen_kwargs={'''files''': files}			)]
       _snake_case					     =       []
       for split_name, files in data_files.items():
         if isinstance(_lowerCamelCase    ,      _lowerCamelCase			):
           _snake_case					     =       [files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         _snake_case					     =       [dl_manager.iter_files(_lowerCamelCase			) for file in files]
         # Infer features is they are stoed in the arrow schema
         if self.info.features is None:
           for file in itertools.chain.from_iterable(_lowerCamelCase			):
             with open(_lowerCamelCase    ,      '''rb'''			) as f:
               _snake_case					     =       datasets.Features.from_arrow_schema(pq.read_schema(_lowerCamelCase			)			)
             break
         splits.append(datasets.SplitGenerator(name=_lowerCamelCase    ,      gen_kwargs={'''files''': files}			)			)
       return splits







     def      lowercase	(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			pa.Table			):
       if self.info.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
         _snake_case					     =       table_cast(_lowerCamelCase    ,      self.info.features.arrow_schema			)
       return pa_table







     def      lowercase	(							self			:			Dict    ,      _lowerCamelCase			:			Tuple			):
       _snake_case					     =       self.info.features.arrow_schema if self.info.features is not None else None
       if self.info.features is not None and self.config.columns is not None:
         if sorted(field.name for field in schema			) != sorted(self.config.columns			):
           raise ValueError(
               f'''Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\''''			)
       for file_idx, file in enumerate(itertools.chain.from_iterable(_lowerCamelCase			)			):
         with open(_lowerCamelCase    ,      '''rb'''			) as f:
           _snake_case					     =       pq.ParquetFile(_lowerCamelCase			)
           try:
             for batch_idx, record_batch in enumerate(
                 parquet_file.iter_batches(batch_size=self.config.batch_size    ,      columns=self.config.columns			)			):
               _snake_case					     =       pa.Table.from_batches([record_batch]			)
               # Uncomment for debugging (will print the Arrow table size and elements)
               # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
               # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
               yield f'''{file_idx}_{batch_idx}''', self._cast_table(_lowerCamelCase			)
           except ValueError as e:
             logger.error(f'''Failed to read file \'{file}\' with error {type(_lowerCamelCase			)}: {e}'''			)
             raise





"""simple docstring"""



import logging

from transformers.configuration_utils import PretrainedConfig


UpperCAmelCase__          = logging.getLogger(__name__)

class       lowerCAmelCase__				(						A_	):
     __a						=					"""masked_bert"""







     def __init__(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			Any=30522    ,      _lowerCamelCase			:			Union[str, Any]=768    ,      _lowerCamelCase			:			Tuple=12    ,      _lowerCamelCase			:			Any=12    ,      _lowerCamelCase			:			str=3072    ,      _lowerCamelCase			:			str="gelu"    ,      _lowerCamelCase			:			int=0.1    ,      _lowerCamelCase			:			Optional[int]=0.1    ,      _lowerCamelCase			:			Dict=512    ,      _lowerCamelCase			:			List[Any]=2    ,      _lowerCamelCase			:			int=0.0_2    ,      _lowerCamelCase			:			Union[str, Any]=1e-12    ,      _lowerCamelCase			:			Union[str, Any]=0    ,      _lowerCamelCase			:			List[str]="topK"    ,      _lowerCamelCase			:			Optional[Any]="constant"    ,      _lowerCamelCase			:			Optional[Any]=0.0    ,      **_lowerCamelCase			:			str    ,      ):
       super().__init__(pad_token_id=_lowerCamelCase    ,      **_lowerCamelCase			)

       _snake_case					     =       vocab_size
       _snake_case					     =       hidden_size
       _snake_case					     =       num_hidden_layers
       _snake_case					     =       num_attention_heads
       _snake_case					     =       hidden_act
       _snake_case					     =       intermediate_size
       _snake_case					     =       hidden_dropout_prob
       _snake_case					     =       attention_probs_dropout_prob
       _snake_case					     =       max_position_embeddings
       _snake_case					     =       type_vocab_size
       _snake_case					     =       initializer_range
       _snake_case					     =       layer_norm_eps
       _snake_case					     =       pruning_method
       _snake_case					     =       mask_init
       _snake_case					     =       mask_scale



