



"""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


lowercase__		:    int				   =     datasets.utils.logging.get_logger(__name__)







@dataclass
class 				UpperCamelCase__       (     datasets.BuilderConfig  ):
      _SCREAMING_SNAKE_CASE      =					1_0000
      _SCREAMING_SNAKE_CASE      =					None
      _SCREAMING_SNAKE_CASE      =					None







class 				UpperCamelCase__       (     datasets.ArrowBasedBuilder  ):
      _SCREAMING_SNAKE_CASE      =					ParquetConfig



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict	):
        return datasets.DatasetInfo(features=self.config.features	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]   ,      SCREAMING_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}"	)
        lowerCAmelCase_       :    List[str]								=			dl_manager.download_and_extract(self.config.data_files	)
        if isinstance(SCREAMING_SNAKE_CASE_   ,      (str, list, tuple)	):
          lowerCAmelCase_       :    Optional[int]								=			data_files
          if isinstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	):
            lowerCAmelCase_       :    Dict								=			[files]
          # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
          lowerCAmelCase_       :    List[str]								=			[dl_manager.iter_files(SCREAMING_SNAKE_CASE_	) for file in files]
          return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,      gen_kwargs={'files': files}	)]
        lowerCAmelCase_       :    Optional[int]								=			[]
        for split_name, files in data_files.items():
          if isinstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	):
            lowerCAmelCase_       :    Any								=			[files]
          # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
          lowerCAmelCase_       :    Tuple								=			[dl_manager.iter_files(SCREAMING_SNAKE_CASE_	) 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(SCREAMING_SNAKE_CASE_	):
              with open(SCREAMING_SNAKE_CASE_   ,      'rb'	) as f:
                lowerCAmelCase_       :    List[str]								=			datasets.Features.from_arrow_schema(pq.read_schema(SCREAMING_SNAKE_CASE_	)	)
              break
          splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE_   ,      gen_kwargs={'files': files}	)	)
        return splits



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				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
          lowerCAmelCase_       :    int								=			table_cast(SCREAMING_SNAKE_CASE_   ,      self.info.features.arrow_schema	)
        return pa_table





      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]	):
        lowerCAmelCase_       :    str								=			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(SCREAMING_SNAKE_CASE_	)	):
          with open(SCREAMING_SNAKE_CASE_   ,      'rb'	) as f:
            lowerCAmelCase_       :    Optional[Any]								=			pq.ParquetFile(SCREAMING_SNAKE_CASE_	)
            try:
              for batch_idx, record_batch in enumerate(
                  parquet_file.iter_batches(batch_size=self.config.batch_size   ,      columns=self.config.columns	)	):
                lowerCAmelCase_       :    Optional[int]								=			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(SCREAMING_SNAKE_CASE_	)
            except ValueError as e:
              logger.error(F"Failed to read file '{file}' with error {type(SCREAMING_SNAKE_CASE_	)}: {e}"	)
              raise










"""simple docstring"""







import logging

from transformers.configuration_utils import PretrainedConfig


lowercase__		:    List[Any]				   =     logging.getLogger(__name__)







class 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					"""masked_bert"""



      def __init__(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=3_0_5_2_2   ,      SCREAMING_SNAKE_CASE_    :				int=7_6_8   ,      SCREAMING_SNAKE_CASE_    :				Tuple=1_2   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=1_2   ,      SCREAMING_SNAKE_CASE_    :				Tuple=3_0_7_2   ,      SCREAMING_SNAKE_CASE_    :				List[str]="gelu"   ,      SCREAMING_SNAKE_CASE_    :				Tuple=0.1   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=0.1   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=5_1_2   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=2   ,      SCREAMING_SNAKE_CASE_    :				Dict=0.02   ,      SCREAMING_SNAKE_CASE_    :				Any=1E-12   ,      SCREAMING_SNAKE_CASE_    :				List[str]=0   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]="topK"   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]="constant"   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=0.0   ,      **SCREAMING_SNAKE_CASE_    :				Any   ,      ):
        super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    List[Any]								=			vocab_size
        lowerCAmelCase_       :    str								=			hidden_size
        lowerCAmelCase_       :    Optional[int]								=			num_hidden_layers
        lowerCAmelCase_       :    Dict								=			num_attention_heads
        lowerCAmelCase_       :    List[str]								=			hidden_act
        lowerCAmelCase_       :    List[Any]								=			intermediate_size
        lowerCAmelCase_       :    Any								=			hidden_dropout_prob
        lowerCAmelCase_       :    str								=			attention_probs_dropout_prob
        lowerCAmelCase_       :    Any								=			max_position_embeddings
        lowerCAmelCase_       :    Dict								=			type_vocab_size
        lowerCAmelCase_       :    Tuple								=			initializer_range
        lowerCAmelCase_       :    List[Any]								=			layer_norm_eps
        lowerCAmelCase_       :    str								=			pruning_method
        lowerCAmelCase_       :    Optional[Any]								=			mask_init
        lowerCAmelCase_       :    int								=			mask_scale





