



'''simple docstring'''





import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


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

lowercase_	 =						["names", "prefix"]
lowercase_	 =						["warn_bad_lines", "error_bad_lines", "mangle_dupe_cols"]
lowercase_	 =						["encoding_errors", "on_bad_lines"]
lowercase_	 =						["date_format"]






@dataclass
class __A    (						datasets.BuilderConfig		):



 '''simple docstring'''







 __lowerCamelCase					: str    = ","
 __lowerCamelCase					: Optional[str]    = None
 __lowerCamelCase					: Optional[Union[int, List[int], str]]    = "infer"
 __lowerCamelCase					: Optional[List[str]]    = None
 __lowerCamelCase					: Optional[List[str]]    = None
 __lowerCamelCase					: Optional[Union[int, str, List[int], List[str]]]    = None
 __lowerCamelCase					: Optional[Union[List[int], List[str]]]    = None
 __lowerCamelCase					: Optional[str]    = None
 __lowerCamelCase					: bool    = True
 __lowerCamelCase					: Optional[Literal["c", "python", "pyarrow"]]    = None
 __lowerCamelCase					: Dict[Union[int, str], Callable[[Any], Any]]    = None
 __lowerCamelCase					: Optional[list]    = None
 __lowerCamelCase					: Optional[list]    = None
 __lowerCamelCase					: bool    = False
 __lowerCamelCase					: Optional[Union[int, List[int]]]    = None
 __lowerCamelCase					: Optional[int]    = None
 __lowerCamelCase					: Optional[Union[str, List[str]]]    = None
 __lowerCamelCase					: bool    = True
 __lowerCamelCase					: bool    = True
 __lowerCamelCase					: bool    = False
 __lowerCamelCase					: bool    = True
 __lowerCamelCase					: Optional[str]    = None
 __lowerCamelCase					: str    = "."
 __lowerCamelCase					: Optional[str]    = None
 __lowerCamelCase					: str    = '"'
 __lowerCamelCase					: int    = 0
 __lowerCamelCase					: Optional[str]    = None
 __lowerCamelCase					: Optional[str]    = None
 __lowerCamelCase					: Optional[str]    = None
 __lowerCamelCase					: Optional[str]    = None
 __lowerCamelCase					: bool    = True
 __lowerCamelCase					: bool    = True
 __lowerCamelCase					: int    = 0
 __lowerCamelCase					: bool    = True
 __lowerCamelCase					: bool    = False
 __lowerCamelCase					: Optional[str]    = None
 __lowerCamelCase					: int    = 10_000
 __lowerCamelCase					: Optional[datasets.Features]    = None
 __lowerCamelCase					: Optional[str]    = "strict"
 __lowerCamelCase					: Literal["error", "warn", "skip"]    = "error"
 __lowerCamelCase					: Optional[str]    = None



 def       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



      if self.delimiter is not None:
           _a											=						self.delimiter
      if self.column_names is not None:
           _a											=						self.column_names



 @property
 def       a__						(self )					->  str:


      """simple docstring"""



      _a											=						{
          '''sep''': self.sep,
          '''header''': self.header,
          '''names''': self.names,
          '''index_col''': self.index_col,
          '''usecols''': self.usecols,
          '''prefix''': self.prefix,
          '''mangle_dupe_cols''': self.mangle_dupe_cols,
          '''engine''': self.engine,
          '''converters''': self.converters,
          '''true_values''': self.true_values,
          '''false_values''': self.false_values,
          '''skipinitialspace''': self.skipinitialspace,
          '''skiprows''': self.skiprows,
          '''nrows''': self.nrows,
          '''na_values''': self.na_values,
          '''keep_default_na''': self.keep_default_na,
          '''na_filter''': self.na_filter,
          '''verbose''': self.verbose,
          '''skip_blank_lines''': self.skip_blank_lines,
          '''thousands''': self.thousands,
          '''decimal''': self.decimal,
          '''lineterminator''': self.lineterminator,
          '''quotechar''': self.quotechar,
          '''quoting''': self.quoting,
          '''escapechar''': self.escapechar,
          '''comment''': self.comment,
          '''encoding''': self.encoding,
          '''dialect''': self.dialect,
          '''error_bad_lines''': self.error_bad_lines,
          '''warn_bad_lines''': self.warn_bad_lines,
          '''skipfooter''': self.skipfooter,
          '''doublequote''': self.doublequote,
          '''memory_map''': self.memory_map,
          '''float_precision''': self.float_precision,
          '''chunksize''': self.chunksize,
          '''encoding_errors''': self.encoding_errors,
          '''on_bad_lines''': self.on_bad_lines,
          '''date_format''': self.date_format,
      }

      # some kwargs must not be passed if they don't have a default value
      # some others are deprecated and we can also not pass them if they are the default value
      for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
           if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()				,     A ):
                del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
      if not (datasets.config.PANDAS_VERSION.major >= 2):
           for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
                del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
      if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
           for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
                del pd_read_csv_kwargs[pd_read_csv_parameter]

      return pd_read_csv_kwargs






class __A    (						datasets.ArrowBasedBuilder		):



 '''simple docstring'''







 __lowerCamelCase					: Union[str, Any]    = CsvConfig



 def       a__						(self )					->  Tuple:


      """simple docstring"""



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



 def       a__						(self				,     A )					->  Tuple:


      """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}''' )
      _a											=						dl_manager.download_and_extract(self.config.data_files )
      if isinstance(A				,     (str, list, tuple) ):
           _a											=						data_files
           if isinstance(A				,     A ):
                _a											=						[files]
           _a											=						[dl_manager.iter_files(A ) for file in files]
           return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,     gen_kwargs={'''files''': files} )]
      _a											=						[]
      for split_name, files in data_files.items():
           if isinstance(A				,     A ):
                _a											=						[files]
           _a											=						[dl_manager.iter_files(A ) for file in files]
           splits.append(datasets.SplitGenerator(name=A				,     gen_kwargs={'''files''': files} ) )
      return splits



 def       a__						(self				,     A )					->  pa.Table:


      """simple docstring"""



      if self.config.features is not None:
           _a											=						self.config.features.arrow_schema
           if all(not require_storage_cast(A ) for feature in self.config.features.values() ):
                # cheaper cast
                _a											=						pa.Table.from_arrays([pa_table[field.name] for field in schema]				,     schema=A )
           else:
                # more expensive cast; allows str <-> int/float or str to Audio for example
                _a											=						table_cast(A				,     A )
      return pa_table



 def       a__						(self				,     A )					->  int:


      """simple docstring"""



      _a											=						self.config.features.arrow_schema if self.config.features else None
      # dtype allows reading an int column as str
      _a											=						(
          {
              name: dtype.to_pandas_dtype() if not require_storage_cast(A ) else object
              for name, dtype, feature in zip(schema.names				,     schema.types				,     self.config.features.values() )
          }
          if schema is not None
          else None
      )
      for file_idx, file in enumerate(itertools.chain.from_iterable(A ) ):
           _a											=						pd.read_csv(A				,     iterator=A				,     dtype=A				,     **self.config.pd_read_csv_kwargs )
           try:
                for batch_idx, df in enumerate(A ):
                     _a											=						pa.Table.from_pandas(A )
                     # 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 (file_idx, batch_idx), self._cast_table(A )
           except ValueError as e:
                logger.error(f'''Failed to read file \'{file}\' with error {type(A )}: {e}''' )
                raise








'''simple docstring'''





from ....configuration_utils import PretrainedConfig
from ....utils import logging


lowercase_	 =						logging.get_logger(__name__)

# TODO: upload to AWS
lowercase_	 =						{
    "yjernite/retribert-base-uncased": (
        "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"
    ),
}






class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: Optional[Any]    = 'retribert'



 def __init__(self				,     A=30_522				,     A=768				,     A=8				,     A=12				,     A=3_072				,     A="gelu"				,     A=0.1				,     A=0.1				,     A=512				,     A=2				,     A=0.02				,     A=1E-12				,     A=True				,     A=128				,     A=0				,     **A				,     )					->  Any:


      """simple docstring"""



      super().__init__(pad_token_id=A				,     **A )

      _a											=						vocab_size
      _a											=						hidden_size
      _a											=						num_hidden_layers
      _a											=						num_attention_heads
      _a											=						hidden_act
      _a											=						intermediate_size
      _a											=						hidden_dropout_prob
      _a											=						attention_probs_dropout_prob
      _a											=						max_position_embeddings
      _a											=						type_vocab_size
      _a											=						initializer_range
      _a											=						layer_norm_eps
      _a											=						share_encoders
      _a											=						projection_dim



