






"""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


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

UpperCamelCase							       =    ['names', 'prefix']
UpperCamelCase							       =    ['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
UpperCamelCase							       =    ['encoding_errors', 'on_bad_lines']
UpperCamelCase							       =    ['date_format']




@dataclass
class    lowercase_    (datasets.BuilderConfig     ):
						A__	:		str       			=				","
						A__	:		Optional[str]       			=				None
						A__	:		Optional[Union[int, List[int], str]]       			=				"infer"
						A__	:		Optional[List[str]]       			=				None
						A__	:		Optional[List[str]]       			=				None
						A__	:		Optional[Union[int, str, List[int], List[str]]]       			=				None
						A__	:		Optional[Union[List[int], List[str]]]       			=				None
						A__	:		Optional[str]       			=				None
						A__	:		bool       			=				True
						A__	:		Optional[Literal["c", "python", "pyarrow"]]       			=				None
						A__	:		Dict[Union[int, str], Callable[[Any], Any]]       			=				None
						A__	:		Optional[list]       			=				None
						A__	:		Optional[list]       			=				None
						A__	:		bool       			=				False
						A__	:		Optional[Union[int, List[int]]]       			=				None
						A__	:		Optional[int]       			=				None
						A__	:		Optional[Union[str, List[str]]]       			=				None
						A__	:		bool       			=				True
						A__	:		bool       			=				True
						A__	:		bool       			=				False
						A__	:		bool       			=				True
						A__	:		Optional[str]       			=				None
						A__	:		str       			=				"."
						A__	:		Optional[str]       			=				None
						A__	:		str       			=				'"'
						A__	:		int       			=				0
						A__	:		Optional[str]       			=				None
						A__	:		Optional[str]       			=				None
						A__	:		Optional[str]       			=				None
						A__	:		Optional[str]       			=				None
						A__	:		bool       			=				True
						A__	:		bool       			=				True
						A__	:		int       			=				0
						A__	:		bool       			=				True
						A__	:		bool       			=				False
						A__	:		Optional[str]       			=				None
						A__	:		int       			=				10000
						A__	:		Optional[datasets.Features]       			=				None
						A__	:		Optional[str]       			=				"strict"
						A__	:		Literal["error", "warn", "skip"]       			=				"error"
						A__	:		Optional[str]       			=				None






						def 			lowerCamelCase__    (    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 			lowerCamelCase__    (    self	)	->List[Any]:







								'''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    lowercase_    (datasets.ArrowBasedBuilder     ):
						A__	:		Tuple       			=				CsvConfig






						def 			lowerCamelCase__    (    self	)	->int:







								'''simple docstring'''


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






						def 			lowerCamelCase__    (    self     ,     a_	)	->List[Any]:







								'''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 			lowerCamelCase__    (    self     ,     a_	)	->Optional[int]:







								'''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 			lowerCamelCase__    (    self     ,     a_	)	->Optional[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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase							       =    {"""configuration_fnet""": ["""FNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FNetConfig"""]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase							       =    ["""FNetTokenizer"""]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase							       =    ["""FNetTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase							       =    [
         """FNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """FNetForMaskedLM""",
         """FNetForMultipleChoice""",
         """FNetForNextSentencePrediction""",
         """FNetForPreTraining""",
         """FNetForQuestionAnswering""",
         """FNetForSequenceClassification""",
         """FNetForTokenClassification""",
         """FNetLayer""",
         """FNetModel""",
         """FNetPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet import FNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet_fast import FNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_fnet import (
              FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              FNetForMaskedLM,
              FNetForMultipleChoice,
              FNetForNextSentencePrediction,
              FNetForPreTraining,
              FNetForQuestionAnswering,
              FNetForSequenceClassification,
              FNetForTokenClassification,
              FNetLayer,
              FNetModel,
              FNetPreTrainedModel,
          )


else:
     import sys

     UpperCamelCase							       =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


