



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_fnet import FNetTokenizer
else:
	__A       =						None

__A       =						logging.get_logger(__name__)
__A       =						{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

__A       =						{
    '''vocab_file''': {
        '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/spiece.model''',
        '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json''',
        '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json''',
    },
}

__A       =						{
    '''google/fnet-base''': 512,
    '''google/fnet-large''': 512,
}

__A       =						'''▁'''






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	a__				: Any		       =							VOCAB_FILES_NAMES
	a__				: Dict		       =							PRETRAINED_VOCAB_FILES_MAP
	a__				: Dict		       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a__				: List[Any]		       =							["input_ids", "token_type_ids"]
	a__				: List[str]		       =							FNetTokenizer




	def __init__(     self     :						Optional[int]						,  __UpperCAmelCase     :						str=None						,  __UpperCAmelCase     :						List[Any]=None						,  __UpperCAmelCase     :						Optional[Any]=False						,  __UpperCAmelCase     :						str=True						,  __UpperCAmelCase     :						Optional[int]=True						,  __UpperCAmelCase     :						List[str]="<unk>"						,  __UpperCAmelCase     :						Any="[SEP]"						,  __UpperCAmelCase     :						int="<pad>"						,  __UpperCAmelCase     :						Any="[CLS]"						,  __UpperCAmelCase     :						Optional[Any]="[MASK]"						,  **__UpperCAmelCase     :						int						,  )      ->			Optional[int]:
							# Mask token behave like a normal word, i.e. include the space before it and
							# is included in the raw text, there should be a match in a non-normalized sentence.
							UpperCAmelCase_=       (
							    AddedToken(__UpperCAmelCase						,  lstrip=__UpperCAmelCase						,  rstrip=__UpperCAmelCase						,  normalized=__UpperCAmelCase					)
							    if isinstance(__UpperCAmelCase						,  __UpperCAmelCase					)
							    else mask_token
							)

							super().__init__(
							    __UpperCAmelCase						,  tokenizer_file=__UpperCAmelCase						,  do_lower_case=__UpperCAmelCase						,  remove_space=__UpperCAmelCase						,  keep_accents=__UpperCAmelCase						,  unk_token=__UpperCAmelCase						,  sep_token=__UpperCAmelCase						,  pad_token=__UpperCAmelCase						,  cls_token=__UpperCAmelCase						,  mask_token=__UpperCAmelCase						,  **__UpperCAmelCase						,  )

							UpperCAmelCase_=       do_lower_case
							UpperCAmelCase_=       remove_space
							UpperCAmelCase_=       keep_accents
							UpperCAmelCase_=       vocab_file
							UpperCAmelCase_=       False if not self.vocab_file else True




	def   _SCREAMING_SNAKE_CASE		(     self     :						int						,  __UpperCAmelCase     :						List[int]						,  __UpperCAmelCase     :						Optional[List[int]] = None					)      ->			List[int]:
							UpperCAmelCase_=       [self.sep_token_id]
							UpperCAmelCase_=       [self.cls_token_id]
							if token_ids_a is None:
													return cls + token_ids_a + sep
							return cls + token_ids_a + sep + token_ids_a + sep




	def   _SCREAMING_SNAKE_CASE		(     self     :						str						,  __UpperCAmelCase     :						List[int]						,  __UpperCAmelCase     :						Optional[List[int]] = None					)      ->			List[int]:
							UpperCAmelCase_=       [self.sep_token_id]
							UpperCAmelCase_=       [self.cls_token_id]

							if token_ids_a is None:
													return len(cls + token_ids_a + sep					) * [0]
							return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple						,  __UpperCAmelCase     :						str						,  __UpperCAmelCase     :						Optional[str] = None					)      ->			Tuple[str]:
							if not os.path.isdir(__UpperCAmelCase					):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""					)
													return
							UpperCAmelCase_=       os.path.join(
							    __UpperCAmelCase						,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

							if os.path.abspath(self.vocab_file					) != os.path.abspath(__UpperCAmelCase					):
													copyfile(self.vocab_file						,  __UpperCAmelCase					)

							return (out_vocab_file,)









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


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

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






@dataclass
class 		lowercase						(			datasets.BuilderConfig):






	"""simple docstring"""


	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		       =							1_0000
	a__				: Optional[datasets.Features]		       =							None
	a__				: Optional[str]		       =							"strict"
	a__				: Literal["error", "warn", "skip"]		       =							"error"
	a__				: Optional[str]		       =							None




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			int:
							if self.delimiter is not None:
													UpperCAmelCase_=       self.delimiter
							if self.column_names is not None:
													UpperCAmelCase_=       self.column_names




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Tuple:
							UpperCAmelCase_=       {
							    """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()						,  __UpperCAmelCase					):
																			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):






	"""simple docstring"""


	a__				: int		       =							CsvConfig




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Any:
							return datasets.DatasetInfo(features=self.config.features					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						Dict					)      ->			Optional[int]:
							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(__UpperCAmelCase						,  (str, list, tuple)					):
													UpperCAmelCase_=       data_files
													if isinstance(__UpperCAmelCase						,  __UpperCAmelCase					):
																			UpperCAmelCase_=       [files]
													UpperCAmelCase_=       [dl_manager.iter_files(__UpperCAmelCase					) 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(__UpperCAmelCase						,  __UpperCAmelCase					):
																			UpperCAmelCase_=       [files]
													UpperCAmelCase_=       [dl_manager.iter_files(__UpperCAmelCase					) for file in files]
													splits.append(datasets.SplitGenerator(name=__UpperCAmelCase						,  gen_kwargs={"""files""": files}					)					)
							return splits




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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						List[Any]					)      ->			List[str]:
							UpperCAmelCase_=       self.config.features.arrow_schema if self.config.features else None
							# dtype allows reading an int column as str
							UpperCAmelCase_=       (
							    {
							        name: dtype.to_pandas_dtype() if not require_storage_cast(__UpperCAmelCase					) 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(__UpperCAmelCase					)					):
													UpperCAmelCase_=       pd.read_csv(__UpperCAmelCase						,  iterator=__UpperCAmelCase						,  dtype=__UpperCAmelCase						,  **self.config.pd_read_csv_kwargs					)
													try:
																			for batch_idx, df in enumerate(__UpperCAmelCase					):
																									UpperCAmelCase_=       pa.Table.from_pandas(__UpperCAmelCase					)
																									# 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(__UpperCAmelCase					)
													except ValueError as e:
																			logger.error(F"""Failed to read file '{file}' with error {type(__UpperCAmelCase					)}: {e}"""					)
																			raise




