





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


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

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

@dataclass
class 		SCREAMING_SNAKE_CASE__    (	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							=   10_000
      A_						:    Optional[datasets.Features]							=   None
      A_						:    Optional[str]							=   "strict"
      A_						:    Literal["error", "warn", "skip"]							=   "error"
      A_						:    Optional[str]							=   None


      def        a			(self	:				Dict				):





         """simple docstring"""



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


      @property
      def        a			(self	:				Optional[int]				):





         """simple docstring"""



         __snake_case								=       {
             '''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 		SCREAMING_SNAKE_CASE__    (	datasets.ArrowBasedBuilder  ):
      A_						:    List[str]							=   CsvConfig


      def        a			(self	:				List[Any]				):





         """simple docstring"""



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


      def        a			(self	:				str    ,      a__	:				Dict				):





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


      def        a			(self	:				Dict    ,      a__	:				pa.Table				):





         """simple docstring"""



         if self.config.features is not None:
            __snake_case								=       self.config.features.arrow_schema
            if all(not require_storage_cast(a__				) for feature in self.config.features.values()				):
               # cheaper cast
               __snake_case								=       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
               __snake_case								=       table_cast(a__    ,      a__				)
         return pa_table


      def        a			(self	:				Tuple    ,      a__	:				str				):





         """simple docstring"""



         __snake_case								=       self.config.features.arrow_schema if self.config.features else None
         # dtype allows reading an int column as str
         __snake_case								=       (
             {
                 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__				)				):
            __snake_case								=       pd.read_csv(a__    ,      iterator=a__    ,      dtype=a__    ,      **self.config.pd_read_csv_kwargs				)
            try:
               for batch_idx, df in enumerate(a__				):
                  __snake_case								=       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








import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase						,				unittest.TestCase  ):
      A_						:    int							=   XLMTokenizer
      A_						:    Optional[Any]							=   False


      def        a			(self	:				Optional[int]				):





         """simple docstring"""



         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         __snake_case								=       [
             '''l''',
             '''o''',
             '''w''',
             '''e''',
             '''r''',
             '''s''',
             '''t''',
             '''i''',
             '''d''',
             '''n''',
             '''w</w>''',
             '''r</w>''',
             '''t</w>''',
             '''lo''',
             '''low''',
             '''er</w>''',
             '''low</w>''',
             '''lowest</w>''',
             '''newer</w>''',
             '''wider</w>''',
             '''<unk>''',
         ]
         __snake_case								=       dict(zip(a__    ,      range(len(a__				)				)				)				)
         __snake_case								=       ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

         __snake_case								=       os.path.join(self.tmpdirname    ,      VOCAB_FILES_NAMES['''vocab_file''']				)
         __snake_case								=       os.path.join(self.tmpdirname    ,      VOCAB_FILES_NAMES['''merges_file''']				)
         with open(self.vocab_file    ,      '''w'''				) as fp:
            fp.write(json.dumps(a__				)				)
         with open(self.merges_file    ,      '''w'''				) as fp:
            fp.write('''\n'''.join(a__				)				)


      def        a			(self	:				int    ,      a__	:				Any				):





         """simple docstring"""



         __snake_case								=       '''lower newer'''
         __snake_case								=       '''lower newer'''
         return input_text, output_text


      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         __snake_case								=       XLMTokenizer(self.vocab_file    ,      self.merges_file				)

         __snake_case								=       '''lower'''
         __snake_case								=       ['''low''', '''er</w>''']
         __snake_case								=       tokenizer.tokenize(a__				)
         self.assertListEqual(a__    ,      a__				)

         __snake_case								=       tokens + ['''<unk>''']
         __snake_case								=       [14, 15, 20]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(a__				)    ,      a__				)


      @slow
      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''				)

         __snake_case								=       tokenizer.encode('''sequence builders'''    ,      add_special_tokens=a__				)
         __snake_case								=       tokenizer.encode('''multi-sequence build'''    ,      add_special_tokens=a__				)

         __snake_case								=       tokenizer.build_inputs_with_special_tokens(a__				)
         __snake_case								=       tokenizer.build_inputs_with_special_tokens(a__    ,      a__				)

         assert encoded_sentence == [0] + text + [1]
         assert encoded_pair == [0] + text + [1] + text_a + [1]

