



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		:List[Any]   	=  datasets.utils.logging.get_logger(__name__)

__snake_case		:Dict   	=  ['''names''', '''prefix''']
__snake_case		:Tuple   	=  ['''warn_bad_lines''', '''error_bad_lines''', '''mangle_dupe_cols''']
__snake_case		:Tuple   	=  ['''encoding_errors''', '''on_bad_lines''']
__snake_case		:int   	=  ['''date_format''']

@dataclass
class 					_A (			datasets.BuilderConfig					):
       UpperCamelCase__						:					str		      =		","
       UpperCamelCase__						:					Optional[str]		      =		None
       UpperCamelCase__						:					Optional[Union[int, List[int], str]]		      =		"infer"
       UpperCamelCase__						:					Optional[List[str]]		      =		None
       UpperCamelCase__						:					Optional[List[str]]		      =		None
       UpperCamelCase__						:					Optional[Union[int, str, List[int], List[str]]]		      =		None
       UpperCamelCase__						:					Optional[Union[List[int], List[str]]]		      =		None
       UpperCamelCase__						:					Optional[str]		      =		None
       UpperCamelCase__						:					bool		      =		True
       UpperCamelCase__						:					Optional[Literal["c", "python", "pyarrow"]]		      =		None
       UpperCamelCase__						:					Dict[Union[int, str], Callable[[Any], Any]]		      =		None
       UpperCamelCase__						:					Optional[list]		      =		None
       UpperCamelCase__						:					Optional[list]		      =		None
       UpperCamelCase__						:					bool		      =		False
       UpperCamelCase__						:					Optional[Union[int, List[int]]]		      =		None
       UpperCamelCase__						:					Optional[int]		      =		None
       UpperCamelCase__						:					Optional[Union[str, List[str]]]		      =		None
       UpperCamelCase__						:					bool		      =		True
       UpperCamelCase__						:					bool		      =		True
       UpperCamelCase__						:					bool		      =		False
       UpperCamelCase__						:					bool		      =		True
       UpperCamelCase__						:					Optional[str]		      =		None
       UpperCamelCase__						:					str		      =		"."
       UpperCamelCase__						:					Optional[str]		      =		None
       UpperCamelCase__						:					str		      =		'"'
       UpperCamelCase__						:					int		      =		0
       UpperCamelCase__						:					Optional[str]		      =		None
       UpperCamelCase__						:					Optional[str]		      =		None
       UpperCamelCase__						:					Optional[str]		      =		None
       UpperCamelCase__						:					Optional[str]		      =		None
       UpperCamelCase__						:					bool		      =		True
       UpperCamelCase__						:					bool		      =		True
       UpperCamelCase__						:					int		      =		0
       UpperCamelCase__						:					bool		      =		True
       UpperCamelCase__						:					bool		      =		False
       UpperCamelCase__						:					Optional[str]		      =		None
       UpperCamelCase__						:					int		      =		10_000
       UpperCamelCase__						:					Optional[datasets.Features]		      =		None
       UpperCamelCase__						:					Optional[str]		      =		"strict"
       UpperCamelCase__						:					Literal["error", "warn", "skip"]		      =		"error"
       UpperCamelCase__						:					Optional[str]		      =		None



       def 	_lowerCamelCase    (	self							:  List[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							:  int):


           '''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()  ,	__SCREAMING_SNAKE_CASE):
                   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					):
       UpperCamelCase__						:					List[Any]		      =		CsvConfig



       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

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



       def 	_lowerCamelCase    (	self							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  str):


           '''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(__SCREAMING_SNAKE_CASE  ,	(str, list, tuple)):
               __a		 =       data_files
               if isinstance(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE):
                   __a		 =       [files]
               __a		 =       [dl_manager.iter_files(__SCREAMING_SNAKE_CASE) 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(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE):
                   __a		 =       [files]
               __a		 =       [dl_manager.iter_files(__SCREAMING_SNAKE_CASE) for file in files]
               splits.append(datasets.SplitGenerator(name=__SCREAMING_SNAKE_CASE  ,	gen_kwargs={'''files''': files}))
           return splits



       def 	_lowerCamelCase    (	self							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  pa.Table):


           '''simple docstring'''

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





       def 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]):


           '''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(__SCREAMING_SNAKE_CASE) 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(__SCREAMING_SNAKE_CASE)):
               __a		 =       pd.read_csv(__SCREAMING_SNAKE_CASE  ,	iterator=__SCREAMING_SNAKE_CASE  ,	dtype=__SCREAMING_SNAKE_CASE  ,	**self.config.pd_read_csv_kwargs)
               try:
                   for batch_idx, df in enumerate(__SCREAMING_SNAKE_CASE):
                       __a		 =       pa.Table.from_pandas(__SCREAMING_SNAKE_CASE)
                       # 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(__SCREAMING_SNAKE_CASE)
               except ValueError as e:
                   logger.error(F'Failed to read file \'{file}\' with error {type(__SCREAMING_SNAKE_CASE)}: {e}')
                   raise










import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__snake_case		:Tuple   	=  logging.getLogger(__name__)

if __name__ == "__main__":
 __snake_case		:Union[str, Any]   	=  argparse.ArgumentParser(
     description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
 )
 parser.add_argument(
     '''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
 )
 parser.add_argument(
     '''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
 )
 parser.add_argument('''--vocab_size''', default=3_0522, type=int)
 __snake_case		:List[str]   	=  parser.parse_args()

 logger.info(f'Loading data from {args.data_file}')
 with open(args.data_file, '''rb''') as fp:
  __snake_case		:Optional[Any]   	=  pickle.load(fp)

 logger.info('''Counting occurrences for MLM.''')
 __snake_case		:Dict   	=  Counter()
 for tk_ids in data:
  counter.update(tk_ids)
 __snake_case		:Optional[Any]   	=  [0] * args.vocab_size
 for k, v in counter.items():
  __snake_case		:Any   	=  v

 logger.info(f'Dump to {args.token_counts_dump}')
 with open(args.token_counts_dump, '''wb''') as handle:
  pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)





