

'''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


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

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






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



 def 			snake_case__  (   self		:   Dict      ):
      if self.delimiter is not None:
           __magic_name__						       =    self.delimiter
      if self.column_names is not None:
           __magic_name__						       =    self.column_names



 @property
 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    {
          '''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     ):
 __SCREAMING_SNAKE_CASE					:List[str]      	=					CsvConfig



 def 			snake_case__  (   self		:   List[Any]      ):
      return datasets.DatasetInfo(features=self.config.features      )



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



 def 			snake_case__  (   self		:   Dict			,				a__		:   pa.Table      ):
      if self.config.features is not None:
           __magic_name__						       =    self.config.features.arrow_schema
           if all(not require_storage_cast(a__      ) for feature in self.config.features.values()      ):
                # cheaper cast
                __magic_name__						       =    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
                __magic_name__						       =    table_cast(a__			,				a__      )
      return pa_table



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




def 						UpperCamelCase	(			a		,						a		,						a		,						a = False		,						a = False		,						a = 5		,						)				->     list:






     '''simple docstring'''
     __magic_name__						       =    [[-1] * number_of_cells]  # Create a highway without any car
     __magic_name__						       =    0
     __magic_name__						       =    max(a		,						0			)
     while i < number_of_cells:
          __magic_name__						       =    (
              randint(0		,						a			) if random_speed else initial_speed
          )  # Place the cars
          i += (
              randint(1		,						max_speed * 2			) if random_frequency else frequency
          )  # Arbitrary number, may need tuning
     return highway




def 						UpperCamelCase	(			a		,						a			)				->     int:






     '''simple docstring'''
     __magic_name__						       =    0
     __magic_name__						       =    highway_now[car_index + 1 :]
     for cell in range(len(a			)			):  # May need a better name for this
          if cells[cell] != -1:  # If the cell is not empty then
               return distance  # we have the distance we wanted
          distance += 1
     # Here if the car is near the end of the highway
     return distance + get_distance(a		,						-1			)




def 						UpperCamelCase	(			a		,						a		,						a			)				->     list:






     '''simple docstring'''
     __magic_name__						       =    len(a			)
     # Beforce calculations, the highway is empty
     __magic_name__						       =    [-1] * number_of_cells

     for car_index in range(a			):
          if highway_now[car_index] != -1:
               # Add 1 to the current speed of the car and cap the speed
               __magic_name__						       =    min(highway_now[car_index] + 1		,						a			)
               # Number of empty cell before the next car
               __magic_name__						       =    get_distance(a		,						a			) - 1
               # We can't have the car causing an accident
               __magic_name__						       =    min(next_highway[car_index]		,						a			)
               if random() < probability:
                    # Randomly, a driver will slow down
                    __magic_name__						       =    max(next_highway[car_index] - 1		,						0			)
     return next_highway




def 						UpperCamelCase	(			a		,						a		,						a		,						a			)				->     list:






     '''simple docstring'''
     __magic_name__						       =    len(highway[0]			)

     for i in range(a			):
          __magic_name__						       =    update(highway[i]		,						a		,						a			)
          __magic_name__						       =    [-1] * number_of_cells

          for car_index in range(a			):
               __magic_name__						       =    next_speeds_calculated[car_index]
               if speed != -1:
                    # Change the position based on the speed (with % to create the loop)
                    __magic_name__						       =    (car_index + speed) % number_of_cells
                    # Commit the change of position
                    __magic_name__						       =    speed
          highway.append(a			)

     return highway


if __name__ == "__main__":
      import doctest

      doctest.testmod()






