
"""simple docstring"""






from __future__ import annotations




def       A_  (				__lowercase	):
 UpperCamelCase_     :					Tuple   	=len(__lowercase	)
 # We need to create solution object to save path.
 UpperCamelCase_     :					Any   	=[[0 for _ in range(__lowercase	)] for _ in range(__lowercase	)]
 UpperCamelCase_     :					Optional[Any]   	=run_maze(__lowercase						,	0						,	0						,	__lowercase	)
 if solved:
  print('\n'.join(str(__lowercase	) for row in solutions	)	)
 else:
  print('No solution exists!'	)
 return solved




def       A_  (				__lowercase						,	__lowercase						,	__lowercase						,	__lowercase	):
 UpperCamelCase_     :					Union[str, Any]   	=len(__lowercase	)
 # Final check point.
 if i == j == (size - 1):
  UpperCamelCase_     :					Union[str, Any]   	=1
  return True

 UpperCamelCase_     :					Optional[Any]   	=(not i < 0) and (not j < 0)  # Check lower bounds
 UpperCamelCase_     :					Dict   	=(i < size) and (j < size)  # Check upper bounds

 if lower_flag and upper_flag:
  # check for already visited and block points.
  UpperCamelCase_     :					Tuple   	=(not solutions[i][j]) and (not maze[i][j])
  if block_flag:
   # check visited
   UpperCamelCase_     :					Union[str, Any]   	=1

   # check for directions
   if (
       run_maze(__lowercase						,	i + 1						,	__lowercase						,	__lowercase	)
       or run_maze(__lowercase						,	__lowercase						,	j + 1						,	__lowercase	)
       or run_maze(__lowercase						,	i - 1						,	__lowercase						,	__lowercase	)
       or run_maze(__lowercase						,	__lowercase						,	j - 1						,	__lowercase	)
   ):
    return True

   UpperCamelCase_     :					Any   	=0
   return False
 return False


if __name__ == "__main__":
 import doctest

 doctest.testmod()


"""simple docstring"""






import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


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





@dataclass
class 				a__					(						datasets.BuilderConfig       ):
     UpperCAmelCase__         =      None
     UpperCAmelCase__         =      "utf-8"
     UpperCAmelCase__         =      None
     UpperCAmelCase__         =      None
     UpperCAmelCase__         =      True  # deprecated
     UpperCAmelCase__         =      None  # deprecated
     UpperCAmelCase__         =      10 << 20  # 10MB
     UpperCAmelCase__         =      None





class 				a__					(						datasets.ArrowBasedBuilder       ):
     UpperCAmelCase__         =      JsonConfig



     def  lowerCamelCase_						(							self :List[str]   ):







      '''simple docstring'''



      if self.config.block_size is not None:
       logger.warning('The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'   )
       UpperCamelCase_     :					List[Any]   	=self.config.block_size
      if self.config.use_threads is not True:
       logger.warning(
           'The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.'   )
      if self.config.newlines_in_values is not None:
       raise ValueError('The JSON loader parameter `newlines_in_values` is no longer supported'   )
      return datasets.DatasetInfo(features=self.config.features   )



     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :List[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}'''   )
      UpperCamelCase_     :					int   	=dl_manager.download_and_extract(self.config.data_files   )
      if isinstance(_lowerCamelCase      ,  (str, list, tuple)   ):
       UpperCamelCase_     :					Tuple   	=data_files
       if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ):
        UpperCamelCase_     :					Union[str, Any]   	=[files]
       UpperCamelCase_     :					List[str]   	=[dl_manager.iter_files(_lowerCamelCase   ) for file in files]
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,  gen_kwargs={'files': files}   )]
      UpperCamelCase_     :					Optional[Any]   	=[]
      for split_name, files in data_files.items():
       if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ):
        UpperCamelCase_     :					Tuple   	=[files]
       UpperCamelCase_     :					Optional[Any]   	=[dl_manager.iter_files(_lowerCamelCase   ) for file in files]
       splits.append(datasets.SplitGenerator(name=_lowerCamelCase      ,  gen_kwargs={'files': files}   )   )
      return splits



     def  lowerCamelCase_						(							self :int      ,  _lowerCamelCase :pa.Table   ):







      '''simple docstring'''



      if self.config.features is not None:
       # adding missing columns
       for column_name in set(self.config.features   ) - set(pa_table.column_names   ):
        UpperCamelCase_     :					Dict   	=self.config.features.arrow_schema.field(_lowerCamelCase   ).type
        UpperCamelCase_     :					Optional[Any]   	=pa_table.append_column(_lowerCamelCase      ,  pa.array([None] * len(_lowerCamelCase   )      ,  type=_lowerCamelCase   )   )
       # more expensive cast to support nested structures with keys in a different order
       # allows str <-> int/float or str to Audio for example
       UpperCamelCase_     :					int   	=table_cast(_lowerCamelCase      ,  self.config.features.arrow_schema   )
      return pa_table




     def  lowerCamelCase_						(							self :Dict      ,  _lowerCamelCase :Optional[int]   ):







      '''simple docstring'''



      for file_idx, file in enumerate(itertools.chain.from_iterable(_lowerCamelCase   )   ):
       # If the file is one json object and if we need to look at the list of items in one specific field
       if self.config.field is not None:
        with open(_lowerCamelCase      ,  encoding=self.config.encoding      ,  errors=self.config.encoding_errors   ) as f:
         UpperCamelCase_     :					List[str]   	=json.load(_lowerCamelCase   )

        # We keep only the field we are interested in
        UpperCamelCase_     :					Tuple   	=dataset[self.config.field]

        # We accept two format: a list of dicts or a dict of lists
        if isinstance(_lowerCamelCase      ,  (list, tuple)   ):
         UpperCamelCase_     :					Tuple   	=set().union(*[row.keys() for row in dataset]   )
         UpperCamelCase_     :					str   	={col: [row.get(_lowerCamelCase   ) for row in dataset] for col in keys}
        else:
         UpperCamelCase_     :					Any   	=dataset
        UpperCamelCase_     :					List[Any]   	=pa.Table.from_pydict(_lowerCamelCase   )
        yield file_idx, self._cast_table(_lowerCamelCase   )

       # If the file has one json object per line
       else:
        with open(_lowerCamelCase      ,  'rb'   ) as f:
         UpperCamelCase_     :					Any   	=0
         # Use block_size equal to the chunk size divided by 32 to leverage multithreading
         # Set a default minimum value of 16kB if the chunk size is really small
         UpperCamelCase_     :					List[str]   	=max(self.config.chunksize // 32      ,  16 << 10   )
         UpperCamelCase_     :					Dict   	=(
             self.config.encoding_errors if self.config.encoding_errors is not None else 'strict'
         )
         while True:
          UpperCamelCase_     :					Optional[Any]   	=f.read(self.config.chunksize   )
          if not batch:
           break
          # Finish current line
          try:
           batch += f.readline()
          except (AttributeError, io.UnsupportedOperation):
           batch += readline(_lowerCamelCase   )
          # PyArrow only accepts utf-8 encoded bytes
          if self.config.encoding != "utf-8":
           UpperCamelCase_     :					List[Any]   	=batch.decode(self.config.encoding      ,  errors=_lowerCamelCase   ).encode('utf-8'   )
          try:
           while True:
            try:
             UpperCamelCase_     :					str   	=paj.read_json(
                 io.BytesIO(_lowerCamelCase   )      ,  read_options=paj.ReadOptions(block_size=_lowerCamelCase   )   )
             break
            except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
             if (
                 isinstance(_lowerCamelCase      ,  pa.ArrowInvalid   )
                 and "straddling" not in str(_lowerCamelCase   )
                 or block_size > len(_lowerCamelCase   )
             ):
              raise
             else:
              # Increase the block size in case it was too small.
              # The block size will be reset for the next file.
              logger.debug(
                  f'''Batch of {len(_lowerCamelCase   )} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.'''   )
              block_size *= 2
          except pa.ArrowInvalid as e:
           try:
            with open(
                _lowerCamelCase      ,  encoding=self.config.encoding      ,  errors=self.config.encoding_errors   ) as f:
             UpperCamelCase_     :					List[Any]   	=json.load(_lowerCamelCase   )
           except json.JSONDecodeError:
            logger.error(f'''Failed to read file \'{file}\' with error {type(_lowerCamelCase   )}: {e}'''   )
            raise e
           # If possible, parse the file as a list of json objects and exit the loop
           if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ):  # list is the only sequence type supported in JSON
            try:
             UpperCamelCase_     :					str   	=set().union(*[row.keys() for row in dataset]   )
             UpperCamelCase_     :					List[Any]   	={col: [row.get(_lowerCamelCase   ) for row in dataset] for col in keys}
             UpperCamelCase_     :					Optional[Any]   	=pa.Table.from_pydict(_lowerCamelCase   )
            except (pa.ArrowInvalid, AttributeError) as e:
             logger.error(f'''Failed to read file \'{file}\' with error {type(_lowerCamelCase   )}: {e}'''   )
             raise ValueError(f'''Not able to read records in the JSON file at {file}.'''   ) from None
            yield file_idx, self._cast_table(_lowerCamelCase   )
            break
           else:
            logger.error(f'''Failed to read file \'{file}\' with error {type(_lowerCamelCase   )}: {e}'''   )
            raise ValueError(
                f'''Not able to read records in the JSON file at {file}. '''
                f'''You should probably indicate the field of the JSON file containing your records. '''
                f'''This JSON file contain the following fields: {str(list(dataset.keys()   )   )}. '''
                f'''Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. '''   ) from None
                        # 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(_lowerCamelCase   )
          batch_idx += 1
