
"""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


_a      :			Tuple					=      datasets.utils.logging.get_logger(__name__)






@dataclass
class      _UpperCAmelCase				(					datasets.BuilderConfig):
    __lowercase							: Optional[datasets.Features]  =					None
    __lowercase							: str  =					"utf-8"
    __lowercase							: Optional[str]  =					None
    __lowercase							: Optional[str]  =					None
    __lowercase							: bool  =					True  # deprecated
    __lowercase							: Optional[int]  =					None  # deprecated
    __lowercase							: int  =					1_0 << 2_0  # 10MB
    __lowercase							: Optional[bool]  =					None






class      _UpperCAmelCase				(					datasets.ArrowBasedBuilder):
    __lowercase							: int  =					JsonConfig
    def 					lowerCamelCase__	(    self     ):
      if self.config.block_size is not None:
        logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"     )
        _snake_case     :						List[str]						=   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						,    snake_case_     ):
      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     :						Union[str, Any]						=   dl_manager.download_and_extract(self.config.data_files     )
      if isinstance(snake_case_						,    (str, list, tuple)     ):
        _snake_case     :						List[Any]						=   data_files
        if isinstance(snake_case_						,    snake_case_     ):
          _snake_case     :						Dict						=   [files]
        _snake_case     :						str						=   [dl_manager.iter_files(snake_case_     ) for file in files]
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,    gen_kwargs={"files": files}     )]
      _snake_case     :						List[str]						=   []
      for split_name, files in data_files.items():
        if isinstance(snake_case_						,    snake_case_     ):
          _snake_case     :						int						=   [files]
        _snake_case     :						Optional[int]						=   [dl_manager.iter_files(snake_case_     ) for file in files]
        splits.append(datasets.SplitGenerator(name=snake_case_						,    gen_kwargs={"files": files}     )     )
      return splits
    def 					lowerCamelCase__	(    self						,    snake_case_     ):
      if self.config.features is not None:
        # adding missing columns
        for column_name in set(self.config.features     ) - set(pa_table.column_names     ):
          _snake_case     :						Optional[int]						=   self.config.features.arrow_schema.field(snake_case_     ).type
          _snake_case     :						Union[str, Any]						=   pa_table.append_column(snake_case_						,    pa.array([None] * len(snake_case_     )						,    type=snake_case_     )     )
        # more expensive cast to support nested structures with keys in a different order
        # allows str <-> int/float or str to Audio for example
        _snake_case     :						Tuple						=   table_cast(snake_case_						,    self.config.features.arrow_schema     )
      return pa_table


    def 					lowerCamelCase__	(    self						,    snake_case_     ):
      for file_idx, file in enumerate(itertools.chain.from_iterable(snake_case_     )     ):
        # 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(snake_case_						,    encoding=self.config.encoding						,    errors=self.config.encoding_errors     ) as f:
            _snake_case     :						Union[str, Any]						=   json.load(snake_case_     )

          # We keep only the field we are interested in
          _snake_case     :						List[str]						=   dataset[self.config.field]

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

        # If the file has one json object per line
        else:
          with open(snake_case_						,    "rb"     ) as f:
            _snake_case     :						Union[str, 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
            _snake_case     :						Optional[Any]						=   max(self.config.chunksize // 32						,    16 << 10     )
            _snake_case     :						Tuple						=   (
                self.config.encoding_errors if self.config.encoding_errors is not None else "strict"
            )
            while True:
              _snake_case     :						int						=   f.read(self.config.chunksize     )
              if not batch:
                break
              # Finish current line
              try:
                batch += f.readline()
              except (AttributeError, io.UnsupportedOperation):
                batch += readline(snake_case_     )
              # PyArrow only accepts utf-8 encoded bytes
              if self.config.encoding != "utf-8":
                _snake_case     :						Optional[int]						=   batch.decode(self.config.encoding						,    errors=snake_case_     ).encode("utf-8"     )
              try:
                while True:
                  try:
                    _snake_case     :						List[Any]						=   paj.read_json(
                        io.BytesIO(snake_case_     )						,    read_options=paj.ReadOptions(block_size=snake_case_     )     )
                    break
                  except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                    if (
                        isinstance(snake_case_						,    pa.ArrowInvalid     )
                        and "straddling" not in str(snake_case_     )
                        or block_size > len(snake_case_     )
                    ):
                      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(snake_case_     )} 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(
                      snake_case_						,    encoding=self.config.encoding						,    errors=self.config.encoding_errors     ) as f:
                    _snake_case     :						Optional[Any]						=   json.load(snake_case_     )
                except json.JSONDecodeError:
                  logger.error(F'Failed to read file \'{file}\' with error {type(snake_case_     )}: {e}'     )
                  raise e
                # If possible, parse the file as a list of json objects and exit the loop
                if isinstance(snake_case_						,    snake_case_     ):  # list is the only sequence type supported in JSON
                  try:
                    _snake_case     :						Union[str, Any]						=   set().union(*[row.keys() for row in dataset]     )
                    _snake_case     :						Union[str, Any]						=   {col: [row.get(snake_case_     ) for row in dataset] for col in keys}
                    _snake_case     :						Union[str, Any]						=   pa.Table.from_pydict(snake_case_     )
                  except (pa.ArrowInvalid, AttributeError) as e:
                    logger.error(F'Failed to read file \'{file}\' with error {type(snake_case_     )}: {e}'     )
                    raise ValueError(F'Not able to read records in the JSON file at {file}.'     ) from None
                  yield file_idx, self._cast_table(snake_case_     )
                  break
                else:
                  logger.error(F'Failed to read file \'{file}\' with error {type(snake_case_     )}: {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(snake_case_     )
              batch_idx += 1



"""simple docstring"""
from __future__ import annotations






class      _UpperCAmelCase				:
    def __init__(    self						,    snake_case_						,    snake_case_     ):
      _snake_case			,					_snake_case     :						Dict						=   text, pattern
      _snake_case			,					_snake_case     :						int						=   len(snake_case_     ), len(snake_case_     )
    def 					lowerCamelCase__	(    self						,    snake_case_     ):

      for i in range(self.patLen - 1						,    -1						,    -1     ):
        if char == self.pattern[i]:
          return i
      return -1
    def 					lowerCamelCase__	(    self						,    snake_case_     ):

      for i in range(self.patLen - 1						,    -1						,    -1     ):
        if self.pattern[i] != self.text[current_pos + i]:
          return current_pos + i
      return -1


    def 					lowerCamelCase__	(    self     ):
      # searches pattern in text and returns index positions
      _snake_case     :						List[str]						=   []
      for i in range(self.textLen - self.patLen + 1     ):
        _snake_case     :						Union[str, Any]						=   self.mismatch_in_text(snake_case_     )
        if mismatch_index == -1:
          positions.append(snake_case_     )
        else:
          _snake_case     :						Tuple						=   self.match_in_pattern(self.text[mismatch_index]     )
          _snake_case     :						Tuple						=   (
              mismatch_index - match_index
          )  # shifting index lgtm [py/multiple-definition]
      return positions


_a      :			List[Any]					=							"""ABAABA"""
_a      :			str					=							"""AB"""
_a      :			List[Any]					=							BoyerMooreSearch(text, pattern)
_a      :			Any					=							bms.bad_character_heuristic()

if len(positions) == 0:
    print("""No match found""")
else:
    print("""Pattern found in following positions: """)
    print(positions)

