

import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class  UpperCAmelCase__	( A__ ):






		"""simple docstring"""


		def __init__(							self		:	str			,     __lowerCamelCase		:	NestedDataStructureLike[PathLike]			,     __lowerCamelCase		:	Optional[NamedSplit] = None			,     __lowerCamelCase		:	Optional[Features] = None			,     __lowerCamelCase		:	str = None			,     __lowerCamelCase		:	bool = False			,     __lowerCamelCase		:	bool = False			,     __lowerCamelCase		:	Optional[str] = None			,     __lowerCamelCase		:	Optional[int] = None			,     **__lowerCamelCase		:	Tuple			,     )	->							Union[str, Any]:
							super().__init__(
							    __lowerCamelCase			,     split=__lowerCamelCase			,     features=__lowerCamelCase			,     cache_dir=__lowerCamelCase			,     keep_in_memory=__lowerCamelCase			,     streaming=__lowerCamelCase			,     num_proc=__lowerCamelCase			,     **__lowerCamelCase			,     )
							SCREAMING_SNAKE_CASE__					=					field
							SCREAMING_SNAKE_CASE__					=					path_or_paths if isinstance(__lowerCamelCase			,     __lowerCamelCase      ) else {self.split: path_or_paths}
							SCREAMING_SNAKE_CASE__					=					Json(
							    cache_dir=__lowerCamelCase			,     data_files=__lowerCamelCase			,     features=__lowerCamelCase			,     field=__lowerCamelCase			,     **__lowerCamelCase			,     )







		def    lowercase_	(							self		:	List[str]      )	->							Dict:
							# Build iterable dataset
							if self.streaming:
												SCREAMING_SNAKE_CASE__					=					self.builder.as_streaming_dataset(split=self.split      )
							# Build regular (map-style) dataset
							else:
												SCREAMING_SNAKE_CASE__					=					None
												SCREAMING_SNAKE_CASE__					=					None
												SCREAMING_SNAKE_CASE__					=					None
												SCREAMING_SNAKE_CASE__					=					None

												self.builder.download_and_prepare(
												    download_config=__lowerCamelCase			,     download_mode=__lowerCamelCase			,     verification_mode=__lowerCamelCase			,     base_path=__lowerCamelCase			,     num_proc=self.num_proc			,     )
												SCREAMING_SNAKE_CASE__					=					self.builder.as_dataset(
												    split=self.split			,     verification_mode=__lowerCamelCase			,     in_memory=self.keep_in_memory      )
							return dataset






class  UpperCAmelCase__	:






		"""simple docstring"""


		def __init__(							self		:	str			,     __lowerCamelCase		:	Dataset			,     __lowerCamelCase		:	Union[PathLike, BinaryIO]			,     __lowerCamelCase		:	Optional[int] = None			,     __lowerCamelCase		:	Optional[int] = None			,     **__lowerCamelCase		:	Optional[int]			,     )	->							str:
							if num_proc is not None and num_proc <= 0:
												raise ValueError(f'''num_proc {num_proc} must be an integer > 0.'''      )

							SCREAMING_SNAKE_CASE__					=					dataset
							SCREAMING_SNAKE_CASE__					=					path_or_buf
							SCREAMING_SNAKE_CASE__					=					batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
							SCREAMING_SNAKE_CASE__					=					num_proc
							SCREAMING_SNAKE_CASE__					=					'''utf-8'''
							SCREAMING_SNAKE_CASE__					=					to_json_kwargs






		def    lowercase_	(							self		:	Optional[Any]      )	->							int:
							SCREAMING_SNAKE_CASE__					=					self.to_json_kwargs.pop('''path_or_buf'''			,     __lowerCamelCase      )
							SCREAMING_SNAKE_CASE__					=					self.to_json_kwargs.pop('''orient'''			,     '''records'''      )
							SCREAMING_SNAKE_CASE__					=					self.to_json_kwargs.pop('''lines'''			,     True if orient == '''records''' else False      )
							SCREAMING_SNAKE_CASE__					=					self.to_json_kwargs.pop('''index'''			,     False if orient in ['''split''', '''table'''] else True      )
							SCREAMING_SNAKE_CASE__					=					self.to_json_kwargs.pop('''compression'''			,     __lowerCamelCase      )

							if compression not in [None, "infer", "gzip", "bz2", "xz"]:
												raise NotImplementedError(f'''`datasets` currently does not support {compression} compression'''      )

							if isinstance(self.path_or_buf			,     (str, bytes, os.PathLike)      ):
												with fsspec.open(self.path_or_buf			,     '''wb'''			,     compression=__lowerCamelCase      ) as buffer:
																	SCREAMING_SNAKE_CASE__					=					self._write(file_obj=__lowerCamelCase			,     orient=__lowerCamelCase			,     lines=__lowerCamelCase			,     index=__lowerCamelCase			,     **self.to_json_kwargs      )
							else:
												if compression:
																	raise NotImplementedError(
																	    f'''The compression parameter is not supported when writing to a buffer, but compression={compression}'''
																	    ''' was passed. Please provide a local path instead.'''      )
												SCREAMING_SNAKE_CASE__					=					self._write(
												    file_obj=self.path_or_buf			,     orient=__lowerCamelCase			,     lines=__lowerCamelCase			,     index=__lowerCamelCase			,     **self.to_json_kwargs      )
							return written






		def    lowercase_	(							self		:	Any			,     __lowerCamelCase		:	Optional[int]      )	->							Union[str, Any]:
							SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					args

							SCREAMING_SNAKE_CASE__					=					query_table(
							    table=self.dataset.data			,     key=slice(__lowerCamelCase			,     offset + self.batch_size      )			,     indices=self.dataset._indices			,     )
							SCREAMING_SNAKE_CASE__					=					batch.to_pandas().to_json(
							    path_or_buf=__lowerCamelCase			,     orient=__lowerCamelCase			,     lines=__lowerCamelCase			,     index=__lowerCamelCase			,     **__lowerCamelCase      )
							if not json_str.endswith('''\n'''      ):
												json_str += "\n"
							return json_str.encode(self.encoding      )







		def    lowercase_	(							self		:	str			,     __lowerCamelCase		:	BinaryIO			,     __lowerCamelCase		:	Union[str, Any]			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	str			,     **__lowerCamelCase		:	Any			,     )	->							int:
							SCREAMING_SNAKE_CASE__					=					0

							if self.num_proc is None or self.num_proc == 1:
												for offset in logging.tqdm(
												    range(0			,     len(self.dataset      )			,     self.batch_size      )			,     unit='''ba'''			,     disable=not logging.is_progress_bar_enabled()			,     desc='''Creating json from Arrow format'''			,     ):
																	SCREAMING_SNAKE_CASE__					=					self._batch_json((offset, orient, lines, index, to_json_kwargs)      )
																	written += file_obj.write(__lowerCamelCase      )
							else:
												SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					len(self.dataset      ), self.batch_size
												with multiprocessing.Pool(self.num_proc      ) as pool:
																	for json_str in logging.tqdm(
																	    pool.imap(
																	        self._batch_json			,     [(offset, orient, lines, index, to_json_kwargs) for offset in range(0			,     __lowerCamelCase			,     __lowerCamelCase      )]			,     )			,     total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size			,     unit='''ba'''			,     disable=not logging.is_progress_bar_enabled()			,     desc='''Creating json from Arrow format'''			,     ):
																						written += file_obj.write(__lowerCamelCase      )

							return written





import doctest
from collections import deque

import numpy as np






class  UpperCAmelCase__	:






  """simple docstring"""


  def __init__(							self		:	Optional[Any]      )	->							None:
       SCREAMING_SNAKE_CASE__					=					[2, 1, 2, -1]
       SCREAMING_SNAKE_CASE__					=					[1, 2, 3, 4]







  def    lowercase_	(							self		:	List[str]      )	->							list[float]:
       SCREAMING_SNAKE_CASE__					=					len(self.first_signal      )
       SCREAMING_SNAKE_CASE__					=					len(self.second_signal      )

       SCREAMING_SNAKE_CASE__					=					max(__lowerCamelCase			,     __lowerCamelCase      )

       # create a zero matrix of max_length x max_length
       SCREAMING_SNAKE_CASE__					=					[[0] * max_length for i in range(__lowerCamelCase      )]

       # fills the smaller signal with zeros to make both signals of same length
       if length_first_signal < length_second_signal:
            self.first_signal += [0] * (max_length - length_first_signal)
       elif length_first_signal > length_second_signal:
            self.second_signal += [0] * (max_length - length_second_signal)
       for i in range(__lowerCamelCase      ):
            SCREAMING_SNAKE_CASE__					=					deque(self.second_signal      )
            rotated_signal.rotate(__lowerCamelCase      )
            for j, item in enumerate(__lowerCamelCase      ):
                 matrix[i][j] += item

        # multiply the matrix with the first signal
       SCREAMING_SNAKE_CASE__					=					np.matmul(np.transpose(__lowerCamelCase      )			,     np.transpose(self.first_signal      )      )

       # rounding-off to two decimal places
       return [round(__lowerCamelCase			,     2      ) for i in final_signal]


if __name__ == "__main__":
       doctest.testmod()


