
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


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




@dataclass
class 	__A(				datasets.BuilderConfig ):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__			    =			None
  SCREAMING_SNAKE_CASE__			    =			"""utf-8"""
  SCREAMING_SNAKE_CASE__			    =			None
  SCREAMING_SNAKE_CASE__			    =			None
  SCREAMING_SNAKE_CASE__			    =			True  # deprecated
  SCREAMING_SNAKE_CASE__			    =			None  # deprecated
  SCREAMING_SNAKE_CASE__			    =			10 << 20  # 10MB
  SCREAMING_SNAKE_CASE__			    =			None






class 	__A(				datasets.ArrowBasedBuilder ):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__			    =			JsonConfig







  def        UpperCAmelCase_					(self						):






       if self.config.block_size is not None:
            logger.warning("""The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"""						)
            UpperCamelCase__				=				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        UpperCAmelCase_					(self       ,			SCREAMING_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}"						)
       UpperCamelCase__				=				dl_manager.download_and_extract(self.config.data_files						)
       if isinstance(_UpperCAmelCase       ,			(str, list, tuple)						):
            UpperCamelCase__				=				data_files
            if isinstance(_UpperCAmelCase       ,			_UpperCAmelCase						):
                 UpperCamelCase__				=				[files]
            UpperCamelCase__				=				[dl_manager.iter_files(_UpperCAmelCase						) for file in files]
            return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,			gen_kwargs={"""files""": files}						)]
       UpperCamelCase__				=				[]
       for split_name, files in data_files.items():
            if isinstance(_UpperCAmelCase       ,			_UpperCAmelCase						):
                 UpperCamelCase__				=				[files]
            UpperCamelCase__				=				[dl_manager.iter_files(_UpperCAmelCase						) for file in files]
            splits.append(datasets.SplitGenerator(name=_UpperCAmelCase       ,			gen_kwargs={"""files""": files}						)						)
       return splits







  def        UpperCAmelCase_					(self       ,			SCREAMING_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						):
                 UpperCamelCase__				=				self.config.features.arrow_schema.field(_UpperCAmelCase						).type
                 UpperCamelCase__				=				pa_table.append_column(_UpperCAmelCase       ,			pa.array([None] * len(_UpperCAmelCase						)       ,			type=_UpperCAmelCase						)						)
            # more expensive cast to support nested structures with keys in a different order
            # allows str <-> int/float or str to Audio for example
            UpperCamelCase__				=				table_cast(_UpperCAmelCase       ,			self.config.features.arrow_schema						)
       return pa_table







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_						):






       for file_idx, file in enumerate(itertools.chain.from_iterable(_UpperCAmelCase						)						):
            # 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(_UpperCAmelCase       ,			encoding=self.config.encoding       ,			errors=self.config.encoding_errors						) as f:
                      UpperCamelCase__				=				json.load(_UpperCAmelCase						)

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

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

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












'''simple docstring'''




from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros






def        _lowerCAmelCase			(  __snake_case  : Optional[Any] ,							__snake_case  : Optional[int]			)     ->     List[str]:
     __A						: List[str]      =       k_size // 2
     __A ,__A						: List[Any]      =       mgrid[0 - center : k_size - center, 0 - center : k_size - center]
     __A						: List[Any]      =       1 / (2 * pi * sigma) * exp(-(square(__snake_case			) + square(__snake_case			)) / (2 * square(__snake_case			))			)
     return g






def        _lowerCAmelCase			(  __snake_case  : List[str] ,							__snake_case  : List[str] ,							__snake_case  : int			)     ->     Union[str, Any]:
     __A ,__A						: Tuple      =       image.shape[0], image.shape[1]
     # dst image height and width
     __A						: Tuple      =       height - k_size + 1
     __A						: Optional[Any]      =       width - k_size + 1

     # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
     __A						: str      =       zeros((dst_height * dst_width, k_size * k_size)			)
     __A						: Optional[Any]      =       0
     for i, j in product(range(__snake_case			) ,							range(__snake_case			)			):
          __A						: int      =       ravel(image[i : i + k_size, j : j + k_size]			)
          __A						: List[str]      =       window
          row += 1

     #  turn the kernel into shape(k*k, 1)
     __A						: List[Any]      =       gen_gaussian_kernel(__snake_case ,							__snake_case			)
     __A						: Any      =       ravel(__snake_case			)

     # reshape and get the dst image
     __A						: Dict      =       dot(__snake_case ,							__snake_case			).reshape(__snake_case ,							__snake_case			).astype(__snake_case			)

     return dst


if __name__ == "__main__":
  # read original image
  lowercase__    :      List[Any]						=    imread(r'''../image_data/lena.jpg''')
  # turn image in gray scale value
  lowercase__    :      Dict						=    cvtColor(img, COLOR_BGR2GRAY)

  # get values with two different mask size
  lowercase__    :      Any						=    gaussian_filter(gray, 3, sigma=1)
  lowercase__    :      str						=    gaussian_filter(gray, 5, sigma=0.8)

  # show result images
  imshow('''gaussian filter with 3x3 mask''', gaussianaxa)
  imshow('''gaussian filter with 5x5 mask''', gaussianaxa)
  waitKey()