"""simple docstring"""


import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class   UpperCAmelCase_	:
    def __init__(	self			:       Union[str, Any]   ,						_lowercase			:       Optional[Any]   ,						_lowercase			:       int		)						->    List[str]:
       _lowercase							     =       0
       # max greyscale value for #FFFFFF
       _lowercase							     =       int(self.get_greyscale(2_5_5   ,						2_5_5   ,						2_5_5		)		)

       if not self.min_threshold < threshold < self.max_threshold:
          _lowercase							     =       f"""Factor value should be from 0 to {self.max_threshold}"""
          raise ValueError(lowerCamelCase__		)

       _lowercase							     =       input_img
       _lowercase							     =       threshold
       _lowercase							     =       self.input_img.shape[1], self.input_img.shape[0]

       # error table size (+4 columns and +1 row) greater than input image because of
       # lack of if statements
       _lowercase							     =       [
           [0 for _ in range(self.height + 4		)] for __ in range(self.width + 1		)
       ]
       _lowercase							     =       np.ones((self.width, self.height, 3)   ,						np.uinta		) * 2_5_5
    @classmethod
    def 		_lowerCamelCase      (	cls			:       Optional[Any]   ,						_lowercase			:       int   ,						_lowercase			:       int   ,						_lowercase			:       int		)						->    float:
       return 0.1_14 * blue + 0.5_87 * green + 0.21_26 * red




    def 		_lowerCamelCase      (	self			:       List[Any]		)						->    None:







       for y in range(self.height		):
          for x in range(self.width		):
             _lowercase							     =       int(self.get_greyscale(*self.input_img[y][x]		)		)
             if self.threshold > greyscale + self.error_table[y][x]:
                _lowercase							     =       (0, 0, 0)
                _lowercase							     =       greyscale + self.error_table[x][y]
             else:
                _lowercase							     =       (2_5_5, 2_5_5, 2_5_5)
                _lowercase							     =       greyscale + self.error_table[x][y] - 2_5_5
             self.error_table[y][x + 1] += int(8 / 3_2 * current_error		)
             self.error_table[y][x + 2] += int(4 / 3_2 * current_error		)
             self.error_table[y + 1][x] += int(8 / 3_2 * current_error		)
             self.error_table[y + 1][x + 1] += int(4 / 3_2 * current_error		)
             self.error_table[y + 1][x + 2] += int(2 / 3_2 * current_error		)
             self.error_table[y + 1][x - 1] += int(4 / 3_2 * current_error		)
             self.error_table[y + 1][x - 2] += int(2 / 3_2 * current_error		)


if __name__ == "__main__":
  # create Burke's instances with original images in greyscale
  __UpperCamelCase       :    Dict         =		[
      Burkes(imread("image_data/lena.jpg", 1), threshold)
      for threshold in (1, 1_2_6, 1_3_0, 1_4_0)
  ]

  for burkes in burkes_instances:
    burkes.process()

  for burkes in burkes_instances:
    imshow(
        f'''Original image with dithering threshold: {burkes.threshold}''',
        burkes.output_img,
    )

  waitKey(0)
  destroyAllWindows()



'''simple docstring'''


import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			**__lowerCAmelCase ):
		return RunBeamCommand(
		    args.dataset   ,			args.name   ,			args.cache_dir   ,			args.beam_pipeline_options   ,			args.data_dir   ,			args.all_configs   ,			args.save_info or args.save_infos   ,			args.ignore_verifications   ,			args.force_redownload   ,			**__lowerCAmelCase   ,			)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  ArgumentParser			) ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							Union[str, Any]             =   parser.add_parser("run_beam"						,	help="Run a Beam dataset processing pipeline"			)
					run_beam_parser.add_argument("dataset"						,	type=lowerCamelCase__						,	help="Name of the dataset to download"			)
					run_beam_parser.add_argument("--name"						,	type=lowerCamelCase__						,	default=lowerCamelCase__						,	help="Dataset config name"			)
					run_beam_parser.add_argument(
					    "--cache_dir"						,	type=lowerCamelCase__						,	default=lowerCamelCase__						,	help="Cache directory where the datasets are stored"						,	)
					run_beam_parser.add_argument(
					    "--beam_pipeline_options"						,	type=lowerCamelCase__						,	default=""						,	help="Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`"						,	)
					run_beam_parser.add_argument(
					    "--data_dir"						,	type=lowerCamelCase__						,	default=lowerCamelCase__						,	help="Can be used to specify a manual directory to get the files from"						,	)
					run_beam_parser.add_argument("--all_configs"						,	action="store_true"						,	help="Test all dataset configurations"			)
					run_beam_parser.add_argument("--save_info"						,	action="store_true"						,	help="Save the dataset infos file"			)
					run_beam_parser.add_argument(
					    "--ignore_verifications"						,	action="store_true"						,	help="Run the test without checksums and splits checks"			)
					run_beam_parser.add_argument("--force_redownload"						,	action="store_true"						,	help="Force dataset redownload"			)
					# aliases
					run_beam_parser.add_argument("--save_infos"						,	action="store_true"						,	help="alias for save_info"			)
					run_beam_parser.set_defaults(func=lowerCamelCase__			)




			def __init__(		self	:  str						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  bool						,	lowerCamelCase__	:  bool						,	lowerCamelCase__	:  bool						,	lowerCamelCase__	:  bool						,	**lowerCamelCase__	:  Optional[Any]						,	) ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:							Any             =   dataset
					_UpperCAmelCase		:							str             =   name
					_UpperCAmelCase		:							int             =   cache_dir
					_UpperCAmelCase		:							Optional[int]             =   beam_pipeline_options
					_UpperCAmelCase		:							int             =   data_dir
					_UpperCAmelCase		:							Optional[int]             =   all_configs
					_UpperCAmelCase		:							Any             =   save_infos
					_UpperCAmelCase		:							Optional[Any]             =   ignore_verifications
					_UpperCAmelCase		:							Any             =   force_redownload
					_UpperCAmelCase		:							Tuple             =   config_kwargs






			def 				lowerCAmelCase__     (		self	:  Dict			) ->Union[str, Any]:



					'''simple docstring'''







					import apache_beam as beam

					if self._name is not None and self._all_configs:
							print("Both parameters `name` and `all_configs` can't be used at once."			)
							exit(1			)
					_UpperCAmelCase       ,     _UpperCAmelCase		:							Union[str, Any]             =   self._dataset, self._name
					_UpperCAmelCase		:							Union[str, Any]             =   dataset_module_factory(lowerCamelCase__			)
					_UpperCAmelCase		:							Any             =   import_main_class(dataset_module.module_path			)
					_UpperCAmelCase		:							List[DatasetBuilder]             =   []
					if self._beam_pipeline_options:
							_UpperCAmelCase		:							Optional[int]             =   beam.options.pipeline_options.PipelineOptions(
							    flags=[F"""--{opt.strip()}""" for opt in self._beam_pipeline_options.split(","			) if opt]			)
					else:
							_UpperCAmelCase		:							str             =   None
					if self._all_configs and len(builder_cls.BUILDER_CONFIGS			) > 0:
							for builder_config in builder_cls.BUILDER_CONFIGS:
									builders.append(
									    builder_cls(
									        config_name=builder_config.name						,	data_dir=self._data_dir						,	hash=dataset_module.hash						,	beam_options=lowerCamelCase__						,	cache_dir=self._cache_dir						,	base_path=dataset_module.builder_kwargs.get("base_path"			)						,	)			)
					else:
							builders.append(
							    builder_cls(
							        config_name=lowerCamelCase__						,	data_dir=self._data_dir						,	beam_options=lowerCamelCase__						,	cache_dir=self._cache_dir						,	base_path=dataset_module.builder_kwargs.get("base_path"			)						,	**self._config_kwargs						,	)			)

					for builder in builders:
							builder.download_and_prepare(
							    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
							    if not self._force_redownload
							    else DownloadMode.FORCE_REDOWNLOAD						,	download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH			)						,	verification_mode=VerificationMode.NO_CHECKS
							    if self._ignore_verifications
							    else VerificationMode.ALL_CHECKS						,	try_from_hf_gcs=lowerCamelCase__						,	)
							if self._save_infos:
									builder._save_infos()

					print("Apache beam run successful."			)

					# If save_infos=True, the dataset infos file is created next to the loaded module file.
					# Let's move it to the original directory of the dataset script, to allow the user to
					# upload them on S3 at the same time afterwards.
					if self._save_infos:
							_UpperCAmelCase		:							Optional[Any]             =   os.path.join(builder_cls.get_imported_module_dir()						,	config.DATASETDICT_INFOS_FILENAME			)

							_UpperCAmelCase		:							List[str]             =   Path(lowerCamelCase__			).name + ".py"

							_UpperCAmelCase		:							int             =   os.path.join(lowerCamelCase__						,	lowerCamelCase__			)
							if os.path.isfile(lowerCamelCase__			):
									_UpperCAmelCase		:							Any             =   os.path.dirname(lowerCamelCase__			)
							elif os.path.isfile(lowerCamelCase__			):
									_UpperCAmelCase		:							Tuple             =   path
							else:  # in case of a remote dataset
									print(F"""Dataset Infos file saved at {dataset_infos_path}"""			)
									exit(1			)

							# Move datasetinfo back to the user
							_UpperCAmelCase		:							Union[str, Any]             =   os.path.join(lowerCamelCase__						,	config.DATASETDICT_INFOS_FILENAME			)
							copyfile(lowerCamelCase__						,	lowerCamelCase__			)
							print(F"""Dataset Infos file saved at {user_dataset_infos_path}"""			)



