






"""simple docstring"""
import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


lowerCamelCase_					=      logging.get_logger(__name__)







class 			UpperCamelCase_       :
	__magic_name__	      =				None





@experimental
def 				snake_case      (   A__	,A__	,A__	,A__	,A__	,A__	,A__						):
			if ParallelBackendConfig.backend_name is None:
						return _map_with_multiprocessing_pool(
						    A__	,A__	,A__	,A__	,A__	,A__	,A__						)

			return _map_with_joblib(A__	,A__	,A__	,A__	,A__	,A__	,A__						)

def 				snake_case      (   A__	,A__	,A__	,A__	,A__	,A__	,A__						):
			UpperCAmelCase_     : List[Any]			     =					num_proc if num_proc <= len(A__						) else len(A__						)
			UpperCAmelCase_     : Optional[Any]			     =					[]  # We organize the splits ourselve (contiguous splits)
			for index in range(A__						):
						UpperCAmelCase_     : Union[str, Any]			     =					len(A__						) // num_proc
						UpperCAmelCase_     : Dict			     =					len(A__						) % num_proc
						UpperCAmelCase_     : Any			     =					div * index + min(A__	,A__						)
						UpperCAmelCase_     : int			     =					start + div + (1 if index < mod else 0)
						split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)						)

			if len(A__						) != sum(len(i[1]						) for i in split_kwds						):
						raise ValueError(
						    F"""Error dividing inputs iterable among processes. """
						    F"""Total number of objects {len(A__						)}, """
						    F"""length: {sum(len(i[1]						) for i in split_kwds						)}"""						)

			logger.info(
			    F"""Spawning {num_proc} processes for {len(A__						)} objects in slices of {[len(i[1]						) for i in split_kwds]}"""						)
			UpperCAmelCase_     ,	UpperCAmelCase_     : str			     =					None, None
			if not disable_tqdm:
						UpperCAmelCase_     ,	UpperCAmelCase_     : List[str]			     =					(RLock(),), tqdm.set_lock
			with Pool(A__	,initargs=A__	,initializer=A__						) as pool:
						UpperCAmelCase_     : Any			     =					pool.map(A__	,A__						)
			logger.info(F"""Finished {num_proc} processes"""						)
			UpperCAmelCase_     : Tuple			     =					[obj for proc_res in mapped for obj in proc_res]
			logger.info(F"""Unpacked {len(A__						)} objects"""						)

			return mapped

def 				snake_case      (   A__	,A__	,A__	,A__	,A__	,A__	,A__						):
			# progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
			# and it requires monkey-patching joblib internal classes which is subject to change
			import joblib

			with joblib.parallel_backend(ParallelBackendConfig.backend_name	,n_jobs=A__						):
						return joblib.Parallel()(
						    joblib.delayed(A__						)((function, obj, types, None, True, None)						) for obj in iterable						)




@experimental
@contextlib.contextmanager
def 				snake_case      (   A__						):
			UpperCAmelCase_     : Any			     =					backend_name

			if backend_name == "spark":
						from joblibspark import register_spark

						register_spark()

						# TODO: call create_cache_and_write_probe if "download" in steps
						# TODO: raise NotImplementedError when Dataset.map etc is called

			try:
						yield
			finally:
						UpperCAmelCase_     : int			     =					None











"""simple docstring"""
import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt







class 			UpperCamelCase_       :





	def __init__(      self					: str						)						->			Dict:
				UpperCAmelCase_     : List[Any]			     =					""
				UpperCAmelCase_     : int			     =					""
				UpperCAmelCase_     : Dict			     =					[]
				UpperCAmelCase_     : int			     =					0
				UpperCAmelCase_     : List[Any]			     =					256
				UpperCAmelCase_     : Dict			     =					0
				UpperCAmelCase_     : List[Any]			     =					0
				UpperCAmelCase_     : str			     =					0
				UpperCAmelCase_     : List[str]			     =					0





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: Dict						)						->			Optional[Any]:
				UpperCAmelCase_     : Dict			     =					cva.imread(lowerCAmelCase_	,	0						)
				UpperCAmelCase_     : Union[str, Any]			     =					copy.deepcopy(self.img						)
				UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : List[Any]			     =					plt.hist(self.img.ravel()	,	256	,	[0, 256]	,	label="x"						)
				UpperCAmelCase_     : List[Any]			     =					np.sum(lowerCAmelCase_						)
				for i in range(len(lowerCAmelCase_						)						):
							UpperCAmelCase_     : List[Any]			     =					x[i] / self.k
							self.sk += prk
							UpperCAmelCase_     : Optional[Any]			     =					(self.L - 1) * self.sk
							if self.rem != 0:
										UpperCAmelCase_     : Any			     =					int(last % last						)
							UpperCAmelCase_     : List[str]			     =					int(last + 1 if self.rem >= 0.5 else last						)
							self.last_list.append(lowerCAmelCase_						)
							UpperCAmelCase_     : Optional[Any]			     =					int(np.ma.count(self.img						) / self.img[1].size						)
							UpperCAmelCase_     : Dict			     =					self.img[1].size
				for i in range(self.number_of_cols						):
							for j in range(self.number_of_rows						):
										UpperCAmelCase_     : Any			     =					self.img[j][i]
										if num != self.last_list[num]:
													UpperCAmelCase_     : Tuple			     =					self.last_list[num]
				cva.imwrite("output_data/output.jpg"	,	self.img						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			Union[str, Any]:
				plt.hist(self.img.ravel()	,	256	,	[0, 256]						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			str:
				cva.imshow("Output-Image"	,	self.img						)
				cva.imshow("Input-Image"	,	self.original_image						)
				cva.waitKey(5_000						)
				cva.destroyAllWindows()


if __name__ == "__main__":
			lowerCamelCase_					=      os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
			lowerCamelCase_					=      ConstantStretch()
			stretcher.stretch(file_path)
			stretcher.plot_histogram()
			stretcher.show_image()



