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

import cva
import numpy as np
from matplotlib import pyplot as plt
class       lowerCAmelCase_		:



							'''simple docstring'''





							def __init__( self				)       ->  Tuple:
													__lowerCAmelCase     =  """"""
													__lowerCAmelCase     =  """"""
													__lowerCAmelCase     =  []
													__lowerCAmelCase     =  0
													__lowerCAmelCase     =  256
													__lowerCAmelCase     =  0
													__lowerCAmelCase     =  0
													__lowerCAmelCase     =  0
													__lowerCAmelCase     =  0




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




							def    A__						( self				)       ->  str:
													plt.hist(self.img.ravel()    ,				256    ,				[0, 256]				)




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


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


"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_					     =	{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE_					     =	[
			    '''RagModel''',
			    '''RagPreTrainedModel''',
			    '''RagSequenceForGeneration''',
			    '''RagTokenForGeneration''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE_					     =	[
			    '''TFRagModel''',
			    '''TFRagPreTrainedModel''',
			    '''TFRagSequenceForGeneration''',
			    '''TFRagTokenForGeneration''',
			]


if TYPE_CHECKING:
			from .configuration_rag import RagConfig
			from .retrieval_rag import RagRetriever
			from .tokenization_rag import RagTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_rag import (
						    TFRagModel,
						    TFRagPreTrainedModel,
						    TFRagSequenceForGeneration,
						    TFRagTokenForGeneration,
						)

else:
			import sys

			SCREAMING_SNAKE_CASE_					     =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
