


'''simple docstring'''
import copy
import os

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

		"""simple docstring"""

		def __init__(	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				''''''
							UpperCamelCase__     :str							      =				''''''
							UpperCamelCase__     :Tuple							      =				[]
							UpperCamelCase__     :Any							      =				0
							UpperCamelCase__     :Tuple							      =				256
							UpperCamelCase__     :Optional[Any]							      =				0
							UpperCamelCase__     :str							      =				0
							UpperCamelCase__     :Union[str, Any]							      =				0
							UpperCamelCase__     :Union[str, Any]							      =				0




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


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




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							plt.hist(self.img.ravel()					,     256					,     [0, 256]							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							cva.imshow('''Output-Image'''					,     self.img							)
							cva.imshow('''Input-Image'''					,     self.original_image							)
							cva.waitKey(5000							)
							cva.destroyAllWindows()


if __name__ == "__main__":
		__snake_case							   =  os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
		__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_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__snake_case							   =  {
    '''configuration_layoutlmv3''': [
        '''LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LayoutLMv3Config''',
        '''LayoutLMv3OnnxConfig''',
    ],
    '''processing_layoutlmv3''': ['''LayoutLMv3Processor'''],
    '''tokenization_layoutlmv3''': ['''LayoutLMv3Tokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							   =  ['''LayoutLMv3TokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							   =  [
		    '''LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''LayoutLMv3ForQuestionAnswering''',
		    '''LayoutLMv3ForSequenceClassification''',
		    '''LayoutLMv3ForTokenClassification''',
		    '''LayoutLMv3Model''',
		    '''LayoutLMv3PreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							   =  [
		    '''TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFLayoutLMv3ForQuestionAnswering''',
		    '''TFLayoutLMv3ForSequenceClassification''',
		    '''TFLayoutLMv3ForTokenClassification''',
		    '''TFLayoutLMv3Model''',
		    '''TFLayoutLMv3PreTrainedModel''',
		]

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							   =  ['''LayoutLMv3FeatureExtractor''']
		__snake_case							   =  ['''LayoutLMv3ImageProcessor''']


if TYPE_CHECKING:
		from .configuration_layoutlmva import (
		    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    LayoutLMvaConfig,
		    LayoutLMvaOnnxConfig,
		)
		from .processing_layoutlmva import LayoutLMvaProcessor
		from .tokenization_layoutlmva import LayoutLMvaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_layoutlmva import (
				    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LayoutLMvaForQuestionAnswering,
				    LayoutLMvaForSequenceClassification,
				    LayoutLMvaForTokenClassification,
				    LayoutLMvaModel,
				    LayoutLMvaPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_layoutlmva import (
				    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFLayoutLMvaForQuestionAnswering,
				    TFLayoutLMvaForSequenceClassification,
				    TFLayoutLMvaForTokenClassification,
				    TFLayoutLMvaModel,
				    TFLayoutLMvaPreTrainedModel,
				)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
				from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
		import sys

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