

"""simple docstring"""




import copy
import os

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



	def __init__(  self     :       Tuple						):
		__UpperCAmelCase      		= ''''''
		__UpperCAmelCase      		= ''''''
		__UpperCAmelCase      		= []
		__UpperCAmelCase      		= 0
		__UpperCAmelCase      		= 2_56
		__UpperCAmelCase      		= 0
		__UpperCAmelCase      		= 0
		__UpperCAmelCase      		= 0
		__UpperCAmelCase      		= 0



	def        a  (  self     :       List[Any]			,    _lowercase     :       List[Any]						):
		__UpperCAmelCase      		= cva.imread(_lowercase			,    0						)
		__UpperCAmelCase      		= copy.deepcopy(self.img						)
		__UpperCAmelCase		,							__UpperCAmelCase		,							__UpperCAmelCase      		= plt.hist(self.img.ravel()			,    2_56			,    [0, 2_56]			,    label='''x'''						)
		__UpperCAmelCase      		= np.sum(_lowercase						)
		for i in range(len(_lowercase						)						):
			__UpperCAmelCase      		= x[i] / self.k
			self.sk += prk
			__UpperCAmelCase      		= (self.L - 1) * self.sk
			if self.rem != 0:
				__UpperCAmelCase      		= int(last % last						)
			__UpperCAmelCase      		= int(last + 1 if self.rem >= 0.5 else last						)
			self.last_list.append(_lowercase						)
			__UpperCAmelCase      		= int(np.ma.count(self.img						) / self.img[1].size						)
			__UpperCAmelCase      		= self.img[1].size
		for i in range(self.number_of_cols						):
			for j in range(self.number_of_rows						):
				__UpperCAmelCase      		= self.img[j][i]
				if num != self.last_list[num]:
					__UpperCAmelCase      		= self.last_list[num]
		cva.imwrite('''output_data/output.jpg'''			,    self.img						)



	def        a  (  self     :       Tuple						):
		plt.hist(self.img.ravel()			,    2_56			,    [0, 2_56]						)



	def        a  (  self     :       Union[str, Any]						):
		cva.imshow('''Output-Image'''			,    self.img						)
		cva.imshow('''Input-Image'''			,    self.original_image						)
		cva.waitKey(50_00						)
		cva.destroyAllWindows()


if __name__ == "__main__":
				_lowercase   :	Optional[int]												=						os.path.join(os.path.basename(__file__), 'image_data/input.jpg')
				_lowercase   :	Union[str, Any]												=						ConstantStretch()
				stretcher.stretch(file_path)
				stretcher.plot_histogram()
				stretcher.show_image()




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase   :    Tuple     =		{
    "configuration_blenderbot_small": [
        "BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotSmallConfig",
        "BlenderbotSmallOnnxConfig",
    ],
    "tokenization_blenderbot_small": ["BlenderbotSmallTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    List[Any]     =		["BlenderbotSmallTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    List[str]     =		[
		    "BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "BlenderbotSmallForCausalLM",
		    "BlenderbotSmallForConditionalGeneration",
		    "BlenderbotSmallModel",
		    "BlenderbotSmallPreTrainedModel",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    Any     =		[
		    "TFBlenderbotSmallForConditionalGeneration",
		    "TFBlenderbotSmallModel",
		    "TFBlenderbotSmallPreTrainedModel",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    List[Any]     =		[
		    "FlaxBlenderbotSmallForConditionalGeneration",
		    "FlaxBlenderbotSmallModel",
		    "FlaxBlenderbotSmallPreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_blenderbot_small import (
		    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BlenderbotSmallConfig,
		    BlenderbotSmallOnnxConfig,
		)
		from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blenderbot_small import (
				    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlenderbotSmallForCausalLM,
				    BlenderbotSmallForConditionalGeneration,
				    BlenderbotSmallModel,
				    BlenderbotSmallPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_blenderbot_small import (
				    TFBlenderbotSmallForConditionalGeneration,
				    TFBlenderbotSmallModel,
				    TFBlenderbotSmallPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_blenderbot_small import (
				    FlaxBlenderbotSmallForConditionalGeneration,
				    FlaxBlenderbotSmallModel,
				    FlaxBlenderbotSmallPreTrainedModel,
				)

else:
		import sys

		UpperCAmelCase   :    List[Any]     =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

