






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

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


lowerCamelCase_					=      {
    '''configuration_bert''': ['''BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BertConfig''', '''BertOnnxConfig'''],
    '''tokenization_bert''': ['''BasicTokenizer''', '''BertTokenizer''', '''WordpieceTokenizer'''],
}

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_					=      [
			    '''BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''BertForMaskedLM''',
			    '''BertForMultipleChoice''',
			    '''BertForNextSentencePrediction''',
			    '''BertForPreTraining''',
			    '''BertForQuestionAnswering''',
			    '''BertForSequenceClassification''',
			    '''BertForTokenClassification''',
			    '''BertLayer''',
			    '''BertLMHeadModel''',
			    '''BertModel''',
			    '''BertPreTrainedModel''',
			    '''load_tf_weights_in_bert''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_					=      [
			    '''TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFBertEmbeddings''',
			    '''TFBertForMaskedLM''',
			    '''TFBertForMultipleChoice''',
			    '''TFBertForNextSentencePrediction''',
			    '''TFBertForPreTraining''',
			    '''TFBertForQuestionAnswering''',
			    '''TFBertForSequenceClassification''',
			    '''TFBertForTokenClassification''',
			    '''TFBertLMHeadModel''',
			    '''TFBertMainLayer''',
			    '''TFBertModel''',
			    '''TFBertPreTrainedModel''',
			]
try:
			if not is_tensorflow_text_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_					=      ['''TFBertTokenizer''']

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_					=      [
			    '''FlaxBertForCausalLM''',
			    '''FlaxBertForMaskedLM''',
			    '''FlaxBertForMultipleChoice''',
			    '''FlaxBertForNextSentencePrediction''',
			    '''FlaxBertForPreTraining''',
			    '''FlaxBertForQuestionAnswering''',
			    '''FlaxBertForSequenceClassification''',
			    '''FlaxBertForTokenClassification''',
			    '''FlaxBertModel''',
			    '''FlaxBertPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
			from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bert_fast import BertTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bert import (
						    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BertForMaskedLM,
						    BertForMultipleChoice,
						    BertForNextSentencePrediction,
						    BertForPreTraining,
						    BertForQuestionAnswering,
						    BertForSequenceClassification,
						    BertForTokenClassification,
						    BertLayer,
						    BertLMHeadModel,
						    BertModel,
						    BertPreTrainedModel,
						    load_tf_weights_in_bert,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_bert import (
						    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFBertEmbeddings,
						    TFBertForMaskedLM,
						    TFBertForMultipleChoice,
						    TFBertForNextSentencePrediction,
						    TFBertForPreTraining,
						    TFBertForQuestionAnswering,
						    TFBertForSequenceClassification,
						    TFBertForTokenClassification,
						    TFBertLMHeadModel,
						    TFBertMainLayer,
						    TFBertModel,
						    TFBertPreTrainedModel,
						)

			try:
						if not is_tensorflow_text_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bert_tf import TFBertTokenizer

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_bert import (
						    FlaxBertForCausalLM,
						    FlaxBertForMaskedLM,
						    FlaxBertForMultipleChoice,
						    FlaxBertForNextSentencePrediction,
						    FlaxBertForPreTraining,
						    FlaxBertForQuestionAnswering,
						    FlaxBertForSequenceClassification,
						    FlaxBertForTokenClassification,
						    FlaxBertModel,
						    FlaxBertPreTrainedModel,
						)

else:
			import sys

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











"""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()



