
import copy
import os

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






class   UpperCAmelCase		:



   def __init__(self		:					List[str] )		->	Any:


          '''simple docstring'''


          snake_case       :   Tuple							=      ""
          snake_case       :   str							=      ""
          snake_case       :   Dict							=      []
          snake_case       :   str							=      0
          snake_case       :   Tuple							=      2_56
          snake_case       :   Optional[Any]							=      0
          snake_case       :   Union[str, Any]							=      0
          snake_case       :   Any							=      0
          snake_case       :   Union[str, Any]							=      0



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					Tuple )		->	List[Any]:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any] )		->	List[Any]:


          '''simple docstring'''


          plt.hist(self.img.ravel()    ,						2_56    ,						[0, 2_56] )



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	int:


          '''simple docstring'''


          cva.imshow("Output-Image"    ,						self.img )
          cva.imshow("Input-Image"    ,						self.original_image )
          cva.waitKey(50_00 )
          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()









"""simple docstring"""




from typing import TYPE_CHECKING

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


lowercase_			     =  {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  ["RoFormerTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "RoFormerForCausalLM",
				    "RoFormerForMaskedLM",
				    "RoFormerForMultipleChoice",
				    "RoFormerForQuestionAnswering",
				    "RoFormerForSequenceClassification",
				    "RoFormerForTokenClassification",
				    "RoFormerLayer",
				    "RoFormerModel",
				    "RoFormerPreTrainedModel",
				    "load_tf_weights_in_roformer",
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFRoFormerForCausalLM",
				    "TFRoFormerForMaskedLM",
				    "TFRoFormerForMultipleChoice",
				    "TFRoFormerForQuestionAnswering",
				    "TFRoFormerForSequenceClassification",
				    "TFRoFormerForTokenClassification",
				    "TFRoFormerLayer",
				    "TFRoFormerModel",
				    "TFRoFormerPreTrainedModel",
				]


try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "FlaxRoFormerForMaskedLM",
				    "FlaxRoFormerForMultipleChoice",
				    "FlaxRoFormerForQuestionAnswering",
				    "FlaxRoFormerForSequenceClassification",
				    "FlaxRoFormerForTokenClassification",
				    "FlaxRoFormerModel",
				    "FlaxRoFormerPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
				from .tokenization_roformer import RoFormerTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_roformer_fast import RoFormerTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_roformer import (
								    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RoFormerForCausalLM,
								    RoFormerForMaskedLM,
								    RoFormerForMultipleChoice,
								    RoFormerForQuestionAnswering,
								    RoFormerForSequenceClassification,
								    RoFormerForTokenClassification,
								    RoFormerLayer,
								    RoFormerModel,
								    RoFormerPreTrainedModel,
								    load_tf_weights_in_roformer,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_roformer import (
								    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRoFormerForCausalLM,
								    TFRoFormerForMaskedLM,
								    TFRoFormerForMultipleChoice,
								    TFRoFormerForQuestionAnswering,
								    TFRoFormerForSequenceClassification,
								    TFRoFormerForTokenClassification,
								    TFRoFormerLayer,
								    TFRoFormerModel,
								    TFRoFormerPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_roformer import (
								    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    FlaxRoFormerForMaskedLM,
								    FlaxRoFormerForMultipleChoice,
								    FlaxRoFormerForQuestionAnswering,
								    FlaxRoFormerForSequenceClassification,
								    FlaxRoFormerForTokenClassification,
								    FlaxRoFormerModel,
								    FlaxRoFormerPreTrainedModel,
								)


else:
				import sys

				lowercase_			     =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






