


"""simple docstring"""



import copy
import os

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







class _lowerCAmelCase							:







   def __init__(      self		)      ->						Any:

       '''simple docstring'''




       snake_case       : Union[str, Any]    =       ""
       snake_case       : str    =       ""
       snake_case       : str    =       []
       snake_case       : Tuple    =       0
       snake_case       : List[Any]    =       256
       snake_case       : List[Any]    =       0
       snake_case       : Union[str, Any]    =       0
       snake_case       : Optional[Any]    =       0
       snake_case       : int    =       0







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						int:

       '''simple docstring'''




       snake_case       : List[Any]    =       cva.imread(UpperCamelCase__		,      0		)
       snake_case       : Optional[Any]    =       copy.deepcopy(self.img		)
       snake_case   ,snake_case   ,snake_case       : List[str]    =       plt.hist(self.img.ravel()		,      256		,      [0, 256]		,      label="x"		)
       snake_case       : Dict    =       np.sum(UpperCamelCase__		)
       for i in range(len(UpperCamelCase__		)		):
           snake_case       : Any    =       x[i] / self.k
           self.sk += prk
           snake_case       : Any    =       (self.L - 1) * self.sk
           if self.rem != 0:
               snake_case       : Any    =       int(last % last		)
           snake_case       : str    =       int(last + 1 if self.rem >= 0.5 else last		)
           self.last_list.append(UpperCamelCase__		)
           snake_case       : Optional[int]    =       int(np.ma.count(self.img		) / self.img[1].size		)
           snake_case       : Any    =       self.img[1].size
       for i in range(self.number_of_cols		):
           for j in range(self.number_of_rows		):
               snake_case       : 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 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		)      ->						Optional[Any]:

       '''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"""



import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()







def     __lowerCAmelCase		(		lowercase    :    List[Any]		,				lowercase    :    Dict		,				lowercase    :    str					)      ->		Optional[int]:


    """simple docstring"""





    snake_case       : Tuple    =       AlbertConfig.from_json_file(lowercase					)
    print(F'Building PyTorch model from configuration: {config}'					)
    snake_case       : int    =       AlbertForPreTraining(lowercase					)

    # Load weights from tf checkpoint
    load_tf_weights_in_albert(lowercase		,				lowercase		,				lowercase					)

    # Save pytorch-model
    print(F'Save PyTorch model to {pytorch_dump_path}'					)
    torch.save(model.state_dict()		,				lowercase					)


if __name__ == "__main__":
  __snake_case       =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--albert_config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained ALBERT model. \n"""
          """This specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  __snake_case       =       parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





