

'''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     :  int    ):
   __lowercase													=				''''''
   __lowercase													=				''''''
   __lowercase													=				[]
   __lowercase													=				0
   __lowercase													=				2_5_6
   __lowercase													=				0
   __lowercase													=				0
   __lowercase													=				0
   __lowercase													=				0






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  str    ):
   __lowercase													=				cva.imread(__snake_case      ,0    )
   __lowercase													=				copy.deepcopy(self.img    )
   __lowercase													=				plt.hist(self.img.ravel()      ,2_5_6      ,[0, 2_5_6]      ,label='''x'''    )
   __lowercase													=				np.sum(__snake_case    )
   for i in range(len(__snake_case    )    ):
    __lowercase													=				x[i] / self.k
    self.sk += prk
    __lowercase													=				(self.L - 1) * self.sk
    if self.rem != 0:
     __lowercase													=				int(last % last    )
    __lowercase													=				int(last + 1 if self.rem >= 0.5 else last    )
    self.last_list.append(__snake_case    )
    __lowercase													=				int(np.ma.count(self.img    ) / self.img[1].size    )
    __lowercase													=				self.img[1].size
   for i in range(self.number_of_cols    ):
    for j in range(self.number_of_rows    ):
     __lowercase													=				self.img[j][i]
     if num != self.last_list[num]:
      __lowercase													=				self.last_list[num]
   cva.imwrite('''output_data/output.jpg'''      ,self.img    )






  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   plt.hist(self.img.ravel()      ,2_5_6      ,[0, 2_5_6]    )






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   cva.imshow('''Output-Image'''      ,self.img    )
   cva.imshow('''Input-Image'''      ,self.original_image    )
   cva.waitKey(5_0_0_0    )
   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'''






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def 	_A  (					A__  ,       A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				TaConfig.from_json_file(A__   )
 print(F"Building PyTorch model from configuration: {config}"   )
 __lowercase													=				TaForConditionalGeneration(A__   )

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(A__  ,       A__  ,       A__   )

 # Save pytorch-model
 print(F"Save PyTorch model to {pytorch_dump_path}"   )
 model.save_pretrained(A__   )


if __name__ == "__main__":
 lowerCAmelCase__      		=			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(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 lowerCAmelCase__      		=			parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

