




'''simple docstring'''
import copy
import os

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






class    UpperCAmelCase_				:





 def __init__(    self):
     snake_case_ :    Any     			=						""
     snake_case_ :    Optional[int]     			=						""
     snake_case_ :    List[str]     			=						[]
     snake_case_ :    Any     			=						0
     snake_case_ :    Tuple     			=						2_56
     snake_case_ :    Optional[Any]     			=						0
     snake_case_ :    List[str]     			=						0
     snake_case_ :    int     			=						0
     snake_case_ :    str     			=						0





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





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





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


if __name__ == "__main__":
  a_											=						os.path.join(os.path.basename(__file__), "image_data/input.jpg")
  a_											=						ConstantStretch()
  stretcher.stretch(file_path)
  stretcher.plot_histogram()
  stretcher.show_image()






'''simple docstring'''
import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


a_											=						["small", "medium", "large"]

a_											=						"lm_head.decoder.weight"
a_											=						"lm_head.weight"

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						torch.load(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    List[Any]     			=						d.pop(__SCREAMING_SNAKE_CASE       )
    os.makedirs(__SCREAMING_SNAKE_CASE,		exist_ok=__SCREAMING_SNAKE_CASE       )
    torch.save(__SCREAMING_SNAKE_CASE,		os.path.join(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )       )


if __name__ == "__main__":
  a_											=						argparse.ArgumentParser()
  parser.add_argument("--dialogpt_path", default=".", type=str)
  a_											=						parser.parse_args()
  for MODEL in DIALOGPT_MODELS:
    a_											=						os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
    a_											=						f"""./DialoGPT-{MODEL}"""
    convert_dialogpt_checkpoint(
        checkpoint_path,
        pytorch_dump_folder_path,
    )




