
"""simple docstring"""


import copy
import os

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







class  lowerCAmelCase_ :



						'''simple docstring'''


						def __init__(       self   :					List[str]					)   ->      int:
							A        =      ''
							A        =      ''
							A        =      []
							A        =      0
							A        =      256
							A        =      0
							A        =      0
							A        =      0
							A        =      0


						def   _SCREAMING_SNAKE_CASE					(       self   :					str     ,A_   :					List[str]					)   ->      int:
							A        =      cva.imread(A_     ,0					)
							A        =      copy.deepcopy(self.img					)
							A      ,					A      ,					A        =      plt.hist(self.img.ravel()     ,256     ,[0, 256]     ,label='x'					)
							A        =      np.sum(A_					)
							for i in range(len(A_					)					):
								A        =      x[i] / self.k
								self.sk += prk
								A        =      (self.L - 1) * self.sk
								if self.rem != 0:
									A        =      int(last % last					)
								A        =      int(last + 1 if self.rem >= 0.5 else last					)
								self.last_list.append(A_					)
								A        =      int(np.ma.count(self.img					) / self.img[1].size					)
								A        =      self.img[1].size
							for i in range(self.number_of_cols					):
								for j in range(self.number_of_rows					):
									A        =      self.img[j][i]
									if num != self.last_list[num]:
										A        =      self.last_list[num]
							cva.imwrite('output_data/output.jpg'     ,self.img					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]					)   ->      int:
							plt.hist(self.img.ravel()     ,256     ,[0, 256]					)




						def   _SCREAMING_SNAKE_CASE					(       self   :					List[Any]					)   ->      str:
							cva.imshow('Output-Image'     ,self.img					)
							cva.imshow('Input-Image'     ,self.original_image					)
							cva.waitKey(5000					)
							cva.destroyAllWindows()


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






"""simple docstring"""
import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig



def      __lowerCamelCase   (							a_     :		str ,   a_     :		Dict ,   a_     :		Any ,   a_     :		str							)			->      str:
      __SCREAMING_SNAKE_CASE   :int						=  s.rsplit(a_ ,   a_							)
      return new.join(a_							)



def      __lowerCamelCase   (							a_     :		List[str]							)			->      Dict:
      # encoder.embeddings are double copied in original FLAVA
      return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items()							)



def      __lowerCamelCase   (							a_     :		Optional[int]							)			->      Any:
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  {}

      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  ['''group_1''', '''group_2''', '''group_3''', '''group_4''']
      for key, value in state_dict.items():
            for group_key in group_keys:
                  if group_key in key:
                        __SCREAMING_SNAKE_CASE   :Optional[Any]						=  key.replace(f'''{group_key}.''' ,   f'''{group_key}.group.'''							)

            if "res_path" in key:
                  __SCREAMING_SNAKE_CASE   :str						=  key.replace('''res_path.''' ,   '''res_path.path.'''							)

            if key.endswith('''.w'''							):
                  __SCREAMING_SNAKE_CASE   :List[Any]						=  rreplace(a_ ,   '''.w''' ,   '''.weight''' ,   1							)
            if key.endswith('''.b'''							):
                  __SCREAMING_SNAKE_CASE   :List[Any]						=  rreplace(a_ ,   '''.b''' ,   '''.bias''' ,   1							)

            __SCREAMING_SNAKE_CASE   :Optional[Any]						=  value.float()

      return upgrade



@torch.no_grad()
def      __lowerCamelCase   (							a_     :		List[Any] ,   a_     :		Optional[int] ,   a_     :		Optional[int]=None ,   a_     :		Dict=True							)			->      Union[str, Any]:
      from dall_e import Encoder

      __SCREAMING_SNAKE_CASE   :int						=  Encoder()
      if os.path.exists(a_							):
            __SCREAMING_SNAKE_CASE   :Dict						=  torch.load(a_							)
      else:
            __SCREAMING_SNAKE_CASE   :List[str]						=  torch.hub.load_state_dict_from_url(a_							)

      if isinstance(a_ ,   a_							):
            __SCREAMING_SNAKE_CASE   :List[str]						=  ckpt.state_dict()
      encoder.load_state_dict(a_							)

      if config_path is not None:
            __SCREAMING_SNAKE_CASE   :Any						=  FlavaImageCodebookConfig.from_pretrained(a_							)
      else:
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  FlavaImageCodebookConfig()

      __SCREAMING_SNAKE_CASE   :Tuple						=  FlavaImageCodebook(a_							).eval()
      __SCREAMING_SNAKE_CASE   :List[str]						=  encoder.state_dict()

      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  upgrade_state_dict(a_							)
      hf_model.load_state_dict(a_							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  hf_model.state_dict()
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  count_parameters(a_							)
      __SCREAMING_SNAKE_CASE   :Any						=  count_parameters(a_							)

      assert torch.allclose(a_ ,   a_ ,   atol=1e-3							)

      if save_checkpoint:
            hf_model.save_pretrained(a_							)
      else:
            return hf_state_dict


if __name__ == "__main__":
  lowerCamelCase_								=		argparse.ArgumentParser()
  parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
  parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
  lowerCamelCase_								=		parser.parse_args()

  convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)