



'''simple docstring'''






import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase: List[Any]							=						logging.get_logger(__name__)
def 				lowerCamelCase__	(						_A			,      _A    ):
  a						: Optional[int]					       =					RobertaPreLayerNormConfig.from_pretrained(
      _A			,      architectures=['RobertaPreLayerNormForMaskedLM']    )

  # convert state_dict
  a						: Dict					       =					torch.load(hf_hub_download(repo_id=_A			,      filename='pytorch_model.bin'    )    )
  a						: Optional[int]					       =					{}
  for tensor_key, tensor_value in original_state_dict.items():
    # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
    if tensor_key.startswith('roberta.'    ):
      a						: int					       =					'roberta_prelayernorm.' + tensor_key[len('roberta.'    ) :]

    # The original implementation contains weights which are not used, remove them from the state_dict
    if tensor_key.endswith('.self.LayerNorm.weight'    ) or tensor_key.endswith('.self.LayerNorm.bias'    ):
      continue

    a						: Dict					       =					tensor_value

  a						: str					       =					RobertaPreLayerNormForMaskedLM.from_pretrained(
      pretrained_model_name_or_path=_A			,      config=_A			,      state_dict=_A    )
  model.save_pretrained(_A    )

  # convert tokenizer
  a						: int					       =					AutoTokenizer.from_pretrained(_A    )
  tokenizer.save_pretrained(_A    )


if __name__ == "__main__":
     lowerCAmelCase: Any							=						argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--checkpoint-repo',
         default=None,
         type=str,
         required=True,
         help='Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     lowerCAmelCase: int							=						parser.parse_args()
     convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)




'''simple docstring'''






import copy
import os

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

class 							a__:







    def __init__(				self			:  Optional[int]     ):
      a						: int					       =					''
      a						: List[str]					       =					''
      a						: int					       =					[]
      a						: Optional[Any]					       =					0
      a						: Optional[Any]					       =					2_56
      a						: int					       =					0
      a						: Optional[int]					       =					0
      a						: str					       =					0
      a						: int					       =					0







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







    def       lowercase_	(				self			:  Union[str, Any]     ):
      plt.hist(self.img.ravel()						,      2_56						,      [0, 2_56]     )







    def       lowercase_	(				self			:  Any     ):
      cva.imshow('Output-Image'						,      self.img     )
      cva.imshow('Input-Image'						,      self.original_image     )
      cva.waitKey(50_00     )
      cva.destroyAllWindows()


if __name__ == "__main__":
     lowerCAmelCase: Dict							=						os.path.join(os.path.basename(__file__), 'image_data/input.jpg')
     lowerCAmelCase: Optional[Any]							=						ConstantStretch()
     stretcher.stretch(file_path)
     stretcher.plot_histogram()
     stretcher.show_image()