


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

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig


def       _lowercase							(					UpperCamelCase__							:       Union[str, Any],					UpperCamelCase__							:       Union[str, Any],					UpperCamelCase__							:       Optional[Any],					UpperCamelCase__							:       Optional[Any]      ):
 __A	:		Tuple	  =      s.rsplit(UpperCamelCase__,					UpperCamelCase__      )
 return new.join(UpperCamelCase__      )


def       _lowercase							(					UpperCamelCase__							:       Union[str, Any]      ):
 # 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       _lowercase							(					UpperCamelCase__							:       Optional[Any]      ):
 __A	:		List[str]	  =      {}

 __A	:		List[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:
    __A	:		str	  =      key.replace(f"""{group_key}.""",					f"""{group_key}.group."""      )

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

  if key.endswith('.w'      ):
   __A	:		Optional[Any]	  =      rreplace(UpperCamelCase__,					'.w',					'.weight',					1      )
  if key.endswith('.b'      ):
   __A	:		Union[str, Any]	  =      rreplace(UpperCamelCase__,					'.b',					'.bias',					1      )

  __A	:		str	  =      value.float()

 return upgrade


@torch.no_grad()
def       _lowercase							(					UpperCamelCase__							:       Optional[int],					UpperCamelCase__							:       str,					UpperCamelCase__							:       str=None,					UpperCamelCase__							:       List[Any]=True      ):
 from dall_e import Encoder

 __A	:		Optional[Any]	  =      Encoder()
 if os.path.exists(UpperCamelCase__      ):
  __A	:		int	  =      torch.load(UpperCamelCase__      )
 else:
  __A	:		List[Any]	  =      torch.hub.load_state_dict_from_url(UpperCamelCase__      )

 if isinstance(UpperCamelCase__,					UpperCamelCase__      ):
  __A	:		Optional[Any]	  =      ckpt.state_dict()
 encoder.load_state_dict(UpperCamelCase__      )

 if config_path is not None:
  __A	:		List[str]	  =      FlavaImageCodebookConfig.from_pretrained(UpperCamelCase__      )
 else:
  __A	:		str	  =      FlavaImageCodebookConfig()

 __A	:		Any	  =      FlavaImageCodebook(UpperCamelCase__      ).eval()
 __A	:		str	  =      encoder.state_dict()

 __A	:		Any	  =      upgrade_state_dict(UpperCamelCase__      )
 hf_model.load_state_dict(UpperCamelCase__      )
 __A	:		Optional[Any]	  =      hf_model.state_dict()
 __A	:		str	  =      count_parameters(UpperCamelCase__      )
 __A	:		Tuple	  =      count_parameters(UpperCamelCase__      )

 assert torch.allclose(UpperCamelCase__,					UpperCamelCase__,					atol=1E-3      )

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


if __name__ == "__main__":
 UpperCAmelCase_			:  str										=   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')
 UpperCAmelCase_			:  Optional[int]										=   parser.parse_args()

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










'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self   ,   __lowercase   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  if dst_width < 0 or dst_height < 0:
   raise ValueError('Destination width/height should be > 0'    )

  __A	:		Dict	  =      img
  __A	:		Dict	  =      img.shape[1]
  __A	:		Tuple	  =      img.shape[0]
  __A	:		List[str]	  =      dst_width
  __A	:		Tuple	  =      dst_height

  __A	:		Optional[Any]	  =      self.src_w / self.dst_w
  __A	:		List[str]	  =      self.src_h / self.dst_h

  __A	:		Dict	  =      (
      np.ones((self.dst_h, self.dst_w, 3)   ,   np.uinta    ) * 255
  )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  for i in range(self.dst_h    ):
   for j in range(self.dst_w    ):
    __A	:		List[str]	  =      self.img[self.get_y(__lowercase    )][self.get_x(__lowercase    )]


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  return int(self.ratio_x * x    )







 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  return int(self.ratio_y * y    )


if __name__ == "__main__":
 UpperCAmelCase_				,      UpperCAmelCase_			:  str										=   8_0_0, 6_0_0
 UpperCAmelCase_			:  str										=   imread('image_data/lena.jpg', 1)
 UpperCAmelCase_			:  List[str]										=   NearestNeighbour(im, dst_w, dst_h)
 n.process()

 imshow(
     f'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
 )
 waitKey(0)
 destroyAllWindows()






