

"""simple docstring"""






import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint





def 	lowercase      (   __UpperCamelCase						) ->      Optional[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      ,   __UpperCamelCase						) ->      Any:
 __magic_name__     =      {}

 for key, value in state_dict.items():
  if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
   continue

  __magic_name__     =      key.replace('''heads.cmd.mim_head.cls.predictions'''      ,   '''mmm_image_head'''						)
  __magic_name__     =      key.replace('''heads.cmd.mlm_head.cls.predictions'''      ,   '''mmm_text_head'''						)
  __magic_name__     =      key.replace('''heads.cmd.itm_head.cls'''      ,   '''itm_head'''						)
  __magic_name__     =      key.replace('''heads.cmd.itm_head.pooler'''      ,   '''itm_head.pooler'''						)
  __magic_name__     =      key.replace('''heads.cmd.clip_head.logit_scale'''      ,   '''flava.logit_scale'''						)
  __magic_name__     =      key.replace('''heads.fairseq_mlm.cls.predictions'''      ,   '''mlm_head'''						)
  __magic_name__     =      key.replace('''heads.imagenet.mim_head.cls.predictions'''      ,   '''mim_head'''						)
  __magic_name__     =      key.replace('''mm_text_projection'''      ,   '''flava.text_to_mm_projection'''						)
  __magic_name__     =      key.replace('''mm_image_projection'''      ,   '''flava.image_to_mm_projection'''						)
  __magic_name__     =      key.replace('''image_encoder.module'''      ,   '''flava.image_model'''						)
  __magic_name__     =      key.replace('''text_encoder.module'''      ,   '''flava.text_model'''						)
  __magic_name__     =      key.replace('''mm_encoder.module.encoder.cls_token'''      ,   '''flava.multimodal_model.cls_token'''						)
  __magic_name__     =      key.replace('''mm_encoder.module'''      ,   '''flava.multimodal_model'''						)
  __magic_name__     =      key.replace('''text_projection'''      ,   '''flava.text_projection'''						)
  __magic_name__     =      key.replace('''image_projection'''      ,   '''flava.image_projection'''						)

  __magic_name__     =      value.float()

 for key, value in codebook_state_dict.items():
  __magic_name__     =      value

 return upgrade





@torch.no_grad()
def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase=None						) ->      List[Any]:
 if config_path is not None:
  __magic_name__     =      FlavaConfig.from_pretrained(__UpperCamelCase						)
 else:
  __magic_name__     =      FlavaConfig()

 __magic_name__     =      FlavaForPreTraining(__UpperCamelCase						).eval()

 __magic_name__     =      convert_dalle_checkpoint(__UpperCamelCase      ,   __UpperCamelCase      ,   save_checkpoint=__UpperCamelCase						)

 if os.path.exists(__UpperCamelCase						):
  __magic_name__     =      torch.load(__UpperCamelCase      ,   map_location='''cpu'''						)
 else:
  __magic_name__     =      torch.hub.load_state_dict_from_url(__UpperCamelCase      ,   map_location='''cpu'''						)

 __magic_name__     =      upgrade_state_dict(__UpperCamelCase      ,   __UpperCamelCase						)
 hf_model.load_state_dict(__UpperCamelCase						)
 __magic_name__     =      hf_model.state_dict()
 __magic_name__     =      count_parameters(__UpperCamelCase						)
 __magic_name__     =      count_parameters(__UpperCamelCase						) + count_parameters(__UpperCamelCase						)

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

 hf_model.save_pretrained(__UpperCamelCase						)


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("--codebook_path", default=None, type=str, help="Path to flava codebook 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_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)




"""simple docstring"""






from collections.abc import Sequence
from queue import Queue
class        _lowercase					:







     def __init__(      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_=None ,						UpperCamelCase_=None       ):
      __magic_name__     =      start
      __magic_name__     =      end
      __magic_name__     =      val
      __magic_name__     =      (start + end) // 2
      __magic_name__     =      left
      __magic_name__     =      right







     def __repr__(      self       ):
      return f'''SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'''


class        _lowercase					:







     def __init__(      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      __magic_name__     =      collection
      __magic_name__     =      function
      if self.collection:
       __magic_name__     =      self._build_tree(0 ,						len(UpperCamelCase_       ) - 1       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      self._update_tree(self.root ,						UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      return self._query_range(self.root ,						UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      if start == end:
       return SegmentTreeNode(UpperCamelCase_ ,						UpperCamelCase_ ,						self.collection[start]       )
      __magic_name__     =      (start + end) // 2
      __magic_name__     =      self._build_tree(UpperCamelCase_ ,						UpperCamelCase_       )
      __magic_name__     =      self._build_tree(mid + 1 ,						UpperCamelCase_       )
      return SegmentTreeNode(UpperCamelCase_ ,						UpperCamelCase_ ,						self.fn(left.val ,						right.val       ) ,						UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      if node.start == i and node.end == i:
       __magic_name__     =      val
       return
      if i <= node.mid:
       self._update_tree(node.left ,						UpperCamelCase_ ,						UpperCamelCase_       )
      else:
       self._update_tree(node.right ,						UpperCamelCase_ ,						UpperCamelCase_       )
      __magic_name__     =      self.fn(node.left.val ,						node.right.val       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      if node.start == i and node.end == j:
       return node.val

      if i <= node.mid:
       if j <= node.mid:
        # range in left child tree
        return self._query_range(node.left ,						UpperCamelCase_ ,						UpperCamelCase_       )
       else:
        # range in left child tree and right child tree
        return self.fn(
            self._query_range(node.left ,						UpperCamelCase_ ,						node.mid       ) ,						self._query_range(node.right ,						node.mid + 1 ,						UpperCamelCase_       ) ,						)
      else:
       # range in right child tree
       return self._query_range(node.right ,						UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      if self.root is not None:
       __magic_name__     =      Queue()
       queue.put(self.root       )
       while not queue.empty():
        __magic_name__     =      queue.get()
        yield node

        if node.left is not None:
         queue.put(node.left       )

        if node.right is not None:
         queue.put(node.right       )


if __name__ == "__main__":
 import operator

 for fn in [operator.add, max, min]:
  print("*" * 50)
  __lowerCamelCase										=		SegmentTree([2, 1, 5, 3, 4], fn)
  for node in arr.traverse():
   print(node)
  print()

  arr.update(1, 5)
  for node in arr.traverse():
   print(node)
  print()

  print(arr.query_range(3, 4))  # 7
  print(arr.query_range(2, 2))  # 5
  print(arr.query_range(1, 3))  # 13
  print()

