
from collections.abc import Sequence
from queue import Queue




class 	__A:

  """simple docstring"""







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







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






class 	__A:

  """simple docstring"""







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







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						):
       self._update_tree(self.root       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						)







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						):
       return self._query_range(self.root       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						)







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						):

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







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						):

       if node.start == i and node.end == i:
            UpperCamelCase__				=				val
            return
       if i <= node.mid:
            self._update_tree(node.left       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						)
       else:
            self._update_tree(node.right       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=				self.fn(node.left.val       ,			node.right.val						)







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						):

       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       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						)
            else:
                 # range in left child tree and right child tree
                 return self.fn(
                     self._query_range(node.left       ,			SCREAMING_SNAKE_CASE_       ,			node.mid						)       ,			self._query_range(node.right       ,			node.mid + 1       ,			SCREAMING_SNAKE_CASE_						)       ,			)
       else:
            # range in right child tree
            return self._query_range(node.right       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						)







  def        UpperCAmelCase_					(self						):

       if self.root is not None:
            UpperCamelCase__				=				Queue()
            queue.put(self.root						)
            while not queue.empty():
                 UpperCamelCase__				=				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()












import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase      :       Any									=  logging.get_logger(__name__)



def A_	(		A__				)  ->		List[str]:
       a__						:	Union[str, Any] =				torch.load(A__		,      map_location='cpu'				)
       if "model" in sd.keys():
              a__						:	Tuple =				torch.load(A__		,      map_location='cpu'				)['model']

       # pop unnecessary weights
       a__						:	Optional[Any] =				[
           'decoder.version',
           'decoder.output_projection.weight',
       ]
       for key in keys_to_delete:
              if key in sd:
                     sd.pop(A__				)

       a__						:	Union[str, Any] =				{
           'decoder.project_in_dim.weight': 'decoder.project_in.weight',
           'decoder.project_out_dim.weight': 'decoder.project_out.weight',
           'decoder.layer_norm.weight': 'decoder.final_layer_norm.weight',
           'decoder.layer_norm.bias': 'decoder.final_layer_norm.bias',
       }
       for old_key, new_key in keys_to_rename.items():
              if old_key in sd:
                     a__						:	Any =				sd.pop(A__				)

       a__						:	Optional[int] =				list(sd.keys()				)
       for key in keys:
              if ".qkv_proj." in key:
                     a__						:	str =				sd[key]
                     # We split QKV in separate Q,K,V

                     a__						:	Union[str, Any] =				key.replace('.qkv_proj.'		,      '.q_proj.'				)
                     a__						:	int =				key.replace('.qkv_proj.'		,      '.k_proj.'				)
                     a__						:	Optional[int] =				key.replace('.qkv_proj.'		,      '.v_proj.'				)

                     a__						:	List[Any] =				value.shape[0]
                     assert depth % 3 == 0
                     # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
                     # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
                     a__ ,							a__ ,							a__						:	Any =				torch.split(A__		,      depth // 3		,      dim=0				)

                     a__						:	int =				q
                     a__						:	List[Any] =				k
                     a__						:	int =				v
                     del sd[key]

       return sd



@torch.no_grad()
def A_	(		A__		,      A__		,      A__=None				)  ->		Union[str, Any]:
       a__						:	Union[str, Any] =				load_checkpoint(A__				)

       if config is not None:
              a__						:	List[Any] =				OPTConfig.from_pretrained(A__				)
       else:
              a__						:	int =				OPTConfig()

       a__						:	Optional[int] =				OPTModel(A__				).half().eval()
       model.load_state_dict(A__				)

       # Check results
       Path(A__				).mkdir(exist_ok=A__				)
       model.save_pretrained(A__				)


if __name__ == "__main__":
    lowercase      :       str									=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--fairseq_path""",
        type=str,
        help=(
            """path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
            """ https://huggingface.co/models?other=opt_metasq"""
        ),
    )
    parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
    parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
    lowercase      :       Any									=  parser.parse_args()
    convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)


