


"""simple docstring"""



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






       def __init__(   self  :    Optional[Any]  ,     snake_case__  :    Union[str, Any]  ,     snake_case__  :    Any  ,     snake_case__  :    str  ,     snake_case__  :    Dict=None  ,     snake_case__  :    Tuple=None				):

              '''simple docstring'''






              lowercase		:int       							=				start
              lowercase		:Optional[int]       							=				end
              lowercase		:List[Any]       							=				val
              lowercase		:Tuple       							=				(start + end) // 2
              lowercase		:str       							=				left
              lowercase		:Optional[int]       							=				right







       def __repr__(   self  :    Optional[Any]				):

              '''simple docstring'''






              return f"""SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"""







class 					__magic_name__		:






       def __init__(   self  :    Optional[Any]  ,     snake_case__  :    Sequence  ,     snake_case__  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Dict       							=				collection
              lowercase		:Dict       							=				function
              if self.collection:
                     lowercase		:Dict       							=				self._build_tree(0  ,     len(snake_case__				) - 1				)






       def  __snake_case						(   self  :    Dict  ,     snake_case__  :    str  ,     snake_case__  :    List[str]				):

              '''simple docstring'''






              self._update_tree(self.root  ,     snake_case__  ,     snake_case__				)






       def  __snake_case						(   self  :    str  ,     snake_case__  :    int  ,     snake_case__  :    Dict				):

              '''simple docstring'''






              return self._query_range(self.root  ,     snake_case__  ,     snake_case__				)






       def  __snake_case						(   self  :    Tuple  ,     snake_case__  :    Dict  ,     snake_case__  :    Optional[Any]				):

              '''simple docstring'''






              if start == end:
                     return SegmentTreeNode(snake_case__  ,     snake_case__  ,     self.collection[start]				)
              lowercase		:Optional[Any]       							=				(start + end) // 2
              lowercase		:List[Any]       							=				self._build_tree(snake_case__  ,     snake_case__				)
              lowercase		:List[Any]       							=				self._build_tree(mid + 1  ,     snake_case__				)
              return SegmentTreeNode(snake_case__  ,     snake_case__  ,     self.fn(left.val  ,     right.val				)  ,     snake_case__  ,     snake_case__				)






       def  __snake_case						(   self  :    Any  ,     snake_case__  :    Dict  ,     snake_case__  :    int  ,     snake_case__  :    Union[str, Any]				):

              '''simple docstring'''






              if node.start == i and node.end == i:
                     lowercase		:Union[str, Any]       							=				val
                     return
              if i <= node.mid:
                     self._update_tree(node.left  ,     snake_case__  ,     snake_case__				)
              else:
                     self._update_tree(node.right  ,     snake_case__  ,     snake_case__				)
              lowercase		:Any       							=				self.fn(node.left.val  ,     node.right.val				)






       def  __snake_case						(   self  :    Union[str, Any]  ,     snake_case__  :    List[str]  ,     snake_case__  :    Optional[Any]  ,     snake_case__  :    List[str]				):

              '''simple docstring'''






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







       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              if self.root is not None:
                     lowercase		:List[Any]       							=				Queue()
                     queue.put(self.root				)
                     while not queue.empty():
                            lowercase		:int       							=				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)
  UpperCAmelCase      		=			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()





"""simple docstring"""



import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


UpperCAmelCase      		=			[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['''memory_attention''', '''encoder_attn'''],
    ['''attention''', '''attn'''],
    ['''/''', '''.'''],
    ['''.LayerNorm.gamma''', '''_layer_norm.weight'''],
    ['''.LayerNorm.beta''', '''_layer_norm.bias'''],
    ['''r.layer_''', '''r.layers.'''],
    ['''output_proj''', '''out_proj'''],
    ['''ffn.dense_1.''', '''fc2.'''],
    ['''ffn.dense.''', '''fc1.'''],
    ['''ffn_layer_norm''', '''final_layer_norm'''],
    ['''kernel''', '''weight'''],
    ['''encoder_layer_norm.''', '''encoder.layer_norm.'''],
    ['''decoder_layer_norm.''', '''decoder.layer_norm.'''],
    ['''embeddings.weights''', '''shared.weight'''],
]






def       lowerCamelCase					(a_						:Union[str, Any])     ->							Optional[Any]:
       for pegasus_name, hf_name in PATTERNS:
              lowercase		:List[Any]       							=				k.replace(a_				,				a_)
       return k






def       lowerCamelCase					(a_						:dict				,				a_						:dict)     ->							PegasusForConditionalGeneration:
       lowercase		:Union[str, Any]       							=				DEFAULTS.copy()
       cfg_kwargs.update(a_)
       lowercase		:Optional[Any]       							=				PegasusConfig(**a_)
       lowercase		:int       							=				PegasusForConditionalGeneration(a_)
       lowercase		:Optional[int]       							=				torch_model.model.state_dict()
       lowercase		:Tuple       							=				{}
       for k, v in tf_weights.items():
              lowercase		:Dict       							=				rename_state_dict_key(a_)
              if new_k not in sd:
                     raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""")

              if "dense" in k or "proj" in new_k:
                     lowercase		:Union[str, Any]       							=				v.T
              lowercase		:List[str]       							=				torch.tensor(a_				,				dtype=sd[new_k].dtype)
              assert v.shape == sd[new_k].shape, F"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
       # make sure embedding.padding_idx is respected
       lowercase		:Optional[int]       							=				torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1])
       lowercase		:Union[str, Any]       							=				mapping['''shared.weight''']
       lowercase		:Dict       							=				mapping['''shared.weight''']
       lowercase		:int       							=				{k: torch.zeros_like(a_) for k, v in sd.items() if k.endswith('''bias''') and k not in mapping}
       mapping.update(**a_)
       lowercase							,  lowercase		:Tuple       							=				torch_model.model.load_state_dict(a_				,				strict=a_)
       lowercase		:Dict       							=				[
           k for k in missing if k not in ['''encoder.embed_positions.weight''', '''decoder.embed_positions.weight''']
       ]
       assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
       assert extra == [], F"""no matches found for the following tf keys {extra}"""
       return torch_model






def       lowerCamelCase					(a_						:Union[str, Any]="./ckpt/aeslc/model.ckpt-32000")     ->							Dict:
       lowercase		:Any       							=				tf.train.list_variables(a_)
       lowercase		:str       							=				{}
       lowercase		:Optional[Any]       							=				['''Adafactor''', '''global_step''']
       for name, shape in tqdm(a_				,				desc='''converting tf checkpoint to dict'''):
              lowercase		:str       							=				any(pat in name for pat in ignore_name)
              if skip_key:
                     continue
              lowercase		:str       							=				tf.train.load_variable(a_				,				a_)
              lowercase		:Dict       							=				array
       return tf_weights






def       lowerCamelCase					(a_						:str				,				a_						:str)     ->							List[str]:
       # save tokenizer first
       lowercase		:List[str]       							=				Path(a_).parent.name
       lowercase		:List[Any]       							=				task_specific_params[F"""summarization_{dataset}"""]['''max_position_embeddings''']
       lowercase		:List[Any]       							=				PegasusTokenizer.from_pretrained('''sshleifer/pegasus'''				,				model_max_length=a_)
       assert tok.model_max_length == desired_max_model_length
       tok.save_pretrained(a_)

       # convert model
       lowercase		:Any       							=				get_tf_weights_as_numpy(a_)
       lowercase		:str       							=				task_specific_params[F"""summarization_{dataset}"""]
       if dataset == "large":
              lowercase		:int       							=				task_specific_params
       lowercase		:List[Any]       							=				convert_pegasus(a_				,				a_)
       torch_model.save_pretrained(a_)
       lowercase		:Union[str, Any]       							=				torch_model.state_dict()
       sd.pop('''model.decoder.embed_positions.weight''')
       sd.pop('''model.encoder.embed_positions.weight''')
       torch.save(a_				,				Path(a_) / '''pytorch_model.bin''')


if __name__ == "__main__":
 UpperCAmelCase      		=			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('''tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
 parser.add_argument('''save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
 UpperCAmelCase      		=			parser.parse_args()
 if args.save_dir is None:
  UpperCAmelCase      		=			Path(args.tf_ckpt_path).parent.name
  UpperCAmelCase      		=			os.path.join('''pegasus''', dataset)
 convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)

