



"""simple docstring"""




from collections.abc import Callable






class      UpperCAmelCase_  :



      def __init__(					self					:     Dict					,     A					:     Callable | None = None						):
           # Stores actual heap items.
           _UpperCAmelCase      :     list        =   []
           # Stores indexes of each item for supporting updates and deletion.
           _UpperCAmelCase      :     dict        =   {}
           # Stores current size of heap.
           _UpperCAmelCase      :     Tuple        =   0
           # Stores function used to evaluate the score of an item on which basis ordering
           # will be done.
           _UpperCAmelCase      :     Any        =   key or (lambda A						: x)



      def 						snake_case_ (					self					:     List[Any]					,     A					:     int						):
           return int((i - 1) / 2						) if i > 0 else None



      def 						snake_case_ (					self					:     List[Any]					,     A					:     int						):
           _UpperCAmelCase      :     Tuple        =   int(2 * i + 1						)
           return left if 0 < left < self.size else None



      def 						snake_case_ (					self					:     List[Any]					,     A					:     int						):
           _UpperCAmelCase      :     Tuple        =   int(2 * i + 2						)
           return right if 0 < right < self.size else None



      def 						snake_case_ (					self					:     Optional[int]					,     A					:     int					,     A					:     int						):
           _UpperCAmelCase    ,	_UpperCAmelCase      :     int        =   (
               self.pos_map[self.arr[j][0]],
               self.pos_map[self.arr[i][0]],
           )
           # Then swap the items in the list.
           _UpperCAmelCase    ,	_UpperCAmelCase      :     Any        =   self.arr[j], self.arr[i]



      def 						snake_case_ (					self					:     List[str]					,     A					:     int					,     A					:     int						):
           return self.arr[i][1] < self.arr[j][1]



      def 						snake_case_ (					self					:     Dict					,     A					:     int						):
           _UpperCAmelCase      :     str        =   self._left(A						)
           _UpperCAmelCase      :     str        =   self._right(A						)
           _UpperCAmelCase      :     List[Any]        =   i

           if left is not None and not self._cmp(A					,     A						):
                _UpperCAmelCase      :     Optional[int]        =   left
           if right is not None and not self._cmp(A					,     A						):
                _UpperCAmelCase      :     Any        =   right

           return valid_parent



      def 						snake_case_ (					self					:     Tuple					,     A					:     int						):
           _UpperCAmelCase      :     Tuple        =   self._parent(A						)
           while parent is not None and not self._cmp(A					,     A						):
                self._swap(A					,     A						)
                _UpperCAmelCase    ,	_UpperCAmelCase      :     Dict        =   parent, self._parent(A						)



      def 						snake_case_ (					self					:     Optional[int]					,     A					:     int						):
           _UpperCAmelCase      :     Tuple        =   self._get_valid_parent(A						)
           while valid_parent != index:
                self._swap(A					,     A						)
                _UpperCAmelCase    ,	_UpperCAmelCase      :     Optional[Any]        =   valid_parent, self._get_valid_parent(A						)



      def 						snake_case_ (					self					:     Dict					,     A					:     int					,     A					:     int						):
           if item not in self.pos_map:
                return
           _UpperCAmelCase      :     Any        =   self.pos_map[item]
           _UpperCAmelCase      :     Optional[int]        =   [item, self.key(A						)]
           # Make sure heap is right in both up and down direction.
           # Ideally only one of them will make any change.
           self._heapify_up(A						)
           self._heapify_down(A						)



      def 						snake_case_ (					self					:     List[str]					,     A					:     int						):
           if item not in self.pos_map:
                return
           _UpperCAmelCase      :     str        =   self.pos_map[item]
           del self.pos_map[item]
           _UpperCAmelCase      :     Tuple        =   self.arr[self.size - 1]
           _UpperCAmelCase      :     List[Any]        =   index
           self.size -= 1
           # Make sure heap is right in both up and down direction. Ideally only one
           # of them will make any change- so no performance loss in calling both.
           if self.size > index:
                self._heapify_up(A						)
                self._heapify_down(A						)



      def 						snake_case_ (					self					:     Any					,     A					:     int					,     A					:     int						):
           _UpperCAmelCase      :     Any        =   len(self.arr						)
           if arr_len == self.size:
                self.arr.append([item, self.key(A						)]						)
           else:
                _UpperCAmelCase      :     Any        =   [item, self.key(A						)]
           _UpperCAmelCase      :     List[Any]        =   self.size
           self.size += 1
           self._heapify_up(self.size - 1						)



      def 						snake_case_ (					self					:     Tuple						):
           return self.arr[0] if self.size else None



      def 						snake_case_ (					self					:     Any						):
           _UpperCAmelCase      :     Dict        =   self.get_top()
           if top_item_tuple:
                self.delete_item(top_item_tuple[0]						)
           return top_item_tuple







def 						__snake_case						(				)  ->       None:





     '''simple docstring'''



if __name__ == "__main__":
 import doctest

 doctest.testmod()





"""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


_lowerCAmelCase    : List[str]							=							[
    # 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 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Dict    )  ->       List[str]:





     '''simple docstring'''

     for pegasus_name, hf_name in PATTERNS:
          _UpperCAmelCase      :     str        =   k.replace(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )
     return k

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						dict						,  SCREAMING_SNAKE_CASE__     :						dict    )  ->       PegasusForConditionalGeneration:





     '''simple docstring'''

     _UpperCAmelCase      :     List[Any]        =   DEFAULTS.copy()
     cfg_kwargs.update(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[Any]        =   PegasusConfig(**SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[Any]        =   PegasusForConditionalGeneration(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[int]        =   torch_model.model.state_dict()
     _UpperCAmelCase      :     Union[str, Any]        =   {}
     for k, v in tf_weights.items():
          _UpperCAmelCase      :     Union[str, Any]        =   rename_state_dict_key(SCREAMING_SNAKE_CASE__    )
          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:
               _UpperCAmelCase      :     Any        =   v.T
          _UpperCAmelCase      :     str        =   torch.tensor(SCREAMING_SNAKE_CASE__						,  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
     _UpperCAmelCase      :     Tuple        =   torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]    )
     _UpperCAmelCase      :     Any        =   mapping["shared.weight"]
     _UpperCAmelCase      :     Dict        =   mapping["shared.weight"]
     _UpperCAmelCase      :     Dict        =   {k: torch.zeros_like(SCREAMING_SNAKE_CASE__    ) for k, v in sd.items() if k.endswith("bias"    ) and k not in mapping}
     mapping.update(**SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase    ,	_UpperCAmelCase      :     List[Any]        =   torch_model.model.load_state_dict(SCREAMING_SNAKE_CASE__						,  strict=SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     int        =   [
         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 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Dict="./ckpt/aeslc/model.ckpt-32000"    )  ->       Dict:





     '''simple docstring'''

     _UpperCAmelCase      :     Optional[int]        =   tf.train.list_variables(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Union[str, Any]        =   {}
     _UpperCAmelCase      :     Optional[Any]        =   ["Adafactor", "global_step"]
     for name, shape in tqdm(SCREAMING_SNAKE_CASE__						,  desc="converting tf checkpoint to dict"    ):
          _UpperCAmelCase      :     Union[str, Any]        =   any(pat in name for pat in ignore_name    )
          if skip_key:
               continue
          _UpperCAmelCase      :     int        =   tf.train.load_variable(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )
          _UpperCAmelCase      :     Dict        =   array
     return tf_weights

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						str						,  SCREAMING_SNAKE_CASE__     :						str    )  ->       Dict:





     '''simple docstring'''

     _UpperCAmelCase      :     Dict        =   Path(SCREAMING_SNAKE_CASE__    ).parent.name
     _UpperCAmelCase      :     Tuple        =   task_specific_params[f'summarization_{dataset}']["max_position_embeddings"]
     _UpperCAmelCase      :     Dict        =   PegasusTokenizer.from_pretrained("sshleifer/pegasus"						,  model_max_length=SCREAMING_SNAKE_CASE__    )
     assert tok.model_max_length == desired_max_model_length
     tok.save_pretrained(SCREAMING_SNAKE_CASE__    )

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


if __name__ == "__main__":
 _lowerCAmelCase    : Union[str, Any]							=							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.")
 _lowerCAmelCase    : Union[str, Any]							=							parser.parse_args()
 if args.save_dir is None:
  _lowerCAmelCase    : Tuple							=							Path(args.tf_ckpt_path).parent.name
  _lowerCAmelCase    : Dict							=							os.path.join("pegasus", dataset)
 convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
