


'''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 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     for pegasus_name, hf_name in PATTERNS:
          __A	:       Optional[int]      =							k.replace(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
     return k


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       Any      =							DEFAULTS.copy()
     cfg_kwargs.update(SCREAMING_SNAKE_CASE							)
     __A	:       Tuple      =							PegasusConfig(**SCREAMING_SNAKE_CASE							)
     __A	:       Dict      =							PegasusForConditionalGeneration(SCREAMING_SNAKE_CASE							)
     __A	:       Any      =							torch_model.model.state_dict()
     __A	:       Union[str, Any]      =							{}
     for k, v in tf_weights.items():
          __A	:       Optional[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:
               __A	:       Optional[Any]      =							v.T
          __A	:       int      =							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
     __A	:       int      =							torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]							)
     __A	:       List[Any]      =							mapping["shared.weight"]
     __A	:       Tuple      =							mapping["shared.weight"]
     __A	:       Optional[int]      =							{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							)
     __A  ,__A	:       Union[str, Any]      =							torch_model.model.load_state_dict(SCREAMING_SNAKE_CASE       ,					strict=SCREAMING_SNAKE_CASE							)
     __A	:       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 		_lowercase (SCREAMING_SNAKE_CASE="./ckpt/aeslc/model.ckpt-32000"							):




     '''simple docstring'''





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


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       Any      =							Path(SCREAMING_SNAKE_CASE							).parent.name
     __A	:       Optional[int]      =							task_specific_params[f"summarization_{dataset}"]["max_position_embeddings"]
     __A	:       Tuple      =							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
     __A	:       Union[str, Any]      =							get_tf_weights_as_numpy(SCREAMING_SNAKE_CASE							)
     __A	:       int      =							task_specific_params[f"summarization_{dataset}"]
     if dataset == "large":
          __A	:       Any      =							task_specific_params
     __A	:       Any      =							convert_pegasus(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
     torch_model.save_pretrained(SCREAMING_SNAKE_CASE							)
     __A	:       Union[str, 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__":
      _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)





'''simple docstring'''


import functools


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       int      =							len(SCREAMING_SNAKE_CASE							)
     __A	:       int      =							len(SCREAMING_SNAKE_CASE							)

     @functools.cache
     def min_distance(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							) -> int:
          # if first word index is overflow - delete all from the second word
          if indexa >= len_worda:
               return len_worda - indexa
          # if second word index is overflow - delete all from the first word
          if indexa >= len_worda:
               return len_worda - indexa
          __A	:       Optional[int]      =							int(worda[indexa] != worda[indexa]							)  # current letters not identical
          return min(
              1 + min_distance(indexa + 1       ,					SCREAMING_SNAKE_CASE							)       ,					1 + min_distance(SCREAMING_SNAKE_CASE       ,					indexa + 1							)       ,					diff + min_distance(indexa + 1       ,					indexa + 1							)       ,					)

     return min_distance(0       ,					0							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()

