



from __future__ import annotations

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						str						,  SCREAMING_SNAKE_CASE__     :						list[str] | None = None    )  ->       list[list[str]]:





     '''simple docstring'''

     _UpperCAmelCase      :     int        =   word_bank or []
     # create a table
     _UpperCAmelCase      :     int        =   len(SCREAMING_SNAKE_CASE__    ) + 1

     _UpperCAmelCase      :     list[list[list[str]]]        =   []
     for _ in range(SCREAMING_SNAKE_CASE__    ):
          table.append([]    )
     # seed value
     _UpperCAmelCase      :     List[Any]        =   [[]]  # because empty string has empty combination

     # iterate through the indices
     for i in range(SCREAMING_SNAKE_CASE__    ):
          # condition
          if table[i] != []:
               for word in word_bank:
                    # slice condition
                    if target[i : i + len(SCREAMING_SNAKE_CASE__    )] == word:
                         _UpperCAmelCase      :     list[list[str]]        =   [
                             [word, *way] for way in table[i]
                         ]
                         # adds the word to every combination the current position holds
                         # now,push that combination to the table[i+len(word)]
                         table[i + len(SCREAMING_SNAKE_CASE__    )] += new_combinations

    # combinations are in reverse order so reverse for better output
     for combination in table[len(SCREAMING_SNAKE_CASE__    )]:
          combination.reverse()

     return table[len(SCREAMING_SNAKE_CASE__    )]


if __name__ == "__main__":
 print(all_construct("jwajalapa", ["jwa", "j", "w", "a", "la", "lapa"]))
 print(all_construct("rajamati", ["s", "raj", "amat", "raja", "ma", "i", "t"]))
 print(
     all_construct(
         "hexagonosaurus",
         ["h", "ex", "hex", "ag", "ago", "ru", "auru", "rus", "go", "no", "o", "s"],
     )
 )





"""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)
