




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      :						int							=  [
    # 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 SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			int:



       """simple docstring"""

       for pegasus_name, hf_name in PATTERNS:
              A__ 					=       k.replace(__snake_case					,							__snake_case		)
       return k







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			PegasusForConditionalGeneration:



       """simple docstring"""

       A__ 					=       DEFAULTS.copy()
       cfg_kwargs.update(__snake_case		)
       A__ 					=       PegasusConfig(**__snake_case		)
       A__ 					=       PegasusForConditionalGeneration(__snake_case		)
       A__ 					=       torch_model.model.state_dict()
       A__ 					=       {}
       for k, v in tf_weights.items():
              A__ 					=       rename_state_dict_key(__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__ 					=       v.T
              A__ 					=       torch.tensor(__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__ 					=       torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1]		)
       A__ 					=       mapping['shared.weight']
       A__ 					=       mapping['shared.weight']
       A__ 					=       {k: torch.zeros_like(__snake_case		) for k, v in sd.items() if k.endswith('''bias'''		) and k not in mapping}
       mapping.update(**__snake_case		)
       A__ 					=       torch_model.model.load_state_dict(__snake_case					,							strict=__snake_case		)
       A__ 					=       [
           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 SCREAMING_SNAKE_CASE							(			lowercase_="./ckpt/aeslc/model.ckpt-32000"		)  ->			Dict:



       """simple docstring"""

       A__ 					=       tf.train.list_variables(__snake_case		)
       A__ 					=       {}
       A__ 					=       ['Adafactor', 'global_step']
       for name, shape in tqdm(__snake_case					,							desc='''converting tf checkpoint to dict'''		):
              A__ 					=       any(pat in name for pat in ignore_name		)
              if skip_key:
                     continue
              A__ 					=       tf.train.load_variable(__snake_case					,							__snake_case		)
              A__ 					=       array
       return tf_weights







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			Tuple:



       """simple docstring"""

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

       # convert model
       A__ 					=       get_tf_weights_as_numpy(__snake_case		)
       A__ 					=       task_specific_params[f"""summarization_{dataset}"""]
       if dataset == "large":
              A__ 					=       task_specific_params
       A__ 					=       convert_pegasus(__snake_case					,							__snake_case		)
       torch_model.save_pretrained(__snake_case		)
       A__ 					=       torch_model.state_dict()
       sd.pop('''model.decoder.embed_positions.weight'''		)
       sd.pop('''model.encoder.embed_positions.weight'''		)
       torch.save(__snake_case					,							Path(__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      :						Any							=  parser.parse_args()
  if args.save_dir is None:
    _lowerCamelCase      :						Tuple							=  Path(args.tf_ckpt_path).parent.name
    _lowerCamelCase      :						Any							=  os.path.join("""pegasus""", dataset)
  convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)









'''simple docstring'''




import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer






def 			_lowerCAmelCase     (    __snake_case    :					str					,				__snake_case    :					str					,				**__snake_case    :					List[Any]     )	->	Any:
     __A		:					Optional[Any]						=			AutoConfig.from_pretrained(__snake_case					,				**__snake_case     )
     __A		:					int						=			AutoModelForSeqaSeqLM.from_config(__snake_case     )
     model.save_pretrained(__snake_case     )
     AutoTokenizer.from_pretrained(__snake_case     ).save_pretrained(__snake_case     )
     return model


if __name__ == "__main__":
  fire.Fire(save_randomly_initialized_version)