





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							:					Dict		    =		[
    # 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  (    _lowerCAmelCase       :       Optional[Any]		)       ->  Dict:
   for pegasus_name, hf_name in PATTERNS:
      A_    : List[Any]								=	k.replace(_lowerCAmelCase , _lowerCAmelCase		)
   return k

def    __snake_case  (    _lowerCAmelCase       :       Dict , _lowerCAmelCase       :       Dict		)       ->  PegasusForConditionalGeneration:
   A_    : str								=	DEFAULTS.copy()
   cfg_kwargs.update(_lowerCAmelCase		)
   A_    : Optional[Any]								=	PegasusConfig(**_lowerCAmelCase		)
   A_    : int								=	PegasusForConditionalGeneration(_lowerCAmelCase		)
   A_    : List[str]								=	torch_model.model.state_dict()
   A_    : int								=	{}
   for k, v in tf_weights.items():
      A_    : Optional[int]								=	rename_state_dict_key(_lowerCAmelCase		)
      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[int]								=	v.T
      A_    : Optional[int]								=	torch.tensor(_lowerCAmelCase , 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_    : Optional[Any]								=	torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]		)
   A_    : Optional[int]								=	mapping['shared.weight']
   A_    : Optional[Any]								=	mapping['shared.weight']
   A_    : List[str]								=	{k: torch.zeros_like(_lowerCAmelCase		) for k, v in sd.items() if k.endswith("bias"		) and k not in mapping}
   mapping.update(**_lowerCAmelCase		)
   A_    : Any								=	torch_model.model.load_state_dict(_lowerCAmelCase , strict=_lowerCAmelCase		)
   A_    : Any								=	[
       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  (    _lowerCAmelCase       :       Any="./ckpt/aeslc/model.ckpt-32000"		)       ->  Dict:
   A_    : List[Any]								=	tf.train.list_variables(_lowerCAmelCase		)
   A_    : Union[str, Any]								=	{}
   A_    : Optional[Any]								=	['Adafactor', 'global_step']
   for name, shape in tqdm(_lowerCAmelCase , desc="converting tf checkpoint to dict"		):
      A_    : List[Any]								=	any(pat in name for pat in ignore_name		)
      if skip_key:
         continue
      A_    : Optional[int]								=	tf.train.load_variable(_lowerCAmelCase , _lowerCAmelCase		)
      A_    : Dict								=	array
   return tf_weights

def    __snake_case  (    _lowerCAmelCase       :       Any , _lowerCAmelCase       :       Union[str, Any]		)       ->  str:
   # save tokenizer first
   A_    : List[str]								=	Path(_lowerCAmelCase		).parent.name
   A_    : Optional[Any]								=	task_specific_params[f"summarization_{dataset}"]['max_position_embeddings']
   A_    : str								=	PegasusTokenizer.from_pretrained("sshleifer/pegasus" , model_max_length=_lowerCAmelCase		)
   assert tok.model_max_length == desired_max_model_length
   tok.save_pretrained(_lowerCAmelCase		)

   # convert model
   A_    : Optional[Any]								=	get_tf_weights_as_numpy(_lowerCAmelCase		)
   A_    : Any								=	task_specific_params[f"summarization_{dataset}"]
   if dataset == "large":
      A_    : Tuple								=	task_specific_params
   A_    : Tuple								=	convert_pegasus(_lowerCAmelCase , _lowerCAmelCase		)
   torch_model.save_pretrained(_lowerCAmelCase		)
   A_    : Dict								=	torch_model.state_dict()
   sd.pop("model.decoder.embed_positions.weight"		)
   sd.pop("model.encoder.embed_positions.weight"		)
   torch.save(_lowerCAmelCase , Path(_lowerCAmelCase		) / "pytorch_model.bin"		)


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







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      str:
					if number > 0:
										raise ValueError('input must be a negative integer'  )
					__A	:						Optional[int]       =				len(bin(a  )[3:]  )
					__A	:						Dict       =				bin(abs(a  ) - (1 << binary_number_length)  )[3:]
					__A	:						int       =				(
					    (
					        '1'
					        + '0' * (binary_number_length - len(a  ))
					        + twos_complement_number
					    )
					    if number < 0
					    else '0'
					)
					return "0b" + twos_complement_number


if __name__ == "__main__":
				import doctest

				doctest.testmod()




