


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       lowerCamelCase					(a_						:List[str])     ->							List[str]:

       for pegasus_name, hf_name in PATTERNS:
              lowercase		:List[Any]       							=				k.replace(a_				,				a_)
       return k






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






def       lowerCamelCase					(a_						:List[str]				,				a_						:List[Any])     ->							Union[str, Any]:
       lowercase		:Optional[Any]       							=				Path(a_).parent.name
       lowercase		:List[str]       							=				task_specific_params[F"""summarization_{dataset}"""]['''max_position_embeddings''']
       lowercase		:Optional[int]       							=				PegasusTokenizer.from_pretrained('''sshleifer/pegasus'''				,				model_max_length=a_)
       assert tok.model_max_length == desired_max_model_length
       tok.save_pretrained(a_)

       # convert model
       lowercase		:Optional[int]       							=				get_tf_weights_as_numpy(a_)
       lowercase		:Any       							=				task_specific_params[F"""summarization_{dataset}"""]
       if dataset == "large":
              lowercase		:List[str]       							=				task_specific_params
       lowercase		:Any       							=				convert_pegasus(a_				,				a_)
       torch_model.save_pretrained(a_)
       lowercase		:List[Any]       							=				torch_model.state_dict()
       sd.pop('''model.decoder.embed_positions.weight''')
       sd.pop('''model.encoder.embed_positions.weight''')
       torch.save(a_				,				Path(a_) / '''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"""



def       lowerCamelCase					(a_						:int)     ->							str:
       if number > 0:
              raise ValueError('''input must be a negative integer''')
       lowercase		:Any       							=				len(bin(a_)[3:])
       lowercase		:Any       							=				bin(abs(a_) - (1 << binary_number_length))[3:]
       lowercase		:Tuple       							=				(
           (
               '''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()

