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     :							Any    =[
    # 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							( __lowerCAmelCase      )			->     Union[str, Any]:


     for pegasus_name, hf_name in PATTERNS:
          UpperCamelCase__      :       Union[str, Any]	    =  k.replace(_UpperCAmelCase ,   _UpperCAmelCase      )
     return k







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







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     Optional[Any]:
     UpperCamelCase__      :       Any	    =  Path(_UpperCAmelCase      ).parent.name
     UpperCamelCase__      :       int	    =  task_specific_params[f'summarization_{dataset}']['max_position_embeddings']
     UpperCamelCase__      :       List[Any]	    =  PegasusTokenizer.from_pretrained("sshleifer/pegasus" ,   model_max_length=_UpperCAmelCase      )
     assert tok.model_max_length == desired_max_model_length
     tok.save_pretrained(_UpperCAmelCase      )

     # convert model
     UpperCamelCase__      :       List[Any]	    =  get_tf_weights_as_numpy(_UpperCAmelCase      )
     UpperCamelCase__      :       Optional[int]	    =  task_specific_params[f'summarization_{dataset}']
     if dataset == "large":
          UpperCamelCase__      :       int	    =  task_specific_params
     UpperCamelCase__      :       Optional[Any]	    =  convert_pegasus(_UpperCAmelCase ,   _UpperCAmelCase      )
     torch_model.save_pretrained(_UpperCAmelCase      )
     UpperCamelCase__      :       Optional[Any]	    =  torch_model.state_dict()
     sd.pop("model.decoder.embed_positions.weight"      )
     sd.pop("model.encoder.embed_positions.weight"      )
     torch.save(_UpperCAmelCase ,   Path(_UpperCAmelCase      ) / "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     :							int    =parser.parse_args()
 if args.save_dir is None:
  lowerCamelCase     :							int    =Path(args.tf_ckpt_path).parent.name
  lowerCamelCase     :							str    =os.path.join('''pegasus''', dataset)
 convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)







from __future__ import annotations







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     bool:
     if len(__lowerCAmelCase      ) == 0:
          return False
     UpperCamelCase__      :       Any	    =  len(__lowerCAmelCase      ) // 2
     if a_list[midpoint] == item:
          return True
     if item < a_list[midpoint]:
          return binary_search(a_list[:midpoint] ,   __lowerCAmelCase      )
     else:
          return binary_search(a_list[midpoint + 1 :] ,   __lowerCAmelCase      )


if __name__ == "__main__":
 lowerCamelCase     :							Any    =input('''Enter numbers separated by comma:\n''').strip()
 lowerCamelCase     :							Dict    =[int(item.strip()) for item in user_input.split(''',''')]
 lowerCamelCase     :							List[str]    =int(input('''Enter the number to be found in the list:\n''').strip())
 lowerCamelCase     :							Union[str, Any]    ='''''' if binary_search(sequence, target) else '''not '''
 print(F"""{target} was {not_str}found in {sequence}""")