


'''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


lowercase				=[
    # 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__     (		__lowerCamelCase			:				str				):







   '''simple docstring'''




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





def lowerCamelCase__     (		__lowerCamelCase			:				dict			,       __lowerCamelCase			:				dict				):







   '''simple docstring'''




   _UpperCAmelCase  :  Dict				       =DEFAULTS.copy()
   cfg_kwargs.update(__lowerCamelCase				)
   _UpperCAmelCase  :  Union[str, Any]				       =PegasusConfig(**__lowerCamelCase				)
   _UpperCAmelCase  :  Dict				       =PegasusForConditionalGeneration(__lowerCamelCase				)
   _UpperCAmelCase  :  Tuple				       =torch_model.model.state_dict()
   _UpperCAmelCase  :  int				       ={}
   for k, v in tf_weights.items():
      _UpperCAmelCase  :  Any				       =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:
         _UpperCAmelCase  :  Any				       =v.T
      _UpperCAmelCase  :  List[Any]				       =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
   _UpperCAmelCase  :  Union[str, Any]				       =torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]				)
   _UpperCAmelCase  :  Any				       =mapping['shared.weight']
   _UpperCAmelCase  :  Tuple				       =mapping['shared.weight']
   _UpperCAmelCase  :  str				       ={k: torch.zeros_like(__lowerCamelCase				) for k, v in sd.items() if k.endswith('bias'				) and k not in mapping}
   mapping.update(**__lowerCamelCase				)
   _UpperCAmelCase       ,			_UpperCAmelCase  :  str				       =torch_model.model.load_state_dict(__lowerCamelCase			,       strict=__lowerCamelCase				)
   _UpperCAmelCase  :  Tuple				       =[
       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__     (		__lowerCamelCase			:				Optional[Any]="./ckpt/aeslc/model.ckpt-32000"				):







   '''simple docstring'''




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





def lowerCamelCase__     (		__lowerCamelCase			:				str			,       __lowerCamelCase			:				str				):







   '''simple docstring'''




   _UpperCAmelCase  :  Optional[Any]				       =Path(__lowerCamelCase				).parent.name
   _UpperCAmelCase  :  int				       =task_specific_params[f"summarization_{dataset}"]['max_position_embeddings']
   _UpperCAmelCase  :  List[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
   _UpperCAmelCase  :  Optional[Any]				       =get_tf_weights_as_numpy(__lowerCamelCase				)
   _UpperCAmelCase  :  Any				       =task_specific_params[f"summarization_{dataset}"]
   if dataset == "large":
      _UpperCAmelCase  :  List[str]				       =task_specific_params
   _UpperCAmelCase  :  Optional[int]				       =convert_pegasus(__lowerCamelCase			,       __lowerCamelCase				)
   torch_model.save_pretrained(__lowerCamelCase				)
   _UpperCAmelCase  :  Union[str, Any]				       =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__":
 lowercase				=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.')
 lowercase				=parser.parse_args()
 if args.save_dir is None:
  lowercase				=Path(args.tf_ckpt_path).parent.name
  lowercase				=os.path.join('pegasus', dataset)
 convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)





'''simple docstring'''






def lowerCamelCase__     (		__lowerCamelCase			:				str = "The quick brown fox jumps over the lazy dog"			,       ):







   '''simple docstring'''




   _UpperCAmelCase  :  Optional[Any]				       =set()

   # Replace all the whitespace in our sentence
   _UpperCAmelCase  :  Dict				       =input_str.replace(' '			,       ''				)
   for alpha in input_str:
      if "a" <= alpha.lower() <= "z":
         frequency.add(alpha.lower()				)
   return len(__lowerCamelCase				) == 2_6





def lowerCamelCase__     (		__lowerCamelCase			:				str = "The quick brown fox jumps over the lazy dog"			,       ):







   '''simple docstring'''




   _UpperCAmelCase  :  Tuple				       =[False] * 2_6
   for char in input_str:
      if char.islower():
         _UpperCAmelCase  :  Dict				       =True
      elif char.isupper():
         _UpperCAmelCase  :  List[str]				       =True
   return all(__lowerCamelCase				)





def lowerCamelCase__     (		__lowerCamelCase			:				str = "The quick brown fox jumps over the lazy dog"			,       ):







   '''simple docstring'''




   return len({char for char in input_str.lower() if char.isalpha()}				) == 2_6





def lowerCamelCase__     (		):







   '''simple docstring'''




   from timeit import timeit

   _UpperCAmelCase  :  List[Any]				       ='from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'
   print(timeit('is_pangram()'			,       setup=__lowerCamelCase				)				)
   print(timeit('is_pangram_faster()'			,       setup=__lowerCamelCase				)				)
   print(timeit('is_pangram_fastest()'			,       setup=__lowerCamelCase				)				)
   # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
   # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 benchmark()

