




'''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


UpperCAmelCase_							:	Optional[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       _UpperCamelCase			(_lowerCamelCase				:  str						)-> List[Any]:




     '''simple docstring'''





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







def       _UpperCamelCase			(_lowerCamelCase				:  dict ,						_lowerCamelCase				:  dict						)-> PegasusForConditionalGeneration:




     '''simple docstring'''





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




     '''simple docstring'''





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







def       _UpperCamelCase			(_lowerCamelCase				:  str ,						_lowerCamelCase				:  str						)-> List[Any]:




     '''simple docstring'''





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

     # convert model
     __snake_case            =	get_tf_weights_as_numpy(_lowerCamelCase						)
     __snake_case            =	task_specific_params[f'''summarization_{dataset}''']
     if dataset == "large":
          __snake_case            =	task_specific_params
     __snake_case            =	convert_pegasus(_lowerCamelCase ,						_lowerCamelCase						)
     torch_model.save_pretrained(_lowerCamelCase						)
     __snake_case            =	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__":
     UpperCAmelCase_							:	Dict	     =		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_							:	str	     =		parser.parse_args()
     if args.save_dir is None:
          UpperCAmelCase_							:	Optional[int]	     =		Path(args.tf_ckpt_path).parent.name
          UpperCAmelCase_							:	Tuple	     =		os.path.join('''pegasus''', dataset)
     convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)












'''simple docstring'''



import argparse
import os
import re


UpperCAmelCase_							:	List[str]	     =		'''src/transformers/models/auto'''


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
UpperCAmelCase_							:	Tuple	     =		re.compile(R'''[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict''')
# re pattern that matches identifiers in mappings
UpperCAmelCase_							:	Dict	     =		re.compile(R'''\s*\(\s*"(\S[^"]+)"''')







def       _UpperCamelCase			(_lowerCamelCase				:  List[Any] ,						_lowerCamelCase				:  bool = False						)-> str:




     '''simple docstring'''





     with open(_lowerCamelCase ,						'''r''' ,						encoding='''utf-8'''						) as f:
          __snake_case            =	f.read()

     __snake_case            =	content.split('''\n'''						)
     __snake_case            =	[]
     __snake_case            =	0
     while line_idx < len(_lowerCamelCase						):
          if _re_intro_mapping.search(lines[line_idx]						) is not None:
               __snake_case            =	len(re.search(R'''^(\s*)\S''' ,						lines[line_idx]						).groups()[0]						) + 8
               # Start of a new mapping!
               while not lines[line_idx].startswith(''' ''' * indent + '''('''						):
                    new_lines.append(lines[line_idx]						)
                    line_idx += 1

               __snake_case            =	[]
               while lines[line_idx].strip() != "]":
                    # Blocks either fit in one line or not
                    if lines[line_idx].strip() == "(":
                         __snake_case            =	line_idx
                         while not lines[line_idx].startswith(''' ''' * indent + ''')'''						):
                              line_idx += 1
                         blocks.append('''\n'''.join(lines[start_idx : line_idx + 1]						)						)
                    else:
                         blocks.append(lines[line_idx]						)
                    line_idx += 1

               # Sort blocks by their identifiers
               __snake_case            =	sorted(_lowerCamelCase ,						key=lambda _lowerCamelCase						: _re_identifier.search(_lowerCamelCase						).groups()[0]						)
               new_lines += blocks
          else:
               new_lines.append(lines[line_idx]						)
               line_idx += 1

     if overwrite:
          with open(_lowerCamelCase ,						'''w''' ,						encoding='''utf-8'''						) as f:
               f.write('''\n'''.join(_lowerCamelCase						)						)
     elif "\n".join(_lowerCamelCase						) != content:
          return True







def       _UpperCamelCase			(_lowerCamelCase				:  bool = False						)-> Tuple:




     '''simple docstring'''





     __snake_case            =	[os.path.join(_lowerCamelCase ,						_lowerCamelCase						) for f in os.listdir(_lowerCamelCase						) if f.endswith('''.py'''						)]
     __snake_case            =	[sort_auto_mapping(_lowerCamelCase ,						overwrite=_lowerCamelCase						) for fname in fnames]

     if not overwrite and any(_lowerCamelCase						):
          __snake_case            =	[f for f, d in zip(_lowerCamelCase ,						_lowerCamelCase						) if d]
          raise ValueError(
              f'''The following files have auto mappings that need sorting: {", ".join(_lowerCamelCase						)}. Run `make style` to fix'''
              ''' this.'''						)


if __name__ == "__main__":
     UpperCAmelCase_							:	str	     =		argparse.ArgumentParser()
     parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
     UpperCAmelCase_							:	List[Any]	     =		parser.parse_args()

     sort_all_auto_mappings(not args.check_only)






