


"""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


snake_case__		:     List[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      _snake_case						(						_snake_case				:			Dict			):
      for pegasus_name, hf_name in PATTERNS:
            lowerCAmelCase					:	Dict       =   k.replace(_snake_case ,   _snake_case			)
      return k


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


def      _snake_case						(						_snake_case				:			str ,   _snake_case				:			str			):
      # save tokenizer first
      lowerCAmelCase					:	List[str]       =   Path(_snake_case			).parent.name
      lowerCAmelCase					:	List[str]       =   task_specific_params[f'''summarization_{dataset}''']['''max_position_embeddings''']
      lowerCAmelCase					:	Dict       =   PegasusTokenizer.from_pretrained('''sshleifer/pegasus''' ,   model_max_length=_snake_case			)
      assert tok.model_max_length == desired_max_model_length
      tok.save_pretrained(_snake_case			)

      # convert model
      lowerCAmelCase					:	Any       =   get_tf_weights_as_numpy(_snake_case			)
      lowerCAmelCase					:	Union[str, Any]       =   task_specific_params[f'''summarization_{dataset}''']
      if dataset == "large":
            lowerCAmelCase					:	Any       =   task_specific_params
      lowerCAmelCase					:	List[str]       =   convert_pegasus(_snake_case ,   _snake_case			)
      torch_model.save_pretrained(_snake_case			)
      lowerCAmelCase					:	str       =   torch_model.state_dict()
      sd.pop('''model.decoder.embed_positions.weight'''			)
      sd.pop('''model.encoder.embed_positions.weight'''			)
      torch.save(_snake_case ,   Path(_snake_case			) / '''pytorch_model.bin'''			)


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






"""simple docstring"""





from __future__ import annotations



class 		snake_case_:






     def __init__(	self  :					int					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					str				):
           lowerCAmelCase,      lowerCAmelCase					:	List[str]       =   text, pattern
           lowerCAmelCase,      lowerCAmelCase					:	Union[str, Any]       =   len(UpperCamelCase_				), len(UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					List[Any]					,				UpperCamelCase_  :					str				):

           for i in range(self.patLen - 1					,				-1					,				-1				):
                 if char == self.pattern[i]:
                       return i
           return -1






     def      lowerCamelCase__     (	self  :					Optional[Any]					,				UpperCamelCase_  :					int				):

           for i in range(self.patLen - 1					,				-1					,				-1				):
                 if self.pattern[i] != self.text[current_pos + i]:
                       return current_pos + i
           return -1






     def      lowerCamelCase__     (	self  :					Dict				):
           # searches pattern in text and returns index positions
           lowerCAmelCase					:	Union[str, Any]       =   []
           for i in range(self.textLen - self.patLen + 1				):
                 lowerCAmelCase					:	str       =   self.mismatch_in_text(UpperCamelCase_				)
                 if mismatch_index == -1:
                       positions.append(UpperCamelCase_				)
                 else:
                       lowerCAmelCase					:	Optional[Any]       =   self.match_in_pattern(self.text[mismatch_index]				)
                       lowerCAmelCase					:	int       =   (
                           mismatch_index - match_index
                       )  # shifting index lgtm [py/multiple-definition]
           return positions



snake_case__		:     str    				=				'''ABAABA'''
snake_case__		:     List[str]    				=				'''AB'''
snake_case__		:     Union[str, Any]    				=				BoyerMooreSearch(text, pattern)
snake_case__		:     Optional[Any]    				=				bms.bad_character_heuristic()

if len(positions) == 0:
 print('''No match found''')
else:
 print('''Pattern found in following positions: ''')
 print(positions)


