






'''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


SCREAMING_SNAKE_CASE						=	[
    # 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 							lowercase_				(    __A :       Tuple			)       ->      int:



 """simple docstring"""




 for pegasus_name, hf_name in PATTERNS:
  lowercase					:   Optional[int]				  =k.replace(__A					,      __A			)
 return k


def 							lowercase_				(    __A :       dict					,      __A :       dict			)       ->      PegasusForConditionalGeneration:



 """simple docstring"""




 lowercase					:   List[str]				  =DEFAULTS.copy()
 cfg_kwargs.update(__A			)
 lowercase					:   Dict				  =PegasusConfig(**__A			)
 lowercase					:   Optional[int]				  =PegasusForConditionalGeneration(__A			)
 lowercase					:   Union[str, Any]				  =torch_model.model.state_dict()
 lowercase					:   Union[str, Any]				  ={}
 for k, v in tf_weights.items():
  lowercase					:   Dict				  =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					:   Union[str, Any]				  =v.T
  lowercase					:   Any				  =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					:   int				  =torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1]			)
 lowercase					:   Dict				  =mapping['''shared.weight''']
 lowercase					:   List[str]				  =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					:   List[str]				  =torch_model.model.load_state_dict(__A					,      strict=__A			)
 lowercase					:   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 							lowercase_				(    __A :       Optional[int]="./ckpt/aeslc/model.ckpt-32000"			)       ->      Dict:



 """simple docstring"""




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


def 							lowercase_				(    __A :       str					,      __A :       str			)       ->      Dict:



 """simple docstring"""




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











'''simple docstring'''



import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


SCREAMING_SNAKE_CASE						=	logging.get_logger(__name__)


SCREAMING_SNAKE_CASE						=	{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all LED models at https://huggingface.co/models?filter=LED
SCREAMING_SNAKE_CASE						=	{
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

SCREAMING_SNAKE_CASE						=	{
    'allenai/led-base-16384': 16_384,
}


@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 							lowercase_				(    )       ->      Any:



 """simple docstring"""




 lowercase					:   int				  =(
     list(range(ord('''!'''			)					,      ord('''~'''			) + 1			)			) + list(range(ord('''¡'''			)					,      ord('''¬'''			) + 1			)			) + list(range(ord('''®'''			)					,      ord('''ÿ'''			) + 1			)			)
 )
 lowercase					:   Union[str, Any]				  =bs[:]
 lowercase					:   Tuple				  =0
 for b in range(2**8			):
  if b not in bs:
   bs.append(__A			)
   cs.append(2**8 + n			)
   n += 1
 lowercase					:   Optional[Any]				  =[chr(__A			) for n in cs]
 return dict(zip(__A					,      __A			)			)


def 							lowercase_				(    __A :       str			)       ->      List[Any]:



 """simple docstring"""




 lowercase					:   Optional[Any]				  =set()
 lowercase					:   Tuple				  =word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)			)
  lowercase					:   List[str]				  =char
 return pairs





class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			VOCAB_FILES_NAMES
      UpperCamelCase_								=			PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_								=			['''input_ids''', '''attention_mask''']






      def __init__( self			:		Tuple     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		str="replace"     ,      UpperCAmelCase			:		int="<s>"     ,      UpperCAmelCase			:		Optional[int]="</s>"     ,      UpperCAmelCase			:		Optional[int]="</s>"     ,      UpperCAmelCase			:		List[Any]="<s>"     ,      UpperCAmelCase			:		str="<unk>"     ,      UpperCAmelCase			:		Dict="<pad>"     ,      UpperCAmelCase			:		Union[str, Any]="<mask>"     ,      UpperCAmelCase			:		str=False     ,      **UpperCAmelCase			:		int     ,      )  ->  Dict:


       '''simple docstring'''

       lowercase					:   int				  =AddedToken(UpperCAmelCase     ,      lstrip=UpperCAmelCase     ,      rstrip=UpperCAmelCase		) if isinstance(UpperCAmelCase     ,      UpperCAmelCase		) else bos_token
       lowercase					:   Union[str, Any]				  =AddedToken(UpperCAmelCase     ,      lstrip=UpperCAmelCase     ,      rstrip=UpperCAmelCase		) if isinstance(UpperCAmelCase     ,      UpperCAmelCase		) else eos_token
       lowercase					:   str				  =AddedToken(UpperCAmelCase     ,      lstrip=UpperCAmelCase     ,      rstrip=UpperCAmelCase		) if isinstance(UpperCAmelCase     ,      UpperCAmelCase		) else sep_token
       lowercase					:   Optional[int]				  =AddedToken(UpperCAmelCase     ,      lstrip=UpperCAmelCase     ,      rstrip=UpperCAmelCase		) if isinstance(UpperCAmelCase     ,      UpperCAmelCase		) else cls_token
       lowercase					:   Union[str, Any]				  =AddedToken(UpperCAmelCase     ,      lstrip=UpperCAmelCase     ,      rstrip=UpperCAmelCase		) if isinstance(UpperCAmelCase     ,      UpperCAmelCase		) else unk_token
       lowercase					:   List[Any]				  =AddedToken(UpperCAmelCase     ,      lstrip=UpperCAmelCase     ,      rstrip=UpperCAmelCase		) if isinstance(UpperCAmelCase     ,      UpperCAmelCase		) else pad_token

       # Mask token behave like a normal word, i.e. include the space before it
       lowercase					:   Any				  =AddedToken(UpperCAmelCase     ,      lstrip=UpperCAmelCase     ,      rstrip=UpperCAmelCase		) if isinstance(UpperCAmelCase     ,      UpperCAmelCase		) else mask_token

       super().__init__(
           errors=UpperCAmelCase     ,      bos_token=UpperCAmelCase     ,      eos_token=UpperCAmelCase     ,      unk_token=UpperCAmelCase     ,      sep_token=UpperCAmelCase     ,      cls_token=UpperCAmelCase     ,      pad_token=UpperCAmelCase     ,      mask_token=UpperCAmelCase     ,      add_prefix_space=UpperCAmelCase     ,      **UpperCAmelCase     ,      )

       with open(UpperCAmelCase     ,      encoding='''utf-8'''		) as vocab_handle:
        lowercase					:   str				  =json.load(UpperCAmelCase		)
       lowercase					:   Optional[int]				  ={v: k for k, v in self.encoder.items()}
       lowercase					:   Optional[int]				  =errors  # how to handle errors in decoding
       lowercase					:   Tuple				  =bytes_to_unicode()
       lowercase					:   int				  ={v: k for k, v in self.byte_encoder.items()}
       with open(UpperCAmelCase     ,      encoding='''utf-8'''		) as merges_handle:
        lowercase					:   Union[str, Any]				  =merges_handle.read().split('''\n'''		)[1:-1]
       lowercase					:   Optional[Any]				  =[tuple(merge.split()		) for merge in bpe_merges]
       lowercase					:   Optional[int]				  =dict(zip(UpperCAmelCase     ,      range(len(UpperCAmelCase		)		)		)		)
       lowercase					:   Optional[int]				  ={}
       lowercase					:   Any				  =add_prefix_space

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       lowercase					:   str				  =re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''		)






      @property
      # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
      def 				A__      ( self			:		Union[str, Any]		)  ->  Optional[int]:


       '''simple docstring'''

       return len(self.encoder		)






      def 				A__      ( self			:		Union[str, Any]		)  ->  Union[str, Any]:


       '''simple docstring'''

       return dict(self.encoder     ,      **self.added_tokens_encoder		)






      def 				A__      ( self			:		int     ,      UpperCAmelCase			:		str		)  ->  Optional[Any]:


       '''simple docstring'''

       if token in self.cache:
        return self.cache[token]
       lowercase					:   List[str]				  =tuple(UpperCAmelCase		)
       lowercase					:   List[str]				  =get_pairs(UpperCAmelCase		)

       if not pairs:
        return token

       while True:
        lowercase					:   Tuple				  =min(UpperCAmelCase     ,      key=lambda UpperCAmelCase		: self.bpe_ranks.get(UpperCAmelCase     ,      float('''inf'''		)		)		)
        if bigram not in self.bpe_ranks:
         break
        lowercase  ,		lowercase					:   Optional[int]				  =bigram
        lowercase					:   Union[str, Any]				  =[]
        lowercase					:   Optional[Any]				  =0
        while i < len(UpperCAmelCase		):
         try:
          lowercase					:   Dict				  =word.index(UpperCAmelCase     ,      UpperCAmelCase		)
         except ValueError:
          new_word.extend(word[i:]		)
          break
         else:
          new_word.extend(word[i:j]		)
          lowercase					:   Optional[int]				  =j

         if word[i] == first and i < len(UpperCAmelCase		) - 1 and word[i + 1] == second:
          new_word.append(first + second		)
          i += 2
         else:
          new_word.append(word[i]		)
          i += 1
        lowercase					:   List[str]				  =tuple(UpperCAmelCase		)
        lowercase					:   str				  =new_word
        if len(UpperCAmelCase		) == 1:
         break
        else:
         lowercase					:   Optional[Any]				  =get_pairs(UpperCAmelCase		)
       lowercase					:   Optional[Any]				  =''' '''.join(UpperCAmelCase		)
       lowercase					:   Union[str, Any]				  =word
       return word






      def 				A__      ( self			:		int     ,      UpperCAmelCase			:		Optional[Any]		)  ->  Union[str, Any]:


       '''simple docstring'''

       lowercase					:   Dict				  =[]
       for token in re.findall(self.pat     ,      UpperCAmelCase		):
        lowercase					:   Optional[int]				  =''''''.join(
            self.byte_encoder[b] for b in token.encode('''utf-8'''		)		)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
        bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCAmelCase		).split(''' '''		)		)
       return bpe_tokens






      def 				A__      ( self			:		Union[str, Any]     ,      UpperCAmelCase			:		Union[str, Any]		)  ->  List[str]:


       '''simple docstring'''

       return self.encoder.get(UpperCAmelCase     ,      self.encoder.get(self.unk_token		)		)






      def 				A__      ( self			:		Dict     ,      UpperCAmelCase			:		Optional[int]		)  ->  Any:


       '''simple docstring'''

       return self.decoder.get(UpperCAmelCase		)






      def 				A__      ( self			:		List[str]     ,      UpperCAmelCase			:		List[Any]		)  ->  Union[str, Any]:


       '''simple docstring'''

       lowercase					:   str				  =''''''.join(UpperCAmelCase		)
       lowercase					:   Dict				  =bytearray([self.byte_decoder[c] for c in text]		).decode('''utf-8'''     ,      errors=self.errors		)
       return text






      def 				A__      ( self			:		Any     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		Optional[str] = None		)  ->  Tuple[str]:


       '''simple docstring'''

       if not os.path.isdir(UpperCAmelCase		):
        logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
        return
       lowercase					:   Optional[Any]				  =os.path.join(
           UpperCAmelCase     ,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)
       lowercase					:   List[Any]				  =os.path.join(
           UpperCAmelCase     ,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']		)

       with open(UpperCAmelCase     ,      '''w'''     ,      encoding='''utf-8'''		) as f:
        f.write(json.dumps(self.encoder     ,      indent=2     ,      sort_keys=UpperCAmelCase     ,      ensure_ascii=UpperCAmelCase		) + '''\n'''		)

       lowercase					:   List[str]				  =0
       with open(UpperCAmelCase     ,      '''w'''     ,      encoding='''utf-8'''		) as writer:
        writer.write('''#version: 0.2\n'''		)
        for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,      key=lambda UpperCAmelCase		: kv[1]		):
         if index != token_index:
          logger.warning(
              f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
              ''' Please check that the tokenizer is not corrupted!'''		)
          lowercase					:   Any				  =token_index
         writer.write(''' '''.join(UpperCAmelCase		) + '''\n'''		)
         index += 1

       return vocab_file, merge_file






      def 				A__      ( self			:		str     ,      UpperCAmelCase			:		List[int]     ,      UpperCAmelCase			:		Optional[List[int]] = None		)  ->  List[int]:


       '''simple docstring'''

       if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       lowercase					:   Optional[int]				  =[self.cls_token_id]
       lowercase					:   List[Any]				  =[self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep






      def 				A__      ( self			:		Optional[int]     ,      UpperCAmelCase			:		List[int]     ,      UpperCAmelCase			:		Optional[List[int]] = None     ,      UpperCAmelCase			:		bool = False		)  ->  List[int]:


       '''simple docstring'''

       if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=UpperCAmelCase     ,      token_ids_a=UpperCAmelCase     ,      already_has_special_tokens=UpperCAmelCase		)

       if token_ids_a is None:
        return [1] + ([0] * len(UpperCAmelCase		)) + [1]
       return [1] + ([0] * len(UpperCAmelCase		)) + [1, 1] + ([0] * len(UpperCAmelCase		)) + [1]






      def 				A__      ( self			:		Any     ,      UpperCAmelCase			:		List[int]     ,      UpperCAmelCase			:		Optional[List[int]] = None		)  ->  List[int]:


       '''simple docstring'''

       lowercase					:   Dict				  =[self.sep_token_id]
       lowercase					:   Optional[int]				  =[self.cls_token_id]

       if token_ids_a is None:
        return len(cls + token_ids_a + sep		) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]






      def 				A__      ( self			:		Optional[int]     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		Optional[int]=False     ,      **UpperCAmelCase			:		Optional[Any]		)  ->  str:


       '''simple docstring'''

       lowercase					:   Tuple				  =kwargs.pop('''add_prefix_space'''     ,      self.add_prefix_space		)
       if (is_split_into_words or add_prefix_space) and (len(UpperCAmelCase		) > 0 and not text[0].isspace()):
        lowercase					:   Union[str, Any]				  =''' ''' + text
       return (text, kwargs)






      def 				A__      ( self			:		Any     ,      UpperCAmelCase			:		Union[Dict[str, EncodedInput], BatchEncoding]     ,      UpperCAmelCase			:		Optional[int] = None     ,      UpperCAmelCase			:		PaddingStrategy = PaddingStrategy.DO_NOT_PAD     ,      UpperCAmelCase			:		Optional[int] = None     ,      UpperCAmelCase			:		Optional[bool] = None     ,      )  ->  dict:


       '''simple docstring'''

       lowercase					:   Optional[int]				  =super()._pad(
           encoded_inputs=UpperCAmelCase     ,      max_length=UpperCAmelCase     ,      padding_strategy=UpperCAmelCase     ,      pad_to_multiple_of=UpperCAmelCase     ,      return_attention_mask=UpperCAmelCase     ,      )

       # Load from model defaults
       if return_attention_mask is None:
        lowercase					:   Tuple				  ='''attention_mask''' in self.model_input_names

       if return_attention_mask and "global_attention_mask" in encoded_inputs:
        lowercase					:   Optional[Any]				  =encoded_inputs[self.model_input_names[0]]
        # `global_attention_mask` need to have the same length as other (sequential) inputs.
        lowercase					:   str				  =len(encoded_inputs['''global_attention_mask''']		) != len(UpperCAmelCase		)

        if needs_to_be_padded:
         lowercase					:   Tuple				  =len(UpperCAmelCase		) - len(encoded_inputs['''global_attention_mask''']		)

         if self.padding_side == "right":
          # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
          lowercase					:   List[str]				  =(
              encoded_inputs['''global_attention_mask'''] + [-1] * difference
          )
         elif self.padding_side == "left":
          lowercase					:   Any				  =[-1] * difference + encoded_inputs[
              '''global_attention_mask'''
          ]
         else:
          raise ValueError('''Invalid padding strategy:''' + str(self.padding_side		)		)

       return encoded_inputs



