



"""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


lowerCamelCase  			=		logging.get_logger(__name__)


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

# See all LED models at https://huggingface.co/models?filter=LED
lowerCamelCase  			=		{
    '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',
    },
}

lowerCamelCase  			=		{
    'allenai/led-base-16384': 16_384,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def   a__      ( ):
      UpperCAmelCase_     					=				(
          list(range(ord("!" )      ,     ord("~" ) + 1 ) ) + list(range(ord("¡" )      ,     ord("¬" ) + 1 ) ) + list(range(ord("®" )      ,     ord("ÿ" ) + 1 ) )
      )
      UpperCAmelCase_     					=				bs[:]
      UpperCAmelCase_     					=				0
      for b in range(2**8 ):
            if b not in bs:
                  bs.append(__UpperCAmelCase )
                  cs.append(2**8 + n )
                  n += 1
      UpperCAmelCase_     					=				[chr(__UpperCAmelCase ) for n in cs]
      return dict(zip(__UpperCAmelCase      ,     __UpperCAmelCase ) )
def   a__      ( lowerCAmelCase__ ):
      UpperCAmelCase_     					=				set()
      UpperCAmelCase_     					=				word[0]
      for char in word[1:]:
            pairs.add((prev_char, char) )
            UpperCAmelCase_     					=				char
      return pairs


class  lowercase__  (       _lowercase ):







       '''simple docstring'''


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



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


             '''simple docstring'''




             UpperCAmelCase_     					=				AddedToken(_UpperCAmelCase						,							lstrip=_UpperCAmelCase						,							rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase						,							_UpperCAmelCase       ) else bos_token
             UpperCAmelCase_     					=				AddedToken(_UpperCAmelCase						,							lstrip=_UpperCAmelCase						,							rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase						,							_UpperCAmelCase       ) else eos_token
             UpperCAmelCase_     					=				AddedToken(_UpperCAmelCase						,							lstrip=_UpperCAmelCase						,							rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase						,							_UpperCAmelCase       ) else sep_token
             UpperCAmelCase_     					=				AddedToken(_UpperCAmelCase						,							lstrip=_UpperCAmelCase						,							rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase						,							_UpperCAmelCase       ) else cls_token
             UpperCAmelCase_     					=				AddedToken(_UpperCAmelCase						,							lstrip=_UpperCAmelCase						,							rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase						,							_UpperCAmelCase       ) else unk_token
             UpperCAmelCase_     					=				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
             UpperCAmelCase_     					=				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:
                   UpperCAmelCase_     					=				json.load(_UpperCAmelCase       )
             UpperCAmelCase_     					=				{v: k for k, v in self.encoder.items()}
             UpperCAmelCase_     					=				errors  # how to handle errors in decoding
             UpperCAmelCase_     					=				bytes_to_unicode()
             UpperCAmelCase_     					=				{v: k for k, v in self.byte_encoder.items()}
             with open(_UpperCAmelCase						,							encoding="utf-8"       ) as merges_handle:
                   UpperCAmelCase_     					=				merges_handle.read().split("\n"       )[1:-1]
             UpperCAmelCase_     					=				[tuple(merge.split()       ) for merge in bpe_merges]
             UpperCAmelCase_     					=				dict(zip(_UpperCAmelCase						,							range(len(_UpperCAmelCase       )       )       )       )
             UpperCAmelCase_     					=				{}
             UpperCAmelCase_     					=				add_prefix_space

             # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
             UpperCAmelCase_     					=				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 	lowercase__							(				self     :    int       )					->		List[Any]:


             '''simple docstring'''




             return len(self.encoder       )



       def 	lowercase__							(				self     :    Tuple       )					->		List[str]:


             '''simple docstring'''




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



       def 	lowercase__							(				self     :    Optional[int]						,							_UpperCAmelCase     :    Optional[Any]       )					->		Optional[Any]:


             '''simple docstring'''




             if token in self.cache:
                   return self.cache[token]
             UpperCAmelCase_     					=				tuple(_UpperCAmelCase       )
             UpperCAmelCase_     					=				get_pairs(_UpperCAmelCase       )

             if not pairs:
                   return token

             while True:
                   UpperCAmelCase_     					=				min(_UpperCAmelCase						,							key=lambda _UpperCAmelCase       : self.bpe_ranks.get(_UpperCAmelCase						,							float("inf"       )       )       )
                   if bigram not in self.bpe_ranks:
                         break
                   UpperCAmelCase_					,       UpperCAmelCase_     					=				bigram
                   UpperCAmelCase_     					=				[]
                   UpperCAmelCase_     					=				0
                   while i < len(_UpperCAmelCase       ):
                         try:
                               UpperCAmelCase_     					=				word.index(_UpperCAmelCase						,							_UpperCAmelCase       )
                         except ValueError:
                               new_word.extend(word[i:]       )
                               break
                         else:
                               new_word.extend(word[i:j]       )
                               UpperCAmelCase_     					=				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
                   UpperCAmelCase_     					=				tuple(_UpperCAmelCase       )
                   UpperCAmelCase_     					=				new_word
                   if len(_UpperCAmelCase       ) == 1:
                         break
                   else:
                         UpperCAmelCase_     					=				get_pairs(_UpperCAmelCase       )
             UpperCAmelCase_     					=				" ".join(_UpperCAmelCase       )
             UpperCAmelCase_     					=				word
             return word



       def 	lowercase__							(				self     :    str						,							_UpperCAmelCase     :    Optional[int]       )					->		List[Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				[]
             for token in re.findall(self.pat						,							_UpperCAmelCase       ):
                   UpperCAmelCase_     					=				"".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 	lowercase__							(				self     :    List[Any]						,							_UpperCAmelCase     :    List[str]       )					->		List[Any]:


             '''simple docstring'''




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



       def 	lowercase__							(				self     :    Union[str, Any]						,							_UpperCAmelCase     :    List[str]       )					->		int:


             '''simple docstring'''




             return self.decoder.get(_UpperCAmelCase       )



       def 	lowercase__							(				self     :    Union[str, Any]						,							_UpperCAmelCase     :    int       )					->		str:


             '''simple docstring'''




             UpperCAmelCase_     					=				"".join(_UpperCAmelCase       )
             UpperCAmelCase_     					=				bytearray([self.byte_decoder[c] for c in text]       ).decode("utf-8"						,							errors=self.errors       )
             return text



       def 	lowercase__							(				self     :    Any						,							_UpperCAmelCase     :    Union[str, Any]						,							_UpperCAmelCase     :    Optional[Any] = None       )					->		Tuple[str]:


             '''simple docstring'''




             if not os.path.isdir(_UpperCAmelCase       ):
                   logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
                   return
             UpperCAmelCase_     					=				os.path.join(
                 _UpperCAmelCase						,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )
             UpperCAmelCase_     					=				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"       )

             UpperCAmelCase_     					=				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!"       )
                               UpperCAmelCase_     					=				token_index
                         writer.write(" ".join(_UpperCAmelCase       ) + "\n"       )
                         index += 1

             return vocab_file, merge_file



       def 	lowercase__							(				self     :    Tuple						,							_UpperCAmelCase     :    List[str]						,							_UpperCAmelCase     :    List[str] = None       )					->		List[int]:


             '''simple docstring'''




             if token_ids_a is None:
                   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
             UpperCAmelCase_     					=				[self.cls_token_id]
             UpperCAmelCase_     					=				[self.sep_token_id]
             return cls + token_ids_a + sep + sep + token_ids_a + sep



       def 	lowercase__							(				self     :    Any						,							_UpperCAmelCase     :    List[Any]						,							_UpperCAmelCase     :    Dict = None						,							_UpperCAmelCase     :    List[Any] = 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 	lowercase__							(				self     :    Union[str, Any]						,							_UpperCAmelCase     :    List[str]						,							_UpperCAmelCase     :    int = None       )					->		List[int]:


             '''simple docstring'''




             UpperCAmelCase_     					=				[self.sep_token_id]
             UpperCAmelCase_     					=				[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 	lowercase__							(				self     :    List[Any]						,							_UpperCAmelCase     :    Optional[Any]						,							_UpperCAmelCase     :    List[Any]=False						,							**_UpperCAmelCase     :    List[Any]       )					->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_     					=				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()):
                   UpperCAmelCase_     					=				" " + text
             return (text, kwargs)







       def 	lowercase__							(				self     :    Union[str, Any]						,							_UpperCAmelCase     :    int						,							_UpperCAmelCase     :    Any = None						,							_UpperCAmelCase     :    Optional[Any] = PaddingStrategy.DO_NOT_PAD						,							_UpperCAmelCase     :    int = None						,							_UpperCAmelCase     :    List[str] = None						,							)					->		dict:


             '''simple docstring'''




             UpperCAmelCase_     					=				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:
                   UpperCAmelCase_     					=				"attention_mask" in self.model_input_names

             if return_attention_mask and "global_attention_mask" in encoded_inputs:
                   UpperCAmelCase_     					=				encoded_inputs[self.model_input_names[0]]
                   # `global_attention_mask` need to have the same length as other (sequential) inputs.
                   UpperCAmelCase_     					=				len(encoded_inputs["global_attention_mask"]       ) != len(_UpperCAmelCase       )

                   if needs_to_be_padded:
                         UpperCAmelCase_     					=				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`
                               UpperCAmelCase_     					=				(
                                   encoded_inputs["global_attention_mask"] + [-1] * difference
                               )
                         elif self.padding_side == "left":
                               UpperCAmelCase_     					=				[-1] * difference + encoded_inputs[
                                   "global_attention_mask"
                               ]
                         else:
                               raise ValueError("Invalid padding strategy:" + str(self.padding_side       )       )

             return encoded_inputs









import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= ('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

 a__												= (
     ('''layer.''', '''layer_'''),
     ('''word_embeddings.weight''', '''word_embeddings'''),
     ('''position_embeddings.weight''', '''position_embeddings'''),
     ('''token_type_embeddings.weight''', '''token_type_embeddings'''),
     ('''.''', '''/'''),
     ('''LayerNorm/weight''', '''LayerNorm/gamma'''),
     ('''LayerNorm/bias''', '''LayerNorm/beta'''),
     ('''weight''', '''kernel'''),
 )

 if not os.path.isdir(__UpperCAmelCase       ):
  os.makedirs(__UpperCAmelCase       )

 a__												= model.state_dict()

 def to_tf_var_name(__UpperCAmelCase       ):
  for patt, repl in iter(__UpperCAmelCase       ):
   a__												= name.replace(__UpperCAmelCase							,  __UpperCAmelCase       )
  return f"bert/{name}"

 def create_tf_var(__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
  a__												= tf.dtypes.as_dtype(tensor.dtype       )
  a__												= tf.get_variable(dtype=__UpperCAmelCase							,  shape=tensor.shape							,  name=__UpperCAmelCase							,  initializer=tf.zeros_initializer()       )
  session.run(tf.variables_initializer([tf_var]       )       )
  session.run(__UpperCAmelCase       )
  return tf_var

 tf.reset_default_graph()
 with tf.Session() as session:
  for var_name in state_dict:
   a__												= to_tf_var_name(__UpperCAmelCase       )
   a__												= state_dict[var_name].numpy()
   if any(x in var_name for x in tensors_to_transpose       ):
    a__												= torch_tensor.T
   a__												= create_tf_var(tensor=__UpperCAmelCase							,  name=__UpperCAmelCase							,  session=__UpperCAmelCase       )
   tf.keras.backend.set_value(__UpperCAmelCase							,  __UpperCAmelCase       )
   a__												= session.run(__UpperCAmelCase       )
   print(f"Successfully created {tf_name}: {np.allclose(__UpperCAmelCase							,  __UpperCAmelCase       )}"       )

  a__												= tf.train.Saver(tf.trainable_variables()       )
  saver.save(__UpperCAmelCase							,  os.path.join(__UpperCAmelCase							,  model_name.replace('''-'''							,  '''_'''       ) + '''.ckpt'''       )       )






def     __a					(		__UpperCAmelCase=None       ):
 a__												= argparse.ArgumentParser()
 parser.add_argument('''--model_name'''							,  type=__UpperCAmelCase							,  required=__UpperCAmelCase							,  help='''model name e.g. bert-base-uncased'''       )
 parser.add_argument(
     '''--cache_dir'''							,  type=__UpperCAmelCase							,  default=__UpperCAmelCase							,  required=__UpperCAmelCase							,  help='''Directory containing pytorch model'''       )
 parser.add_argument('''--pytorch_model_path'''							,  type=__UpperCAmelCase							,  required=__UpperCAmelCase							,  help='''/path/to/<pytorch-model-name>.bin'''       )
 parser.add_argument('''--tf_cache_dir'''							,  type=__UpperCAmelCase							,  required=__UpperCAmelCase							,  help='''Directory in which to save tensorflow model'''       )
 a__												= parser.parse_args(__UpperCAmelCase       )

 a__												= BertModel.from_pretrained(
     pretrained_model_name_or_path=args.model_name							,  state_dict=torch.load(args.pytorch_model_path       )							,  cache_dir=args.cache_dir							,  )

 convert_pytorch_checkpoint_to_tf(model=__UpperCAmelCase							,  ckpt_dir=args.tf_cache_dir							,  model_name=args.model_name       )


if __name__ == "__main__":
     main()





