



"""simple docstring"""





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

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE				:     Optional[Any]          =	logging.get_logger(__name__)


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

# See all MVP models at https://huggingface.co/models?filter=mvp
SCREAMING_SNAKE_CASE				:     Union[str, Any]          =	{
    '''vocab_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
    },
    '''added_tokens.json''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
    },
    '''merges_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
    },
}

SCREAMING_SNAKE_CASE				:     Union[str, Any]          =	{
    '''RUCAIBox/mvp''': 1_0_2_4,
}






@lru_cache()
def 	__UpperCAmelCase					(       )							->	Tuple:






   """simple docstring"""


   _lowerCAmelCase					     =		(
       list(range(ord("""!"""   )   ,	ord("""~"""   ) + 1   )   ) + list(range(ord("""¡"""   )   ,	ord("""¬"""   ) + 1   )   ) + list(range(ord("""®"""   )   ,	ord("""ÿ"""   ) + 1   )   )
   )
   _lowerCAmelCase					     =		bs[:]
   _lowerCAmelCase					     =		0
   for b in range(2**8   ):
      if b not in bs:
         bs.append(snake_case_   )
         cs.append(2**8 + n   )
         n += 1
   _lowerCAmelCase					     =		[chr(snake_case_   ) for n in cs]
   return dict(zip(snake_case_   ,	snake_case_   )   )






def 	__UpperCAmelCase					(       snake_case_  :  Dict   )							->	List[Any]:






   """simple docstring"""


   _lowerCAmelCase					     =		set()
   _lowerCAmelCase					     =		word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)   )
      _lowerCAmelCase					     =		char
   return pairs



class       __lowerCamelCase	(   __lowercase    ):
   __UpperCamelCase									=   VOCAB_FILES_NAMES
   __UpperCamelCase									=   PRETRAINED_VOCAB_FILES_MAP
   __UpperCamelCase									=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCamelCase									=   ['input_ids', 'attention_mask']







   def __init__(self       ,	lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase="replace"       ,	lowerCamelCase="<s>"       ,	lowerCamelCase="</s>"       ,	lowerCamelCase="</s>"       ,	lowerCamelCase="<s>"       ,	lowerCamelCase="<unk>"       ,	lowerCamelCase="<pad>"       ,	lowerCamelCase="<mask>"       ,	lowerCamelCase=False       ,	**lowerCamelCase       ,	):




      '''simple docstring'''




      _lowerCAmelCase					     =		AddedToken(lowerCamelCase       ,	lstrip=lowerCamelCase       ,	rstrip=lowerCamelCase				) if isinstance(lowerCamelCase       ,	lowerCamelCase				) else bos_token
      _lowerCAmelCase					     =		AddedToken(lowerCamelCase       ,	lstrip=lowerCamelCase       ,	rstrip=lowerCamelCase				) if isinstance(lowerCamelCase       ,	lowerCamelCase				) else eos_token
      _lowerCAmelCase					     =		AddedToken(lowerCamelCase       ,	lstrip=lowerCamelCase       ,	rstrip=lowerCamelCase				) if isinstance(lowerCamelCase       ,	lowerCamelCase				) else sep_token
      _lowerCAmelCase					     =		AddedToken(lowerCamelCase       ,	lstrip=lowerCamelCase       ,	rstrip=lowerCamelCase				) if isinstance(lowerCamelCase       ,	lowerCamelCase				) else cls_token
      _lowerCAmelCase					     =		AddedToken(lowerCamelCase       ,	lstrip=lowerCamelCase       ,	rstrip=lowerCamelCase				) if isinstance(lowerCamelCase       ,	lowerCamelCase				) else unk_token
      _lowerCAmelCase					     =		AddedToken(lowerCamelCase       ,	lstrip=lowerCamelCase       ,	rstrip=lowerCamelCase				) if isinstance(lowerCamelCase       ,	lowerCamelCase				) else pad_token

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

      super().__init__(
          errors=lowerCamelCase       ,	bos_token=lowerCamelCase       ,	eos_token=lowerCamelCase       ,	unk_token=lowerCamelCase       ,	sep_token=lowerCamelCase       ,	cls_token=lowerCamelCase       ,	pad_token=lowerCamelCase       ,	mask_token=lowerCamelCase       ,	add_prefix_space=lowerCamelCase       ,	**lowerCamelCase       ,	)
      with open(lowerCamelCase       ,	encoding="""utf-8"""				) as vocab_handle:
         _lowerCAmelCase					     =		json.load(lowerCamelCase				)
      _lowerCAmelCase					     =		{v: k for k, v in self.encoder.items()}
      _lowerCAmelCase					     =		errors  # how to handle errors in decoding
      _lowerCAmelCase					     =		bytes_to_unicode()
      _lowerCAmelCase					     =		{v: k for k, v in self.byte_encoder.items()}
      with open(lowerCamelCase       ,	encoding="""utf-8"""				) as merges_handle:
         _lowerCAmelCase					     =		merges_handle.read().split("""\n"""				)[1:-1]
      _lowerCAmelCase					     =		[tuple(merge.split()				) for merge in bpe_merges]
      _lowerCAmelCase					     =		dict(zip(lowerCamelCase       ,	range(len(lowerCamelCase				)				)				)				)
      _lowerCAmelCase					     =		{}
      _lowerCAmelCase					     =		add_prefix_space

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







   @property
   def 							A__						(self				):




      '''simple docstring'''




      return len(self.encoder				)







   def 							A__						(self				):




      '''simple docstring'''




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







   def 							A__						(self       ,	lowerCamelCase				):




      '''simple docstring'''




      if token in self.cache:
         return self.cache[token]
      _lowerCAmelCase					     =		tuple(lowerCamelCase				)
      _lowerCAmelCase					     =		get_pairs(lowerCamelCase				)

      if not pairs:
         return token

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

            if word[i] == first and i < len(lowerCamelCase				) - 1 and word[i + 1] == second:
               new_word.append(first + second				)
               i += 2
            else:
               new_word.append(word[i]				)
               i += 1
         _lowerCAmelCase					     =		tuple(lowerCamelCase				)
         _lowerCAmelCase					     =		new_word
         if len(lowerCamelCase				) == 1:
            break
         else:
            _lowerCAmelCase					     =		get_pairs(lowerCamelCase				)
      _lowerCAmelCase					     =		""" """.join(lowerCamelCase				)
      _lowerCAmelCase					     =		word
      return word







   def 							A__						(self       ,	lowerCamelCase				):




      '''simple docstring'''




      _lowerCAmelCase					     =		[]
      for token in re.findall(self.pat       ,	lowerCamelCase				):
         _lowerCAmelCase					     =		"""""".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(lowerCamelCase				).split(""" """				)				)
      return bpe_tokens







   def 							A__						(self       ,	lowerCamelCase				):




      '''simple docstring'''




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







   def 							A__						(self       ,	lowerCamelCase				):




      '''simple docstring'''




      return self.decoder.get(lowerCamelCase				)







   def 							A__						(self       ,	lowerCamelCase				):




      '''simple docstring'''




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







   def 							A__						(self       ,	lowerCamelCase       ,	lowerCamelCase = None				):




      '''simple docstring'''




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

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

      _lowerCAmelCase					     =		0
      with open(lowerCamelCase       ,	"""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 lowerCamelCase				: 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!"""				)
               _lowerCAmelCase					     =		token_index
            writer.write(""" """.join(lowerCamelCase				) + """\n"""				)
            index += 1

      return vocab_file, merge_file







   def 							A__						(self       ,	lowerCamelCase       ,	lowerCamelCase = None				):




      '''simple docstring'''




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







   def 							A__						(self       ,	lowerCamelCase       ,	lowerCamelCase = None       ,	lowerCamelCase = False				):




      '''simple docstring'''




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

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







   def 							A__						(self       ,	lowerCamelCase       ,	lowerCamelCase = None				):




      '''simple docstring'''




      _lowerCAmelCase					     =		[self.sep_token_id]
      _lowerCAmelCase					     =		[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       ,	lowerCamelCase       ,	lowerCamelCase=False       ,	**lowerCamelCase				):




      '''simple docstring'''




      _lowerCAmelCase					     =		kwargs.pop("""add_prefix_space"""       ,	self.add_prefix_space				)
      if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase				) > 0 and not text[0].isspace()):
         _lowerCAmelCase					     =		""" """ + text
      return (text, kwargs)




"""simple docstring"""





import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
  raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE				:     Tuple          =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE				:     List[Any]          =	'''Hello world! cécé herlolip'''






def 	__UpperCAmelCase					(       snake_case_  :  str   ,	snake_case_  :  str   ,	snake_case_  :  bool   )							->	Optional[int]:






   """simple docstring"""


   _lowerCAmelCase					     =		FairseqRobertaModel.from_pretrained(snake_case_   )
   roberta.eval()  # disable dropout
   _lowerCAmelCase					     =		roberta.model.encoder.sentence_encoder
   _lowerCAmelCase					     =		RobertaConfig(
       vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings   ,	hidden_size=roberta.args.encoder_embed_dim   ,	num_hidden_layers=roberta.args.encoder_layers   ,	num_attention_heads=roberta.args.encoder_attention_heads   ,	intermediate_size=roberta.args.encoder_ffn_embed_dim   ,	max_position_embeddings=514   ,	type_vocab_size=1   ,	layer_norm_eps=1e-5   ,	)
   if classification_head:
      _lowerCAmelCase					     =		roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
   print("""Our BERT config:"""   ,	snake_case_   )

   _lowerCAmelCase					     =		RobertaForSequenceClassification(snake_case_   ) if classification_head else RobertaForMaskedLM(snake_case_   )
   model.eval()

   # Now let's copy all the weights.
   # Embeddings
   _lowerCAmelCase					     =		roberta_sent_encoder.embed_tokens.weight
   _lowerCAmelCase					     =		roberta_sent_encoder.embed_positions.weight
   _lowerCAmelCase					     =		torch.zeros_like(
       model.roberta.embeddings.token_type_embeddings.weight   )  # just zero them out b/c RoBERTa doesn't use them.
   _lowerCAmelCase					     =		roberta_sent_encoder.emb_layer_norm.weight
   _lowerCAmelCase					     =		roberta_sent_encoder.emb_layer_norm.bias

   for i in range(config.num_hidden_layers   ):
      # Encoder: start of layer
      _lowerCAmelCase					     =		model.roberta.encoder.layer[i]
      _lowerCAmelCase					     =		roberta_sent_encoder.layers[i]

      # self attention
      _lowerCAmelCase					     =		layer.attention.self
      assert (
          roberta_layer.self_attn.k_proj.weight.data.shape
          == roberta_layer.self_attn.q_proj.weight.data.shape
          == roberta_layer.self_attn.v_proj.weight.data.shape
          == torch.Size((config.hidden_size, config.hidden_size)   )
      )

      _lowerCAmelCase					     =		roberta_layer.self_attn.q_proj.weight
      _lowerCAmelCase					     =		roberta_layer.self_attn.q_proj.bias
      _lowerCAmelCase					     =		roberta_layer.self_attn.k_proj.weight
      _lowerCAmelCase					     =		roberta_layer.self_attn.k_proj.bias
      _lowerCAmelCase					     =		roberta_layer.self_attn.v_proj.weight
      _lowerCAmelCase					     =		roberta_layer.self_attn.v_proj.bias

      # self-attention output
      _lowerCAmelCase					     =		layer.attention.output
      assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
      _lowerCAmelCase					     =		roberta_layer.self_attn.out_proj.weight
      _lowerCAmelCase					     =		roberta_layer.self_attn.out_proj.bias
      _lowerCAmelCase					     =		roberta_layer.self_attn_layer_norm.weight
      _lowerCAmelCase					     =		roberta_layer.self_attn_layer_norm.bias

      # intermediate
      _lowerCAmelCase					     =		layer.intermediate
      assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
      _lowerCAmelCase					     =		roberta_layer.fca.weight
      _lowerCAmelCase					     =		roberta_layer.fca.bias

      # output
      _lowerCAmelCase					     =		layer.output
      assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
      _lowerCAmelCase					     =		roberta_layer.fca.weight
      _lowerCAmelCase					     =		roberta_layer.fca.bias
      _lowerCAmelCase					     =		roberta_layer.final_layer_norm.weight
      _lowerCAmelCase					     =		roberta_layer.final_layer_norm.bias
      # end of layer

   if classification_head:
      _lowerCAmelCase					     =		roberta.model.classification_heads["""mnli"""].dense.weight
      _lowerCAmelCase					     =		roberta.model.classification_heads["""mnli"""].dense.bias
      _lowerCAmelCase					     =		roberta.model.classification_heads["""mnli"""].out_proj.weight
      _lowerCAmelCase					     =		roberta.model.classification_heads["""mnli"""].out_proj.bias
   else:
      # LM Head
      _lowerCAmelCase					     =		roberta.model.encoder.lm_head.dense.weight
      _lowerCAmelCase					     =		roberta.model.encoder.lm_head.dense.bias
      _lowerCAmelCase					     =		roberta.model.encoder.lm_head.layer_norm.weight
      _lowerCAmelCase					     =		roberta.model.encoder.lm_head.layer_norm.bias
      _lowerCAmelCase					     =		roberta.model.encoder.lm_head.weight
      _lowerCAmelCase					     =		roberta.model.encoder.lm_head.bias

   # Let's check that we get the same results.
   _lowerCAmelCase					     =		roberta.encode(snake_case_   ).unsqueeze(0   )  # batch of size 1

   _lowerCAmelCase					     =		model(snake_case_   )[0]
   if classification_head:
      _lowerCAmelCase					     =		roberta.model.classification_heads["""mnli"""](roberta.extract_features(snake_case_   )   )
   else:
      _lowerCAmelCase					     =		roberta.model(snake_case_   )[0]
   print(our_output.shape   ,	their_output.shape   )
   _lowerCAmelCase					     =		torch.max(torch.abs(our_output - their_output   )   ).item()
   print(F"""max_absolute_diff = {max_absolute_diff}"""   )  # ~ 1e-7
   _lowerCAmelCase					     =		torch.allclose(snake_case_   ,	snake_case_   ,	atol=1e-3   )
   print("""Do both models output the same tensors?"""   ,	"""🔥""" if success else """💩"""   )
   if not success:
      raise Exception("""Something went wRoNg"""   )

   pathlib.Path(snake_case_   ).mkdir(parents=snake_case_   ,	exist_ok=snake_case_   )
   print(F"""Saving model to {pytorch_dump_folder_path}"""   )
   model.save_pretrained(snake_case_   )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE				:     Any          =	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--roberta_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  parser.add_argument(
      '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
  )
  SCREAMING_SNAKE_CASE				:     Any          =	parser.parse_args()
  convert_roberta_checkpoint_to_pytorch(
      args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )