






'''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 XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('1.0.0a'):
  raise Exception('requires fairseq >= 1.0.0a')

logging.set_verbosity_info()
a_					=	logging.get_logger(__name__)

a_					=	"Hello world! cécé herlolip"





def 	_a(					UpperCamelCase__			:  Optional[int],		UpperCamelCase__			:  Optional[int],		UpperCamelCase__			:  Any							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			str													=FairseqRobertaModel.from_pretrained(lowerCamelCase_							)
      roberta.eval()  # disable dropout
      SCREAMING_SNAKE_CASE__       :			Any													=roberta.model.encoder.sentence_encoder
      SCREAMING_SNAKE_CASE__       :			int													=XLMRobertaConfig(
          vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings,		hidden_size=roberta.cfg.model.encoder_embed_dim,		num_hidden_layers=roberta.cfg.model.encoder_layers,		num_attention_heads=roberta.cfg.model.encoder_attention_heads,		intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim,		max_position_embeddings=5_1_4,		type_vocab_size=1,		layer_norm_eps=1e-5,		)
      if classification_head:
            SCREAMING_SNAKE_CASE__       :			List[Any]													=roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

      print('''Our RoBERTa config:''',		lowerCamelCase_							)

      SCREAMING_SNAKE_CASE__       :			Any													=XLMRobertaXLForSequenceClassification(lowerCamelCase_							) if classification_head else XLMRobertaXLForMaskedLM(lowerCamelCase_							)
      model.eval()

      # Now let's copy all the weights.
      # Embeddings
      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=roberta_sent_encoder.embed_tokens.weight
      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=roberta_sent_encoder.embed_positions.weight
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=torch.zeros_like(
          model.roberta.embeddings.token_type_embeddings.weight							)  # just zero them out b/c RoBERTa doesn't use them.

      SCREAMING_SNAKE_CASE__       :			Any													=roberta_sent_encoder.layer_norm.weight
      SCREAMING_SNAKE_CASE__       :			Tuple													=roberta_sent_encoder.layer_norm.bias

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

            SCREAMING_SNAKE_CASE__       :			RobertaAttention													=layer.attention
            SCREAMING_SNAKE_CASE__       :			int													=roberta_layer.self_attn_layer_norm.weight
            SCREAMING_SNAKE_CASE__       :			int													=roberta_layer.self_attn_layer_norm.bias

            # self attention
            SCREAMING_SNAKE_CASE__       :			BertSelfAttention													=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)							)
            )

            SCREAMING_SNAKE_CASE__       :			Dict													=roberta_layer.self_attn.q_proj.weight
            SCREAMING_SNAKE_CASE__       :			Tuple													=roberta_layer.self_attn.q_proj.bias
            SCREAMING_SNAKE_CASE__       :			str													=roberta_layer.self_attn.k_proj.weight
            SCREAMING_SNAKE_CASE__       :			List[Any]													=roberta_layer.self_attn.k_proj.bias
            SCREAMING_SNAKE_CASE__       :			List[Any]													=roberta_layer.self_attn.v_proj.weight
            SCREAMING_SNAKE_CASE__       :			Dict													=roberta_layer.self_attn.v_proj.bias

            # self-attention output
            SCREAMING_SNAKE_CASE__       :			BertSelfOutput													=layer.attention.output
            assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
            SCREAMING_SNAKE_CASE__       :			Any													=roberta_layer.self_attn.out_proj.weight
            SCREAMING_SNAKE_CASE__       :			Tuple													=roberta_layer.self_attn.out_proj.bias

            # this one is final layer norm
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=roberta_layer.final_layer_norm.weight
            SCREAMING_SNAKE_CASE__       :			Tuple													=roberta_layer.final_layer_norm.bias

            # intermediate
            SCREAMING_SNAKE_CASE__       :			BertIntermediate													=layer.intermediate
            assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
            SCREAMING_SNAKE_CASE__       :			List[Any]													=roberta_layer.fca.weight
            SCREAMING_SNAKE_CASE__       :			Tuple													=roberta_layer.fca.bias

            # output
            SCREAMING_SNAKE_CASE__       :			BertOutput													=layer.output
            assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=roberta_layer.fca.weight
            SCREAMING_SNAKE_CASE__       :			Dict													=roberta_layer.fca.bias
            # end of layer

      if classification_head:
            SCREAMING_SNAKE_CASE__       :			int													=roberta.model.classification_heads['mnli'].dense.weight
            SCREAMING_SNAKE_CASE__       :			Tuple													=roberta.model.classification_heads['mnli'].dense.bias
            SCREAMING_SNAKE_CASE__       :			List[str]													=roberta.model.classification_heads['mnli'].out_proj.weight
            SCREAMING_SNAKE_CASE__       :			Dict													=roberta.model.classification_heads['mnli'].out_proj.bias
      else:
            # LM Head
            SCREAMING_SNAKE_CASE__       :			List[str]													=roberta.model.encoder.lm_head.dense.weight
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=roberta.model.encoder.lm_head.dense.bias
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=roberta.model.encoder.lm_head.layer_norm.weight
            SCREAMING_SNAKE_CASE__       :			List[str]													=roberta.model.encoder.lm_head.layer_norm.bias
            SCREAMING_SNAKE_CASE__       :			Dict													=roberta.model.encoder.lm_head.weight
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=roberta.model.encoder.lm_head.bias

      # Let's check that we get the same results.
      SCREAMING_SNAKE_CASE__       :			torch.Tensor													=roberta.encode(lowerCamelCase_							).unsqueeze(0							)  # batch of size 1

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

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


if __name__ == "__main__":
  a_					=	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.'
  )
  a_					=	parser.parse_args()
  convert_xlm_roberta_xl_checkpoint_to_pytorch(
      args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )







'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a_					=	logging.get_logger(__name__)


a_					=	{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

a_					=	{
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

a_					=	{'facebook/blenderbot_small-90M': 5_1_2}





def 	_a(					UpperCamelCase__			:  Dict							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=set()
      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)							)
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=char

      SCREAMING_SNAKE_CASE__       :			Any													=set(UpperCamelCase__							)
      return pairs







class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):
 snake_case_												=							VOCAB_FILES_NAMES
 snake_case_												=							PRETRAINED_VOCAB_FILES_MAP
 snake_case_												=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 snake_case_												=							["""input_ids""", """attention_mask"""]




 def __init__( self		:							Tuple   , __lowercase		:							int   , __lowercase		:							Optional[int]   , __lowercase		:							List[str]="__start__"   , __lowercase		:							Union[str, Any]="__end__"   , __lowercase		:							str="__unk__"   , __lowercase		:							Union[str, Any]="__null__"   , **__lowercase		:							List[str]   , )		->  Optional[int]:
       super().__init__(unk_token=__lowercase   , bos_token=__lowercase   , eos_token=__lowercase   , pad_token=__lowercase   , **__lowercase )

       with open(__lowercase   , encoding='''utf-8''' ) as vocab_handle:
             SCREAMING_SNAKE_CASE__       :			Any													=json.load(__lowercase )
       SCREAMING_SNAKE_CASE__       :			Dict													={v: k for k, v in self.encoder.items()}
       with open(__lowercase   , encoding='''utf-8''' ) as merges_handle:
             SCREAMING_SNAKE_CASE__       :			int													=merges_handle.read().split('''\n''' )[1:-1]
       SCREAMING_SNAKE_CASE__       :			List[Any]													=[tuple(merge.split() ) for merge in merges]
       SCREAMING_SNAKE_CASE__       :			int													=dict(zip(__lowercase   , range(len(__lowercase ) ) ) )
       SCREAMING_SNAKE_CASE__       :			Dict													={}




 @property
 def        __magic_name__		( self		:							Any )		->  int:
       return len(self.encoder )




 def        __magic_name__		( self		:							Tuple )		->  Dict:
       return dict(self.encoder   , **self.added_tokens_encoder )




 def        __magic_name__		( self		:							Optional[Any]   , __lowercase		:							str )		->  str:
       if token in self.cache:
             return self.cache[token]
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=re.sub('''([.,!?()])'''   , r''' \1'''   , __lowercase )
       SCREAMING_SNAKE_CASE__       :			int													=re.sub('''(\')'''   , r''' \1 '''   , __lowercase )
       SCREAMING_SNAKE_CASE__       :			Dict													=re.sub(r'''\s{2,}'''   , ''' '''   , __lowercase )
       if "\n" in token:
             SCREAMING_SNAKE_CASE__       :			List[str]													=token.replace('''\n'''   , ''' __newln__''' )

       SCREAMING_SNAKE_CASE__       :			Dict													=token.split(''' ''' )
       SCREAMING_SNAKE_CASE__       :			Dict													=[]
       for token in tokens:
             if not len(__lowercase ):
                   continue

             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=token.lower()
             SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tuple(__lowercase )
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=tuple(list(word[:-1] ) + [word[-1] + '''</w>'''] )
             SCREAMING_SNAKE_CASE__       :			Optional[Any]													=get_pairs(__lowercase )

             if not pairs:
                   words.append(__lowercase )
                   continue

             while True:
                   SCREAMING_SNAKE_CASE__       :			int													=min(__lowercase   , key=lambda __lowercase : self.bpe_ranks.get(__lowercase   , float('''inf''' ) ) )
                   if bigram not in self.bpe_ranks:
                         break
                   SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Any													=bigram
                   SCREAMING_SNAKE_CASE__       :			List[Any]													=[]
                   SCREAMING_SNAKE_CASE__       :			Tuple													=0

                   while i < len(__lowercase ):
                         try:
                               SCREAMING_SNAKE_CASE__       :			Optional[Any]													=word.index(__lowercase   , __lowercase )
                               new_word.extend(word[i:j] )
                               SCREAMING_SNAKE_CASE__       :			Optional[int]													=j
                         except ValueError:
                               new_word.extend(word[i:] )
                               break

                         if word[i] == first and i < len(__lowercase ) - 1 and word[i + 1] == second:
                               new_word.append(first + second )
                               i += 2
                         else:
                               new_word.append(word[i] )
                               i += 1
                   SCREAMING_SNAKE_CASE__       :			int													=tuple(__lowercase )
                   SCREAMING_SNAKE_CASE__       :			int													=new_word
                   if len(__lowercase ) == 1:
                         break
                   else:
                         SCREAMING_SNAKE_CASE__       :			Any													=get_pairs(__lowercase )
             SCREAMING_SNAKE_CASE__       :			Optional[int]													='''@@ '''.join(__lowercase )
             SCREAMING_SNAKE_CASE__       :			List[Any]													=word[:-4]

             SCREAMING_SNAKE_CASE__       :			str													=word
             words.append(__lowercase )
       return " ".join(__lowercase )




 def        __magic_name__		( self		:							str   , __lowercase		:							str )		->  List[str]:
       SCREAMING_SNAKE_CASE__       :			Dict													=[]

       SCREAMING_SNAKE_CASE__       :			List[Any]													=re.findall(r'''\S+\n?'''   , __lowercase )

       for token in words:
             split_tokens.extend(list(self.bpe(__lowercase ).split(''' ''' ) ) )
       return split_tokens




 def        __magic_name__		( self		:							List[Any]   , __lowercase		:							str )		->  int:
       SCREAMING_SNAKE_CASE__       :			int													=token.lower()
       return self.encoder.get(__lowercase   , self.encoder.get(self.unk_token ) )




 def        __magic_name__		( self		:							int   , __lowercase		:							int )		->  str:
       return self.decoder.get(__lowercase   , self.unk_token )




 def        __magic_name__		( self		:							List[Any]   , __lowercase		:							List[str] )		->  str:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=''' '''.join(__lowercase ).replace('''@@ '''   , '''''' ).strip()
       return out_string




 def        __magic_name__		( self		:							Tuple   , __lowercase		:							str   , __lowercase		:							Optional[str] = None )		->  Tuple[str]:
       if not os.path.isdir(__lowercase ):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
             return
       SCREAMING_SNAKE_CASE__       :			str													=os.path.join(
           __lowercase   , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
       SCREAMING_SNAKE_CASE__       :			int													=os.path.join(
           __lowercase   , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )

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

       SCREAMING_SNAKE_CASE__       :			List[str]													=0
       with open(__lowercase   , '''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 __lowercase : 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!''' )
                         SCREAMING_SNAKE_CASE__       :			Any													=token_index
                   writer.write(''' '''.join(__lowercase ) + '''\n''' )
                   index += 1

       return vocab_file, merge_file