






import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.12.2"):
 raise Exception("requires fairseq >= 0.12.2")
if version.parse(fairseq.__version__) > version.parse("2"):
 raise Exception("requires fairseq < v2")

logging.set_verbosity_info()
lowerCamelCase       :	Any									=     logging.get_logger(__name__)

lowerCamelCase       :	Tuple									=     "Hello, World!"
lowerCamelCase       :	Union[str, Any]									=     "en_XX"

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str			,							lowercase			: str			,							lowercase			: bool  ):


      '''simple docstring'''



      lowerCamelCase_	  =					Path('data_bin'  )
      lowerCamelCase_	  =					FairseqXmodModel.from_pretrained(
          model_name_or_path=str(Path(lowercase  ).parent  )			,							checkpoint_file=Path(lowercase  ).name			,							_name='xmod_base'			,							arch='xmod_base'			,							task='multilingual_masked_lm'			,							data_name_or_path=str(lowercase  )			,							bpe='sentencepiece'			,							sentencepiece_model=str(Path(lowercase  ).parent / 'sentencepiece.bpe.model'  )			,							src_dict=str(data_dir / 'dict.txt'  )			,							)
      xmod.eval()  # disable dropout
      print(lowercase  )

      lowerCamelCase_	  =					xmod.model.encoder.sentence_encoder
      lowerCamelCase_	  =					XmodConfig(
          vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings			,							hidden_size=xmod.cfg.model.encoder_embed_dim			,							num_hidden_layers=xmod.cfg.model.encoder_layers			,							num_attention_heads=xmod.cfg.model.encoder_attention_heads			,							intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim			,							max_position_embeddings=5_14			,							type_vocab_size=1			,							layer_norm_eps=1e-5			,							pre_norm=xmod.cfg.model.encoder_normalize_before			,							adapter_reduction_factor=getattr(xmod.cfg.model			,							'bottleneck'			,							2  )			,							adapter_layer_norm=xmod.cfg.model.adapter_layer_norm			,							adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm			,							ln_before_adapter=xmod.cfg.model.ln_before_adapter			,							languages=xmod.cfg.model.languages			,							)
      if classification_head:
            lowerCamelCase_	  =					xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

      print('Our X-MOD config:'			,							lowercase  )

      lowerCamelCase_	  =					XmodForSequenceClassification(lowercase  ) if classification_head else XmodForMaskedLM(lowercase  )
      model.eval()

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

      lowerCamelCase_	  =					xmod_sent_encoder.layernorm_embedding.weight
      lowerCamelCase_	  =					xmod_sent_encoder.layernorm_embedding.bias

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

            # self attention
            lowerCamelCase_	  =					layer.attention.self
            if not (
                xmod_layer.self_attn.k_proj.weight.data.shape
                == xmod_layer.self_attn.q_proj.weight.data.shape
                == xmod_layer.self_attn.v_proj.weight.data.shape
                == torch.Size((config.hidden_size, config.hidden_size)  )
            ):
                  raise AssertionError('Dimensions of self-attention weights do not match.'  )

            lowerCamelCase_	  =					xmod_layer.self_attn.q_proj.weight
            lowerCamelCase_	  =					xmod_layer.self_attn.q_proj.bias
            lowerCamelCase_	  =					xmod_layer.self_attn.k_proj.weight
            lowerCamelCase_	  =					xmod_layer.self_attn.k_proj.bias
            lowerCamelCase_	  =					xmod_layer.self_attn.v_proj.weight
            lowerCamelCase_	  =					xmod_layer.self_attn.v_proj.bias

            # self-attention output
            lowerCamelCase_	  =					layer.attention.output
            if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
                  raise AssertionError('Dimensions of self-attention output weights do not match.'  )
            lowerCamelCase_	  =					xmod_layer.self_attn.out_proj.weight
            lowerCamelCase_	  =					xmod_layer.self_attn.out_proj.bias
            lowerCamelCase_	  =					xmod_layer.self_attn_layer_norm.weight
            lowerCamelCase_	  =					xmod_layer.self_attn_layer_norm.bias

            # intermediate
            lowerCamelCase_	  =					layer.intermediate
            if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
                  raise AssertionError('Dimensions of intermediate weights do not match.'  )
            lowerCamelCase_	  =					xmod_layer.fca.weight
            lowerCamelCase_	  =					xmod_layer.fca.bias

            # output
            lowerCamelCase_	  =					layer.output
            if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
                  raise AssertionError('Dimensions of feed-forward weights do not match.'  )
            lowerCamelCase_	  =					xmod_layer.fca.weight
            lowerCamelCase_	  =					xmod_layer.fca.bias
            lowerCamelCase_	  =					xmod_layer.final_layer_norm.weight
            lowerCamelCase_	  =					xmod_layer.final_layer_norm.bias
            if bert_output.adapter_layer_norm is not None:
                  lowerCamelCase_	  =					xmod_layer.adapter_layer_norm.weight
                  lowerCamelCase_	  =					xmod_layer.adapter_layer_norm.bias

            if sorted(bert_output.adapter_modules.keys()  ) != sorted(xmod_layer.adapter_modules.keys()  ):
                  raise AssertionError('Lists of language adapters do not match.'  )
            for lang_code, adapter in xmod_layer.adapter_modules.items():
                  lowerCamelCase_	  =					bert_output.adapter_modules[lang_code]
                  lowerCamelCase_	  =					xmod_layer.adapter_modules[lang_code]
                  lowerCamelCase_	  =					from_adapter.fca.weight
                  lowerCamelCase_	  =					from_adapter.fca.bias
                  lowerCamelCase_	  =					from_adapter.fca.weight
                  lowerCamelCase_	  =					from_adapter.fca.bias

            # end of layer

      if xmod_sent_encoder.layer_norm is not None:
            lowerCamelCase_	  =					xmod_sent_encoder.layer_norm.weight
            lowerCamelCase_	  =					xmod_sent_encoder.layer_norm.bias

      if classification_head:
            lowerCamelCase_	  =					xmod.model.classification_heads['mnli'].dense.weight
            lowerCamelCase_	  =					xmod.model.classification_heads['mnli'].dense.bias
            lowerCamelCase_	  =					xmod.model.classification_heads['mnli'].out_proj.weight
            lowerCamelCase_	  =					xmod.model.classification_heads['mnli'].out_proj.bias
      else:
            # LM Head
            lowerCamelCase_	  =					xmod.model.encoder.lm_head.dense.weight
            lowerCamelCase_	  =					xmod.model.encoder.lm_head.dense.bias
            lowerCamelCase_	  =					xmod.model.encoder.lm_head.layer_norm.weight
            lowerCamelCase_	  =					xmod.model.encoder.lm_head.layer_norm.bias
            lowerCamelCase_	  =					xmod.model.encoder.lm_head.weight
            lowerCamelCase_	  =					xmod.model.encoder.lm_head.bias

      # Let's check that we get the same results.
      lowerCamelCase_	  =					xmod.encode(lowercase  ).unsqueeze(0  )  # batch of size 1
      model.roberta.set_default_language(lowercase  )

      lowerCamelCase_	  =					model(lowercase  )[0]
      if classification_head:
            lowerCamelCase_	  =					xmod.model.classification_heads['mnli'](xmod.extract_features(lowercase  )  )
      else:
            lowerCamelCase_	  =					xmod.model(lowercase			,							lang_id=[SAMPLE_LANGUAGE]  )[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(lowercase			,							lowercase			,							atol=1e-3  )
      print('Do both models output the same tensors?'			,							'🔥' if success else '💩'  )
      if not success:
            raise Exception('Something went wRoNg'  )

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


if __name__ == "__main__":
 lowerCamelCase       :	Dict									=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--xmod_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."
 )
 lowerCamelCase       :	List[Any]									=     parser.parse_args()
 convert_xmod_checkpoint_to_pytorch(
     args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )









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

import regex as re

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


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation

lowerCamelCase       :	str									=     logging.get_logger(__name__)


lowerCamelCase       :	Optional[Any]									=     {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowerCamelCase       :	int									=     {
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

lowerCamelCase       :	Tuple									=     {"facebook/blenderbot-3B": 128}

@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def     _SCREAMING_SNAKE_CASE    (							):


      '''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(lowercase  )
                  cs.append(2**8 + n  )
                  n += 1
      lowerCamelCase_	  =					[chr(lowercase  ) for n in cs]
      return dict(zip(lowercase			,							lowercase  )  )

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int  ):


      '''simple docstring'''



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







class 						A(		UpperCamelCase     ):



 '''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[Any]		,							A_      :						List[Any]		,							A_      :						List[Any]		,							A_      :						Union[str, Any]="replace"		,							A_      :						Dict="<s>"		,							A_      :						Optional[int]="</s>"		,							A_      :						Optional[Any]="</s>"		,							A_      :						Dict="<s>"		,							A_      :						Dict="<unk>"		,							A_      :						Any="<pad>"		,							A_      :						Dict="<mask>"		,							A_      :						Union[str, Any]=False		,							**A_      :						List[str]		,							)     ->			Tuple:

       """simple docstring"""






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

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

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

       with open(A_		,							encoding='utf-8'						) as vocab_handle:
             lowerCamelCase_	  =					json.load(A_						)
       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(A_		,							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(A_		,							range(len(A_						)						)						)						)
       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
 # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
 def        a__				(       self      :						Optional[Any]						)     ->			Dict:

       """simple docstring"""






       return len(self.encoder						)

 def        a__				(       self      :						List[Any]						)     ->			Dict:

       """simple docstring"""






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

 def        a__				(       self      :						Tuple		,							A_      :						Tuple						)     ->			Optional[Any]:

       """simple docstring"""






       if token in self.cache:
             return self.cache[token]
       lowerCamelCase_	  =					tuple(A_						)
       lowerCamelCase_	  =					get_pairs(A_						)

       if not pairs:
             return token

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

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

 def        a__				(       self      :						str		,							A_      :						List[str]						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					[]
       for token in re.findall(self.pat		,							A_						):
             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(A_						).split(' '						)						)
       return bpe_tokens

 def        a__				(       self      :						Tuple		,							A_      :						str						)     ->			Optional[Any]:

       """simple docstring"""






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

 def        a__				(       self      :						Tuple		,							A_      :						Dict						)     ->			List[Any]:

       """simple docstring"""






       return self.decoder.get(A_						)

 def        a__				(       self      :						Optional[int]		,							A_      :						List[Any]						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					''.join(A_						)
       lowerCamelCase_	  =					bytearray([self.byte_decoder[c] for c in text]						).decode('utf-8'		,							errors=self.errors						)
       return text

 def        a__				(       self      :						Tuple		,							A_      :						str		,							A_      :						Optional[str] = None						)     ->			Tuple[str]:

       """simple docstring"""






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

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

       lowerCamelCase_	  =					0
       with open(A_		,							'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 A_						: 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(A_						) + '\n'						)
                   index += 1

       return vocab_file, merge_file

 def        a__				(       self      :						str		,							A_      :						List[int]		,							A_      :						Optional[List[int]] = None		,							A_      :						bool = False						)     ->			List[int]:

       """simple docstring"""






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

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

 def        a__				(       self      :						int		,							A_      :						List[int]		,							A_      :						Optional[List[int]] = None						)     ->			List[int]:

       """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      :						str		,							A_      :						Optional[Any]		,							A_      :						Union[str, Any]=False		,							**A_      :						List[str]						)     ->			List[Any]:

       """simple docstring"""






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

 def        a__				(       self      :						List[Any]		,							A_      :						List[int]		,							A_      :						Optional[List[int]] = None						)     ->			Dict:

       """simple docstring"""






       return token_ids_a + [self.eos_token_id]





 def        a__				(       self      :						Optional[int]		,							A_      :						"Conversation"						)     ->			List[int]:

       """simple docstring"""






       lowerCamelCase_	  =					[]
       for is_user, text in conversation.iter_texts():
             if is_user:
                   # We need to space prefix as it's being done within blenderbot
                   inputs.append(' ' + text						)
             else:
                   # Generated responses should contain them already.
                   inputs.append(A_						)

       lowerCamelCase_	  =					'  '.join(A_						)
       lowerCamelCase_	  =					self.encode(A_						)
       if len(A_						) > self.model_max_length:
             lowerCamelCase_	  =					input_ids[-self.model_max_length :]
             logger.warning(f"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens."""						)
       return input_ids



