import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
   import jieba

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


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

__lowerCamelCase			: Optional[Any]							   =		{'''vocab_file''': '''vocab.txt'''}

__lowerCamelCase			: Optional[int]							   =		{
    '''vocab_file''': {
        '''openbmb/cpm-ant-10b''': '''https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt''',
    },
}

__lowerCamelCase			: Dict							   =		{
    '''openbmb/cpm-ant-10b''': 1024,
}



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Union[str, Any]	)							->					Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      collections.OrderedDict()
  with open(__UpperCamelCase ,     """r""" ,     encoding="""utf-8"""	) as reader:
    SCREAMING_SNAKE_CASE__										=      reader.readlines()
  for index, token in enumerate(__UpperCamelCase	):
    SCREAMING_SNAKE_CASE__										=      token.rstrip("""\n"""	)
    SCREAMING_SNAKE_CASE__										=      index
  return vocab







class __snake_case				(						lowerCamelCase_		):




   def __init__(    self			:				Any       ,	_lowercase			:				Union[str, Any]       ,	_lowercase			:				Optional[Any]="<unk>"       ,	_lowercase			:				Optional[Any]=2_00      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      vocab
     SCREAMING_SNAKE_CASE__										=      unk_token
     SCREAMING_SNAKE_CASE__										=      max_input_chars_per_word




   def 	__a						(    self			:				Optional[Any]       ,	_lowercase			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      list(_lowercase      )
     if len(_lowercase      ) > self.max_input_chars_per_word:
       return [self.unk_token]

     SCREAMING_SNAKE_CASE__										=      0
     SCREAMING_SNAKE_CASE__										=      []
     while start < len(_lowercase      ):
       SCREAMING_SNAKE_CASE__										=      len(_lowercase      )
       SCREAMING_SNAKE_CASE__										=      None
       while start < end:
         SCREAMING_SNAKE_CASE__										=      """""".join(chars[start:end]      )
         if substr in self.vocab:
           SCREAMING_SNAKE_CASE__										=      substr
           break
         end -= 1
       if cur_substr is None:
         sub_tokens.append(self.unk_token      )
         start += 1
       else:
         sub_tokens.append(_lowercase      )
         SCREAMING_SNAKE_CASE__										=      end

     return sub_tokens







class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							VOCAB_FILES_NAMES
   lowerCAmelCase_														=							PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase_														=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCAmelCase_														=							["input_ids", "attention_mask"]
   lowerCAmelCase_														=							False




   def __init__(    self			:				Dict       ,	_lowercase			:				List[Any]       ,	_lowercase			:				List[str]="<d>"       ,	_lowercase			:				List[Any]="</d>"       ,	_lowercase			:				int="<s>"       ,	_lowercase			:				Union[str, Any]="</s>"       ,	_lowercase			:				Optional[Any]="<pad>"       ,	_lowercase			:				List[Any]="<unk>"       ,	_lowercase			:				Any="</n>"       ,	_lowercase			:				str="</_>"       ,	_lowercase			:				Union[str, Any]="left"       ,	**_lowercase			:				List[str]       ,	):



     """simple docstring"""






     requires_backends(self       ,	["""jieba"""]      )
     super().__init__(
         bod_token=_lowercase       ,	eod_token=_lowercase       ,	bos_token=_lowercase       ,	eos_token=_lowercase       ,	pad_token=_lowercase       ,	unk_token=_lowercase       ,	line_token=_lowercase       ,	space_token=_lowercase       ,	padding_side=_lowercase       ,	**_lowercase       ,	)
     SCREAMING_SNAKE_CASE__										=      bod_token
     SCREAMING_SNAKE_CASE__										=      eod_token
     SCREAMING_SNAKE_CASE__										=      load_vocab(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      self.encoder[space_token]
     SCREAMING_SNAKE_CASE__										=      self.encoder[line_token]

     del self.encoder[space_token]
     del self.encoder[line_token]

     SCREAMING_SNAKE_CASE__										=      collections.OrderedDict(sorted(self.encoder.items()       ,	key=lambda _lowercase      : x[1]      )      )
     SCREAMING_SNAKE_CASE__										=      {v: k for k, v in self.encoder.items()}

     SCREAMING_SNAKE_CASE__										=      WordpieceTokenizer(vocab=self.encoder       ,	unk_token=self.unk_token      )




   @property
   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     return self.encoder[self.bod_token]




   @property
   def 	__a						(    self			:				int      ):



     """simple docstring"""






     return self.encoder[self.eod_token]




   @property
   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     return self.encoder["\n"]




   @property
   def 	__a						(    self			:				Dict      ):



     """simple docstring"""






     return len(self.encoder      )




   def 	__a						(    self			:				int      ):



     """simple docstring"""






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




   def 	__a						(    self			:				List[str]       ,	_lowercase			:				str      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      []
     for x in jieba.cut(_lowercase       ,	cut_all=_lowercase      ):
       output_tokens.extend(self.wordpiece_tokenizer.tokenize(_lowercase      )      )
     return output_tokens




   def 	__a						(    self			:				List[Any]       ,	_lowercase			:				List[str]       ,	**_lowercase			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      [i for i in token_ids if i >= 0]
     SCREAMING_SNAKE_CASE__										=      [
         x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
     ]
     return super()._decode(_lowercase       ,	**_lowercase      )




   def 	__a						(    self			:				List[Any]       ,	_lowercase			:				Optional[Any]      ):



     """simple docstring"""






     return token in self.encoder




   def 	__a						(    self			:				Optional[int]       ,	_lowercase			:				List[str]      ):



     """simple docstring"""






     return "".join(_lowercase      )




   def 	__a						(    self			:				str       ,	_lowercase			:				List[Any]      ):



     """simple docstring"""






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




   def 	__a						(    self			:				Optional[Any]       ,	_lowercase			:				Union[str, Any]      ):



     """simple docstring"""






     return self.decoder.get(_lowercase       ,	self.unk_token      )




   def 	__a						(    self			:				List[str]       ,	_lowercase			:				str       ,	_lowercase			:				Optional[str] = None      ):



     """simple docstring"""






     if os.path.isdir(_lowercase      ):
       SCREAMING_SNAKE_CASE__										=      os.path.join(
           _lowercase       ,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )
     else:
       SCREAMING_SNAKE_CASE__										=      (filename_prefix + """-""" if filename_prefix else """""") + save_directory
     SCREAMING_SNAKE_CASE__										=      0
     if " " in self.encoder:
       SCREAMING_SNAKE_CASE__										=      self.encoder[""" """]
       del self.encoder[" "]
     if "\n" in self.encoder:
       SCREAMING_SNAKE_CASE__										=      self.encoder["""\n"""]
       del self.encoder["\n"]
     SCREAMING_SNAKE_CASE__										=      collections.OrderedDict(sorted(self.encoder.items()       ,	key=lambda _lowercase      : x[1]      )      )
     with open(_lowercase       ,	"""w"""       ,	encoding="""utf-8"""      ) as writer:
       for token, token_index in self.encoder.items():
         if index != token_index:
           logger.warning(
               f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
               """ Please check that the vocabulary is not corrupted!"""      )
           SCREAMING_SNAKE_CASE__										=      token_index
         writer.write(token + """\n"""      )
         index += 1
     return (vocab_file,)




   def 	__a						(    self			:				Optional[Any]       ,	_lowercase			:				List[int]       ,	_lowercase			:				List[int] = None      ):



     """simple docstring"""






     if token_ids_a is None:
       return [self.bos_token_id] + token_ids_a
     return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a




   def 	__a						(    self			:				str       ,	_lowercase			:				List[int]       ,	_lowercase			:				Optional[List[int]] = None       ,	_lowercase			:				bool = False      ):



     """simple docstring"""






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

     if token_ids_a is not None:
       return [1] + ([0] * len(_lowercase      )) + [1] + ([0] * len(_lowercase      ))
     return [1] + ([0] * len(_lowercase      ))

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			: Dict							   =		logging.get_logger(__name__)

__lowerCamelCase			: Dict							   =		'''Hello, World!'''
__lowerCamelCase			: Optional[Any]							   =		'''en_XX'''



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str ,     __UpperCamelCase    :       str ,     __UpperCamelCase    :       bool	)							->					Any:







  """simple docstring"""





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

  SCREAMING_SNAKE_CASE__										=      xmod.model.encoder.sentence_encoder
  SCREAMING_SNAKE_CASE__										=      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:
    SCREAMING_SNAKE_CASE__										=      xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

  print("""Our X-MOD config:""" ,     __UpperCamelCase	)

  SCREAMING_SNAKE_CASE__										=      XmodForSequenceClassification(__UpperCamelCase	) if classification_head else XmodForMaskedLM(__UpperCamelCase	)
  model.eval()

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

  SCREAMING_SNAKE_CASE__										=      xmod_sent_encoder.layernorm_embedding.weight
  SCREAMING_SNAKE_CASE__										=      xmod_sent_encoder.layernorm_embedding.bias

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

    # self attention
    SCREAMING_SNAKE_CASE__										=      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."""	)

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

    # self-attention output
    SCREAMING_SNAKE_CASE__										=      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."""	)
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.out_proj.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.out_proj.bias
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn_layer_norm.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn_layer_norm.bias

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

    # output
    SCREAMING_SNAKE_CASE__										=      layer.output
    if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
      raise AssertionError("""Dimensions of feed-forward weights do not match."""	)
    SCREAMING_SNAKE_CASE__										=      xmod_layer.fca.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.fca.bias
    SCREAMING_SNAKE_CASE__										=      xmod_layer.final_layer_norm.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.final_layer_norm.bias
    if bert_output.adapter_layer_norm is not None:
      SCREAMING_SNAKE_CASE__										=      xmod_layer.adapter_layer_norm.weight
      SCREAMING_SNAKE_CASE__										=      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():
      SCREAMING_SNAKE_CASE__										=      bert_output.adapter_modules[lang_code]
      SCREAMING_SNAKE_CASE__										=      xmod_layer.adapter_modules[lang_code]
      SCREAMING_SNAKE_CASE__										=      from_adapter.fca.weight
      SCREAMING_SNAKE_CASE__										=      from_adapter.fca.bias
      SCREAMING_SNAKE_CASE__										=      from_adapter.fca.weight
      SCREAMING_SNAKE_CASE__										=      from_adapter.fca.bias

    # end of layer

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

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

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

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

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


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			: str							   =		parser.parse_args()
   convert_xmod_checkpoint_to_pytorch(
       args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )
