


'''simple docstring'''






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()
__A   :    Union[str, Any]											= logging.get_logger(__name__)

__A   :    Tuple											= "Hello, World!"
__A   :    Tuple											= "en_XX"







def 							UpperCamelCase_     (       A__   :  str				,       A__   :  str				,       A__   :  bool   ):
  '''simple docstring'''





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

  lowerCAmelCase_     : str     =  xmod.model.encoder.sentence_encoder
  lowerCAmelCase_     : Optional[int]     =  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_     : int     =  xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

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

  lowerCAmelCase_     : Any     =  XmodForSequenceClassification(A__   ) if classification_head else XmodForMaskedLM(A__   )
  model.eval()

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

  lowerCAmelCase_     : Tuple     =  xmod_sent_encoder.layernorm_embedding.weight
  lowerCAmelCase_     : int     =  xmod_sent_encoder.layernorm_embedding.bias

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

    # self attention
    lowerCAmelCase_     : List[str]     =  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_     : Optional[Any]     =  xmod_layer.self_attn.q_proj.weight
    lowerCAmelCase_     : Union[str, Any]     =  xmod_layer.self_attn.q_proj.bias
    lowerCAmelCase_     : Optional[int]     =  xmod_layer.self_attn.k_proj.weight
    lowerCAmelCase_     : int     =  xmod_layer.self_attn.k_proj.bias
    lowerCAmelCase_     : int     =  xmod_layer.self_attn.v_proj.weight
    lowerCAmelCase_     : Optional[Any]     =  xmod_layer.self_attn.v_proj.bias

    # self-attention output
    lowerCAmelCase_     : Tuple     =  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_     : Tuple     =  xmod_layer.self_attn.out_proj.weight
    lowerCAmelCase_     : List[Any]     =  xmod_layer.self_attn.out_proj.bias
    lowerCAmelCase_     : Tuple     =  xmod_layer.self_attn_layer_norm.weight
    lowerCAmelCase_     : Optional[Any]     =  xmod_layer.self_attn_layer_norm.bias

    # intermediate
    lowerCAmelCase_     : Any     =  layer.intermediate
    if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
      raise AssertionError("""Dimensions of intermediate weights do not match."""   )
    lowerCAmelCase_     : List[str]     =  xmod_layer.fca.weight
    lowerCAmelCase_     : Dict     =  xmod_layer.fca.bias

    # output
    lowerCAmelCase_     : Tuple     =  layer.output
    if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
      raise AssertionError("""Dimensions of feed-forward weights do not match."""   )
    lowerCAmelCase_     : Tuple     =  xmod_layer.fca.weight
    lowerCAmelCase_     : Optional[Any]     =  xmod_layer.fca.bias
    lowerCAmelCase_     : Dict     =  xmod_layer.final_layer_norm.weight
    lowerCAmelCase_     : Tuple     =  xmod_layer.final_layer_norm.bias
    if bert_output.adapter_layer_norm is not None:
      lowerCAmelCase_     : List[str]     =  xmod_layer.adapter_layer_norm.weight
      lowerCAmelCase_     : Optional[Any]     =  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_     : int     =  bert_output.adapter_modules[lang_code]
      lowerCAmelCase_     : int     =  xmod_layer.adapter_modules[lang_code]
      lowerCAmelCase_     : str     =  from_adapter.fca.weight
      lowerCAmelCase_     : Tuple     =  from_adapter.fca.bias
      lowerCAmelCase_     : Tuple     =  from_adapter.fca.weight
      lowerCAmelCase_     : List[str]     =  from_adapter.fca.bias

    # end of layer

  if xmod_sent_encoder.layer_norm is not None:
    lowerCAmelCase_     : str     =  xmod_sent_encoder.layer_norm.weight
    lowerCAmelCase_     : Tuple     =  xmod_sent_encoder.layer_norm.bias

  if classification_head:
    lowerCAmelCase_     : List[str]     =  xmod.model.classification_heads["""mnli"""].dense.weight
    lowerCAmelCase_     : Tuple     =  xmod.model.classification_heads["""mnli"""].dense.bias
    lowerCAmelCase_     : Any     =  xmod.model.classification_heads["""mnli"""].out_proj.weight
    lowerCAmelCase_     : Optional[Any]     =  xmod.model.classification_heads["""mnli"""].out_proj.bias
  else:
    # LM Head
    lowerCAmelCase_     : Any     =  xmod.model.encoder.lm_head.dense.weight
    lowerCAmelCase_     : Optional[Any]     =  xmod.model.encoder.lm_head.dense.bias
    lowerCAmelCase_     : int     =  xmod.model.encoder.lm_head.layer_norm.weight
    lowerCAmelCase_     : List[str]     =  xmod.model.encoder.lm_head.layer_norm.bias
    lowerCAmelCase_     : Optional[Any]     =  xmod.model.encoder.lm_head.weight
    lowerCAmelCase_     : Optional[int]     =  xmod.model.encoder.lm_head.bias

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

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

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


if __name__ == "__main__":
  __A   :    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."
  )
  __A   :    Any											= parser.parse_args()
  convert_xmod_checkpoint_to_pytorch(
      args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )







'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A   :    str											= logging.get_logger(__name__)

__A   :    int											= {
    "weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}






class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase  					=   'roc_bert'

    def __init__(  self       :    str       ,		lowerCamelCase       :    Optional[Any]=3_05_22       ,		lowerCamelCase       :    List[Any]=7_68       ,		lowerCamelCase       :    int=12       ,		lowerCamelCase       :    str=12       ,		lowerCamelCase       :    Any=30_72       ,		lowerCamelCase       :    List[Any]="gelu"       ,		lowerCamelCase       :    Optional[int]=0.1       ,		lowerCamelCase       :    List[str]=0.1       ,		lowerCamelCase       :    int=5_12       ,		lowerCamelCase       :    List[Any]=2       ,		lowerCamelCase       :    int=0.02       ,		lowerCamelCase       :    str=1E-12       ,		lowerCamelCase       :    int=True       ,		lowerCamelCase       :    Optional[Any]=0       ,		lowerCamelCase       :    List[Any]="absolute"       ,		lowerCamelCase       :    str=None       ,		lowerCamelCase       :    str=True       ,		lowerCamelCase       :    Tuple=True       ,		lowerCamelCase       :    str=7_68       ,		lowerCamelCase       :    str=9_10       ,		lowerCamelCase       :    Tuple=5_12       ,		lowerCamelCase       :    List[str]=2_48_58       ,		lowerCamelCase       :    Dict=True       ,		**lowerCamelCase       :    Tuple       ,		)  ->    Optional[int]:
      lowerCAmelCase_     : Optional[Any]     =  vocab_size
      lowerCAmelCase_     : Any     =  max_position_embeddings
      lowerCAmelCase_     : Any     =  hidden_size
      lowerCAmelCase_     : int     =  num_hidden_layers
      lowerCAmelCase_     : Tuple     =  num_attention_heads
      lowerCAmelCase_     : List[Any]     =  intermediate_size
      lowerCAmelCase_     : Tuple     =  hidden_act
      lowerCAmelCase_     : List[Any]     =  hidden_dropout_prob
      lowerCAmelCase_     : Dict     =  attention_probs_dropout_prob
      lowerCAmelCase_     : Any     =  initializer_range
      lowerCAmelCase_     : Any     =  type_vocab_size
      lowerCAmelCase_     : Optional[int]     =  layer_norm_eps
      lowerCAmelCase_     : Tuple     =  use_cache
      lowerCAmelCase_     : List[Any]     =  enable_pronunciation
      lowerCAmelCase_     : List[Any]     =  enable_shape
      lowerCAmelCase_     : List[str]     =  pronunciation_embed_dim
      lowerCAmelCase_     : List[str]     =  pronunciation_vocab_size
      lowerCAmelCase_     : Dict     =  shape_embed_dim
      lowerCAmelCase_     : Optional[Any]     =  shape_vocab_size
      lowerCAmelCase_     : List[Any]     =  concat_input
      lowerCAmelCase_     : Any     =  position_embedding_type
      lowerCAmelCase_     : List[str]     =  classifier_dropout
      super().__init__(pad_token_id=lowerCamelCase       ,		**lowerCamelCase						)



