

'''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_       						=							logging.get_logger(__name__)

A_       						=							"Hello, World!"
A_       						=							"en_XX"





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

  SCREAMING_SNAKE_CASE:Tuple						      =					xmod.model.encoder.sentence_encoder
  SCREAMING_SNAKE_CASE:Tuple						      =					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=514      ,       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:Tuple						      =					xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

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

  SCREAMING_SNAKE_CASE:List[Any]						      =					XmodForSequenceClassification(snake_case ) if classification_head else XmodForMaskedLM(snake_case )
  model.eval()

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

  SCREAMING_SNAKE_CASE:Tuple						      =					xmod_sent_encoder.layernorm_embedding.weight
  SCREAMING_SNAKE_CASE:List[str]						      =					xmod_sent_encoder.layernorm_embedding.bias

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

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

    # self-attention output
    SCREAMING_SNAKE_CASE:List[Any]						      =					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:Union[str, Any]						      =					xmod_layer.self_attn.out_proj.weight
    SCREAMING_SNAKE_CASE:List[Any]						      =					xmod_layer.self_attn.out_proj.bias
    SCREAMING_SNAKE_CASE:Any						      =					xmod_layer.self_attn_layer_norm.weight
    SCREAMING_SNAKE_CASE:List[Any]						      =					xmod_layer.self_attn_layer_norm.bias

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

    # output
    SCREAMING_SNAKE_CASE:str						      =					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:List[str]						      =					xmod_layer.fca.weight
    SCREAMING_SNAKE_CASE:List[str]						      =					xmod_layer.fca.bias
    SCREAMING_SNAKE_CASE:Tuple						      =					xmod_layer.final_layer_norm.weight
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					xmod_layer.final_layer_norm.bias
    if bert_output.adapter_layer_norm is not None:
      SCREAMING_SNAKE_CASE:Tuple						      =					xmod_layer.adapter_layer_norm.weight
      SCREAMING_SNAKE_CASE:List[str]						      =					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:List[str]						      =					bert_output.adapter_modules[lang_code]
      SCREAMING_SNAKE_CASE:Optional[Any]						      =					xmod_layer.adapter_modules[lang_code]
      SCREAMING_SNAKE_CASE:Optional[int]						      =					from_adapter.fca.weight
      SCREAMING_SNAKE_CASE:Union[str, Any]						      =					from_adapter.fca.bias
      SCREAMING_SNAKE_CASE:List[Any]						      =					from_adapter.fca.weight
      SCREAMING_SNAKE_CASE:Optional[Any]						      =					from_adapter.fca.bias

    # end of layer

  if xmod_sent_encoder.layer_norm is not None:
    SCREAMING_SNAKE_CASE:List[str]						      =					xmod_sent_encoder.layer_norm.weight
    SCREAMING_SNAKE_CASE:str						      =					xmod_sent_encoder.layer_norm.bias

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

  # Let's check that we get the same results.
  SCREAMING_SNAKE_CASE:Optional[int]						      =					xmod.encode(snake_case ).unsqueeze(0 )  # batch of size 1
  model.roberta.set_default_language(snake_case )

  SCREAMING_SNAKE_CASE:Optional[int]						      =					model(snake_case )[0]
  if classification_head:
    SCREAMING_SNAKE_CASE:Tuple						      =					xmod.model.classification_heads["mnli"](xmod.extract_features(snake_case ) )
  else:
    SCREAMING_SNAKE_CASE:Tuple						      =					xmod.model(snake_case      ,       lang_id=[SAMPLE_LANGUAGE] )[0]
  print(our_output.shape      ,       their_output.shape )
  SCREAMING_SNAKE_CASE:Tuple						      =					torch.max(torch.abs(our_output - their_output ) ).item()
  print(F'''max_absolute_diff = {max_absolute_diff}''' )  # ~ 1e-7
  SCREAMING_SNAKE_CASE:Tuple						      =					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" )

  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__":
    A_       						=							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_       						=							parser.parse_args()
    convert_xmod_checkpoint_to_pytorch(
        args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
    )









'''simple docstring'''

def    A_  ( snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					word.split()

  def justify(snake_case      ,       snake_case      ,       snake_case ) -> str:
    SCREAMING_SNAKE_CASE:str						      =					max_width - width
    SCREAMING_SNAKE_CASE:Dict						      =					len(snake_case )
    if len(snake_case ) == 1:
      # if there is only word in line
      # just insert overall_spaces_count for the remainder of line
      return line[0] + " " * overall_spaces_count
    else:
      SCREAMING_SNAKE_CASE:Optional[int]						      =					words_count - 1
      # num_spaces_between_words_list[i] : tells you to insert
      # num_spaces_between_words_list[i] spaces
      # after word on line[i]
      SCREAMING_SNAKE_CASE:List[Any]						      =					spaces_to_insert_between_words * [
          overall_spaces_count // spaces_to_insert_between_words
      ]
      SCREAMING_SNAKE_CASE:List[Any]						      =					(
          overall_spaces_count % spaces_to_insert_between_words
      )
      # distribute spaces via round robin to the left words
      for i in range(snake_case ):
        num_spaces_between_words_list[i] += 1
      SCREAMING_SNAKE_CASE:str						      =					[]
      for i in range(snake_case ):
        # add the word
        aligned_words_list.append(line[i] )
        # add the spaces to insert
        aligned_words_list.append(num_spaces_between_words_list[i] * " " )
      # just add the last word to the sentence
      aligned_words_list.append(line[-1] )
      # join the aligned words list to form a justified line
      return "".join(snake_case )

  SCREAMING_SNAKE_CASE:Any						      =					[]
  SCREAMING_SNAKE_CASE:list[str]						      =					[]
  SCREAMING_SNAKE_CASE:List[str]						      =					0
  for word in words:
    if width + len(snake_case ) + len(snake_case ) <= max_width:
      # keep adding words until we can fill out max_width
      # width = sum of length of all words (without overall_spaces_count)
      # len(word) = length of current word
      # len(line) = number of overall_spaces_count to insert between words
      line.append(snake_case )
      width += len(snake_case )
    else:
      # justify the line and add it to result
      answer.append(justify(snake_case      ,       snake_case      ,       snake_case ) )
      # reset new line and new width
      SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					[word], len(snake_case )
  SCREAMING_SNAKE_CASE:Optional[int]						      =					max_width - width - len(snake_case )
  answer.append(" ".join(snake_case ) + (remaining_spaces + 1) * " " )
  return answer


if __name__ == "__main__":
    from doctest import testmod

    testmod()






