

"""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()
__lowerCamelCase										=		logging.get_logger(__name__)

__lowerCamelCase										=		"Hello, World!"
__lowerCamelCase										=		"en_XX"





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase						) ->      int:
 __magic_name__     =      Path('''data_bin'''						)
 __magic_name__     =      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						)

 __magic_name__     =      xmod.model.encoder.sentence_encoder
 __magic_name__     =      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:
  __magic_name__     =      xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

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

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

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

 __magic_name__     =      xmod_sent_encoder.layernorm_embedding.weight
 __magic_name__     =      xmod_sent_encoder.layernorm_embedding.bias

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

  # self attention
  __magic_name__     =      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.'''						)

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

  # self-attention output
  __magic_name__     =      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.'''						)
  __magic_name__     =      xmod_layer.self_attn.out_proj.weight
  __magic_name__     =      xmod_layer.self_attn.out_proj.bias
  __magic_name__     =      xmod_layer.self_attn_layer_norm.weight
  __magic_name__     =      xmod_layer.self_attn_layer_norm.bias

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

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

  # end of layer

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

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

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

 __magic_name__     =      model(__UpperCamelCase						)[0]
 if classification_head:
  __magic_name__     =      xmod.model.classification_heads['''mnli'''](xmod.extract_features(__UpperCamelCase						)						)
 else:
  __magic_name__     =      xmod.model(__UpperCamelCase      ,   lang_id=[SAMPLE_LANGUAGE]						)[0]
 print(our_output.shape      ,   their_output.shape						)
 __magic_name__     =      torch.max(torch.abs(our_output - their_output						)						).item()
 print(f'''max_absolute_diff = {max_absolute_diff}'''						)  # ~ 1e-7
 __magic_name__     =      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										=		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										=		parser.parse_args()
 convert_xmod_checkpoint_to_pytorch(
     args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )




"""simple docstring"""






class        _lowercase					:







     def __init__(      self ,						UpperCamelCase_       ):
      __magic_name__     =      size
      __magic_name__     =      [0] * size
      __magic_name__     =      [0] * size







     @staticmethod
     def 	lowerCAmelCase__    (      UpperCamelCase_       ):
      return index | (index + 1)







     @staticmethod
     def 	lowerCAmelCase__    (      UpperCamelCase_       ):
      return (index & (index + 1)) - 1







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      __magic_name__     =      value
      while index < self.size:
       __magic_name__     =      self.get_prev(UpperCamelCase_       ) + 1
       if current_left_border == index:
        __magic_name__     =      value
       else:
        __magic_name__     =      max(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       )
       __magic_name__     =      self.get_next(UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      right -= 1  # Because of right is exclusive
      __magic_name__     =      0
      while left <= right:
       __magic_name__     =      self.get_prev(UpperCamelCase_       )
       if left <= current_left:
        __magic_name__     =      max(UpperCamelCase_ ,						self.tree[right]       )
        __magic_name__     =      current_left
       else:
        __magic_name__     =      max(UpperCamelCase_ ,						self.arr[right]       )
        right -= 1
      return result


if __name__ == "__main__":
 import doctest

 doctest.testmod()

