


"""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 					a__			(				SCREAMING_SNAKE_CASE    :    str				,      SCREAMING_SNAKE_CASE    :    str				,      SCREAMING_SNAKE_CASE    :    bool				):


  '''simple docstring'''







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

  lowerCAmelCase     :     List[str]								=							xmod.model.encoder.sentence_encoder
  lowerCAmelCase     :     List[Any]								=							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_1_4				,      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     :     List[str]								=							xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

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

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

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

  lowerCAmelCase     :     Union[str, Any]								=							xmod_sent_encoder.layernorm_embedding.weight
  lowerCAmelCase     :     List[str]								=							xmod_sent_encoder.layernorm_embedding.bias

  for i in range(config.num_hidden_layers				):
    # Encoder: start of layer
    lowerCAmelCase     :     str								=							model.roberta.encoder.layer[i]
    lowerCAmelCase     :     Dict								=							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     :     Dict								=							xmod_layer.self_attn.q_proj.bias
    lowerCAmelCase     :     str								=							xmod_layer.self_attn.k_proj.weight
    lowerCAmelCase     :     List[Any]								=							xmod_layer.self_attn.k_proj.bias
    lowerCAmelCase     :     List[str]								=							xmod_layer.self_attn.v_proj.weight
    lowerCAmelCase     :     int								=							xmod_layer.self_attn.v_proj.bias

    # self-attention output
    lowerCAmelCase     :     Optional[int]								=							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     :     Optional[int]								=							xmod_layer.self_attn.out_proj.weight
    lowerCAmelCase     :     Dict								=							xmod_layer.self_attn.out_proj.bias
    lowerCAmelCase     :     List[Any]								=							xmod_layer.self_attn_layer_norm.weight
    lowerCAmelCase     :     Optional[Any]								=							xmod_layer.self_attn_layer_norm.bias

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

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

    # end of layer

  if xmod_sent_encoder.layer_norm is not None:
    lowerCAmelCase     :     Any								=							xmod_sent_encoder.layer_norm.weight
    lowerCAmelCase     :     Dict								=							xmod_sent_encoder.layer_norm.bias

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

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

  lowerCAmelCase     :     Optional[Any]								=							model(SCREAMING_SNAKE_CASE				)[0]
  if classification_head:
    lowerCAmelCase     :     Optional[Any]								=							xmod.model.classification_heads["mnli"](xmod.extract_features(SCREAMING_SNAKE_CASE				)				)
  else:
    lowerCAmelCase     :     Optional[int]								=							xmod.model(SCREAMING_SNAKE_CASE				,      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     :     Union[str, Any]								=							torch.allclose(SCREAMING_SNAKE_CASE				,      SCREAMING_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(SCREAMING_SNAKE_CASE				).mkdir(parents=SCREAMING_SNAKE_CASE				,      exist_ok=SCREAMING_SNAKE_CASE				)
  print(f"""Saving model to {pytorch_dump_folder_path}"""				)
  model.save_pretrained(SCREAMING_SNAKE_CASE				)


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"""







def 					a__			(				SCREAMING_SNAKE_CASE    :    float				,      SCREAMING_SNAKE_CASE    :    float				,      SCREAMING_SNAKE_CASE    :    int				):


  '''simple docstring'''







  if principal <= 0:
    raise Exception("Principal borrowed must be > 0"				)
  if rate_per_annum < 0:
    raise Exception("Rate of interest must be >= 0"				)
  if years_to_repay <= 0 or not isinstance(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				):
    raise Exception("Years to repay must be an integer > 0"				)

  # Yearly rate is divided by 12 to get monthly rate
  lowerCAmelCase     :     Tuple								=							rate_per_annum / 1_2

  # Years to repay is multiplied by 12 to get number of payments as payment is monthly
  lowerCAmelCase     :     List[Any]								=							years_to_repay * 1_2

  return (
      principal
      * rate_per_month
      * (1 + rate_per_month) ** number_of_payments
      / ((1 + rate_per_month) ** number_of_payments - 1)
  )


if __name__ == "__main__":
     import doctest

     doctest.testmod()





