





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()
UpperCAmelCase	:	int		     =       logging.get_logger(__name__)

UpperCAmelCase	:	Any		     =       """Hello, World!"""
UpperCAmelCase	:	int		     =       """en_XX"""
def      _A       (		SCREAMING_SNAKE_CASE     :   str       ,       SCREAMING_SNAKE_CASE     :   str       ,       SCREAMING_SNAKE_CASE     :   bool						):





  """simple docstring"""



  a__	:					Any										=Path("data_bin"						)
  a__	:					Optional[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						)

  a__	:					int										=xmod.model.encoder.sentence_encoder
  a__	:					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=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:
    a__	:					Union[str, Any]										=xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

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

  a__	:					str										=XmodForSequenceClassification(SCREAMING_SNAKE_CASE						) if classification_head else XmodForMaskedLM(SCREAMING_SNAKE_CASE						)
  model.eval()

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

  a__	:					Tuple										=xmod_sent_encoder.layernorm_embedding.weight
  a__	:					Any										=xmod_sent_encoder.layernorm_embedding.bias

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

    # self attention
    a__	:					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."						)

    a__	:					Any										=xmod_layer.self_attn.q_proj.weight
    a__	:					Optional[Any]										=xmod_layer.self_attn.q_proj.bias
    a__	:					Optional[int]										=xmod_layer.self_attn.k_proj.weight
    a__	:					Optional[int]										=xmod_layer.self_attn.k_proj.bias
    a__	:					Any										=xmod_layer.self_attn.v_proj.weight
    a__	:					List[str]										=xmod_layer.self_attn.v_proj.bias

    # self-attention output
    a__	:					Union[str, 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."						)
    a__	:					Any										=xmod_layer.self_attn.out_proj.weight
    a__	:					str										=xmod_layer.self_attn.out_proj.bias
    a__	:					Dict										=xmod_layer.self_attn_layer_norm.weight
    a__	:					Any										=xmod_layer.self_attn_layer_norm.bias

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

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

    # end of layer

  if xmod_sent_encoder.layer_norm is not None:
    a__	:					Any										=xmod_sent_encoder.layer_norm.weight
    a__	:					Tuple										=xmod_sent_encoder.layer_norm.bias

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

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

  a__	:					List[Any]										=model(SCREAMING_SNAKE_CASE						)[0]
  if classification_head:
    a__	:					Optional[int]										=xmod.model.classification_heads["mnli"](xmod.extract_features(SCREAMING_SNAKE_CASE						)						)
  else:
    a__	:					Any										=xmod.model(SCREAMING_SNAKE_CASE       ,       lang_id=[SAMPLE_LANGUAGE]						)[0]
  print(our_output.shape       ,       their_output.shape						)
  a__	:					Any										=torch.max(torch.abs(our_output - their_output						)						).item()
  print(f'''max_absolute_diff = {max_absolute_diff}'''						)  # ~ 1e-7
  a__	:					List[str]										=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__":
     UpperCAmelCase	:	Tuple		     =       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."""
     )
     UpperCAmelCase	:	List[Any]		     =       parser.parse_args()
     convert_xmod_checkpoint_to_pytorch(
         args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )













def      _A       (		SCREAMING_SNAKE_CASE     :   int = 50						):





  """simple docstring"""



  a__	:					Any										=[1] * (length + 1)

  for row_length in range(3       ,       length + 1						):
    for block_length in range(3       ,       row_length + 1						):
      for block_start in range(row_length - block_length						):
        ways_number[row_length] += ways_number[
            row_length - block_start - block_length - 1
        ]

      ways_number[row_length] += 1

  return ways_number[length]


if __name__ == "__main__":
     print(F"""{solution() = }""")






