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

UpperCAmelCase__  		=						"""Hello, World!"""
UpperCAmelCase__  		=						"""en_XX"""






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      Path("""data_bin"""		)
  _UpperCAmelCase						 =      FairseqXmodModel.from_pretrained(
      model_name_or_path=str(Path(lowercase		).parent		)   ,checkpoint_file=Path(lowercase		).name   ,_name="""xmod_base"""   ,arch="""xmod_base"""   ,task="""multilingual_masked_lm"""   ,data_name_or_path=str(lowercase		)   ,bpe="""sentencepiece"""   ,sentencepiece_model=str(Path(lowercase		).parent / """sentencepiece.bpe.model"""		)   ,src_dict=str(data_dir / """dict.txt"""		)   ,)
  xmod.eval()  # disable dropout
  print(lowercase		)

  _UpperCAmelCase						 =      xmod.model.encoder.sentence_encoder
  _UpperCAmelCase						 =      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:
    _UpperCAmelCase						 =      xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

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

  _UpperCAmelCase						 =      XmodForSequenceClassification(lowercase		) if classification_head else XmodForMaskedLM(lowercase		)
  model.eval()

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

  _UpperCAmelCase						 =      xmod_sent_encoder.layernorm_embedding.weight
  _UpperCAmelCase						 =      xmod_sent_encoder.layernorm_embedding.bias

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

    # self attention
    _UpperCAmelCase						 =      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."""		)

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

    # self-attention output
    _UpperCAmelCase						 =      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."""		)
    _UpperCAmelCase						 =      xmod_layer.self_attn.out_proj.weight
    _UpperCAmelCase						 =      xmod_layer.self_attn.out_proj.bias
    _UpperCAmelCase						 =      xmod_layer.self_attn_layer_norm.weight
    _UpperCAmelCase						 =      xmod_layer.self_attn_layer_norm.bias

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

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

    # end of layer

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

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

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

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

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


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

"""simple docstring"""


from abc import ABC, abstractmethod
from argparse import ArgumentParser


class        a				(		lowerCAmelCase_   ):


    @staticmethod
    @abstractmethod
    def      lowerCAmelCase_  (					__lowerCAmelCase	:  ArgumentParser					):
      raise NotImplementedError()


    @abstractmethod
    def      lowerCAmelCase_  (					self	:  int					):
      raise NotImplementedError()
