'''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    __UpperCAmelCase			(  a_:       str,					a_:       str,					a_:       bool							):
   _UpperCAmelCase							:	Union[str, Any]							=   Path("data_bin"							)
   _UpperCAmelCase							:	Tuple							=   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							)

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

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

   _UpperCAmelCase							:	Any							=   XmodForSequenceClassification(__UpperCamelCase							) if classification_head else XmodForMaskedLM(__UpperCamelCase							)
   model.eval()

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

   _UpperCAmelCase							:	Dict							=   xmod_sent_encoder.layernorm_embedding.weight
   _UpperCAmelCase							:	int							=   xmod_sent_encoder.layernorm_embedding.bias

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

      # self attention
      _UpperCAmelCase							:	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."							)

      _UpperCAmelCase							:	Optional[int]							=   xmod_layer.self_attn.q_proj.weight
      _UpperCAmelCase							:	int							=   xmod_layer.self_attn.q_proj.bias
      _UpperCAmelCase							:	Tuple							=   xmod_layer.self_attn.k_proj.weight
      _UpperCAmelCase							:	List[str]							=   xmod_layer.self_attn.k_proj.bias
      _UpperCAmelCase							:	int							=   xmod_layer.self_attn.v_proj.weight
      _UpperCAmelCase							:	Union[str, Any]							=   xmod_layer.self_attn.v_proj.bias

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

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

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

      # end of layer

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

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

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

   _UpperCAmelCase							:	Optional[Any]							=   model(__UpperCamelCase							)[0]
   if classification_head:
      _UpperCAmelCase							:	Dict							=   xmod.model.classification_heads["mnli"](xmod.extract_features(__UpperCamelCase							)							)
   else:
      _UpperCAmelCase							:	str							=   xmod.model(__UpperCamelCase,					lang_id=[SAMPLE_LANGUAGE]							)[0]
   print(our_output.shape,					their_output.shape							)
   _UpperCAmelCase							:	Tuple							=   torch.max(torch.abs(our_output - their_output							)							).item()
   print(f"""max_absolute_diff = {max_absolute_diff}"""							)  # ~ 1e-7
   _UpperCAmelCase							:	str							=   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__":
     __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
     )
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp



def 	__SCREAMING_SNAKE_CASE  (					)							->					Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      ArgumentParser(
      description=(
          """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
      )	)

  # Optional arguments for the launch helper
  parser.add_argument("""--num_cores""" ,     type=__UpperCamelCase ,     default=1 ,     help="""Number of TPU cores to use (1 or 8)."""	)

  # positional
  parser.add_argument(
      """training_script""" ,     type=__UpperCamelCase ,     help=(
          """The full path to the single TPU training """
          """program/script to be launched in parallel, """
          """followed by all the arguments for the """
          """training script"""
      ) ,     )

  # rest from the training program
  parser.add_argument("""training_script_args""" ,     nargs=__UpperCamelCase	)

  return parser.parse_args()



def 	__SCREAMING_SNAKE_CASE  (					)							->					Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      parse_args()

  # Import training_script as a module.
  SCREAMING_SNAKE_CASE__										=      Path(args.training_script	)
  sys.path.append(str(script_fpath.parent.resolve()	)	)
  SCREAMING_SNAKE_CASE__										=      script_fpath.stem
  SCREAMING_SNAKE_CASE__										=      importlib.import_module(__UpperCamelCase	)

  # Patch sys.argv
  SCREAMING_SNAKE_CASE__										=      [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores	)]

  xmp.spawn(mod._mp_fn ,     args=() ,     nprocs=args.num_cores	)


if __name__ == "__main__":
   main()
