


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()
lowercase_	   =     logging.get_logger(__name__)

lowercase_	   =     'Hello, World!'
lowercase_	   =     'en_XX'
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							int		 =						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		:							Dict		 =						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=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:
  __lowerCamelCase		:							List[Any]		 =						xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

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

 __lowerCamelCase		:							Tuple		 =						XmodForSequenceClassification(SCREAMING_SNAKE_CASE__						) if classification_head else XmodForMaskedLM(SCREAMING_SNAKE_CASE__						)
 model.eval()

 # Now let's copy all the weights.
 # Embeddings
 __lowerCamelCase		:							Optional[int]		 =						xmod_sent_encoder.embed_tokens.weight
 __lowerCamelCase		:							Dict		 =						xmod_sent_encoder.embed_positions.weight
 __lowerCamelCase		:							List[str]		 =						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		:							Union[str, Any]		 =						xmod_sent_encoder.layernorm_embedding.bias

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

  # self attention
  __lowerCamelCase		:							Optional[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.'						)

  __lowerCamelCase		:							int		 =						xmod_layer.self_attn.q_proj.weight
  __lowerCamelCase		:							Any		 =						xmod_layer.self_attn.q_proj.bias
  __lowerCamelCase		:							Tuple		 =						xmod_layer.self_attn.k_proj.weight
  __lowerCamelCase		:							Optional[int]		 =						xmod_layer.self_attn.k_proj.bias
  __lowerCamelCase		:							List[Any]		 =						xmod_layer.self_attn.v_proj.weight
  __lowerCamelCase		:							Dict		 =						xmod_layer.self_attn.v_proj.bias

  # self-attention output
  __lowerCamelCase		:							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.'						)
  __lowerCamelCase		:							Any		 =						xmod_layer.self_attn.out_proj.weight
  __lowerCamelCase		:							int		 =						xmod_layer.self_attn.out_proj.bias
  __lowerCamelCase		:							Optional[Any]		 =						xmod_layer.self_attn_layer_norm.weight
  __lowerCamelCase		:							Optional[int]		 =						xmod_layer.self_attn_layer_norm.bias

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

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

  # end of layer

 if xmod_sent_encoder.layer_norm is not None:
  __lowerCamelCase		:							Optional[int]		 =						xmod_sent_encoder.layer_norm.weight
  __lowerCamelCase		:							Optional[int]		 =						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		:							Tuple		 =						xmod.model.classification_heads['mnli'].out_proj.weight
  __lowerCamelCase		:							Any		 =						xmod.model.classification_heads['mnli'].out_proj.bias
 else:
  # LM Head
  __lowerCamelCase		:							Any		 =						xmod.model.encoder.lm_head.dense.weight
  __lowerCamelCase		:							Optional[Any]		 =						xmod.model.encoder.lm_head.dense.bias
  __lowerCamelCase		:							Union[str, Any]		 =						xmod.model.encoder.lm_head.layer_norm.weight
  __lowerCamelCase		:							Tuple		 =						xmod.model.encoder.lm_head.layer_norm.bias
  __lowerCamelCase		:							List[Any]		 =						xmod.model.encoder.lm_head.weight
  __lowerCamelCase		:							Union[str, Any]		 =						xmod.model.encoder.lm_head.bias

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

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





from __future__ import annotations
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							List[Any]		 =						str(SCREAMING_SNAKE_CASE__						)
 return len(SCREAMING_SNAKE_CASE__						) == 9 and set(SCREAMING_SNAKE_CASE__						) == set('123456789'						)
def 				UpperCamelCase__					(  ):
 for base_num in range(9_999      ,	4_999      ,	-1						):
  __lowerCamelCase		:							Tuple		 =						100_002 * base_num
  if is_9_pandigital(SCREAMING_SNAKE_CASE__						):
   return candidate

 for base_num in range(333      ,	99      ,	-1						):
  __lowerCamelCase		:							Union[str, Any]		 =						1_002_003 * base_num
  if is_9_pandigital(SCREAMING_SNAKE_CASE__						):
   return candidate

 return None


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

