

'''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()
lowercase__         =      logging.get_logger(__name__)

lowercase__         =      "Hello, World!"
lowercase__         =      "en_XX"




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     List[Any]						 =			Path('data_bin'     )
 UpperCAmelCase	:     List[str]						 =			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	:     List[Any]						 =			xmod.model.encoder.sentence_encoder
 UpperCAmelCase	:     Union[str, 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_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	:     Dict						 =			xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

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

 UpperCAmelCase	:     Optional[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	:     List[Any]						 =			xmod_sent_encoder.embed_positions.weight
 UpperCAmelCase	:     Tuple						 =			torch.zeros_like(
     model.roberta.embeddings.token_type_embeddings.weight     )  # just zero them out b/c xmod doesn't use them.

 UpperCAmelCase	:     Union[str, Any]						 =			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	:     int						 =			model.roberta.encoder.layer[i]
  UpperCAmelCase	:     int						 =			xmod_sent_encoder.layers[i]

  # self attention
  UpperCAmelCase	:     int						 =			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	:     List[Any]						 =			xmod_layer.self_attn.q_proj.weight
  UpperCAmelCase	:     Dict						 =			xmod_layer.self_attn.q_proj.bias
  UpperCAmelCase	:     Optional[Any]						 =			xmod_layer.self_attn.k_proj.weight
  UpperCAmelCase	:     Optional[int]						 =			xmod_layer.self_attn.k_proj.bias
  UpperCAmelCase	:     List[Any]						 =			xmod_layer.self_attn.v_proj.weight
  UpperCAmelCase	:     Optional[int]						 =			xmod_layer.self_attn.v_proj.bias

  # self-attention output
  UpperCAmelCase	:     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	:     Tuple						 =			xmod_layer.self_attn.out_proj.weight
  UpperCAmelCase	:     Optional[Any]						 =			xmod_layer.self_attn.out_proj.bias
  UpperCAmelCase	:     int						 =			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	:     Union[str, Any]						 =			xmod_layer.fca.weight
  UpperCAmelCase	:     str						 =			xmod_layer.fca.bias

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

  # end of layer

 if xmod_sent_encoder.layer_norm is not None:
  UpperCAmelCase	:     List[str]						 =			xmod_sent_encoder.layer_norm.weight
  UpperCAmelCase	:     int						 =			xmod_sent_encoder.layer_norm.bias

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

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

 UpperCAmelCase	:     Optional[int]						 =			model(UpperCAmelCase_     )[0]
 if classification_head:
  UpperCAmelCase	:     Tuple						 =			xmod.model.classification_heads['mnli'](xmod.extract_features(UpperCAmelCase_     )     )
 else:
  UpperCAmelCase	:     Tuple						 =			xmod.model(UpperCAmelCase_  ,	lang_id=[SAMPLE_LANGUAGE]     )[0]
 print(our_output.shape  ,	their_output.shape     )
 UpperCAmelCase	:     int						 =			torch.max(torch.abs(our_output - their_output     )     ).item()
 print(F"""max_absolute_diff = {max_absolute_diff}"""     )  # ~ 1e-7
 UpperCAmelCase	:     List[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__":
 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
 )







'''simple docstring'''

def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 while a != 0:
  UpperCAmelCase ,			UpperCAmelCase	:     Tuple						 =			b % a, a
 return b




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 if gcd(UpperCAmelCase_  ,	UpperCAmelCase_     ) != 1:
  UpperCAmelCase	:     List[str]						 =			F"""mod inverse of {a!r} and {m!r} does not exist"""
  raise ValueError(UpperCAmelCase_     )
 UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase	:     Any						 =			1, 0, a
 UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase	:     Dict						 =			0, 1, m
 while va != 0:
  UpperCAmelCase	:     Tuple						 =			ua // va
  UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase	:     List[Any]						 =			(ua - q * va), (ua - q * va), (ua - q * va), va, va, va
 return ua % m




