


import math




def 			a_			(					SCREAMING_SNAKE_CASE__      :   Optional[Any]  ):
       '''simple docstring'''





       if not isinstance(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  ):
              _lowerCamelCase :	int						=F'''Input value of [number={number}] must be an integer'''
              raise TypeError(SCREAMING_SNAKE_CASE__  )
       if number < 1:
              _lowerCamelCase :	Dict						=F'''Input value of [number={number}] must be a positive integer'''
              raise ValueError(SCREAMING_SNAKE_CASE__  )
       _lowerCamelCase :	Tuple						=[number]
       while number != 1:
              if number % 2 == 0:
                     _lowerCamelCase :	Dict						=math.floor(math.sqrt(SCREAMING_SNAKE_CASE__  )  )
              else:
                     _lowerCamelCase :	Optional[Any]						=math.floor(
                         math.sqrt(SCREAMING_SNAKE_CASE__  ) * math.sqrt(SCREAMING_SNAKE_CASE__  ) * math.sqrt(SCREAMING_SNAKE_CASE__  )  )
              sequence.append(SCREAMING_SNAKE_CASE__  )
       return sequence


if __name__ == "__main__":
 import doctest

 doctest.testmod()






import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.9.0'):
 raise Exception('requires fairseq >= 0.9.0')


logging.set_verbosity_info()
lowerCamelCase		      = logging.get_logger(__name__)

lowerCamelCase		      = 'Hello world! cécé herlolip'




def 			a_   (					SCREAMING_SNAKE_CASE__      :   str	,    SCREAMING_SNAKE_CASE__      :   str	,    SCREAMING_SNAKE_CASE__      :   bool  ):
       '''simple docstring'''





       _lowerCamelCase :	Dict						=FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE__  )
       roberta.eval()  # disable dropout
       _lowerCamelCase :	int						=roberta.model.encoder.sentence_encoder
       _lowerCamelCase :	Union[str, Any]						=RobertaConfig(
           vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings	,    hidden_size=roberta.args.encoder_embed_dim	,    num_hidden_layers=roberta.args.encoder_layers	,    num_attention_heads=roberta.args.encoder_attention_heads	,    intermediate_size=roberta.args.encoder_ffn_embed_dim	,    max_position_embeddings=514	,    type_vocab_size=1	,    layer_norm_eps=1e-5	,    )
       if classification_head:
              _lowerCamelCase :	int						=roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
       print('Our BERT config:'	,    SCREAMING_SNAKE_CASE__  )

       _lowerCamelCase :	Optional[int]						=RobertaForSequenceClassification(SCREAMING_SNAKE_CASE__  ) if classification_head else RobertaForMaskedLM(SCREAMING_SNAKE_CASE__  )
       model.eval()

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

       for i in range(config.num_hidden_layers  ):
              # Encoder: start of layer
              _lowerCamelCase :	BertLayer						=model.roberta.encoder.layer[i]
              _lowerCamelCase :	TransformerSentenceEncoderLayer						=roberta_sent_encoder.layers[i]

              # self attention
              _lowerCamelCase :	BertSelfAttention						=layer.attention.self
              assert (
                  roberta_layer.self_attn.k_proj.weight.data.shape
                  == roberta_layer.self_attn.q_proj.weight.data.shape
                  == roberta_layer.self_attn.v_proj.weight.data.shape
                  == torch.Size((config.hidden_size, config.hidden_size)  )
              )

              _lowerCamelCase :	Dict						=roberta_layer.self_attn.q_proj.weight
              _lowerCamelCase :	List[Any]						=roberta_layer.self_attn.q_proj.bias
              _lowerCamelCase :	Optional[Any]						=roberta_layer.self_attn.k_proj.weight
              _lowerCamelCase :	int						=roberta_layer.self_attn.k_proj.bias
              _lowerCamelCase :	Optional[Any]						=roberta_layer.self_attn.v_proj.weight
              _lowerCamelCase :	int						=roberta_layer.self_attn.v_proj.bias

              # self-attention output
              _lowerCamelCase :	BertSelfOutput						=layer.attention.output
              assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
              _lowerCamelCase :	Union[str, Any]						=roberta_layer.self_attn.out_proj.weight
              _lowerCamelCase :	str						=roberta_layer.self_attn.out_proj.bias
              _lowerCamelCase :	List[Any]						=roberta_layer.self_attn_layer_norm.weight
              _lowerCamelCase :	List[Any]						=roberta_layer.self_attn_layer_norm.bias

              # intermediate
              _lowerCamelCase :	BertIntermediate						=layer.intermediate
              assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
              _lowerCamelCase :	str						=roberta_layer.fca.weight
              _lowerCamelCase :	Optional[int]						=roberta_layer.fca.bias

              # output
              _lowerCamelCase :	BertOutput						=layer.output
              assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
              _lowerCamelCase :	str						=roberta_layer.fca.weight
              _lowerCamelCase :	List[Any]						=roberta_layer.fca.bias
              _lowerCamelCase :	List[str]						=roberta_layer.final_layer_norm.weight
              _lowerCamelCase :	Optional[int]						=roberta_layer.final_layer_norm.bias
              # end of layer

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

       # Let's check that we get the same results.
       _lowerCamelCase :	torch.Tensor						=roberta.encode(SCREAMING_SNAKE_CASE__  ).unsqueeze(0  )  # batch of size 1

       _lowerCamelCase :	List[Any]						=model(SCREAMING_SNAKE_CASE__  )[0]
       if classification_head:
              _lowerCamelCase :	int						=roberta.model.classification_heads['mnli'](roberta.extract_features(SCREAMING_SNAKE_CASE__  )  )
       else:
              _lowerCamelCase :	int						=roberta.model(SCREAMING_SNAKE_CASE__  )[0]
       print(our_output.shape	,    their_output.shape  )
       _lowerCamelCase :	Any						=torch.max(torch.abs(our_output - their_output  )  ).item()
       print(F'''max_absolute_diff = {max_absolute_diff}'''  )  # ~ 1e-7
       _lowerCamelCase :	Optional[Any]						=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'  )

       pathlib.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__":
 lowerCamelCase		      = argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--roberta_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.'
 )
 lowerCamelCase		      = parser.parse_args()
 convert_roberta_checkpoint_to_pytorch(
     args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )


