




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()
SCREAMING_SNAKE_CASE__ :   Optional[int]						=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__ :   int						=     'Hello world! cécé herlolip'

def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)			->				Dict:
       lowerCamelCase     :   Union[str, Any]    				=      FairseqRobertaModel.from_pretrained(_SCREAMING_SNAKE_CASE					)
       roberta.eval()  # disable dropout
       lowerCamelCase     :   Tuple    				=      roberta.model.encoder.sentence_encoder
       lowerCamelCase     :   Optional[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     :   Union[str, Any]    				=      roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
       print("Our BERT config:"				,_SCREAMING_SNAKE_CASE					)

       lowerCamelCase     :   Union[str, Any]    				=      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     :   Optional[Any]    				=      roberta_sent_encoder.embed_positions.weight
       lowerCamelCase     :   int    				=      torch.zeros_like(
           model.roberta.embeddings.token_type_embeddings.weight					)  # just zero them out b/c RoBERTa doesn't use them.
       lowerCamelCase     :   str    				=      roberta_sent_encoder.emb_layer_norm.weight
       lowerCamelCase     :   Tuple    				=      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     :   List[str]    				=      roberta_layer.self_attn.q_proj.weight
              lowerCamelCase     :   Optional[Any]    				=      roberta_layer.self_attn.q_proj.bias
              lowerCamelCase     :   Optional[Any]    				=      roberta_layer.self_attn.k_proj.weight
              lowerCamelCase     :   List[str]    				=      roberta_layer.self_attn.k_proj.bias
              lowerCamelCase     :   Union[str, Any]    				=      roberta_layer.self_attn.v_proj.weight
              lowerCamelCase     :   Union[str, Any]    				=      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     :   str    				=      roberta_layer.self_attn.out_proj.weight
              lowerCamelCase     :   int    				=      roberta_layer.self_attn.out_proj.bias
              lowerCamelCase     :   Optional[Any]    				=      roberta_layer.self_attn_layer_norm.weight
              lowerCamelCase     :   int    				=      roberta_layer.self_attn_layer_norm.bias

              # intermediate
              lowerCamelCase     :   BertIntermediate    				=      layer.intermediate
              assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
              lowerCamelCase     :   List[str]    				=      roberta_layer.fca.weight
              lowerCamelCase     :   Union[str, Any]    				=      roberta_layer.fca.bias

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

       if classification_head:
              lowerCamelCase     :   int    				=      roberta.model.classification_heads["mnli"].dense.weight
              lowerCamelCase     :   List[Any]    				=      roberta.model.classification_heads["mnli"].dense.bias
              lowerCamelCase     :   Dict    				=      roberta.model.classification_heads["mnli"].out_proj.weight
              lowerCamelCase     :   str    				=      roberta.model.classification_heads["mnli"].out_proj.bias
       else:
              # LM Head
              lowerCamelCase     :   Tuple    				=      roberta.model.encoder.lm_head.dense.weight
              lowerCamelCase     :   Dict    				=      roberta.model.encoder.lm_head.dense.bias
              lowerCamelCase     :   Dict    				=      roberta.model.encoder.lm_head.layer_norm.weight
              lowerCamelCase     :   Optional[int]    				=      roberta.model.encoder.lm_head.layer_norm.bias
              lowerCamelCase     :   Tuple    				=      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     :   Dict    				=      model(_SCREAMING_SNAKE_CASE					)[0]
       if classification_head:
              lowerCamelCase     :   Dict    				=      roberta.model.classification_heads["mnli"](roberta.extract_features(_SCREAMING_SNAKE_CASE					)					)
       else:
              lowerCamelCase     :   Tuple    				=      roberta.model(_SCREAMING_SNAKE_CASE					)[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     :   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__":
       SCREAMING_SNAKE_CASE__ :   Optional[Any]						=     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.'
       )
       SCREAMING_SNAKE_CASE__ :   Union[str, Any]						=     parser.parse_args()
       convert_roberta_checkpoint_to_pytorch(
           args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
       )












from itertools import compress, repeat
from math import ceil, sqrt

def   A   (   _SCREAMING_SNAKE_CASE					)			->				list[int]:

       if num <= 2:
              return []
       if num == 3:
              return [2]

       # Odd sieve for numbers in range [3, num - 1]
       lowerCamelCase     :   str    				=      bytearray(B"\x01"					) * ((num >> 1) - 1)

       for i in range(3				,int(sqrt(_SCREAMING_SNAKE_CASE					)					) + 1				,2					):
              if sieve[(i >> 1) - 1]:
                     lowerCamelCase     :   Tuple    				=      i**2
                     lowerCamelCase     :   str    				=      repeat(
                         0				,ceil((num - i_squared) / (i << 1)					)					)

       return [2] + list(compress(range(3				,_SCREAMING_SNAKE_CASE				,2					)				,_SCREAMING_SNAKE_CASE					)					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()






