


'''simple docstring'''





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__    (				UpperCamelCase_	:			str,      UpperCamelCase_	:			str,      UpperCamelCase_	:			bool	):
   UpperCAmelCase__  :Optional[Any]            =			FairseqRobertaModel.from_pretrained(UpperCamelCase_	)
   roberta.eval()  # disable dropout
   UpperCAmelCase__  :Optional[int]            =			roberta.model.encoder.sentence_encoder
   UpperCAmelCase__  :Dict            =			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:
      UpperCAmelCase__  :List[str]            =			roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
   print('''Our BERT config:''',      UpperCamelCase_	)

   UpperCAmelCase__  :List[Any]            =			RobertaForSequenceClassification(UpperCamelCase_	) if classification_head else RobertaForMaskedLM(UpperCamelCase_	)
   model.eval()

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

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

      # self attention
      UpperCAmelCase__  :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)	)
      )

      UpperCAmelCase__  :List[Any]            =			roberta_layer.self_attn.q_proj.weight
      UpperCAmelCase__  :List[Any]            =			roberta_layer.self_attn.q_proj.bias
      UpperCAmelCase__  :Any            =			roberta_layer.self_attn.k_proj.weight
      UpperCAmelCase__  :int            =			roberta_layer.self_attn.k_proj.bias
      UpperCAmelCase__  :str            =			roberta_layer.self_attn.v_proj.weight
      UpperCAmelCase__  :List[str]            =			roberta_layer.self_attn.v_proj.bias

      # self-attention output
      UpperCAmelCase__  :BertSelfOutput            =			layer.attention.output
      assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
      UpperCAmelCase__  :Tuple            =			roberta_layer.self_attn.out_proj.weight
      UpperCAmelCase__  :Optional[int]            =			roberta_layer.self_attn.out_proj.bias
      UpperCAmelCase__  :int            =			roberta_layer.self_attn_layer_norm.weight
      UpperCAmelCase__  :Tuple            =			roberta_layer.self_attn_layer_norm.bias

      # intermediate
      UpperCAmelCase__  :BertIntermediate            =			layer.intermediate
      assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
      UpperCAmelCase__  :str            =			roberta_layer.fca.weight
      UpperCAmelCase__  :Any            =			roberta_layer.fca.bias

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

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

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

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

   pathlib.Path(UpperCamelCase_	).mkdir(parents=UpperCamelCase_,      exist_ok=UpperCamelCase_	)
   print(F'''Saving model to {pytorch_dump_folder_path}'''	)
   model.save_pretrained(UpperCamelCase_	)


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
    )







'''simple docstring'''





def 					a__    (				UpperCamelCase_	:			int	):
   UpperCAmelCase__  :str            =			[True for i in range(n + 1	)]
   UpperCAmelCase__  :List[str]            =			[i - 1 for i in range(n + 1	)]
   UpperCAmelCase__  :str            =			[]
   for i in range(2,      n + 1	):
      if is_prime[i]:
         primes.append(UpperCamelCase_	)
      for j in range(0,      len(UpperCamelCase_	)	):
         if i * primes[j] >= n:
            break
         UpperCAmelCase__  :List[str]            =			False

         if i % primes[j] == 0:
            UpperCAmelCase__  :Any            =			totients[i] * primes[j]
            break

         UpperCAmelCase__  :str            =			totients[i] * (primes[j] - 1)

   return totients


if __name__ == "__main__":
    import doctest

    doctest.testmod()



