




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

_SCREAMING_SNAKE_CASE =	"Hello world! cécé herlolip"





def  lowerCamelCase(       SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )		->		str:
   A_       		=   FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE_    )
   roberta.eval()  # disable dropout
   A_       		=   roberta.model.encoder.sentence_encoder
   A_       		=   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:
      A_       		=   roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
   print('Our BERT config:'					,SCREAMING_SNAKE_CASE_    )

   A_       		=   RobertaForSequenceClassification(SCREAMING_SNAKE_CASE_    ) if classification_head else RobertaForMaskedLM(SCREAMING_SNAKE_CASE_    )
   model.eval()

   # Now let's copy all the weights.
   # Embeddings
   A_       		=   roberta_sent_encoder.embed_tokens.weight
   A_       		=   roberta_sent_encoder.embed_positions.weight
   A_       		=   torch.zeros_like(
       model.roberta.embeddings.token_type_embeddings.weight    )  # just zero them out b/c RoBERTa doesn't use them.
   A_       		=   roberta_sent_encoder.emb_layer_norm.weight
   A_       		=   roberta_sent_encoder.emb_layer_norm.bias

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

      # self attention
      A_       		=   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)    )
      )

      A_       		=   roberta_layer.self_attn.q_proj.weight
      A_       		=   roberta_layer.self_attn.q_proj.bias
      A_       		=   roberta_layer.self_attn.k_proj.weight
      A_       		=   roberta_layer.self_attn.k_proj.bias
      A_       		=   roberta_layer.self_attn.v_proj.weight
      A_       		=   roberta_layer.self_attn.v_proj.bias

      # self-attention output
      A_       		=   layer.attention.output
      assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
      A_       		=   roberta_layer.self_attn.out_proj.weight
      A_       		=   roberta_layer.self_attn.out_proj.bias
      A_       		=   roberta_layer.self_attn_layer_norm.weight
      A_       		=   roberta_layer.self_attn_layer_norm.bias

      # intermediate
      A_       		=   layer.intermediate
      assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
      A_       		=   roberta_layer.fca.weight
      A_       		=   roberta_layer.fca.bias

      # output
      A_       		=   layer.output
      assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
      A_       		=   roberta_layer.fca.weight
      A_       		=   roberta_layer.fca.bias
      A_       		=   roberta_layer.final_layer_norm.weight
      A_       		=   roberta_layer.final_layer_norm.bias
      # end of layer

   if classification_head:
      A_       		=   roberta.model.classification_heads['mnli'].dense.weight
      A_       		=   roberta.model.classification_heads['mnli'].dense.bias
      A_       		=   roberta.model.classification_heads['mnli'].out_proj.weight
      A_       		=   roberta.model.classification_heads['mnli'].out_proj.bias
   else:
      # LM Head
      A_       		=   roberta.model.encoder.lm_head.dense.weight
      A_       		=   roberta.model.encoder.lm_head.dense.bias
      A_       		=   roberta.model.encoder.lm_head.layer_norm.weight
      A_       		=   roberta.model.encoder.lm_head.layer_norm.bias
      A_       		=   roberta.model.encoder.lm_head.weight
      A_       		=   roberta.model.encoder.lm_head.bias

   # Let's check that we get the same results.
   A_       		=   roberta.encode(SCREAMING_SNAKE_CASE_    ).unsqueeze(0    )  # batch of size 1

   A_       		=   model(SCREAMING_SNAKE_CASE_    )[0]
   if classification_head:
      A_       		=   roberta.model.classification_heads['mnli'](roberta.extract_features(SCREAMING_SNAKE_CASE_    )    )
   else:
      A_       		=   roberta.model(SCREAMING_SNAKE_CASE_    )[0]
   print(our_output.shape					,their_output.shape    )
   A_       		=   torch.max(torch.abs(our_output - their_output    )    ).item()
   print(F'max_absolute_diff = {max_absolute_diff}'    )  # ~ 1e-7
   A_       		=   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 =	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 =	parser.parse_args()
       convert_roberta_checkpoint_to_pytorch(
           args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
       )










'''simple docstring'''
def  lowerCamelCase(       SCREAMING_SNAKE_CASE_    )		->		list:
			A_       		=   [True for i in range(n + 1    )]
			A_       		=   [i - 1 for i in range(n + 1    )]
			A_       		=   []
			for i in range(2					,n + 1    ):
						if is_prime[i]:
									primes.append(SCREAMING_SNAKE_CASE_    )
						for j in range(0					,len(SCREAMING_SNAKE_CASE_    )    ):
									if i * primes[j] >= n:
												break
									A_       		=   False

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

									A_       		=   totients[i] * (primes[j] - 1)

			return totients


if __name__ == "__main__":
							import doctest

							doctest.testmod()




