



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

lowercase											=      '''Hello world! cécé herlolip'''






def 			UpperCAmelCase_							(		lowercase__							,						lowercase__							,						lowercase__							):


     '''simple docstring'''







     a_									=FairseqRobertaModel.from_pretrained(lowercase__							)
     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=5_1_4							,						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:"							,						lowercase__							)

     a_									=RobertaForSequenceClassification(lowercase__							) if classification_head else RobertaForMaskedLM(lowercase__							)
     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(lowercase__							).unsqueeze(0							)  # batch of size 1

     a_									=model(lowercase__							)[0]
     if classification_head:
          a_									=roberta.model.classification_heads["mnli"](roberta.extract_features(lowercase__							)							)
     else:
          a_									=roberta.model(lowercase__							)[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(lowercase__							,						lowercase__							,						atol=1E-3							)
     print("Do both models output the same tensors?"							,						"🔥" if success else "💩"							)
     if not success:
          raise Exception("Something went wRoNg"							)

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


if __name__ == "__main__":
  lowercase											=      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.'''
  )
  lowercase											=      parser.parse_args()
  convert_roberta_checkpoint_to_pytorch(
      args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )







'''simple docstring'''


lowercase											=      (1, 153, 370, 371, 1_634, 24_678_051, 115_132_219_018_763_992_565_095_597_973_971_522_401)
lowercase											=      (-153, -1, 0, 1.2, 200, "A", [], {}, None)






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     if not isinstance(lowercase__							,						lowercase__							) or n < 1:
          return False

     # Initialization of sum and number of digits.
     a_									=0
     a_									=0
     a_									=n
     # Calculation of digits of the number
     while temp > 0:
          number_of_digits += 1
          temp //= 1_0
     # Dividing number into separate digits and find Armstrong number
     a_									=n
     while temp > 0:
          a_									=temp % 1_0
          total += rem**number_of_digits
          temp //= 1_0
     return n == total






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     if not isinstance(lowercase__							,						lowercase__							) or n < 1:
          return False

     # Init a "histogram" of the digits
     a_									=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
     a_									=0
     a_									=0
     a_									=n
     while temp > 0:
          a_		,    a_									=divmod(lowercase__							,						1_0							)
          digit_histogram[rem] += 1
          digit_total += 1

     for cnt, i in zip(lowercase__							,						range(len(lowercase__							)							)							):
          total += cnt * i**digit_total

     return n == total






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     if not isinstance(lowercase__							,						lowercase__							) or n < 1:
          return False
     a_									=len(str(lowercase__							)							)  # the power that all digits will be raised to
     # check if sum of each digit multiplied expo times is equal to number
     return n == sum(int(lowercase__							) ** expo for i in str(lowercase__							)							)






def 			UpperCAmelCase_							(		):


     '''simple docstring'''







     a_									=int(input("Enter an integer to see if it is an Armstrong number: "							).strip()							)
     print(F"""{num} is {'' if armstrong_number(lowercase__							) else 'not '}an Armstrong number."""							)
     print(F"""{num} is {'' if narcissistic_number(lowercase__							) else 'not '}an Armstrong number."""							)
     print(F"""{num} is {'' if pluperfect_number(lowercase__							) else 'not '}an Armstrong number."""							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()


