


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  if not isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
    raise ValueError('''iterations must be defined as integers'''       )
  if not isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) or not number >= 1:
    raise ValueError(
        '''starting number must be
                         and integer and be more than 0'''       )
  if not iterations >= 1:
    raise ValueError('''Iterations must be done more than 0 times to play FizzBuzz'''       )

  A_      :				int		     =					''''''
  while number <= iterations:
    if number % 3 == 0:
      out += "Fizz"
    if number % 5 == 0:
      out += "Buzz"
    if 0 not in (number % 3, number % 5):
      out += str(SCREAMING_SNAKE_CASE       )

    # print(out)
    number += 1
    out += " "
  return out


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 XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""1.0.0a"""):
     raise Exception("""requires fairseq >= 1.0.0a""")

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

UpperCamelCase								=	"""Hello world! cécé herlolip"""


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				List[Any]		     =					FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE       )
  roberta.eval()  # disable dropout
  A_      :				Dict		     =					roberta.model.encoder.sentence_encoder
  A_      :				Optional[Any]		     =					XLMRobertaConfig(
      vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings ,			hidden_size=roberta.cfg.model.encoder_embed_dim ,			num_hidden_layers=roberta.cfg.model.encoder_layers ,			num_attention_heads=roberta.cfg.model.encoder_attention_heads ,			intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim ,			max_position_embeddings=514 ,			type_vocab_size=1 ,			layer_norm_eps=1e-5 ,			)
  if classification_head:
    A_      :				Optional[int]		     =					roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

  print('''Our RoBERTa config:''' ,			SCREAMING_SNAKE_CASE       )

  A_      :				List[str]		     =					XLMRobertaXLForSequenceClassification(SCREAMING_SNAKE_CASE       ) if classification_head else XLMRobertaXLForMaskedLM(SCREAMING_SNAKE_CASE       )
  model.eval()

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

  A_      :				Union[str, Any]		     =					roberta_sent_encoder.layer_norm.weight
  A_      :				int		     =					roberta_sent_encoder.layer_norm.bias

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

    A_      :				RobertaAttention		     =					layer.attention
    A_      :				Dict		     =					roberta_layer.self_attn_layer_norm.weight
    A_      :				str		     =					roberta_layer.self_attn_layer_norm.bias

    # self attention
    A_      :				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)       )
    )

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

    # self-attention output
    A_      :				BertSelfOutput		     =					layer.attention.output
    assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
    A_      :				Any		     =					roberta_layer.self_attn.out_proj.weight
    A_      :				Optional[Any]		     =					roberta_layer.self_attn.out_proj.bias

    # this one is final layer norm
    A_      :				Any		     =					roberta_layer.final_layer_norm.weight
    A_      :				int		     =					roberta_layer.final_layer_norm.bias

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

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

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

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

  A_      :				Union[str, Any]		     =					model(SCREAMING_SNAKE_CASE       )[0]
  if classification_head:
    A_      :				str		     =					roberta.model.classification_heads['''mnli'''](roberta.extract_features(SCREAMING_SNAKE_CASE       )       )
  else:
    A_      :				int		     =					roberta.model(SCREAMING_SNAKE_CASE       )[0]
  print(our_output.shape ,			their_output.shape       )
  A_      :				Any		     =					torch.max(torch.abs(our_output - their_output       )       ).item()
  print(f'''max_absolute_diff = {max_absolute_diff}'''       )  # ~ 1e-7
  A_      :				Tuple		     =					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__":
     UpperCamelCase								=	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."""
     )
     UpperCamelCase								=	parser.parse_args()
     convert_xlm_roberta_xl_checkpoint_to_pytorch(
         args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )




