


import requests


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				List[str]		     =					{'''Content-Type''': '''application/json'''}
  A_      :				Union[str, Any]		     =					requests.post(SCREAMING_SNAKE_CASE ,			json={'''text''': message_body} ,			headers=SCREAMING_SNAKE_CASE       )
  if response.status_code != 200:
    A_      :				Dict		     =					(
        '''Request to slack returned an error '''
        f'''{response.status_code}, the response is:\n{response.text}'''
    )
    raise ValueError(SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
     # Set the slack url to the one provided by Slack when you create the webhook at
     # https://my.slack.com/services/new/incoming-webhook/
     send_slack_message("""<YOUR MESSAGE BODY>""", """<SLACK CHANNEL URL>""")








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
     )




