



'''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 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()
_lowerCamelCase	:       Any      	=	logging.get_logger(__name__)

_lowerCamelCase	:       Optional[Any]      	=	"Hello world! cécé herlolip"







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	str:






   """simple docstring"""
   UpperCamelCase					=  FairseqRobertaModel.from_pretrained(A__    )
   roberta.eval()  # disable dropout
   UpperCamelCase					=  roberta.model.encoder.sentence_encoder
   UpperCamelCase					=  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:
      UpperCamelCase					=  roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

   print('Our RoBERTa config:'    ,						A__    )

   UpperCamelCase					=  XLMRobertaXLForSequenceClassification(A__    ) if classification_head else XLMRobertaXLForMaskedLM(A__    )
   model.eval()

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

   UpperCamelCase					=  roberta_sent_encoder.layer_norm.weight
   UpperCamelCase					=  roberta_sent_encoder.layer_norm.bias

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

      UpperCamelCase					=  layer.attention
      UpperCamelCase					=  roberta_layer.self_attn_layer_norm.weight
      UpperCamelCase					=  roberta_layer.self_attn_layer_norm.bias

      # self attention
      UpperCamelCase					=  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					=  roberta_layer.self_attn.q_proj.weight
      UpperCamelCase					=  roberta_layer.self_attn.q_proj.bias
      UpperCamelCase					=  roberta_layer.self_attn.k_proj.weight
      UpperCamelCase					=  roberta_layer.self_attn.k_proj.bias
      UpperCamelCase					=  roberta_layer.self_attn.v_proj.weight
      UpperCamelCase					=  roberta_layer.self_attn.v_proj.bias

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

      # this one is final layer norm
      UpperCamelCase					=  roberta_layer.final_layer_norm.weight
      UpperCamelCase					=  roberta_layer.final_layer_norm.bias

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

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

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

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

   UpperCamelCase					=  model(A__    )[0]
   if classification_head:
      UpperCamelCase					=  roberta.model.classification_heads['mnli'](roberta.extract_features(A__    )    )
   else:
      UpperCamelCase					=  roberta.model(A__    )[0]
   print(our_output.shape    ,						their_output.shape    )
   UpperCamelCase					=  torch.max(torch.abs(our_output - their_output    )    ).item()
   print(F"""max_absolute_diff = {max_absolute_diff}"""    )  # ~ 1e-7
   UpperCamelCase					=  torch.allclose(A__    ,						A__    ,						atol=1e-3    )
   print('Do both models output the same tensors?'    ,						'🔥' if success else '💩'    )
   if not success:
      raise Exception('Something went wRoNg'    )

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


if __name__ == "__main__":
   _lowerCamelCase	:       List[Any]      	=	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	:       Any      	=	parser.parse_args()
   convert_xlm_roberta_xl_checkpoint_to_pytorch(
       args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )






'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase	:       Tuple      	=	{"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase	:       Dict      	=	[
       "IBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "IBertForMaskedLM",
       "IBertForMultipleChoice",
       "IBertForQuestionAnswering",
       "IBertForSequenceClassification",
       "IBertForTokenClassification",
       "IBertModel",
       "IBertPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ibert import (
          IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          IBertForMaskedLM,
          IBertForMultipleChoice,
          IBertForQuestionAnswering,
          IBertForSequenceClassification,
          IBertForTokenClassification,
          IBertModel,
          IBertPreTrainedModel,
      )

else:
   import sys

   _lowerCamelCase	:       Union[str, Any]      	=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

