





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()
lowercase :			Tuple     		=    logging.get_logger(__name__)

lowercase :			Dict     		=    "Hello world! cécé herlolip"



def A_      (						A__						, A__						, A__							)    ->						List[Any]:
       a__		:      List[str]					=							FairseqRobertaModel.from_pretrained(_lowerCamelCase							)
       roberta.eval()  # disable dropout
       a__		:      int					=							roberta.model.encoder.sentence_encoder
       a__		:      List[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:'						, _lowerCamelCase							)

       a__		:      Dict					=							XLMRobertaXLForSequenceClassification(_lowerCamelCase							) if classification_head else XLMRobertaXLForMaskedLM(_lowerCamelCase							)
       model.eval()

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

       a__		:      Optional[int]					=							roberta_sent_encoder.layer_norm.weight
       a__		:      Any					=							roberta_sent_encoder.layer_norm.bias

       for i in range(config.num_hidden_layers							):
              # Encoder: start of layer
              a__		:      List[str]					=							model.roberta.encoder.layer[i]
              a__		:      Optional[Any]					=							roberta_sent_encoder.layers[i]

              a__		:      int					=							layer.attention
              a__		:      str					=							roberta_layer.self_attn_layer_norm.weight
              a__		:      Optional[Any]					=							roberta_layer.self_attn_layer_norm.bias

              # self attention
              a__		:      List[Any]					=							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__		:      Optional[Any]					=							roberta_layer.self_attn.q_proj.weight
              a__		:      Optional[int]					=							roberta_layer.self_attn.q_proj.bias
              a__		:      List[Any]					=							roberta_layer.self_attn.k_proj.weight
              a__		:      str					=							roberta_layer.self_attn.k_proj.bias
              a__		:      List[Any]					=							roberta_layer.self_attn.v_proj.weight
              a__		:      Optional[Any]					=							roberta_layer.self_attn.v_proj.bias

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

              # this one is final layer norm
              a__		:      int					=							roberta_layer.final_layer_norm.weight
              a__		:      Optional[int]					=							roberta_layer.final_layer_norm.bias

              # intermediate
              a__		:      List[Any]					=							layer.intermediate
              assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
              a__		:      Union[str, Any]					=							roberta_layer.fca.weight
              a__		:      Union[str, Any]					=							roberta_layer.fca.bias

              # output
              a__		:      Tuple					=							layer.output
              assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
              a__		:      Any					=							roberta_layer.fca.weight
              a__		:      str					=							roberta_layer.fca.bias
              # end of layer

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

       # Let's check that we get the same results.
       a__		:      Union[str, Any]					=							roberta.encode(_lowerCamelCase							).unsqueeze(0							)  # batch of size 1

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

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


if __name__ == "__main__":
    lowercase :			str     		=    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 :			Optional[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 Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin






class   UpperCAmelCase      (			UpperCamelCase__     ,			UpperCamelCase__							):
 @register_to_config
 def __init__(							self :List[str]		,			lowercase_ :int = 7_68		,			)->				str:
     super().__init__()

     A__							=     nn.Parameter(torch.zeros(1		,			lowercase_				)				)
     A__							=     nn.Parameter(torch.ones(1		,			lowercase_				)				)
 def 	UpperCAmelCase_				(							self :List[Any]		,			lowercase_ :Optional[Union[str, torch.device]] = None		,			lowercase_ :Optional[torch.dtype] = None		,			)->				Tuple:
     A__							=     nn.Parameter(self.mean.to(lowercase_				).to(lowercase_				)				)
     A__							=     nn.Parameter(self.std.to(lowercase_				).to(lowercase_				)				)
     return self
 def 	UpperCAmelCase_				(							self :Optional[int]		,			lowercase_ :str				)->				Union[str, Any]:
     A__							=     (embeds - self.mean) * 1.0 / self.std
     return embeds



 def 	UpperCAmelCase_				(							self :Any		,			lowercase_ :List[Any]				)->				Tuple:
     A__							=     (embeds * self.std) + self.mean
     return embeds



