'''simple docstring'''





from __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy




@add_start_docstrings(
    """The RoBERTa Model transformer with early exiting (DeeRoBERTa). """		,     UpperCamelCase__		,     )
class 	__A				(			UpperCamelCase__     ):
    a__	:						int	       =  RobertaConfig
    a__	:						Optional[Any]	       =  """roberta"""



    def __init__(self   :   Dict       ,      __a   :   str			):
         super().__init__(__a			)

         UpperCAmelCase_	   =       RobertaEmbeddings(__a			)
         self.init_weights()




@add_start_docstrings(
    """RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. """		,     UpperCamelCase__		,     )
class 	__A				(			UpperCamelCase__     ):
    a__	:						str	       =  RobertaConfig
    a__	:						Optional[int]	       =  """roberta"""



    def __init__(self   :   List[Any]       ,      __a   :   str			):
         super().__init__(__a			)
         UpperCAmelCase_	   =       config.num_labels
         UpperCAmelCase_	   =       config.num_hidden_layers

         UpperCAmelCase_	   =       DeeRobertaModel(__a			)
         UpperCAmelCase_	   =       nn.Dropout(config.hidden_dropout_prob			)
         UpperCAmelCase_	   =       nn.Linear(config.hidden_size       ,      self.config.num_labels			)






    @add_start_docstrings_to_model_forward(__a			)
    def  _lowercase						(self   :   Optional[int]       ,      __a   :   int=None       ,      __a   :   List[str]=None       ,      __a   :   List[str]=None       ,      __a   :   List[str]=None       ,      __a   :   List[str]=None       ,      __a   :   List[str]=None       ,      __a   :   Union[str, Any]=None       ,      __a   :   Optional[Any]=-1       ,      __a   :   List[str]=False       ,      ):
         UpperCAmelCase_	   =       self.num_layers
         try:
              UpperCAmelCase_	   =       self.roberta(
                  __a       ,      attention_mask=__a       ,      token_type_ids=__a       ,      position_ids=__a       ,      head_mask=__a       ,      inputs_embeds=__a       ,      )

              UpperCAmelCase_	   =       outputs[1]

              UpperCAmelCase_	   =       self.dropout(__a			)
              UpperCAmelCase_	   =       self.classifier(__a			)
              UpperCAmelCase_	   =       (logits,) + outputs[2:]  # add hidden states and attention if they are here
         except HighwayException as e:
              UpperCAmelCase_	   =       e.message
              UpperCAmelCase_	   =       e.exit_layer
              UpperCAmelCase_	   =       outputs[0]

         if not self.training:
              UpperCAmelCase_	   =       entropy(__a			)
              UpperCAmelCase_	   =       []
              UpperCAmelCase_	   =       []
         if labels is not None:
              if self.num_labels == 1:
                   #  We are doing regression
                   UpperCAmelCase_	   =       MSELoss()
                   UpperCAmelCase_	   =       loss_fct(logits.view(-1			)       ,      labels.view(-1			)			)
              else:
                   UpperCAmelCase_	   =       CrossEntropyLoss()
                   UpperCAmelCase_	   =       loss_fct(logits.view(-1       ,      self.num_labels			)       ,      labels.view(-1			)			)

              # work with highway exits
              UpperCAmelCase_	   =       []
              for highway_exit in outputs[-1]:
                   UpperCAmelCase_	   =       highway_exit[0]
                   if not self.training:
                        highway_logits_all.append(__a			)
                        highway_entropy.append(highway_exit[2]			)
                   if self.num_labels == 1:
                        #  We are doing regression
                        UpperCAmelCase_	   =       MSELoss()
                        UpperCAmelCase_	   =       loss_fct(highway_logits.view(-1			)       ,      labels.view(-1			)			)
                   else:
                        UpperCAmelCase_	   =       CrossEntropyLoss()
                        UpperCAmelCase_	   =       loss_fct(highway_logits.view(-1       ,      self.num_labels			)       ,      labels.view(-1			)			)
                   highway_losses.append(__a			)

              if train_highway:
                   UpperCAmelCase_	   =       (sum(highway_losses[:-1]			),) + outputs
                   # exclude the final highway, of course
              else:
                   UpperCAmelCase_	   =       (loss,) + outputs
         if not self.training:
              UpperCAmelCase_	   =       outputs + ((original_entropy, highway_entropy), exit_layer)
              if output_layer >= 0:
                   UpperCAmelCase_	   =       (
                       (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
                   )  # use the highway of the last layer

         return outputs  # (loss), logits, (hidden_states), (attentions), entropy





'''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()
SCREAMING_SNAKE_CASE_:					List[Any]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					Tuple  ='Hello world! cécé herlolip'





def      lowerCAmelCase_    ( snake_case_	:			str ,   snake_case_	:			str ,   snake_case_	:			bool      )   ->				Any:


     '''simple docstring'''




     UpperCAmelCase_	   =       FairseqRobertaModel.from_pretrained(snake_case_      )
     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=5_14 ,   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:" ,   snake_case_      )

     UpperCAmelCase_	   =       XLMRobertaXLForSequenceClassification(snake_case_      ) if classification_head else XLMRobertaXLForMaskedLM(snake_case_      )
     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(snake_case_      ).unsqueeze(0      )  # batch of size 1

     UpperCAmelCase_	   =       model(snake_case_      )[0]
     if classification_head:
          UpperCAmelCase_	   =       roberta.model.classification_heads["mnli"](roberta.extract_features(snake_case_      )      )
     else:
          UpperCAmelCase_	   =       roberta.model(snake_case_      )[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(snake_case_ ,   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(snake_case_      ).mkdir(parents=snake_case_ ,   exist_ok=snake_case_      )
     print(f"""Saving model to {pytorch_dump_folder_path}"""      )
     model.save_pretrained(snake_case_      )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:					Optional[int]  =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.'
    )
    SCREAMING_SNAKE_CASE_:					Union[str, Any]  =parser.parse_args()
    convert_xlm_roberta_xl_checkpoint_to_pytorch(
        args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
    )




