
import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration







def 							UpperCAmelCase_(  a__					):



    """simple docstring"""



    SCREAMING_SNAKE_CASE							:							List[str]								=      [
        """encoder.version""",
        """decoder.version""",
        """model.encoder.version""",
        """model.decoder.version""",
        """_float_tensor""",
        """decoder.output_projection.weight""",
    ]
    for k in ignore_keys:
        state_dict.pop(snake_case_      ,		snake_case_					)







def 							UpperCAmelCase_(  a__					):



    """simple docstring"""



    SCREAMING_SNAKE_CASE							:							List[str]								=      emb.weight.shape
    SCREAMING_SNAKE_CASE							:							Any								=      nn.Linear(snake_case_      ,		snake_case_      ,		bias=snake_case_					)
    SCREAMING_SNAKE_CASE							:							str								=      emb.weight.data
    return lin_layer







def 							UpperCAmelCase_(  a__      ,		a__="facebook/mbart-large-en-ro"      ,		a__=False      ,		a__=False					):



    """simple docstring"""



    SCREAMING_SNAKE_CASE							:							Optional[Any]								=      torch.load(snake_case_      ,		map_location='''cpu'''					)["""model"""]
    remove_ignore_keys_(snake_case_					)
    SCREAMING_SNAKE_CASE							:							Optional[int]								=      state_dict["""encoder.embed_tokens.weight"""].shape[0]

    SCREAMING_SNAKE_CASE							:							Optional[Any]								=      MBartConfig.from_pretrained(snake_case_      ,		vocab_size=snake_case_					)
    if mbart_aa and finetuned:
        SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      """relu"""

    SCREAMING_SNAKE_CASE							:							str								=      state_dict["""decoder.embed_tokens.weight"""]
    SCREAMING_SNAKE_CASE							:							Optional[Any]								=      MBartForConditionalGeneration(snake_case_					)
    model.model.load_state_dict(snake_case_					)

    if finetuned:
        SCREAMING_SNAKE_CASE							:							Dict								=      make_linear_from_emb(model.model.shared					)

    return model


if __name__ == "__main__":
      a__							:	List[str]  						=			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
      )
      parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
      parser.add_argument(
          '''--hf_config''',
          default='''facebook/mbart-large-cc25''',
          type=str,
          help='''Which huggingface architecture to use: mbart-large''',
      )
      parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
      parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
      a__							:	Tuple  						=			parser.parse_args()
      a__							:	str  						=			convert_fairseq_mbart_checkpoint_from_disk(
          args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
      )
      model.save_pretrained(args.pytorch_dump_folder_path)






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 						lowercase							(   UpperCamelCase__		):
     _a							 =						RobertaConfig
     _a							 =						"roberta"

     def __init__(  self		,     _a				)						->							Optional[int]:
           super().__init__(_a				)

           _A							:     Union[str, Any]			=						RobertaEmbeddings(_a				)
           self.init_weights()







@add_start_docstrings(
    "RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n    also takes care of multi-layer training. ",UpperCamelCase__,)
class 						lowercase							(   UpperCamelCase__		):
     _a							 =						RobertaConfig
     _a							 =						"roberta"

     def __init__(  self		,     _a				)						->							str:
           super().__init__(_a				)
           _A							:     Any			=						config.num_labels
           _A							:     Dict			=						config.num_hidden_layers

           _A							:     List[str]			=						DeeRobertaModel(_a				)
           _A							:     int			=						nn.Dropout(config.hidden_dropout_prob				)
           _A							:     int			=						nn.Linear(config.hidden_size		,     self.config.num_labels				)



     @add_start_docstrings_to_model_forward(_a				)
     def 					a__	(  self		,     _a=None		,     _a=None		,     _a=None		,     _a=None		,     _a=None		,     _a=None		,     _a=None		,     _a=-1		,     _a=False		,     )						->							Any:
           _A							:     Optional[int]			=						self.num_layers
           try:
                 _A							:     List[str]			=						self.roberta(
                     _a		,     attention_mask=_a		,     token_type_ids=_a		,     position_ids=_a		,     head_mask=_a		,     inputs_embeds=_a		,     )

                 _A							:     List[str]			=						outputs[1]

                 _A							:     List[str]			=						self.dropout(_a				)
                 _A							:     Optional[Any]			=						self.classifier(_a				)
                 _A							:     List[Any]			=						(logits,) + outputs[2:]  # add hidden states and attention if they are here
           except HighwayException as e:
                 _A							:     List[Any]			=						e.message
                 _A							:     Optional[int]			=						e.exit_layer
                 _A							:     Optional[int]			=						outputs[0]

           if not self.training:
                 _A							:     int			=						entropy(_a				)
                 _A							:     int			=						[]
                 _A							:     int			=						[]
           if labels is not None:
                 if self.num_labels == 1:
                       #  We are doing regression
                       _A							:     Union[str, Any]			=						MSELoss()
                       _A							:     Tuple			=						loss_fct(logits.view(-1				)		,     labels.view(-1				)				)
                 else:
                       _A							:     List[Any]			=						CrossEntropyLoss()
                       _A							:     Dict			=						loss_fct(logits.view(-1		,     self.num_labels				)		,     labels.view(-1				)				)

                 # work with highway exits
                 _A							:     Optional[Any]			=						[]
                 for highway_exit in outputs[-1]:
                       _A							:     Tuple			=						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
                             _A							:     List[str]			=						MSELoss()
                             _A							:     Optional[int]			=						loss_fct(highway_logits.view(-1				)		,     labels.view(-1				)				)
                       else:
                             _A							:     List[Any]			=						CrossEntropyLoss()
                             _A							:     Tuple			=						loss_fct(highway_logits.view(-1		,     self.num_labels				)		,     labels.view(-1				)				)
                       highway_losses.append(_a				)

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

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

