


import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


lowerCamelCase		      = set()
lowerCamelCase		      = get_logger(__name__)




def 			a_			(					SCREAMING_SNAKE_CASE__      :   Optional[str] = None  ):
       '''simple docstring'''





       def decorator(SCREAMING_SNAKE_CASE__      :   Callable  ):
              global _emitted_deprecation_warnings

              if inspect.isclass(SCREAMING_SNAKE_CASE__  ):
                     _lowerCamelCase :	Dict						=deprecated_class_or_function.__init__
                     _lowerCamelCase :	Optional[Any]						=deprecated_class_or_function.__name__
              else:
                     _lowerCamelCase :	Any						=deprecated_class_or_function
                     _lowerCamelCase :	str						=deprecated_function.__name__
                     # Support deprecating __init__ class method: class name instead
                     _lowerCamelCase :	List[Any]						=name if name != '__init__' else deprecated_function.__qualname__.split('.'  )[-2]

              _lowerCamelCase :	Optional[Any]						=(
                  F'''{name} is deprecated and will be removed in the next major version of datasets.''' + F''' {help_message}'''
                  if help_message
                  else ''
              )

              @wraps(SCREAMING_SNAKE_CASE__  )
              def wrapper(*SCREAMING_SNAKE_CASE__      :   Tuple	,    **SCREAMING_SNAKE_CASE__      :   Optional[int]  ):
                     _lowerCamelCase :	Tuple						=hash(SCREAMING_SNAKE_CASE__  )
                     if func_hash not in _emitted_deprecation_warnings:
                            warnings.warn(SCREAMING_SNAKE_CASE__	,    category=SCREAMING_SNAKE_CASE__	,    stacklevel=2  )
                            _emitted_deprecation_warnings.add(SCREAMING_SNAKE_CASE__  )
                     return deprecated_function(*SCREAMING_SNAKE_CASE__	,    **SCREAMING_SNAKE_CASE__  )

              _lowerCamelCase :	Any						='deprecated'

              if inspect.isclass(SCREAMING_SNAKE_CASE__  ):
                     _lowerCamelCase :	Optional[int]						=wrapper
                     return deprecated_class_or_function
              else:
                     return wrapper

       return decorator




class A						(     enum.EnumMeta  ):






      def __getattribute__(      cls		:  Tuple    ,     lowercase_		:  int				)      ->		Any:
             """simple docstring"""




             _lowerCamelCase :	Optional[int]						=super().__getattribute__(lowercase_				)
             if isinstance(lowercase_    ,     enum.Enum				) and obj._on_access:
                    obj._on_access()
             return obj






      def __getitem__(      cls		:  int    ,     lowercase_		:  int				)      ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase :	Any						=super().__getitem__(lowercase_				)
             if member._on_access:
                    member._on_access()
             return member






      def __call__(      cls		:  str    ,     lowercase_		:  Optional[int]    ,     lowercase_		:  Optional[int]=None    ,     *, lowercase_		:  Optional[Any]=None    ,     lowercase_		:  int=None    ,     lowercase_		:  Tuple=None    ,     lowercase_		:  List[Any]=1				)      ->		str:
             """simple docstring"""




             _lowerCamelCase :	List[str]						=super().__call__(lowercase_    ,     lowercase_    ,     module=lowercase_    ,     qualname=lowercase_    ,     type=lowercase_    ,     start=lowercase_				)
             if isinstance(lowercase_    ,     enum.Enum				) and obj._on_access:
                    obj._on_access()
             return obj




class A						(     enum.Enum					,							metaclass=UpperCamelCase_  ):






      def __new__(      cls		:  Union[str, Any]    ,     lowercase_		:  Optional[int]				)      ->		List[str]:
             """simple docstring"""




             _lowerCamelCase :	Dict						=object.__new__(cls				)
             _lowerCamelCase :	List[Any]						=value
             _lowerCamelCase :	Union[str, Any]						=member.deprecate
             return member






      @property
      def  lowerCamelCase (      self		:  Tuple				)      ->		Optional[int]:
             """simple docstring"""




             return ""






      def  lowerCamelCase (      self		:  Dict				)      ->		Any:
             """simple docstring"""




             _lowerCamelCase :	int						=F''' {self.help_message}''' if self.help_message else ''
             warnings.warn(
                 F'''\'{self.__objclass__.__name__}\' is deprecated and will be removed in the next major version of datasets.'''
                 + help_message    ,     lowercase_    ,     stacklevel=3    ,     )






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 RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.9.0'):
 raise Exception('requires fairseq >= 0.9.0')


logging.set_verbosity_info()
lowerCamelCase		      = logging.get_logger(__name__)

lowerCamelCase		      = 'Hello world! cécé herlolip'




def 			a_   (					SCREAMING_SNAKE_CASE__      :   str	,    SCREAMING_SNAKE_CASE__      :   str	,    SCREAMING_SNAKE_CASE__      :   bool  ):
       '''simple docstring'''





       _lowerCamelCase :	Dict						=FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE__  )
       roberta.eval()  # disable dropout
       _lowerCamelCase :	int						=roberta.model.encoder.sentence_encoder
       _lowerCamelCase :	Union[str, Any]						=RobertaConfig(
           vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings	,    hidden_size=roberta.args.encoder_embed_dim	,    num_hidden_layers=roberta.args.encoder_layers	,    num_attention_heads=roberta.args.encoder_attention_heads	,    intermediate_size=roberta.args.encoder_ffn_embed_dim	,    max_position_embeddings=514	,    type_vocab_size=1	,    layer_norm_eps=1e-5	,    )
       if classification_head:
              _lowerCamelCase :	int						=roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
       print('Our BERT config:'	,    SCREAMING_SNAKE_CASE__  )

       _lowerCamelCase :	Optional[int]						=RobertaForSequenceClassification(SCREAMING_SNAKE_CASE__  ) if classification_head else RobertaForMaskedLM(SCREAMING_SNAKE_CASE__  )
       model.eval()

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

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

              # self attention
              _lowerCamelCase :	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)  )
              )

              _lowerCamelCase :	Dict						=roberta_layer.self_attn.q_proj.weight
              _lowerCamelCase :	List[Any]						=roberta_layer.self_attn.q_proj.bias
              _lowerCamelCase :	Optional[Any]						=roberta_layer.self_attn.k_proj.weight
              _lowerCamelCase :	int						=roberta_layer.self_attn.k_proj.bias
              _lowerCamelCase :	Optional[Any]						=roberta_layer.self_attn.v_proj.weight
              _lowerCamelCase :	int						=roberta_layer.self_attn.v_proj.bias

              # self-attention output
              _lowerCamelCase :	BertSelfOutput						=layer.attention.output
              assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
              _lowerCamelCase :	Union[str, Any]						=roberta_layer.self_attn.out_proj.weight
              _lowerCamelCase :	str						=roberta_layer.self_attn.out_proj.bias
              _lowerCamelCase :	List[Any]						=roberta_layer.self_attn_layer_norm.weight
              _lowerCamelCase :	List[Any]						=roberta_layer.self_attn_layer_norm.bias

              # intermediate
              _lowerCamelCase :	BertIntermediate						=layer.intermediate
              assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
              _lowerCamelCase :	str						=roberta_layer.fca.weight
              _lowerCamelCase :	Optional[int]						=roberta_layer.fca.bias

              # output
              _lowerCamelCase :	BertOutput						=layer.output
              assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
              _lowerCamelCase :	str						=roberta_layer.fca.weight
              _lowerCamelCase :	List[Any]						=roberta_layer.fca.bias
              _lowerCamelCase :	List[str]						=roberta_layer.final_layer_norm.weight
              _lowerCamelCase :	Optional[int]						=roberta_layer.final_layer_norm.bias
              # end of layer

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

       # Let's check that we get the same results.
       _lowerCamelCase :	torch.Tensor						=roberta.encode(SCREAMING_SNAKE_CASE__  ).unsqueeze(0  )  # batch of size 1

       _lowerCamelCase :	List[Any]						=model(SCREAMING_SNAKE_CASE__  )[0]
       if classification_head:
              _lowerCamelCase :	int						=roberta.model.classification_heads['mnli'](roberta.extract_features(SCREAMING_SNAKE_CASE__  )  )
       else:
              _lowerCamelCase :	int						=roberta.model(SCREAMING_SNAKE_CASE__  )[0]
       print(our_output.shape	,    their_output.shape  )
       _lowerCamelCase :	Any						=torch.max(torch.abs(our_output - their_output  )  ).item()
       print(F'''max_absolute_diff = {max_absolute_diff}'''  )  # ~ 1e-7
       _lowerCamelCase :	Optional[Any]						=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__":
 lowerCamelCase		      = 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		      = parser.parse_args()
 convert_roberta_checkpoint_to_pytorch(
     args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )


