'''simple docstring'''





import logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


SCREAMING_SNAKE_CASE_:					Optional[Any]  ={
    'debug': logging.DEBUG,
    'info': logging.INFO,
    'warning': logging.WARNING,
    'error': logging.ERROR,
    'critical': logging.CRITICAL,
}

SCREAMING_SNAKE_CASE_:					Dict  =logging.WARNING





def      lowerCAmelCase_    ( )   ->				Any:


     '''simple docstring'''




     UpperCAmelCase_	   =       os.getenv("DATASETS_VERBOSITY" ,   UpperCamelCase__      )
     if env_level_str:
          if env_level_str in log_levels:
               return log_levels[env_level_str]
          else:
               logging.getLogger().warning(
                   f"""Unknown option DATASETS_VERBOSITY={env_level_str}, """
                   f"""has to be one of: { ", ".join(log_levels.keys()      ) }"""      )
     return _default_log_level





def      lowerCAmelCase_    ( )   ->				Optional[Any]:


     '''simple docstring'''




     return __name__.split("."      )[0]





def      lowerCAmelCase_    ( )   ->				Optional[Any]:


     '''simple docstring'''




     return logging.getLogger(_get_library_name()      )





def      lowerCAmelCase_    ( )   ->				List[str]:


     '''simple docstring'''




     UpperCAmelCase_	   =       _get_library_root_logger()
     library_root_logger.setLevel(_get_default_logging_level()      )





def      lowerCAmelCase_    ( )   ->				Any:


     '''simple docstring'''




     UpperCAmelCase_	   =       _get_library_root_logger()
     library_root_logger.setLevel(logging.NOTSET      )





def      lowerCAmelCase_    ( snake_case_	:			Optional[str] = None      )   ->				Dict:


     '''simple docstring'''




     if name is None:
          UpperCAmelCase_	   =       _get_library_name()
     return logging.getLogger(UpperCamelCase__      )





def      lowerCAmelCase_    ( )   ->				Optional[Any]:


     '''simple docstring'''




     return _get_library_root_logger().getEffectiveLevel()





def      lowerCAmelCase_    ( snake_case_	:			int      )   ->				int:


     '''simple docstring'''




     _get_library_root_logger().setLevel(UpperCamelCase__      )





def      lowerCAmelCase_    ( )   ->				str:


     '''simple docstring'''




     return set_verbosity(UpperCamelCase__      )





def      lowerCAmelCase_    ( )   ->				int:


     '''simple docstring'''




     return set_verbosity(UpperCamelCase__      )





def      lowerCAmelCase_    ( )   ->				Union[str, Any]:


     '''simple docstring'''




     return set_verbosity(UpperCamelCase__      )





def      lowerCAmelCase_    ( )   ->				Optional[Any]:


     '''simple docstring'''




     return set_verbosity(UpperCamelCase__      )





def      lowerCAmelCase_    ( )   ->				List[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       False





def      lowerCAmelCase_    ( )   ->				str:


     '''simple docstring'''




     UpperCAmelCase_	   =       True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()




class 	__A				:



    def __init__(self   :   int       ,      *__a   :   Any       ,      **__a   :   int			):  # pylint: disable=unused-argument
         UpperCAmelCase_	   =       args[0] if args else None



    def __iter__(self   :   Union[str, Any]			):
         return iter(self._iterator			)



    def __getattr__(self   :   Optional[Any]       ,      __a   :   int			):

         def empty_fn(*__a   :   List[str]       ,      **__a   :   Dict			):  # pylint: disable=unused-argument
              return

         return empty_fn



    def __enter__(self   :   Optional[int]			):
         return self






    def __exit__(self   :   Any       ,      __a   :   Tuple       ,      __a   :   List[Any]       ,      __a   :   Union[str, Any]			):
         return




SCREAMING_SNAKE_CASE_:					Union[str, Any]  =True




class 	__A				:



    def __call__(self   :   List[Any]       ,      *__a   :   Optional[Any]       ,      __a   :   List[str]=False       ,      **__a   :   List[Any]			):
         if _tqdm_active and not disable:
              return tqdm_lib.tqdm(*_a       ,      **_a			)
         else:
              return EmptyTqdm(*_a       ,      **_a			)



    def  _lowercase						(self   :   List[Any]       ,      *__a   :   Optional[Any]       ,      **__a   :   Optional[int]			):
         UpperCAmelCase_	   =       None
         if _tqdm_active:
              return tqdm_lib.tqdm.set_lock(*_a       ,      **_a			)






    def  _lowercase						(self   :   Any			):
         if _tqdm_active:
              return tqdm_lib.tqdm.get_lock()




SCREAMING_SNAKE_CASE_:					str  =_tqdm_cls()





def      lowerCAmelCase_    ( )   ->				Optional[int]:


     '''simple docstring'''




     global _tqdm_active
     return bool(_tqdm_active      )





def      lowerCAmelCase_    ( )   ->				int:


     '''simple docstring'''




     global _tqdm_active
     UpperCAmelCase_	   =       True






def      lowerCAmelCase_    ( )   ->				Tuple:


     '''simple docstring'''




     global _tqdm_active
     UpperCAmelCase_	   =       False




'''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
    )




