


"""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()
snake_case__		:     List[str]    				=				logging.get_logger(__name__)

snake_case__		:     Tuple    				=				'''Hello world! cécé herlolip'''


def      _snake_case						(						_snake_case				:			str ,   _snake_case				:			str ,   _snake_case				:			bool			):
      lowerCAmelCase					:	Any       =   FairseqRobertaModel.from_pretrained(_snake_case			)
      roberta.eval()  # disable dropout
      lowerCAmelCase					:	Optional[int]       =   roberta.model.encoder.sentence_encoder
      lowerCAmelCase					:	Union[str, 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:
            lowerCAmelCase					:	Any       =   roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

      print('''Our RoBERTa config:''' ,   _snake_case			)

      lowerCAmelCase					:	List[str]       =   XLMRobertaXLForSequenceClassification(_snake_case			) if classification_head else XLMRobertaXLForMaskedLM(_snake_case			)
      model.eval()

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

      lowerCAmelCase					:	Any       =   roberta_sent_encoder.layer_norm.weight
      lowerCAmelCase					:	Any       =   roberta_sent_encoder.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]

            lowerCAmelCase					:	RobertaAttention       =   layer.attention
            lowerCAmelCase					:	Optional[Any]       =   roberta_layer.self_attn_layer_norm.weight
            lowerCAmelCase					:	str       =   roberta_layer.self_attn_layer_norm.bias

            # 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					:	Tuple       =   roberta_layer.self_attn.q_proj.bias
            lowerCAmelCase					:	Dict       =   roberta_layer.self_attn.k_proj.weight
            lowerCAmelCase					:	Union[str, Any]       =   roberta_layer.self_attn.k_proj.bias
            lowerCAmelCase					:	Tuple       =   roberta_layer.self_attn.v_proj.weight
            lowerCAmelCase					:	str       =   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					:	Optional[Any]       =   roberta_layer.self_attn.out_proj.bias

            # this one is final layer norm
            lowerCAmelCase					:	Any       =   roberta_layer.final_layer_norm.weight
            lowerCAmelCase					:	List[Any]       =   roberta_layer.final_layer_norm.bias

            # intermediate
            lowerCAmelCase					:	BertIntermediate       =   layer.intermediate
            assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
            lowerCAmelCase					:	Any       =   roberta_layer.fca.weight
            lowerCAmelCase					:	Any       =   roberta_layer.fca.bias

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

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

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

      lowerCAmelCase					:	Tuple       =   model(_snake_case			)[0]
      if classification_head:
            lowerCAmelCase					:	int       =   roberta.model.classification_heads['''mnli'''](roberta.extract_features(_snake_case			)			)
      else:
            lowerCAmelCase					:	Any       =   roberta.model(_snake_case			)[0]
      print(our_output.shape ,   their_output.shape			)
      lowerCAmelCase					:	int       =   torch.max(torch.abs(our_output - their_output			)			).item()
      print(f'''max_absolute_diff = {max_absolute_diff}'''			)  # ~ 1e-7
      lowerCAmelCase					:	Dict       =   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__":
 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.'''
 )
 snake_case__		:     int    				=				parser.parse_args()
 convert_xlm_roberta_xl_checkpoint_to_pytorch(
     args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )






"""simple docstring"""





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_big_bird import BigBirdTokenizer
else:
 snake_case__		:     Optional[Any]    				=				None

snake_case__		:     Union[str, Any]    				=				logging.get_logger(__name__)
snake_case__		:     List[str]    				=				{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case__		:     Any    				=				{
    '''vocab_file''': {
        '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
        ),
    },
    '''tokenizer_file''': {
        '''google/bigbird-roberta-base''': (
            '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'''
        ),
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'''
        ),
    },
}

snake_case__		:     int    				=				{
    '''google/bigbird-roberta-base''': 4_096,
    '''google/bigbird-roberta-large''': 4_096,
    '''google/bigbird-base-trivia-itc''': 4_096,
}


snake_case__		:     Optional[Any]    				=				'''▁'''



class 		snake_case_(       a__			):
     __UpperCamelCase  =					VOCAB_FILES_NAMES
     __UpperCamelCase  =					PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase  =					BigBirdTokenizer
     __UpperCamelCase  =					['''input_ids''', '''attention_mask''']
     __UpperCamelCase  =					[]






     def __init__(	self  :					Union[str, Any]					,				UpperCamelCase_  :					str=None					,				UpperCamelCase_  :					Any=None					,				UpperCamelCase_  :					str="<unk>"					,				UpperCamelCase_  :					str="<s>"					,				UpperCamelCase_  :					str="</s>"					,				UpperCamelCase_  :					int="<pad>"					,				UpperCamelCase_  :					List[Any]="[SEP]"					,				UpperCamelCase_  :					Dict="[MASK]"					,				UpperCamelCase_  :					Any="[CLS]"					,				**UpperCamelCase_  :					Any					,				):
           lowerCAmelCase					:	Tuple       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else bos_token
           lowerCAmelCase					:	int       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else eos_token
           lowerCAmelCase					:	List[Any]       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else unk_token
           lowerCAmelCase					:	List[str]       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else pad_token
           lowerCAmelCase					:	Any       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else cls_token
           lowerCAmelCase					:	Tuple       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else sep_token

           # Mask token behave like a normal word, i.e. include the space before it
           lowerCAmelCase					:	Optional[Any]       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else mask_token

           super().__init__(
               UpperCamelCase_					,				tokenizer_file=UpperCamelCase_					,				bos_token=UpperCamelCase_					,				eos_token=UpperCamelCase_					,				unk_token=UpperCamelCase_					,				sep_token=UpperCamelCase_					,				pad_token=UpperCamelCase_					,				cls_token=UpperCamelCase_					,				mask_token=UpperCamelCase_					,				**UpperCamelCase_					,				)

           lowerCAmelCase					:	Optional[int]       =   vocab_file
           lowerCAmelCase					:	Optional[int]       =   False if not self.vocab_file else True






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					List[int]					,				UpperCamelCase_  :					Optional[List[int]] = None				):
           lowerCAmelCase					:	str       =   [self.sep_token_id]
           lowerCAmelCase					:	Tuple       =   [self.cls_token_id]
           if token_ids_a is None:
                 return cls + token_ids_a + sep
           return cls + token_ids_a + sep + token_ids_a + sep






     def      lowerCamelCase__     (	self  :					Dict					,				UpperCamelCase_  :					List[int]					,				UpperCamelCase_  :					Optional[List[int]] = None					,				UpperCamelCase_  :					bool = False				):

           if already_has_special_tokens:
                 if token_ids_a is not None:
                       raise ValueError(
                           '''You should not supply a second sequence if the provided sequence of '''
                           '''ids is already formatted with special tokens for the model.'''				)
                 return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

           if token_ids_a is None:
                 return [1] + ([0] * len(UpperCamelCase_				)) + [1]
           return [1] + ([0] * len(UpperCamelCase_				)) + [1] + ([0] * len(UpperCamelCase_				)) + [1]






     def      lowerCamelCase__     (	self  :					Dict					,				UpperCamelCase_  :					List[int]					,				UpperCamelCase_  :					Optional[List[int]] = None				):
           lowerCAmelCase					:	Tuple       =   [self.sep_token_id]
           lowerCAmelCase					:	Tuple       =   [self.cls_token_id]

           if token_ids_a is None:
                 return len(cls + token_ids_a + sep				) * [0]
           return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					Optional[str] = None				):
           if not self.can_save_slow_tokenizer:
                 raise ValueError(
                     '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                     '''tokenizer.'''				)

           if not os.path.isdir(UpperCamelCase_				):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
                 return
           lowerCAmelCase					:	Optional[int]       =   os.path.join(
               UpperCamelCase_					,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

           if os.path.abspath(self.vocab_file				) != os.path.abspath(UpperCamelCase_				):
                 copyfile(self.vocab_file					,				UpperCamelCase_				)

           return (out_vocab_file,)


