
'''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 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_			:			Any			   =     logging.get_logger(__name__)

lowerCAmelCase_			:			Any			   =     '''Hello world! cécé herlolip'''







def __A  (    lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_       ):
     _UpperCAmelCase					:  Any         =    FairseqRobertaModel.from_pretrained(lowerCAmelCase_       )
     roberta.eval()  # disable dropout
     _UpperCAmelCase					:  Optional[int]         =    roberta.model.encoder.sentence_encoder
     _UpperCAmelCase					:  List[str]         =    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:
          _UpperCAmelCase					:  Optional[int]         =    roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
     print("""Our BERT config:""" ,       lowerCAmelCase_       )

     _UpperCAmelCase					:  str         =    RobertaForSequenceClassification(lowerCAmelCase_       ) if classification_head else RobertaForMaskedLM(lowerCAmelCase_       )
     model.eval()

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

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

          # self attention
          _UpperCAmelCase					:  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)       )
          )

          _UpperCAmelCase					:  List[str]         =    roberta_layer.self_attn.q_proj.weight
          _UpperCAmelCase					:  int         =    roberta_layer.self_attn.q_proj.bias
          _UpperCAmelCase					:  Tuple         =    roberta_layer.self_attn.k_proj.weight
          _UpperCAmelCase					:  str         =    roberta_layer.self_attn.k_proj.bias
          _UpperCAmelCase					:  Optional[int]         =    roberta_layer.self_attn.v_proj.weight
          _UpperCAmelCase					:  int         =    roberta_layer.self_attn.v_proj.bias

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

          # intermediate
          _UpperCAmelCase					:  BertIntermediate         =    layer.intermediate
          assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
          _UpperCAmelCase					:  List[str]         =    roberta_layer.fca.weight
          _UpperCAmelCase					:  str         =    roberta_layer.fca.bias

          # output
          _UpperCAmelCase					:  BertOutput         =    layer.output
          assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
          _UpperCAmelCase					:  Dict         =    roberta_layer.fca.weight
          _UpperCAmelCase					:  Optional[Any]         =    roberta_layer.fca.bias
          _UpperCAmelCase					:  Dict         =    roberta_layer.final_layer_norm.weight
          _UpperCAmelCase					:  Tuple         =    roberta_layer.final_layer_norm.bias
          # end of layer

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

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

     _UpperCAmelCase					:  List[Any]         =    model(lowerCAmelCase_       )[0]
     if classification_head:
          _UpperCAmelCase					:  List[str]         =    roberta.model.classification_heads["""mnli"""](roberta.extract_features(lowerCAmelCase_       )       )
     else:
          _UpperCAmelCase					:  int         =    roberta.model(lowerCAmelCase_       )[0]
     print(our_output.shape ,       their_output.shape       )
     _UpperCAmelCase					:  Dict         =    torch.max(torch.abs(our_output - their_output       )       ).item()
     print(f"max_absolute_diff = {max_absolute_diff}"       )  # ~ 1e-7
     _UpperCAmelCase					:  Tuple         =    torch.allclose(lowerCAmelCase_ ,       lowerCAmelCase_ ,       atol=1e-3       )
     print("""Do both models output the same tensors?""" ,       """🔥""" if success else """💩"""       )
     if not success:
          raise Exception("""Something went wRoNg"""       )

     pathlib.Path(lowerCAmelCase_       ).mkdir(parents=lowerCAmelCase_ ,       exist_ok=lowerCAmelCase_       )
     print(f"Saving model to {pytorch_dump_folder_path}"       )
     model.save_pretrained(lowerCAmelCase_       )


if __name__ == "__main__":
   lowerCAmelCase_			:			List[str]			   =     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_			:			str			   =     parser.parse_args()
   convert_roberta_checkpoint_to_pytorch(
       args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )




'''simple docstring'''

from itertools import permutations







def __A  (    ):
     _UpperCAmelCase					:  Union[str, Any]         =    list(map("""""".join ,       permutations("""0123456789"""       )       )       )
     return result[99_9999]


if __name__ == "__main__":
   print(solution())


