


"""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()
__snake_case												=							logging.get_logger(__name__)

__snake_case												=							'''Hello world! cécé herlolip'''
def    A_ ( _lowerCAmelCase  :       str,  _lowerCAmelCase  :       str,  _lowerCAmelCase  :       bool							):






     """simple docstring"""

     _a     						=					FairseqRobertaModel.from_pretrained(_lowerCAmelCase							)
     roberta.eval()  # disable dropout
     _a     						=					roberta.model.encoder.sentence_encoder
     _a     						=					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=5_14,  type_vocab_size=1,  layer_norm_eps=1e-5,  )
     if classification_head:
          _a     						=					roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
     print('''Our BERT config:''',  _lowerCAmelCase							)

     _a     						=					RobertaForSequenceClassification(_lowerCAmelCase							) if classification_head else RobertaForMaskedLM(_lowerCAmelCase							)
     model.eval()

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

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

          # self attention
          _a     						=					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)							)
          )

          _a     						=					roberta_layer.self_attn.q_proj.weight
          _a     						=					roberta_layer.self_attn.q_proj.bias
          _a     						=					roberta_layer.self_attn.k_proj.weight
          _a     						=					roberta_layer.self_attn.k_proj.bias
          _a     						=					roberta_layer.self_attn.v_proj.weight
          _a     						=					roberta_layer.self_attn.v_proj.bias

          # self-attention output
          _a     						=					layer.attention.output
          assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
          _a     						=					roberta_layer.self_attn.out_proj.weight
          _a     						=					roberta_layer.self_attn.out_proj.bias
          _a     						=					roberta_layer.self_attn_layer_norm.weight
          _a     						=					roberta_layer.self_attn_layer_norm.bias

          # intermediate
          _a     						=					layer.intermediate
          assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
          _a     						=					roberta_layer.fca.weight
          _a     						=					roberta_layer.fca.bias

          # output
          _a     						=					layer.output
          assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
          _a     						=					roberta_layer.fca.weight
          _a     						=					roberta_layer.fca.bias
          _a     						=					roberta_layer.final_layer_norm.weight
          _a     						=					roberta_layer.final_layer_norm.bias
          # end of layer

     if classification_head:
          _a     						=					roberta.model.classification_heads['''mnli'''].dense.weight
          _a     						=					roberta.model.classification_heads['''mnli'''].dense.bias
          _a     						=					roberta.model.classification_heads['''mnli'''].out_proj.weight
          _a     						=					roberta.model.classification_heads['''mnli'''].out_proj.bias
     else:
          # LM Head
          _a     						=					roberta.model.encoder.lm_head.dense.weight
          _a     						=					roberta.model.encoder.lm_head.dense.bias
          _a     						=					roberta.model.encoder.lm_head.layer_norm.weight
          _a     						=					roberta.model.encoder.lm_head.layer_norm.bias
          _a     						=					roberta.model.encoder.lm_head.weight
          _a     						=					roberta.model.encoder.lm_head.bias

     # Let's check that we get the same results.
     _a     						=					roberta.encode(_lowerCAmelCase							).unsqueeze(0							)  # batch of size 1

     _a     						=					model(_lowerCAmelCase							)[0]
     if classification_head:
          _a     						=					roberta.model.classification_heads['''mnli'''](roberta.extract_features(_lowerCAmelCase							)							)
     else:
          _a     						=					roberta.model(_lowerCAmelCase							)[0]
     print(our_output.shape,  their_output.shape							)
     _a     						=					torch.max(torch.abs(our_output - their_output							)							).item()
     print(f'max_absolute_diff = {max_absolute_diff}'							)  # ~ 1e-7
     _a     						=					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__":
   __snake_case												=							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												=							parser.parse_args()
   convert_roberta_checkpoint_to_pytorch(
       args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )



"""simple docstring"""






def    A_ ( _lowerCAmelCase  :       str							):






     """simple docstring"""

     return "".join(chr(ord(_lowerCAmelCase							) + 32							) if '''A''' <= char <= '''Z''' else char for char in word							)


if __name__ == "__main__":
   from doctest import testmod

   testmod()