



'''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		    =     logging.get_logger(__name__)

__lowerCAmelCase		    =     '''Hello world! cécé herlolip'''



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)    ->   Union[str, Any]:
  _a  :   Any				 =							FairseqRobertaModel.from_pretrained(lowerCAmelCase_						)
  roberta.eval()  # disable dropout
  _a  :   List[str]				 =							roberta.model.encoder.sentence_encoder
  _a  :   Dict				 =							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:
    _a  :   int				 =							roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
  print('Our BERT config:'		,    lowerCAmelCase_						)

  _a  :   int				 =							RobertaForSequenceClassification(lowerCAmelCase_						) if classification_head else RobertaForMaskedLM(lowerCAmelCase_						)
  model.eval()

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

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

    # self attention
    _a  :   int				 =							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  :   Tuple				 =							roberta_layer.self_attn.q_proj.weight
    _a  :   List[str]				 =							roberta_layer.self_attn.q_proj.bias
    _a  :   Optional[Any]				 =							roberta_layer.self_attn.k_proj.weight
    _a  :   Dict				 =							roberta_layer.self_attn.k_proj.bias
    _a  :   str				 =							roberta_layer.self_attn.v_proj.weight
    _a  :   List[str]				 =							roberta_layer.self_attn.v_proj.bias

    # self-attention output
    _a  :   Optional[int]				 =							layer.attention.output
    assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
    _a  :   Optional[int]				 =							roberta_layer.self_attn.out_proj.weight
    _a  :   Optional[Any]				 =							roberta_layer.self_attn.out_proj.bias
    _a  :   Tuple				 =							roberta_layer.self_attn_layer_norm.weight
    _a  :   Dict				 =							roberta_layer.self_attn_layer_norm.bias

    # intermediate
    _a  :   str				 =							layer.intermediate
    assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
    _a  :   Dict				 =							roberta_layer.fca.weight
    _a  :   int				 =							roberta_layer.fca.bias

    # output
    _a  :   Dict				 =							layer.output
    assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
    _a  :   Union[str, Any]				 =							roberta_layer.fca.weight
    _a  :   List[str]				 =							roberta_layer.fca.bias
    _a  :   Union[str, Any]				 =							roberta_layer.final_layer_norm.weight
    _a  :   Optional[Any]				 =							roberta_layer.final_layer_norm.bias
    # end of layer

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

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

  _a  :   Optional[Any]				 =							model(lowerCAmelCase_						)[0]
  if classification_head:
    _a  :   str				 =							roberta.model.classification_heads['mnli'](roberta.extract_features(lowerCAmelCase_						)						)
  else:
    _a  :   str				 =							roberta.model(lowerCAmelCase_						)[0]
  print(our_output.shape		,    their_output.shape						)
  _a  :   List[Any]				 =							torch.max(torch.abs(our_output - their_output						)						).item()
  print(f"""max_absolute_diff = {max_absolute_diff}"""						)  # ~ 1e-7
  _a  :   Any				 =							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		    =     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
       )









'''simple docstring'''

from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest

@require_tokenizers
class 			__magic_name__		(					_UpperCamelCase     ):
							lowerCAmelCase :					int            =      SqueezeBertTokenizer
							lowerCAmelCase :					int            =      SqueezeBertTokenizerFast
							lowerCAmelCase :					List[Any]            =      True







							def 							__lowercase    (							self      : List[str]			,**_UpperCAmelCase      : Tuple	):
									return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname			,**_UpperCAmelCase	)







							@slow
							def 							__lowercase    (							self      : Tuple	):
									_a  :   Optional[int]				 =							SqueezeBertTokenizer.from_pretrained('squeezebert/squeezebert-mnli-headless'	)

									_a  :   int				 =							tokenizer.encode('sequence builders'			,add_special_tokens=_UpperCAmelCase	)
									_a  :   List[Any]				 =							tokenizer.encode('multi-sequence build'			,add_special_tokens=_UpperCAmelCase	)

									_a  :   Union[str, Any]				 =							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase	)
									_a  :   Dict				 =							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase			,_UpperCAmelCase	)

									assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
									assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
									    tokenizer.sep_token_id
									]




