






'''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()
UpperCAmelCase__						:Dict          =			logging.get_logger(__name__)

UpperCAmelCase__						:List[str]          =			"""Hello world! cécé herlolip"""
def     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     Any:


     """simple docstring"""
     __lowerCamelCase						:       Dict				   =				FairseqRobertaModel.from_pretrained(_lowercase     )
     roberta.eval()  # disable dropout
     __lowerCamelCase						:       Dict				   =				roberta.model.encoder.sentence_encoder
     __lowerCamelCase						:       str				   =				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						:       List[str]				   =				roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

     print("""Our RoBERTa config:""",						_lowercase     )

     __lowerCamelCase						:       Optional[Any]				   =				XLMRobertaXLForSequenceClassification(_lowercase     ) if classification_head else XLMRobertaXLForMaskedLM(_lowercase     )
     model.eval()

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

     __lowerCamelCase						:       Optional[Any]				   =				roberta_sent_encoder.layer_norm.weight
     __lowerCamelCase						:       Optional[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[int]				   =				roberta_layer.self_attn_layer_norm.weight
          __lowerCamelCase						:       List[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						:       str				   =				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						:       int				   =				roberta_layer.self_attn.k_proj.bias
          __lowerCamelCase						:       List[Any]				   =				roberta_layer.self_attn.v_proj.weight
          __lowerCamelCase						:       Optional[Any]				   =				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						:       str				   =				roberta_layer.self_attn.out_proj.weight
          __lowerCamelCase						:       Tuple				   =				roberta_layer.self_attn.out_proj.bias

          # this one is final layer norm
          __lowerCamelCase						:       Any				   =				roberta_layer.final_layer_norm.weight
          __lowerCamelCase						:       Union[str, Any]				   =				roberta_layer.final_layer_norm.bias

          # intermediate
          __lowerCamelCase						:       BertIntermediate				   =				layer.intermediate
          assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
          __lowerCamelCase						:       List[Any]				   =				roberta_layer.fca.weight
          __lowerCamelCase						:       Optional[int]				   =				roberta_layer.fca.bias

          # output
          __lowerCamelCase						:       BertOutput				   =				layer.output
          assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
          __lowerCamelCase						:       Union[str, Any]				   =				roberta_layer.fca.weight
          __lowerCamelCase						:       int				   =				roberta_layer.fca.bias
          # end of layer

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

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

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

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


if __name__ == "__main__":
   UpperCAmelCase__						: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."""
   )
   UpperCAmelCase__						:List[Any]          =			parser.parse_args()
   convert_xlm_roberta_xl_checkpoint_to_pytorch(
       args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )











'''simple docstring'''

def     __lowercase  (_lowercase     )   ->     list:


     """simple docstring"""

     # bit count represents no. of bits in the gray code
     if bit_count < 0:
          raise ValueError("""The given input must be positive"""     )

     # get the generated string sequence
     __lowerCamelCase						:       Optional[Any]				   =				gray_code_sequence_string(_lowercase     )
     #
     # convert them to integers
     for i in range(len(_lowercase     )     ):
          __lowerCamelCase						:       List[str]				   =				int(sequence[i],						2     )

     return sequence
def     __lowercase  (_lowercase     )   ->     list:


     """simple docstring"""

     # The approach is a recursive one
     # Base case achieved when either n = 0 or n=1
     if bit_count == 0:
          return ["0"]

     if bit_count == 1:
          return ["0", "1"]

     __lowerCamelCase						:       str				   =				1 << bit_count  # defines the length of the sequence
     # 1<< n is equivalent to 2^n

     # recursive answer will generate answer for n-1 bits
     __lowerCamelCase						:       Union[str, Any]				   =				gray_code_sequence_string(bit_count - 1     )

     __lowerCamelCase						:       Optional[int]				   =				[]

     # append 0 to first half of the smaller sequence generated
     for i in range(seq_len // 2     ):
          __lowerCamelCase						:       Union[str, Any]				   =				"""0""" + smaller_sequence[i]
          sequence.append(_lowercase     )

     # append 1 to second half ... start from the end of the list
     for i in reversed(range(seq_len // 2     )     ):
          __lowerCamelCase						:       Optional[Any]				   =				"""1""" + smaller_sequence[i]
          sequence.append(_lowercase     )

     return sequence


if __name__ == "__main__":
   import doctest

   doctest.testmod()



