





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()
__lowerCamelCase :  List[Any]										=  logging.get_logger(__name__)

__lowerCamelCase :  Tuple										=  """Hello world! cécé herlolip"""



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )		->				Union[str, Any]:
 UpperCamelCase			:      List[Any] 			=     FairseqRobertaModel.from_pretrained(_lowerCAmelCase  )
 roberta.eval()  # disable dropout
 UpperCamelCase			:      Any 			=     roberta.model.encoder.sentence_encoder
 UpperCamelCase			:      int 			=     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:
  UpperCamelCase			:      Dict 			=     roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

 print("Our RoBERTa config:"							,     _lowerCAmelCase  )

 UpperCamelCase			:      Any 			=     XLMRobertaXLForSequenceClassification(_lowerCAmelCase  ) if classification_head else XLMRobertaXLForMaskedLM(_lowerCAmelCase  )
 model.eval()

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

 UpperCamelCase			:      Tuple 			=     roberta_sent_encoder.layer_norm.weight
 UpperCamelCase			:      Union[str, Any] 			=     roberta_sent_encoder.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]

  UpperCamelCase			:      RobertaAttention 			=     layer.attention
  UpperCamelCase			:      int 			=     roberta_layer.self_attn_layer_norm.weight
  UpperCamelCase			:      Union[str, Any] 			=     roberta_layer.self_attn_layer_norm.bias

  # 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			:      Dict 			=     roberta_layer.self_attn.q_proj.weight
  UpperCamelCase			:      Optional[int] 			=     roberta_layer.self_attn.q_proj.bias
  UpperCamelCase			:      Optional[Any] 			=     roberta_layer.self_attn.k_proj.weight
  UpperCamelCase			:      int 			=     roberta_layer.self_attn.k_proj.bias
  UpperCamelCase			:      Optional[int] 			=     roberta_layer.self_attn.v_proj.weight
  UpperCamelCase			:      Any 			=     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			:      str 			=     roberta_layer.self_attn.out_proj.weight
  UpperCamelCase			:      Tuple 			=     roberta_layer.self_attn.out_proj.bias

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

  # output
  UpperCamelCase			:      BertOutput 			=     layer.output
  assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
  UpperCamelCase			:      str 			=     roberta_layer.fca.weight
  UpperCamelCase			:      Dict 			=     roberta_layer.fca.bias
  # end of layer

 if classification_head:
  UpperCamelCase			:      Dict 			=     roberta.model.classification_heads["mnli"].dense.weight
  UpperCamelCase			:      List[str] 			=     roberta.model.classification_heads["mnli"].dense.bias
  UpperCamelCase			:      int 			=     roberta.model.classification_heads["mnli"].out_proj.weight
  UpperCamelCase			:      Dict 			=     roberta.model.classification_heads["mnli"].out_proj.bias
 else:
  # LM Head
  UpperCamelCase			:      Union[str, Any] 			=     roberta.model.encoder.lm_head.dense.weight
  UpperCamelCase			:      Union[str, Any] 			=     roberta.model.encoder.lm_head.dense.bias
  UpperCamelCase			:      Tuple 			=     roberta.model.encoder.lm_head.layer_norm.weight
  UpperCamelCase			:      Tuple 			=     roberta.model.encoder.lm_head.layer_norm.bias
  UpperCamelCase			:      Optional[Any] 			=     roberta.model.encoder.lm_head.weight
  UpperCamelCase			:      Tuple 			=     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			:      str 			=     model(_lowerCAmelCase  )[0]
 if classification_head:
  UpperCamelCase			:      str 			=     roberta.model.classification_heads["mnli"](roberta.extract_features(_lowerCAmelCase  )  )
 else:
  UpperCamelCase			:      Union[str, Any] 			=     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			:      str 			=     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 :  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."""
    )
    __lowerCamelCase :  Dict										=  parser.parse_args()
    convert_xlm_roberta_xl_checkpoint_to_pytorch(
        args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
    )







from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream



class       A__     ( __snake_case    ):





      def __init__(				self ,							A_ ,							A_ = None ,							A_ = None ,							A_ = False ,							A_ = False ,							A_ = None ,							A_ = None ,							**A_ ,							):
       '''simple docstring'''




       super().__init__(
           features=A_ ,							cache_dir=A_ ,							keep_in_memory=A_ ,							streaming=A_ ,							num_proc=A_ ,							**A_ ,							)
       UpperCamelCase			:      Optional[int] 			=     Generator(
           cache_dir=A_ ,							features=A_ ,							generator=A_ ,							gen_kwargs=A_ ,							**A_ ,							)





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       if self.streaming:
        UpperCamelCase			:      Optional[Any] 			=     self.builder.as_streaming_dataset(split="train"     )
       # Build regular (map-style) dataset
       else:
        UpperCamelCase			:      Union[str, Any] 			=     None
        UpperCamelCase			:      Union[str, Any] 			=     None
        UpperCamelCase			:      List[Any] 			=     None
        UpperCamelCase			:      List[str] 			=     None

        self.builder.download_and_prepare(
            download_config=A_ ,							download_mode=A_ ,							verification_mode=A_ ,							base_path=A_ ,							num_proc=self.num_proc ,							)
        UpperCamelCase			:      int 			=     self.builder.as_dataset(
            split="train" ,							verification_mode=A_ ,							in_memory=self.keep_in_memory     )
       return dataset
