






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

_UpperCamelCase     			=  "Hello world! cécé herlolip"

def _a (		_snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      UpperCAmelCase							=       FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE_							)
      roberta.eval()  # disable dropout
      UpperCAmelCase							=       roberta.model.encoder.sentence_encoder
      UpperCAmelCase							=       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							=       roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
      print("""Our BERT config:"""      ,       SCREAMING_SNAKE_CASE_							)

      UpperCAmelCase							=       RobertaForSequenceClassification(SCREAMING_SNAKE_CASE_							) if classification_head else RobertaForMaskedLM(SCREAMING_SNAKE_CASE_							)
      model.eval()

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

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

            # self attention
            UpperCAmelCase							=       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							=       roberta_layer.self_attn.q_proj.weight
            UpperCAmelCase							=       roberta_layer.self_attn.q_proj.bias
            UpperCAmelCase							=       roberta_layer.self_attn.k_proj.weight
            UpperCAmelCase							=       roberta_layer.self_attn.k_proj.bias
            UpperCAmelCase							=       roberta_layer.self_attn.v_proj.weight
            UpperCAmelCase							=       roberta_layer.self_attn.v_proj.bias

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

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

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

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

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

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

      pathlib.Path(SCREAMING_SNAKE_CASE_							).mkdir(parents=SCREAMING_SNAKE_CASE_      ,       exist_ok=SCREAMING_SNAKE_CASE_							)
      print(F'''Saving model to {pytorch_dump_folder_path}'''							)
      model.save_pretrained(SCREAMING_SNAKE_CASE_							)


if __name__ == "__main__":
  _UpperCamelCase     			=  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     			=  parser.parse_args()
  convert_roberta_checkpoint_to_pytorch(
      args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )





'''simple docstring'''



from ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    "cross_attention",
    "0.20.0",
    "Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.",
    standard_warn=False,
)


UpperCamelCase__					:						str											=     AttentionProcessor

class       _a (_lowerCamelCase):





       """simple docstring"""




       def __init__(							self				,							*A__				,							**A__					)			->	int:
          _SCREAMING_SNAKE_CASE          = F"{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'					)					)} instead."
          deprecate("""cross_attention"""				,							"""0.20.0"""				,							A__				,							standard_warn=A__					)
          super().__init__(*A__				,							**A__					)






class       _a (_lowerCamelCase):





       """simple docstring"""




       def __init__(							self				,							*A__				,							**A__					)			->	Any:
          _SCREAMING_SNAKE_CASE          = F"{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'					)					)} instead."
          deprecate("""cross_attention"""				,							"""0.20.0"""				,							A__				,							standard_warn=A__					)
          super().__init__(*A__				,							**A__					)






class       _a (_lowerCamelCase):





       """simple docstring"""




       def __init__(							self				,							*A__				,							**A__					)			->	List[str]:
          _SCREAMING_SNAKE_CASE          = F"{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'					)					)} instead."
          deprecate("""cross_attention"""				,							"""0.20.0"""				,							A__				,							standard_warn=A__					)
          super().__init__(*A__				,							**A__					)






class       _a (_lowerCamelCase):





       """simple docstring"""




       def __init__(							self				,							*A__				,							**A__					)			->	Tuple:
          _SCREAMING_SNAKE_CASE          = F"{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'					)					)} instead."
          deprecate("""cross_attention"""				,							"""0.20.0"""				,							A__				,							standard_warn=A__					)
          super().__init__(*A__				,							**A__					)






class       _a (_lowerCamelCase):





       """simple docstring"""




       def __init__(							self				,							*A__				,							**A__					)			->	str:
          _SCREAMING_SNAKE_CASE          = F"{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'					)					)} instead."
          deprecate("""cross_attention"""				,							"""0.20.0"""				,							A__				,							standard_warn=A__					)
          super().__init__(*A__				,							**A__					)






class       _a (_lowerCamelCase):





       """simple docstring"""




       def __init__(							self				,							*A__				,							**A__					)			->	Union[str, Any]:
          _SCREAMING_SNAKE_CASE          = F"{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'					)					)} instead."
          deprecate("""cross_attention"""				,							"""0.20.0"""				,							A__				,							standard_warn=A__					)
          super().__init__(*A__				,							**A__					)






class       _a (_lowerCamelCase):





       """simple docstring"""




       def __init__(							self				,							*A__				,							**A__					)			->	str:
          _SCREAMING_SNAKE_CASE          = F"{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'					)					)} instead."
          deprecate("""cross_attention"""				,							"""0.20.0"""				,							A__				,							standard_warn=A__					)
          super().__init__(*A__				,							**A__					)






class       _a (_lowerCamelCase):





       """simple docstring"""




       def __init__(							self				,							*A__				,							**A__					)			->	Any:
          _SCREAMING_SNAKE_CASE          = F"{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'					)					)} instead."
          deprecate("""cross_attention"""				,							"""0.20.0"""				,							A__				,							standard_warn=A__					)
          super().__init__(*A__				,							**A__					)






