




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()
lowercase_		=							logging.get_logger(__name__)

lowercase_		=							'''Hello world! cécé herlolip'''







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->Optional[int]:






					"""simple docstring"""






					__magic_name__					:							List[Any]		   =							FairseqRobertaModel.from_pretrained(UpperCAmelCase       )
					roberta.eval()  # disable dropout
					__magic_name__					:							Optional[Any]		   =							roberta.model.encoder.sentence_encoder
					__magic_name__					:							int		   =							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:
										__magic_name__					:							Any		   =							roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
					print('''Our BERT config:''',	UpperCAmelCase       )

					__magic_name__					:							Union[str, Any]		   =							RobertaForSequenceClassification(UpperCAmelCase       ) if classification_head else RobertaForMaskedLM(UpperCAmelCase       )
					model.eval()

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

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

										# self attention
										__magic_name__					:							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)       )
										)

										__magic_name__					:							Tuple		   =							roberta_layer.self_attn.q_proj.weight
										__magic_name__					:							Optional[int]		   =							roberta_layer.self_attn.q_proj.bias
										__magic_name__					:							Union[str, Any]		   =							roberta_layer.self_attn.k_proj.weight
										__magic_name__					:							int		   =							roberta_layer.self_attn.k_proj.bias
										__magic_name__					:							Tuple		   =							roberta_layer.self_attn.v_proj.weight
										__magic_name__					:							List[Any]		   =							roberta_layer.self_attn.v_proj.bias

										# self-attention output
										__magic_name__					:							BertSelfOutput		   =							layer.attention.output
										assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
										__magic_name__					:							str		   =							roberta_layer.self_attn.out_proj.weight
										__magic_name__					:							Dict		   =							roberta_layer.self_attn.out_proj.bias
										__magic_name__					:							str		   =							roberta_layer.self_attn_layer_norm.weight
										__magic_name__					:							Any		   =							roberta_layer.self_attn_layer_norm.bias

										# intermediate
										__magic_name__					:							BertIntermediate		   =							layer.intermediate
										assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
										__magic_name__					:							Any		   =							roberta_layer.fca.weight
										__magic_name__					:							int		   =							roberta_layer.fca.bias

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

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

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

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

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


if __name__ == "__main__":
				lowercase_		=							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.'''
				)
				lowercase_		=							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__					)






