

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 	snake_case_      (__A     :						str					,   __A     :						str					,   __A     :						bool     )			->	Union[str, Any]:
   __lowerCAmelCase  :			int					    =						FairseqRobertaModel.from_pretrained(__A     )
   roberta.eval()  # disable dropout
   __lowerCAmelCase  :			Union[str, Any]					    =						roberta.model.encoder.sentence_encoder
   __lowerCAmelCase  :			List[Any]					    =						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=5_1_4					,   type_vocab_size=1					,   layer_norm_eps=1e-5					,   )
   if classification_head:
      __lowerCAmelCase  :			List[Any]					    =						roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
   print("""Our BERT config:"""					,   __A     )

   __lowerCAmelCase  :			List[Any]					    =						RobertaForSequenceClassification(__A     ) if classification_head else RobertaForMaskedLM(__A     )
   model.eval()

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

      # 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  :			List[str]					    =						roberta_layer.self_attn.q_proj.weight
      __lowerCAmelCase  :			List[str]					    =						roberta_layer.self_attn.q_proj.bias
      __lowerCAmelCase  :			Optional[int]					    =						roberta_layer.self_attn.k_proj.weight
      __lowerCAmelCase  :			Optional[int]					    =						roberta_layer.self_attn.k_proj.bias
      __lowerCAmelCase  :			Union[str, 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  :			Any					    =						roberta_layer.self_attn.out_proj.weight
      __lowerCAmelCase  :			Union[str, Any]					    =						roberta_layer.self_attn.out_proj.bias
      __lowerCAmelCase  :			Dict					    =						roberta_layer.self_attn_layer_norm.weight
      __lowerCAmelCase  :			List[Any]					    =						roberta_layer.self_attn_layer_norm.bias

      # intermediate
      __lowerCAmelCase  :			BertIntermediate					    =						layer.intermediate
      assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
      __lowerCAmelCase  :			str					    =						roberta_layer.fca.weight
      __lowerCAmelCase  :			Tuple					    =						roberta_layer.fca.bias

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

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

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

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

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


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
   )





from math import sqrt





def 	snake_case_      (__A     :						int     )			->	list[int]:
   __lowerCAmelCase  :			List[str]					    =						[1]
   for i in range(2					,   int(sqrt(__A     )     ) + 1					,   1     ):
      if number % i == 0:
         values.append(__A     )
         if int(number // i     ) != i:
            values.append(int(number // i     )     )
   return sorted(__A     )





def 	snake_case_      (__A     :						int     )			->	bool:
   return sum(factors(__A     )     ) > n





def 	snake_case_      (__A     :						int     )			->	bool:
   __lowerCAmelCase  :			Any					    =						factors(__A     )
   __lowerCAmelCase  :			Union[str, Any]					    =						len(__A     )
   __lowerCAmelCase  :			str					    =						[[0 for i in range(number + 1     )] for j in range(r + 1     )]
   for i in range(r + 1     ):
      __lowerCAmelCase  :			Tuple					    =						True

   for i in range(1					,   number + 1     ):
      __lowerCAmelCase  :			List[str]					    =						False

   for i in range(1					,   r + 1     ):
      for j in range(1					,   number + 1     ):
         if j < values[i - 1]:
            __lowerCAmelCase  :			Dict					    =						subset[i - 1][j]
         else:
            __lowerCAmelCase  :			Dict					    =						subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

   return subset[r][number] != 0





def 	snake_case_      (__A     :						int     )			->	bool:
   return abundant(__A     ) and not semi_perfect(__A     )


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   for number in (69, 70, 71):
      print(F'{number} is {"" if weird(number) else "not "}weird.')


