






"""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()
A_			: Any				=			logging.get_logger(__name__)

A_			: Tuple				=			"Hello world! cécé herlolip"






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

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

 # Now let's copy all the weights.
 # Embeddings
 lowerCamelCase__     :			List[str]     				=						roberta_sent_encoder.embed_tokens.weight
 lowerCamelCase__     :			int     				=						roberta_sent_encoder.embed_positions.weight
 lowerCamelCase__     :			Dict     				=						torch.zeros_like(
     model.roberta.embeddings.token_type_embeddings.weight				)  # just zero them out b/c RoBERTa doesn't use them.
 lowerCamelCase__     :			List[str]     				=						roberta_sent_encoder.emb_layer_norm.weight
 lowerCamelCase__     :			str     				=						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__     :			Tuple     				=						roberta_layer.self_attn.q_proj.weight
  lowerCamelCase__     :			Optional[int]     				=						roberta_layer.self_attn.q_proj.bias
  lowerCamelCase__     :			Union[str, Any]     				=						roberta_layer.self_attn.k_proj.weight
  lowerCamelCase__     :			int     				=						roberta_layer.self_attn.k_proj.bias
  lowerCamelCase__     :			Tuple     				=						roberta_layer.self_attn.v_proj.weight
  lowerCamelCase__     :			List[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__     :			Dict     				=						roberta_layer.self_attn.out_proj.bias
  lowerCamelCase__     :			str     				=						roberta_layer.self_attn_layer_norm.weight
  lowerCamelCase__     :			Any     				=						roberta_layer.self_attn_layer_norm.bias

  # intermediate
  lowerCamelCase__     :			BertIntermediate     				=						layer.intermediate
  assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
  lowerCamelCase__     :			Any     				=						roberta_layer.fca.weight
  lowerCamelCase__     :			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__     :			Dict     				=						roberta_layer.fca.bias
  lowerCamelCase__     :			Optional[Any]     				=						roberta_layer.final_layer_norm.weight
  lowerCamelCase__     :			int     				=						roberta_layer.final_layer_norm.bias
  # end of layer

 if classification_head:
  lowerCamelCase__     :			Optional[int]     				=						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__     :			Optional[int]     				=						roberta.model.classification_heads['''mnli'''].out_proj.bias
 else:
  # LM Head
  lowerCamelCase__     :			str     				=						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__     :			Optional[int]     				=						roberta.model.encoder.lm_head.layer_norm.bias
  lowerCamelCase__     :			Any     				=						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(_lowerCamelCase				).unsqueeze(0				)  # batch of size 1

 lowerCamelCase__     :			List[str]     				=						model(_lowerCamelCase				)[0]
 if classification_head:
  lowerCamelCase__     :			List[Any]     				=						roberta.model.classification_heads['''mnli'''](roberta.extract_features(_lowerCamelCase				)				)
 else:
  lowerCamelCase__     :			Tuple     				=						roberta.model(_lowerCamelCase				)[0]
 print(our_output.shape			,   their_output.shape				)
 lowerCamelCase__     :			Optional[Any]     				=						torch.max(torch.abs(our_output - their_output				)				).item()
 print(f'''max_absolute_diff = {max_absolute_diff}'''				)  # ~ 1e-7
 lowerCamelCase__     :			Any     				=						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__":
      A_			: Dict				=			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."
      )
      A_			: Optional[int]				=			parser.parse_args()
      convert_roberta_checkpoint_to_pytorch(
          args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )







def  lowerCAmelCase  (	UpperCAmelCase       )  ->int:






					"""simple docstring"""






					return n * (2 * n - 1)







def  lowerCAmelCase  (	UpperCAmelCase       )  ->bool:






					"""simple docstring"""






					__magic_name__					:							Any		   =							(1 + 24 * n) ** 0.5
					return ((1 + root) / 6) % 1 == 0







def  lowerCAmelCase  (	UpperCAmelCase = 144       )  ->int:






					"""simple docstring"""






					__magic_name__					:							Optional[int]		   =							start
					__magic_name__					:							int		   =							hexagonal_num(UpperCAmelCase       )
					while not is_pentagonal(UpperCAmelCase       ):
										n += 1
										__magic_name__					:							Optional[int]		   =							hexagonal_num(UpperCAmelCase       )
					return num


if __name__ == "__main__":
				print(f"{solution()} = ")



