


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()
SCREAMING_SNAKE_CASE__       			=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       			=				"""Hello world! cécé herlolip"""




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		str  ,			__lowerCamelCase:		Optional[int]  ,			__lowerCamelCase:		Optional[Any]  ):







	'''simple docstring'''



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

	lowercase_			 = RobertaForSequenceClassification(lowercase__  ) if classification_head else RobertaForMaskedLM(lowercase__  )
	model.eval()

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

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

		# self attention
		lowercase_			 = 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)  )
		)

		lowercase_			 = roberta_layer.self_attn.q_proj.weight
		lowercase_			 = roberta_layer.self_attn.q_proj.bias
		lowercase_			 = roberta_layer.self_attn.k_proj.weight
		lowercase_			 = roberta_layer.self_attn.k_proj.bias
		lowercase_			 = roberta_layer.self_attn.v_proj.weight
		lowercase_			 = roberta_layer.self_attn.v_proj.bias

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

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

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

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

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

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

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


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






'''simple docstring'''


from math import floor, sqrt






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     a_									=0.0
     a_									=1.0
     a_									=int(sqrt(lowercase__							)							)
     a_									=root
     a_									=0
     while integer_part != 2 * root:
          a_									=denominator * integer_part - numerator
          a_									=(n - numerator**2) / denominator
          a_									=int((root + numerator) / denominator							)
          period += 1
     return period






def 			UpperCAmelCase_							(		lowercase__ = 1_0_0_0_0							):


     '''simple docstring'''







     a_									=0
     for i in range(2							,						n + 1							):
          a_									=sqrt(lowercase__							)
          if sr - floor(lowercase__							) != 0 and continuous_fraction_period(lowercase__							) % 2 == 1:
               count_odd_periods += 1
     return count_odd_periods


if __name__ == "__main__":
  print(F"""{solution(int(input().strip()))}""")


