




'''simple docstring'''



import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.12.2'):
	raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
	raise Exception('requires fairseq < v2')

logging.set_verbosity_info()
lowerCamelCase_			=						logging.get_logger(__name__)

lowerCamelCase_			=						'Hello, World!'
lowerCamelCase_			=						'en_XX'
def 			SCREAMING_SNAKE_CASE_			(							__A    :			str	,					__A    :			str	,					__A    :			bool  )			->			Dict:
			_SCREAMING_SNAKE_CASE	=				Path("data_bin"  )
			_SCREAMING_SNAKE_CASE	=				FairseqXmodModel.from_pretrained(
			    model_name_or_path=str(Path(__A  ).parent  )	,					checkpoint_file=Path(__A  ).name	,					_name="xmod_base"	,					arch="xmod_base"	,					task="multilingual_masked_lm"	,					data_name_or_path=str(__A  )	,					bpe="sentencepiece"	,					sentencepiece_model=str(Path(__A  ).parent / "sentencepiece.bpe.model"  )	,					src_dict=str(data_dir / "dict.txt"  )	,					)
			xmod.eval()  # disable dropout
			print(__A  )

			_SCREAMING_SNAKE_CASE	=				xmod.model.encoder.sentence_encoder
			_SCREAMING_SNAKE_CASE	=				XmodConfig(
			    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings	,					hidden_size=xmod.cfg.model.encoder_embed_dim	,					num_hidden_layers=xmod.cfg.model.encoder_layers	,					num_attention_heads=xmod.cfg.model.encoder_attention_heads	,					intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim	,					max_position_embeddings=5_14	,					type_vocab_size=1	,					layer_norm_eps=1e-5	,					pre_norm=xmod.cfg.model.encoder_normalize_before	,					adapter_reduction_factor=getattr(xmod.cfg.model	,					"bottleneck"	,					2  )	,					adapter_layer_norm=xmod.cfg.model.adapter_layer_norm	,					adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm	,					ln_before_adapter=xmod.cfg.model.ln_before_adapter	,					languages=xmod.cfg.model.languages	,					)
			if classification_head:
						_SCREAMING_SNAKE_CASE	=				xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

			print("Our X-MOD config:"	,					__A  )

			_SCREAMING_SNAKE_CASE	=				XmodForSequenceClassification(__A  ) if classification_head else XmodForMaskedLM(__A  )
			model.eval()

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

			_SCREAMING_SNAKE_CASE	=				xmod_sent_encoder.layernorm_embedding.weight
			_SCREAMING_SNAKE_CASE	=				xmod_sent_encoder.layernorm_embedding.bias

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

						# self attention
						_SCREAMING_SNAKE_CASE	=				layer.attention.self
						if not (
						    xmod_layer.self_attn.k_proj.weight.data.shape
						    == xmod_layer.self_attn.q_proj.weight.data.shape
						    == xmod_layer.self_attn.v_proj.weight.data.shape
						    == torch.Size((config.hidden_size, config.hidden_size)  )
						):
									raise AssertionError("Dimensions of self-attention weights do not match."  )

						_SCREAMING_SNAKE_CASE	=				xmod_layer.self_attn.q_proj.weight
						_SCREAMING_SNAKE_CASE	=				xmod_layer.self_attn.q_proj.bias
						_SCREAMING_SNAKE_CASE	=				xmod_layer.self_attn.k_proj.weight
						_SCREAMING_SNAKE_CASE	=				xmod_layer.self_attn.k_proj.bias
						_SCREAMING_SNAKE_CASE	=				xmod_layer.self_attn.v_proj.weight
						_SCREAMING_SNAKE_CASE	=				xmod_layer.self_attn.v_proj.bias

						# self-attention output
						_SCREAMING_SNAKE_CASE	=				layer.attention.output
						if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
									raise AssertionError("Dimensions of self-attention output weights do not match."  )
						_SCREAMING_SNAKE_CASE	=				xmod_layer.self_attn.out_proj.weight
						_SCREAMING_SNAKE_CASE	=				xmod_layer.self_attn.out_proj.bias
						_SCREAMING_SNAKE_CASE	=				xmod_layer.self_attn_layer_norm.weight
						_SCREAMING_SNAKE_CASE	=				xmod_layer.self_attn_layer_norm.bias

						# intermediate
						_SCREAMING_SNAKE_CASE	=				layer.intermediate
						if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
									raise AssertionError("Dimensions of intermediate weights do not match."  )
						_SCREAMING_SNAKE_CASE	=				xmod_layer.fca.weight
						_SCREAMING_SNAKE_CASE	=				xmod_layer.fca.bias

						# output
						_SCREAMING_SNAKE_CASE	=				layer.output
						if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
									raise AssertionError("Dimensions of feed-forward weights do not match."  )
						_SCREAMING_SNAKE_CASE	=				xmod_layer.fca.weight
						_SCREAMING_SNAKE_CASE	=				xmod_layer.fca.bias
						_SCREAMING_SNAKE_CASE	=				xmod_layer.final_layer_norm.weight
						_SCREAMING_SNAKE_CASE	=				xmod_layer.final_layer_norm.bias
						if bert_output.adapter_layer_norm is not None:
									_SCREAMING_SNAKE_CASE	=				xmod_layer.adapter_layer_norm.weight
									_SCREAMING_SNAKE_CASE	=				xmod_layer.adapter_layer_norm.bias

						if sorted(bert_output.adapter_modules.keys()  ) != sorted(xmod_layer.adapter_modules.keys()  ):
									raise AssertionError("Lists of language adapters do not match."  )
						for lang_code, adapter in xmod_layer.adapter_modules.items():
									_SCREAMING_SNAKE_CASE	=				bert_output.adapter_modules[lang_code]
									_SCREAMING_SNAKE_CASE	=				xmod_layer.adapter_modules[lang_code]
									_SCREAMING_SNAKE_CASE	=				from_adapter.fca.weight
									_SCREAMING_SNAKE_CASE	=				from_adapter.fca.bias
									_SCREAMING_SNAKE_CASE	=				from_adapter.fca.weight
									_SCREAMING_SNAKE_CASE	=				from_adapter.fca.bias

						# end of layer

			if xmod_sent_encoder.layer_norm is not None:
						_SCREAMING_SNAKE_CASE	=				xmod_sent_encoder.layer_norm.weight
						_SCREAMING_SNAKE_CASE	=				xmod_sent_encoder.layer_norm.bias

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

			# Let's check that we get the same results.
			_SCREAMING_SNAKE_CASE	=				xmod.encode(__A  ).unsqueeze(0  )  # batch of size 1
			model.roberta.set_default_language(__A  )

			_SCREAMING_SNAKE_CASE	=				model(__A  )[0]
			if classification_head:
						_SCREAMING_SNAKE_CASE	=				xmod.model.classification_heads["mnli"](xmod.extract_features(__A  )  )
			else:
						_SCREAMING_SNAKE_CASE	=				xmod.model(__A	,					lang_id=[SAMPLE_LANGUAGE]  )[0]
			print(our_output.shape	,					their_output.shape  )
			_SCREAMING_SNAKE_CASE	=				torch.max(torch.abs(our_output - their_output  )  ).item()
			print(f"""max_absolute_diff = {max_absolute_diff}"""  )  # ~ 1e-7
			_SCREAMING_SNAKE_CASE	=				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"  )

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


if __name__ == "__main__":
	lowerCamelCase_			=						argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--xmod_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.'
	)
	lowerCamelCase_			=						parser.parse_args()
	convert_xmod_checkpoint_to_pytorch(
	    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
	)







'''simple docstring'''



def 			SCREAMING_SNAKE_CASE_			(							__A    :			int  )			->			int:
   if n == 1 or not isinstance(__A	,					__A  ):
      return 0
   elif n == 2:
      return 1
   else:
      _SCREAMING_SNAKE_CASE	=				[0, 1]
      for i in range(2	,					n + 1  ):
         sequence.append(sequence[i - 1] + sequence[i - 2]  )

      return sequence[n]
def 			SCREAMING_SNAKE_CASE_			(							__A    :			int  )			->			int:
   _SCREAMING_SNAKE_CASE	=				0
   _SCREAMING_SNAKE_CASE	=				2

   while digits < n:
      index += 1
      _SCREAMING_SNAKE_CASE	=				len(str(fibonacci(__A  )  )  )

   return index
def 			SCREAMING_SNAKE_CASE_			(							__A    :			int = 10_00  )			->			int:
   return fibonacci_digits_index(__A  )


if __name__ == "__main__":
 print(solution(int(str(input()).strip())))

