





'''simple docstring'''

import argparse
import inspect
import logging
from typing import Tuple

import numpy as np
import torch

from transformers import (
    AutoTokenizer,
    BloomForCausalLM,
    BloomTokenizerFast,
    CTRLLMHeadModel,
    CTRLTokenizer,
    GenerationMixin,
    GPTaLMHeadModel,
    GPTaTokenizer,
    GPTJForCausalLM,
    LlamaForCausalLM,
    LlamaTokenizer,
    OpenAIGPTLMHeadModel,
    OpenAIGPTTokenizer,
    OPTForCausalLM,
    TransfoXLLMHeadModel,
    TransfoXLTokenizer,
    XLMTokenizer,
    XLMWithLMHeadModel,
    XLNetLMHeadModel,
    XLNetTokenizer,
)
from transformers.modeling_outputs import CausalLMOutputWithPast


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
lowerCAmelCase__      :   str      	=			logging.getLogger(__name__)

lowerCAmelCase__      :   Tuple      	=			int(1_00_00)  # Hardcoded max length to avoid infinite loop

lowerCAmelCase__      :   List[Any]      	=			{
    "gpt2": (GPTaLMHeadModel, GPTaTokenizer),
    "ctrl": (CTRLLMHeadModel, CTRLTokenizer),
    "openai-gpt": (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
    "xlnet": (XLNetLMHeadModel, XLNetTokenizer),
    "transfo-xl": (TransfoXLLMHeadModel, TransfoXLTokenizer),
    "xlm": (XLMWithLMHeadModel, XLMTokenizer),
    "gptj": (GPTJForCausalLM, AutoTokenizer),
    "bloom": (BloomForCausalLM, BloomTokenizerFast),
    "llama": (LlamaForCausalLM, LlamaTokenizer),
    "opt": (OPTForCausalLM, GPTaTokenizer),
}

# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
# in https://github.com/rusiaaman/XLNet-gen#methodology
# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
lowerCAmelCase__      :   Optional[Any]      	=			"In 1991, the remains of Russian Tsar Nicholas II and his family\n(except for Alexei and Maria) are discovered.\nThe voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the\nremainder of the story. 1883 Western Siberia,\na young Grigori Rasputin is asked by his father and a group of men to perform magic.\nRasputin has a vision and denounces one of the men as a horse thief. Although his\nfather initially slaps him for making such an accusation, Rasputin watches as the\nman is chased outside and beaten. Twenty years later, Rasputin sees a vision of\nthe Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,\nwith people, even a bishop, begging for his blessing. <eod> </s> <eos>"


def 						__UpperCamelCase						(			_UpperCAmelCase  ):
		np.random.seed(args.seed  )
		torch.manual_seed(args.seed  )
		if args.n_gpu > 0:
				torch.cuda.manual_seed_all(args.seed  )


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  ):
		if args.temperature > 0.7:
				logger.info("CTRL typically works better with lower temperatures (and lower top_k)."  )

		__UpperCAmelCase	:							int     =     tokenizer.encode(_UpperCAmelCase,       add_special_tokens=_UpperCAmelCase  )
		if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()  ):
				logger.info("WARNING! You are not starting your generation from a control code so you won't get good results"  )
		return prompt_text


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  ):
		# kwargs = {"language": None, "mask_token_id": None}

		# Set the language
		__UpperCAmelCase	:							int     =     hasattr(model.config,       "use_lang_emb"  ) and model.config.use_lang_emb
		if hasattr(model.config,       "lang2id"  ) and use_lang_emb:
				__UpperCAmelCase	:							List[str]     =     model.config.langaid.keys()
				if args.xlm_language in available_languages:
						__UpperCAmelCase	:							Any     =     args.xlm_language
				else:
						__UpperCAmelCase	:							Tuple     =     None
						while language not in available_languages:
								__UpperCAmelCase	:							Any     =     input("Using XLM. Select language in " + str(list(_UpperCAmelCase  )  ) + " >>> "  )

				__UpperCAmelCase	:							List[str]     =     model.config.langaid[language]
				# kwargs["language"] = tokenizer.lang2id[language]

		# TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
		# XLM masked-language modeling (MLM) models need masked token
		# is_xlm_mlm = "mlm" in args.model_name_or_path
		# if is_xlm_mlm:
		#     kwargs["mask_token_id"] = tokenizer.mask_token_id

		return prompt_text


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  ):
		__UpperCAmelCase	:							str     =     args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
		__UpperCAmelCase	:							Union[str, Any]     =     prefix + prompt_text
		return prompt_text


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  ):
		__UpperCAmelCase	:							List[Any]     =     args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
		__UpperCAmelCase	:							List[Any]     =     prefix + prompt_text
		return prompt_text


lowerCAmelCase__      :   Any      	=			{
    "ctrl": prepare_ctrl_input,
    "xlm": prepare_xlm_input,
    "xlnet": prepare_xlnet_input,
    "transfo-xl": prepare_transfoxl_input,
}


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase  ):
		if length < 0 and max_sequence_length > 0:
				__UpperCAmelCase	:							Any     =     max_sequence_length
		elif 0 < max_sequence_length < length:
				__UpperCAmelCase	:							Tuple     =     max_sequence_length  # No generation bigger than model size
		elif length < 0:
				__UpperCAmelCase	:							str     =     MAX_LENGTH  # avoid infinite loop
		return length


def 						__UpperCamelCase						(			_UpperCAmelCase  ):
		__UpperCAmelCase	:							int     =     None
		if hasattr(_UpperCAmelCase,       "hidden_size"  ):
				__UpperCAmelCase	:							Optional[Any]     =     model_config.hidden_size
		elif hasattr(_UpperCAmelCase,       "n_embed"  ):
				__UpperCAmelCase	:							List[Any]     =     model_config.n_embed
		elif hasattr(_UpperCAmelCase,       "n_embd"  ):
				__UpperCAmelCase	:							List[Any]     =     model_config.n_embd

		__UpperCAmelCase	:							str     =     None
		if hasattr(_UpperCAmelCase,       "num_attention_heads"  ):
				__UpperCAmelCase	:							int     =     model_config.num_attention_heads
		elif hasattr(_UpperCAmelCase,       "n_head"  ):
				__UpperCAmelCase	:							Dict     =     model_config.n_head

		if embedding_size is None or num_head is None or num_head == 0:
				raise ValueError("Check the model config"  )

		__UpperCAmelCase	:							List[Any]     =     int(embedding_size / num_head  )
		if hasattr(_UpperCAmelCase,       "n_layer"  ):
				__UpperCAmelCase	:							int     =     model_config.n_layer
		elif hasattr(_UpperCAmelCase,       "num_hidden_layers"  ):
				__UpperCAmelCase	:							Dict     =     model_config.num_hidden_layers
		else:
				raise ValueError("Number of hidden layers couldn't be determined from the model config"  )

		return num_layer, num_head, num_embedding_size_per_head


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  ):
		__UpperCAmelCase  ,			__UpperCAmelCase  ,			__UpperCAmelCase	:							Union[str, Any]     =     sparse_model_config(model.config  )
		if model.config.model_type == "bloom":
				__UpperCAmelCase	:							Union[str, Any]     =     tuple(
				    (
				        torch.empty(int(num_attention_heads * batch_size  ),       _UpperCAmelCase,       _UpperCAmelCase  )
				        .to(model.dtype  )
				        .to(model.device  ),
				        torch.empty(int(num_attention_heads * batch_size  ),       _UpperCAmelCase,       _UpperCAmelCase  )
				        .to(model.dtype  )
				        .to(model.device  ),
				    )
				    for _ in range(_UpperCAmelCase  )  )
		else:
				__UpperCAmelCase	:							Any     =     tuple(
				    (
				        torch.empty(_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  )
				        .to(model.dtype  )
				        .to(model.device  ),
				        torch.empty(_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  )
				        .to(model.dtype  )
				        .to(model.device  ),
				    )
				    for _ in range(_UpperCAmelCase  )  )
		return past_key_values


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  ):
		__UpperCAmelCase	:							int     =     len(_UpperCAmelCase  )
		__UpperCAmelCase	:							Optional[int]     =     tokenizer.batch_encode_plus(_UpperCAmelCase,       return_tensors="pt"  )
		__UpperCAmelCase	:							Union[str, Any]     =     dummy_input.to(model.device  )
		if model.config.use_cache:
				__UpperCAmelCase	:							Tuple     =     generate_past_key_values(_UpperCAmelCase,       _UpperCAmelCase,       1  )
		__UpperCAmelCase	:							str     =     torch.cat(
		    [
		        torch.zeros(dummy_input["attention_mask"].shape[0],       1  )
		        .to(dummy_input["attention_mask"].dtype  )
		        .to(model.device  ),
		        dummy_input["attention_mask"],
		    ],       -1,       )
		return dummy_input


class      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	('''_optimized''', '''_default''')
					def __init__(   self       :      Any		,	UpperCAmelCase_       :      int		,	UpperCAmelCase_       :      int		):







							"""simple docstring"""
							__UpperCAmelCase	:							Tuple     =     optimized
							__UpperCAmelCase	:							List[str]     =     default
					def __call__(   self       :      int		,	*UpperCAmelCase_       :      List[str]		,	**UpperCAmelCase_       :      Tuple		):







							"""simple docstring"""
							if kwargs["past_key_values"] is None and self._default.config.use_cache:
									__UpperCAmelCase	:							Dict     =     generate_past_key_values(self._default		,	kwargs["input_ids"].shape[0]		,	0		)
							kwargs.pop("position_ids"		,	UpperCAmelCase_		)
							for k in list(kwargs.keys()		):
									if kwargs[k] is None or isinstance(kwargs[k]		,	UpperCAmelCase_		):
											kwargs.pop(UpperCAmelCase_		)
							__UpperCAmelCase	:							Any     =     self._optimized(**UpperCAmelCase_		)
							__UpperCAmelCase	:							Union[str, Any]     =     outputs[0]
							__UpperCAmelCase	:							Any     =     outputs[1]
							__UpperCAmelCase	:							List[str]     =     CausalLMOutputWithPast(
							    loss=UpperCAmelCase_		,	logits=UpperCAmelCase_		,	past_key_values=UpperCAmelCase_		,	hidden_states=UpperCAmelCase_		,	attentions=UpperCAmelCase_		,	)
							return fixed_output
					def __getattr__(   self       :      List[Any]		,	UpperCAmelCase_       :      List[str]		):







							"""simple docstring"""
							return getattr(self._default		,	UpperCAmelCase_		)
					def 		lowerCamelCase_   (   self       :      Optional[Any]		,	UpperCAmelCase_       :      Optional[Any]		,	UpperCAmelCase_       :      Optional[Any]=None		,	UpperCAmelCase_       :      List[str]=None		,	UpperCAmelCase_       :      Optional[int]=None		,	**UpperCAmelCase_       :      List[str]		):







							"""simple docstring"""
							return self._default.prepare_inputs_for_generation(
							    UpperCAmelCase_		,	past_key_values=UpperCAmelCase_		,	inputs_embeds=UpperCAmelCase_		,	use_cache=UpperCAmelCase_		,	**UpperCAmelCase_		)






					def 		lowerCamelCase_   (   self       :      int		,	UpperCAmelCase_       :      Tuple[Tuple[torch.Tensor]]		,	UpperCAmelCase_       :      torch.Tensor		):







							"""simple docstring"""
							return self._default._reorder_cache(UpperCAmelCase_		,	UpperCAmelCase_		)






def 						__UpperCamelCase						(			):
		__UpperCAmelCase	:							Union[str, Any]     =     argparse.ArgumentParser()
		parser.add_argument(
		    "--model_type",       default=_UpperCAmelCase,       type=_UpperCAmelCase,       required=_UpperCAmelCase,       help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()  ),       )
		parser.add_argument(
		    "--model_name_or_path",       default=_UpperCAmelCase,       type=_UpperCAmelCase,       required=_UpperCAmelCase,       help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(MODEL_CLASSES.keys()  ),       )

		parser.add_argument("--prompt",       type=_UpperCAmelCase,       default=""  )
		parser.add_argument("--length",       type=_UpperCAmelCase,       default=20  )
		parser.add_argument("--stop_token",       type=_UpperCAmelCase,       default=_UpperCAmelCase,       help="Token at which text generation is stopped"  )

		parser.add_argument(
		    "--temperature",       type=_UpperCAmelCase,       default=1.0,       help="temperature of 1.0 has no effect, lower tend toward greedy sampling",       )
		parser.add_argument(
		    "--repetition_penalty",       type=_UpperCAmelCase,       default=1.0,       help="primarily useful for CTRL model; in that case, use 1.2"  )
		parser.add_argument("--k",       type=_UpperCAmelCase,       default=0  )
		parser.add_argument("--p",       type=_UpperCAmelCase,       default=0.9  )

		parser.add_argument("--prefix",       type=_UpperCAmelCase,       default="",       help="Text added prior to input."  )
		parser.add_argument("--padding_text",       type=_UpperCAmelCase,       default="",       help="Deprecated, the use of `--prefix` is preferred."  )
		parser.add_argument("--xlm_language",       type=_UpperCAmelCase,       default="",       help="Optional language when used with the XLM model."  )

		parser.add_argument("--seed",       type=_UpperCAmelCase,       default=42,       help="random seed for initialization"  )
		parser.add_argument("--no_cuda",       action="store_true",       help="Avoid using CUDA when available"  )
		parser.add_argument("--num_return_sequences",       type=_UpperCAmelCase,       default=1,       help="The number of samples to generate."  )
		parser.add_argument(
		    "--fp16",       action="store_true",       help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",       )
		parser.add_argument("--jit",       action="store_true",       help="Whether or not to use jit trace to accelerate inference"  )
		__UpperCAmelCase	:							List[str]     =     parser.parse_args()

		__UpperCAmelCase	:							Any     =     torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"  )
		__UpperCAmelCase	:							Tuple     =     0 if args.no_cuda else torch.cuda.device_count()

		logger.warning(F"device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}"  )

		set_seed(_UpperCAmelCase  )

		# Initialize the model and tokenizer
		try:
				__UpperCAmelCase	:							Any     =     args.model_type.lower()
				__UpperCAmelCase  ,			__UpperCAmelCase	:							List[Any]     =     MODEL_CLASSES[args.model_type]
		except KeyError:
				raise KeyError("the model {} you specified is not supported. You are welcome to add it and open a PR :)"  )

		__UpperCAmelCase	:							List[Any]     =     tokenizer_class.from_pretrained(args.model_name_or_path  )
		if tokenizer.pad_token is None:
				__UpperCAmelCase	:							Optional[Any]     =     tokenizer.eos_token
		__UpperCAmelCase	:							str     =     model_class.from_pretrained(args.model_name_or_path  )
		model.to(args.device  )

		if args.fpaa:
				model.half()
		__UpperCAmelCase	:							int     =     getattr(model.config,       "max_position_embeddings",       0  )
		__UpperCAmelCase	:							int     =     adjust_length_to_model(args.length,       max_sequence_length=_UpperCAmelCase  )
		logger.info(_UpperCAmelCase  )

		__UpperCAmelCase	:							str     =     args.prompt if args.prompt else input("Model prompt >>> "  )

		# Different models need different input formatting and/or extra arguments
		__UpperCAmelCase	:							List[str]     =     args.model_type in PREPROCESSING_FUNCTIONS.keys()
		if requires_preprocessing:
				__UpperCAmelCase	:							Dict     =     PREPROCESSING_FUNCTIONS.get(args.model_type  )
				__UpperCAmelCase	:							Optional[Any]     =     prepare_input(_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  )

				if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
						__UpperCAmelCase	:							Any     =     {"add_space_before_punct_symbol": True}
				else:
						__UpperCAmelCase	:							List[Any]     =     {}

				__UpperCAmelCase	:							str     =     tokenizer.encode(
				    _UpperCAmelCase,       add_special_tokens=_UpperCAmelCase,       return_tensors="pt",       **_UpperCAmelCase  )
		else:
				__UpperCAmelCase	:							List[str]     =     args.prefix if args.prefix else args.padding_text
				__UpperCAmelCase	:							Union[str, Any]     =     tokenizer.encode(prefix + prompt_text,       add_special_tokens=_UpperCAmelCase,       return_tensors="pt"  )
		__UpperCAmelCase	:							Optional[Any]     =     encoded_prompt.to(args.device  )

		if encoded_prompt.size()[-1] == 0:
				__UpperCAmelCase	:							Optional[int]     =     None
		else:
				__UpperCAmelCase	:							Any     =     encoded_prompt

		if args.jit:
				__UpperCAmelCase	:							Dict     =     ["enable jit"]
				__UpperCAmelCase	:							Dict     =     prepare_jit_inputs(_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  )
				torch._C._jit_set_texpr_fuser_enabled(_UpperCAmelCase  )
				__UpperCAmelCase	:							Tuple     =     False
				if hasattr(_UpperCAmelCase,       "forward"  ):
						__UpperCAmelCase	:							List[str]     =     inspect.signature(model.forward  )
				else:
						__UpperCAmelCase	:							List[str]     =     inspect.signature(model.__call__  )
				__UpperCAmelCase	:							Optional[int]     =     tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(_UpperCAmelCase,       _UpperCAmelCase  ) is not None  )
				__UpperCAmelCase	:							str     =     torch.jit.trace(_UpperCAmelCase,       _UpperCAmelCase,       strict=_UpperCAmelCase  )
				__UpperCAmelCase	:							str     =     torch.jit.freeze(traced_model.eval()  )
				traced_model(*_UpperCAmelCase  )
				traced_model(*_UpperCAmelCase  )

				__UpperCAmelCase	:							List[str]     =     _ModelFallbackWrapper(_UpperCAmelCase,       _UpperCAmelCase  )

		__UpperCAmelCase	:							Tuple     =     model.generate(
		    input_ids=_UpperCAmelCase,       max_length=args.length + len(encoded_prompt[0]  ),       temperature=args.temperature,       top_k=args.k,       top_p=args.p,       repetition_penalty=args.repetition_penalty,       do_sample=_UpperCAmelCase,       num_return_sequences=args.num_return_sequences,       )

		# Remove the batch dimension when returning multiple sequences
		if len(output_sequences.shape  ) > 2:
				output_sequences.squeeze_()

		__UpperCAmelCase	:							Tuple     =     []

		for generated_sequence_idx, generated_sequence in enumerate(_UpperCAmelCase  ):
				print(F"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="  )
				__UpperCAmelCase	:							List[str]     =     generated_sequence.tolist()

				# Decode text
				__UpperCAmelCase	:							Optional[int]     =     tokenizer.decode(_UpperCAmelCase,       clean_up_tokenization_spaces=_UpperCAmelCase  )

				# Remove all text after the stop token
				__UpperCAmelCase	:							Union[str, Any]     =     text[: text.find(args.stop_token  ) if args.stop_token else None]

				# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
				__UpperCAmelCase	:							Dict     =     (
				    prompt_text + text[len(tokenizer.decode(encoded_prompt[0],       clean_up_tokenization_spaces=_UpperCAmelCase  )  ) :]
				)

				generated_sequences.append(_UpperCAmelCase  )
				print(_UpperCAmelCase  )

		return generated_sequences


if __name__ == "__main__":
			main()











'''simple docstring'''

from __future__ import annotations

lowerCAmelCase__      :   Tuple      	=			{1: 1}


def 						__UpperCamelCase						(			_UpperCAmelCase  ):
		if n in COLLATZ_SEQUENCE_LENGTHS:
				return COLLATZ_SEQUENCE_LENGTHS[n]
		__UpperCAmelCase	:							int     =     n // 2 if n % 2 == 0 else 3 * n + 1
		__UpperCAmelCase	:							str     =     collatz_sequence_length(_UpperCAmelCase  ) + 1
		__UpperCAmelCase	:							Union[str, Any]     =     sequence_length
		return sequence_length


def 						__UpperCamelCase						(			_UpperCAmelCase = 1000000  ):
		__UpperCAmelCase	:							Union[str, Any]     =     max((collatz_sequence_length(_UpperCAmelCase  ), i) for i in range(1,       _UpperCAmelCase  )  )
		return result[1]


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




