






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,
)
__A          = logging.getLogger(__name__)

__A          = int(10000)  # Hardcoded max length to avoid infinite loop

__A          = {
    '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
__A          = '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    __A    (   _lowercase       ):

					'''simple docstring'''







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




def    __A    (   _lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







					if args.temperature > 0.7:
										logger.info('''CTRL typically works better with lower temperatures (and lower top_k).'''       )

					_A									=       tokenizer.encode(_lowercase    ,	add_special_tokens=_lowercase       )
					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    __A    (   _lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       hasattr(model.config    ,	'''use_lang_emb'''       ) and model.config.use_lang_emb
					if hasattr(model.config    ,	'''lang2id'''       ) and use_lang_emb:
										_A									=       model.config.langaid.keys()
										if args.xlm_language in available_languages:
															_A									=       args.xlm_language
										else:
															_A									=       None
															while language not in available_languages:
																				_A									=       input('''Using XLM. Select language in ''' + str(list(_lowercase       )       ) + ''' >>> '''       )

										_A									=       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    __A    (   _lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
					_A									=       prefix + prompt_text
					return prompt_text




def    __A    (   _lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
					_A									=       prefix + prompt_text
					return prompt_text


__A          = {
    'ctrl': prepare_ctrl_input,
    'xlm': prepare_xlm_input,
    'xlnet': prepare_xlnet_input,
    'transfo-xl': prepare_transfoxl_input,
}




def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					if length < 0 and max_sequence_length > 0:
										_A									=       max_sequence_length
					elif 0 < max_sequence_length < length:
										_A									=       max_sequence_length  # No generation bigger than model size
					elif length < 0:
										_A									=       MAX_LENGTH  # avoid infinite loop
					return length




def    __A    (   _lowercase       ):

					'''simple docstring'''







					_A									=       None
					if hasattr(_lowercase    ,	'''hidden_size'''       ):
										_A									=       model_config.hidden_size
					elif hasattr(_lowercase    ,	'''n_embed'''       ):
										_A									=       model_config.n_embed
					elif hasattr(_lowercase    ,	'''n_embd'''       ):
										_A									=       model_config.n_embd

					_A									=       None
					if hasattr(_lowercase    ,	'''num_attention_heads'''       ):
										_A									=       model_config.num_attention_heads
					elif hasattr(_lowercase    ,	'''n_head'''       ):
										_A									=       model_config.n_head

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

					_A									=       int(embedding_size / num_head       )
					if hasattr(_lowercase    ,	'''n_layer'''       ):
										_A									=       model_config.n_layer
					elif hasattr(_lowercase    ,	'''num_hidden_layers'''       ):
										_A									=       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    __A    (   _lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A							,_A							,_A									=       sparse_model_config(model.config       )
					if model.config.model_type == "bloom":
										_A									=       tuple(
										    (
										        torch.empty(int(num_attention_heads * batch_size       )    ,	_lowercase    ,	_lowercase       )
										        .to(model.dtype       )
										        .to(model.device       ),
										        torch.empty(int(num_attention_heads * batch_size       )    ,	_lowercase    ,	_lowercase       )
										        .to(model.dtype       )
										        .to(model.device       ),
										    )
										    for _ in range(_lowercase       )       )
					else:
										_A									=       tuple(
										    (
										        torch.empty(_lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase       )
										        .to(model.dtype       )
										        .to(model.device       ),
										        torch.empty(_lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase       )
										        .to(model.dtype       )
										        .to(model.device       ),
										    )
										    for _ in range(_lowercase       )       )
					return past_key_values




def    __A    (   _lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       len(_lowercase       )
					_A									=       tokenizer.batch_encode_plus(_lowercase    ,	return_tensors='''pt'''       )
					_A									=       dummy_input.to(model.device       )
					if model.config.use_cache:
										_A									=       generate_past_key_values(_lowercase    ,	_lowercase    ,	1       )
					_A									=       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"""

							A_       =    ("_optimized", "_default")

							def __init__(     self:      Optional[int]						,   __A:      Tuple						,   __A:      List[Any]      )			-> Optional[int]:
												_A									=       optimized
												_A									=       default

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

							def __getattr__(     self:      Dict						,   __A:      Any      )			-> Any:
												return getattr(self._default						,   __A      )

							def 					__A       (     self:      Optional[int]						,   __A:      Any						,   __A:      Optional[Any]=None						,   __A:      Union[str, Any]=None						,   __A:      Union[str, Any]=None						,   **__A:      Optional[int]      )			-> Tuple:
												return self._default.prepare_inputs_for_generation(
												    __A						,   past_key_values=__A						,   inputs_embeds=__A						,   use_cache=__A						,   **__A      )



							def 					__A       (     self:      Tuple						,   __A:      Tuple[Tuple[torch.Tensor]]						,   __A:      torch.Tensor      )			-> Tuple[Tuple[torch.Tensor]]:
												return self._default._reorder_cache(__A						,   __A      )




def    __A    (   ):

					'''simple docstring'''







					_A									=       argparse.ArgumentParser()
					parser.add_argument(
					    '''--model_type'''    ,	default=_lowercase    ,	type=_lowercase    ,	required=_lowercase    ,	help='''Model type selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys()       )    ,	)
					parser.add_argument(
					    '''--model_name_or_path'''    ,	default=_lowercase    ,	type=_lowercase    ,	required=_lowercase    ,	help='''Path to pre-trained model or shortcut name selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys()       )    ,	)

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

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

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

					parser.add_argument('''--seed'''    ,	type=_lowercase    ,	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=_lowercase    ,	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'''       )
					_A									=       parser.parse_args()

					_A									=       torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''       )
					_A									=       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(_lowercase       )

					# Initialize the model and tokenizer
					try:
										_A									=       args.model_type.lower()
										_A							,_A									=       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 :)'''       )

					_A									=       tokenizer_class.from_pretrained(args.model_name_or_path       )
					if tokenizer.pad_token is None:
										_A									=       tokenizer.eos_token
					_A									=       model_class.from_pretrained(args.model_name_or_path       )
					model.to(args.device       )

					if args.fpaa:
										model.half()
					_A									=       getattr(model.config    ,	'''max_position_embeddings'''    ,	0       )
					_A									=       adjust_length_to_model(args.length    ,	max_sequence_length=_lowercase       )
					logger.info(_lowercase       )

					_A									=       args.prompt if args.prompt else input('''Model prompt >>> '''       )

					# Different models need different input formatting and/or extra arguments
					_A									=       args.model_type in PREPROCESSING_FUNCTIONS.keys()
					if requires_preprocessing:
										_A									=       PREPROCESSING_FUNCTIONS.get(args.model_type       )
										_A									=       prepare_input(_lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase       )

										if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
															_A									=       {'''add_space_before_punct_symbol''': True}
										else:
															_A									=       {}

										_A									=       tokenizer.encode(
										    _lowercase    ,	add_special_tokens=_lowercase    ,	return_tensors='''pt'''    ,	**_lowercase       )
					else:
										_A									=       args.prefix if args.prefix else args.padding_text
										_A									=       tokenizer.encode(prefix + prompt_text    ,	add_special_tokens=_lowercase    ,	return_tensors='''pt'''       )
					_A									=       encoded_prompt.to(args.device       )

					if encoded_prompt.size()[-1] == 0:
										_A									=       None
					else:
										_A									=       encoded_prompt

					if args.jit:
										_A									=       ['''enable jit''']
										_A									=       prepare_jit_inputs(_lowercase    ,	_lowercase    ,	_lowercase       )
										torch._C._jit_set_texpr_fuser_enabled(_lowercase       )
										_A									=       False
										if hasattr(_lowercase    ,	'''forward'''       ):
															_A									=       inspect.signature(model.forward       )
										else:
															_A									=       inspect.signature(model.__call__       )
										_A									=       tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(_lowercase    ,	_lowercase       ) is not None       )
										_A									=       torch.jit.trace(_lowercase    ,	_lowercase    ,	strict=_lowercase       )
										_A									=       torch.jit.freeze(traced_model.eval()       )
										traced_model(*_lowercase       )
										traced_model(*_lowercase       )

										_A									=       _ModelFallbackWrapper(_lowercase    ,	_lowercase       )

					_A									=       model.generate(
					    input_ids=_lowercase    ,	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=_lowercase    ,	num_return_sequences=args.num_return_sequences    ,	)

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

					_A									=       []

					for generated_sequence_idx, generated_sequence in enumerate(_lowercase       ):
										print(f"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""       )
										_A									=       generated_sequence.tolist()

										# Decode text
										_A									=       tokenizer.decode(_lowercase    ,	clean_up_tokenization_spaces=_lowercase       )

										# Remove all text after the stop token
										_A									=       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
										_A									=       (
										    prompt_text + text[len(tokenizer.decode(encoded_prompt[0]    ,	clean_up_tokenization_spaces=_lowercase       )       ) :]
										)

										generated_sequences.append(_lowercase       )
										print(_lowercase       )

					return generated_sequences


if __name__ == "__main__":
				main()











from math import factorial




def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					return factorial(_lowercase       ) / (factorial(_lowercase       ) * factorial(n - r       ))




def    __A    (   ):

					'''simple docstring'''







					_A									=       0

					for i in range(1    ,	1_01       ):
										for j in range(1    ,	i + 1       ):
															if combinations(_lowercase    ,	_lowercase       ) > 1e6:
																				total += 1
					return total


if __name__ == "__main__":
				print(solution())



