



'''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,
)
a  :     Any       =       logging.getLogger(__name__)

a  :     List[str]       =       int(1_0_0_0_0)  # Hardcoded max length to avoid infinite loop

a  :     int       =       {
    """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  :     List[Any]       =       """In 1991, the remains of Russian Tsar Nicholas II and his family
(except for Alexei and Maria) are discovered.
The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
remainder of the story. 1883 Western Siberia,
a young Grigori Rasputin is asked by his father and a group of men to perform magic.
Rasputin has a vision and denounces one of the men as a horse thief. Although his
father initially slaps him for making such an accusation, Rasputin watches as the
man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
with people, even a bishop, begging for his blessing. <eod> </s> <eos>"""



def  __lowerCamelCase    (   _lowercase						) ->     List[Any]:
							np.random.seed(args.seed						)
							torch.manual_seed(args.seed						)
							if args.n_gpu > 0:
														torch.cuda.manual_seed_all(args.seed						)



def  __lowerCamelCase    (   _lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase						) ->     List[Any]:
							if args.temperature > 0.7:
														logger.info("""CTRL typically works better with lower temperatures (and lower top_k)."""						)

							UpperCAmelCase	:						Union[str, Any]					=						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  __lowerCamelCase    (   _lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase						) ->     List[Any]:
							# 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	:						str					=						model.config.langaid.keys()
														if args.xlm_language in available_languages:
																					UpperCAmelCase	:						List[str]					=						args.xlm_language
														else:
																					UpperCAmelCase	:						Optional[Any]					=						None
																					while language not in available_languages:
																												UpperCAmelCase	:						Optional[int]					=						input("""Using XLM. Select language in """ + str(list(_lowercase						)						) + """ >>> """						)

														UpperCAmelCase	:						int					=						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  __lowerCamelCase    (   _lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase						) ->     Tuple:
							UpperCAmelCase	:						Union[str, Any]					=						args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
							UpperCAmelCase	:						str					=						prefix + prompt_text
							return prompt_text



def  __lowerCamelCase    (   _lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase						) ->     str:
							UpperCAmelCase	:						int					=						args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
							UpperCAmelCase	:						int					=						prefix + prompt_text
							return prompt_text


a  :     Optional[Any]       =       {
    """ctrl""": prepare_ctrl_input,
    """xlm""": prepare_xlm_input,
    """xlnet""": prepare_xlnet_input,
    """transfo-xl""": prepare_transfoxl_input,
}



def  __lowerCamelCase    (   _lowercase      ,							_lowercase						) ->     Tuple:
							if length < 0 and max_sequence_length > 0:
														UpperCAmelCase	:						Optional[Any]					=						max_sequence_length
							elif 0 < max_sequence_length < length:
														UpperCAmelCase	:						Optional[int]					=						max_sequence_length  # No generation bigger than model size
							elif length < 0:
														UpperCAmelCase	:						Union[str, Any]					=						MAX_LENGTH  # avoid infinite loop
							return length



def  __lowerCamelCase    (   _lowercase						) ->     Dict:
							UpperCAmelCase	:						List[Any]					=						None
							if hasattr(_lowercase      ,							"""hidden_size"""						):
														UpperCAmelCase	:						List[str]					=						model_config.hidden_size
							elif hasattr(_lowercase      ,							"""n_embed"""						):
														UpperCAmelCase	:						Tuple					=						model_config.n_embed
							elif hasattr(_lowercase      ,							"""n_embd"""						):
														UpperCAmelCase	:						Any					=						model_config.n_embd

							UpperCAmelCase	:						Union[str, Any]					=						None
							if hasattr(_lowercase      ,							"""num_attention_heads"""						):
														UpperCAmelCase	:						List[str]					=						model_config.num_attention_heads
							elif hasattr(_lowercase      ,							"""n_head"""						):
														UpperCAmelCase	:						List[str]					=						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	:						Union[str, Any]					=						int(embedding_size / num_head						)
							if hasattr(_lowercase      ,							"""n_layer"""						):
														UpperCAmelCase	:						List[Any]					=						model_config.n_layer
							elif hasattr(_lowercase      ,							"""num_hidden_layers"""						):
														UpperCAmelCase	:						int					=						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  __lowerCamelCase    (   _lowercase      ,							_lowercase      ,							_lowercase						) ->     Tuple:
							UpperCAmelCase	,	UpperCAmelCase	,	UpperCAmelCase	:						Optional[Any]					=						sparse_model_config(model.config						)
							if model.config.model_type == "bloom":
														UpperCAmelCase	:						int					=						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:
														UpperCAmelCase	:						Dict					=						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  __lowerCamelCase    (   _lowercase      ,							_lowercase      ,							_lowercase						) ->     Optional[Any]:
							UpperCAmelCase	:						int					=						len(_lowercase						)
							UpperCAmelCase	:						Any					=						tokenizer.batch_encode_plus(_lowercase      ,							return_tensors="""pt"""						)
							UpperCAmelCase	:						str					=						dummy_input.to(model.device						)
							if model.config.use_cache:
														UpperCAmelCase	:						int					=						generate_past_key_values(_lowercase      ,							_lowercase      ,							1						)
							UpperCAmelCase	:						int					=						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      UpperCamelCase_					(				__magic_name__     ):
	lowercase		=					('_optimized', '_default')





	def __init__(    self						,						A						,						A       )     ->     Optional[int]:
								UpperCAmelCase	:						str					=						optimized
								UpperCAmelCase	:						Optional[Any]					=						default





	def __call__(    self						,						*A						,						**A       )     ->     Any:
								if kwargs["past_key_values"] is None and self._default.config.use_cache:
															UpperCAmelCase	:						Union[str, Any]					=						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       )
								UpperCAmelCase	:						List[Any]					=						self._optimized(**A       )
								UpperCAmelCase	:						List[str]					=						outputs[0]
								UpperCAmelCase	:						Any					=						outputs[1]
								UpperCAmelCase	:						str					=						CausalLMOutputWithPast(
								    loss=A						,						logits=A						,						past_key_values=A						,						hidden_states=A						,						attentions=A						,						)
								return fixed_output





	def __getattr__(    self						,						A       )     ->     Optional[Any]:
								return getattr(self._default						,						A       )





	def 					_lowercase(    self						,						A						,						A=None						,						A=None						,						A=None						,						**A       )     ->     str:
								return self._default.prepare_inputs_for_generation(
								    A						,						past_key_values=A						,						inputs_embeds=A						,						use_cache=A						,						**A       )






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




def  __lowerCamelCase    (   ) ->     int:
							UpperCAmelCase	:						Optional[int]					=						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=2_0						)
							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=4_2      ,							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"""						)
							UpperCAmelCase	:						Optional[int]					=						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(_lowercase						)

							# Initialize the model and tokenizer
							try:
														UpperCAmelCase	:						Optional[int]					=						args.model_type.lower()
														UpperCAmelCase	,	UpperCAmelCase	:						int					=						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	:						Dict					=						tokenizer_class.from_pretrained(args.model_name_or_path						)
							if tokenizer.pad_token is None:
														UpperCAmelCase	:						Optional[Any]					=						tokenizer.eos_token
							UpperCAmelCase	:						List[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	:						List[str]					=						adjust_length_to_model(args.length      ,							max_sequence_length=_lowercase						)
							logger.info(_lowercase						)

							UpperCAmelCase	:						List[str]					=						args.prompt if args.prompt else input("""Model prompt >>> """						)

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

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

														UpperCAmelCase	:						Union[str, Any]					=						tokenizer.encode(
														    _lowercase      ,							add_special_tokens=_lowercase      ,							return_tensors="""pt"""      ,							**_lowercase						)
							else:
														UpperCAmelCase	:						int					=						args.prefix if args.prefix else args.padding_text
														UpperCAmelCase	:						str					=						tokenizer.encode(prefix + prompt_text      ,							add_special_tokens=_lowercase      ,							return_tensors="""pt"""						)
							UpperCAmelCase	:						Tuple					=						encoded_prompt.to(args.device						)

							if encoded_prompt.size()[-1] == 0:
														UpperCAmelCase	:						List[Any]					=						None
							else:
														UpperCAmelCase	:						List[str]					=						encoded_prompt

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

														UpperCAmelCase	:						Tuple					=						_ModelFallbackWrapper(_lowercase      ,							_lowercase						)

							UpperCAmelCase	:						Tuple					=						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_()

							UpperCAmelCase	:						Tuple					=						[]

							for generated_sequence_idx, generated_sequence in enumerate(_lowercase						):
														print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''						)
														UpperCAmelCase	:						Any					=						generated_sequence.tolist()

														# Decode text
														UpperCAmelCase	:						Union[str, Any]					=						tokenizer.decode(_lowercase      ,							clean_up_tokenization_spaces=_lowercase						)

														# Remove all text after the stop token
														UpperCAmelCase	:						Dict					=						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	:						Any					=						(
														    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()






'''simple docstring'''




from __future__ import annotations



def  __lowerCamelCase    (   _lowercase						) ->     int | None:
							for r in range(1      ,							_lowercase						):
														UpperCAmelCase	:						Dict					=						[]
														for x in range(n - 1						):
																					UpperCAmelCase	:						List[str]					=						pow(_lowercase      ,							_lowercase      ,							_lowercase						)
																					if val in li:
																												break
																					li.append(_lowercase						)
														else:
																					return r
							return None


if __name__ == "__main__":
							a  :     Dict       =       int(input("""Enter a prime number q: """))
							a  :     Union[str, Any]       =       find_primitive(q)
							if a is None:
														print(F'''Cannot find the primitive for the value: {a!r}''')
							else:
														a  :     str       =       int(input("""Enter private key of A: """))
														a  :     str       =       pow(a, a_private, q)
														a  :     List[Any]       =       int(input("""Enter private key of B: """))
														a  :     str       =       pow(a, b_private, q)

														a  :     Union[str, Any]       =       pow(b_public, a_private, q)
														a  :     List[Any]       =       pow(a_public, b_private, q)

														print("""The key value generated by A is: """, a_secret)
														print("""The key value generated by B is: """, b_secret)

