



"""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 :		List[str]      							=    logging.getLogger(__name__)

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

__A :		Union[str, 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
__A :		Optional[int]      							=    '''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 					A_			(		snake_case_     :       List[str]    ):







    '''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_			(		snake_case_     :       Tuple						,snake_case_     :       str						,snake_case_     :       Union[str, Any]						,snake_case_     :       Optional[Any]    ):







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

    UpperCamelCase				:   Optional[int]          =							tokenizer.encode(snake_case_						,add_special_tokens=snake_case_    )
    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_			(		snake_case_     :       str						,snake_case_     :       Tuple						,snake_case_     :       List[Any]						,snake_case_     :       List[str]    ):







    '''simple docstring'''
    UpperCamelCase				:   str          =							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				:   Tuple          =							args.xlm_language
        else:
            UpperCamelCase				:   int          =							None
            while language not in available_languages:
                UpperCamelCase				:   Any          =							input("""Using XLM. Select language in """ + str(list(snake_case_    )    ) + """ >>> """    )

        UpperCamelCase				:   List[Any]          =							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_			(		snake_case_     :       Union[str, Any]						,snake_case_     :       Optional[int]						,snake_case_     :       Tuple						,snake_case_     :       Tuple    ):







    '''simple docstring'''
    UpperCamelCase				:   Dict          =							args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
    UpperCamelCase				:   Dict          =							prefix + prompt_text
    return prompt_text
def 					A_			(		snake_case_     :       str						,snake_case_     :       Any						,snake_case_     :       Tuple						,snake_case_     :       str    ):







    '''simple docstring'''
    UpperCamelCase				:   Optional[Any]          =							args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
    UpperCamelCase				:   Optional[Any]          =							prefix + prompt_text
    return prompt_text


__A :		str      							=    {
    '''ctrl''': prepare_ctrl_input,
    '''xlm''': prepare_xlm_input,
    '''xlnet''': prepare_xlnet_input,
    '''transfo-xl''': prepare_transfoxl_input,
}
def 					A_			(		snake_case_     :       List[str]						,snake_case_     :       List[Any]    ):







    '''simple docstring'''
    if length < 0 and max_sequence_length > 0:
        UpperCamelCase				:   int          =							max_sequence_length
    elif 0 < max_sequence_length < length:
        UpperCamelCase				:   Tuple          =							max_sequence_length  # No generation bigger than model size
    elif length < 0:
        UpperCamelCase				:   Any          =							MAX_LENGTH  # avoid infinite loop
    return length
def 					A_			(		snake_case_     :       List[str]    ):







    '''simple docstring'''
    UpperCamelCase				:   Dict          =							None
    if hasattr(snake_case_						,"""hidden_size"""    ):
        UpperCamelCase				:   Any          =							model_config.hidden_size
    elif hasattr(snake_case_						,"""n_embed"""    ):
        UpperCamelCase				:   List[str]          =							model_config.n_embed
    elif hasattr(snake_case_						,"""n_embd"""    ):
        UpperCamelCase				:   List[Any]          =							model_config.n_embd

    UpperCamelCase				:   Optional[Any]          =							None
    if hasattr(snake_case_						,"""num_attention_heads"""    ):
        UpperCamelCase				:   int          =							model_config.num_attention_heads
    elif hasattr(snake_case_						,"""n_head"""    ):
        UpperCamelCase				:   int          =							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				:   Any          =							int(embedding_size / num_head    )
    if hasattr(snake_case_						,"""n_layer"""    ):
        UpperCamelCase				:   int          =							model_config.n_layer
    elif hasattr(snake_case_						,"""num_hidden_layers"""    ):
        UpperCamelCase				:   Tuple          =							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_			(		snake_case_     :       str						,snake_case_     :       int						,snake_case_     :       Any    ):







    '''simple docstring'''
    UpperCamelCase				:   Union[str, Any]          =							sparse_model_config(model.config    )
    if model.config.model_type == "bloom":
        UpperCamelCase				:   int          =							tuple(
            (
                torch.empty(int(num_attention_heads * batch_size    )						,snake_case_						,snake_case_    )
                .to(model.dtype    )
                .to(model.device    ),
                torch.empty(int(num_attention_heads * batch_size    )						,snake_case_						,snake_case_    )
                .to(model.dtype    )
                .to(model.device    ),
            )
            for _ in range(snake_case_    )    )
    else:
        UpperCamelCase				:   List[Any]          =							tuple(
            (
                torch.empty(snake_case_						,snake_case_						,snake_case_						,snake_case_    )
                .to(model.dtype    )
                .to(model.device    ),
                torch.empty(snake_case_						,snake_case_						,snake_case_						,snake_case_    )
                .to(model.dtype    )
                .to(model.device    ),
            )
            for _ in range(snake_case_    )    )
    return past_key_values
def 					A_			(		snake_case_     :       Optional[Any]						,snake_case_     :       Optional[int]						,snake_case_     :       Tuple    ):







    '''simple docstring'''
    UpperCamelCase				:   int          =							len(snake_case_    )
    UpperCamelCase				:   Optional[Any]          =							tokenizer.batch_encode_plus(snake_case_						,return_tensors="""pt"""    )
    UpperCamelCase				:   Dict          =							dummy_input.to(model.device    )
    if model.config.use_cache:
        UpperCamelCase				:   List[Any]          =							generate_past_key_values(snake_case_						,snake_case_						,1    )
    UpperCamelCase				:   Tuple          =							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 							lowerCamelCase		( _UpperCAmelCase				):
   lowercase       :  str      =				('_optimized', '_default')







   def __init__(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
       UpperCamelCase				:   Dict          =							optimized
       UpperCamelCase				:   Union[str, Any]          =							default







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







   def __getattr__(							self    ,      SCREAMING_SNAKE_CASE_ ):
       return getattr(self._default    ,      SCREAMING_SNAKE_CASE_ )







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      **SCREAMING_SNAKE_CASE_ ):
       return self._default.prepare_inputs_for_generation(
           SCREAMING_SNAKE_CASE_    ,      past_key_values=SCREAMING_SNAKE_CASE_    ,      inputs_embeds=SCREAMING_SNAKE_CASE_    ,      use_cache=SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
       return self._default._reorder_cache(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )







def 					A_			(		):







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

    parser.add_argument("""--prompt"""						,type=snake_case_						,default=""""""    )
    parser.add_argument("""--length"""						,type=snake_case_						,default=2_0    )
    parser.add_argument("""--stop_token"""						,type=snake_case_						,default=snake_case_						,help="""Token at which text generation is stopped"""    )

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

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

    parser.add_argument("""--seed"""						,type=snake_case_						,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=snake_case_						,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				:   Tuple          =							parser.parse_args()

    UpperCamelCase				:   Tuple          =							torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""    )
    UpperCamelCase				:   Optional[Any]          =							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(snake_case_    )

    # Initialize the model and tokenizer
    try:
        UpperCamelCase				:   Optional[int]          =							args.model_type.lower()
        UpperCamelCase				:   Optional[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				:   Any          =							tokenizer_class.from_pretrained(args.model_name_or_path    )
    if tokenizer.pad_token is None:
        UpperCamelCase				:   Union[str, Any]          =							tokenizer.eos_token
    UpperCamelCase				:   Union[str, Any]          =							model_class.from_pretrained(args.model_name_or_path    )
    model.to(args.device    )

    if args.fpaa:
        model.half()
    UpperCamelCase				:   Optional[Any]          =							getattr(model.config						,"""max_position_embeddings"""						,0    )
    UpperCamelCase				:   Dict          =							adjust_length_to_model(args.length						,max_sequence_length=snake_case_    )
    logger.info(snake_case_    )

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

    # Different models need different input formatting and/or extra arguments
    UpperCamelCase				:   Any          =							args.model_type in PREPROCESSING_FUNCTIONS.keys()
    if requires_preprocessing:
        UpperCamelCase				:   str          =							PREPROCESSING_FUNCTIONS.get(args.model_type    )
        UpperCamelCase				:   Union[str, Any]          =							prepare_input(snake_case_						,snake_case_						,snake_case_						,snake_case_    )

        if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
            UpperCamelCase				:   Tuple          =							{"""add_space_before_punct_symbol""": True}
        else:
            UpperCamelCase				:   Union[str, Any]          =							{}

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

    if encoded_prompt.size()[-1] == 0:
        UpperCamelCase				:   List[str]          =							None
    else:
        UpperCamelCase				:   Optional[int]          =							encoded_prompt

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

        UpperCamelCase				:   List[Any]          =							_ModelFallbackWrapper(snake_case_						,snake_case_    )

    UpperCamelCase				:   Any          =							model.generate(
        input_ids=snake_case_						,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=snake_case_						,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				:   List[str]          =							[]

    for generated_sequence_idx, generated_sequence in enumerate(snake_case_    ):
        print(f'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='    )
        UpperCamelCase				:   str          =							generated_sequence.tolist()

        # Decode text
        UpperCamelCase				:   Any          =							tokenizer.decode(snake_case_						,clean_up_tokenization_spaces=snake_case_    )

        # Remove all text after the stop token
        UpperCamelCase				:   Tuple          =							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				:   Union[str, Any]          =							(
            prompt_text + text[len(tokenizer.decode(encoded_prompt[0]						,clean_up_tokenization_spaces=snake_case_    )    ) :]
        )

        generated_sequences.append(snake_case_    )
        print(snake_case_    )

    return generated_sequences


if __name__ == "__main__":
  main()










"""simple docstring"""




def 					A_			(		snake_case_     :       Tuple						,snake_case_     :       Optional[Any]						,snake_case_     :       Optional[Any]    ):







    '''simple docstring'''
    assert b != 0, "This cannot accept modulo that is == 0"
    if n == 0:
        return 1

    if n % 2 == 1:
        return (bin_exp_mod(snake_case_						,n - 1						,snake_case_    ) * a) % b

    UpperCamelCase				:   List[Any]          =							bin_exp_mod(snake_case_						,n / 2						,snake_case_    )
    return (r * r) % b


if __name__ == "__main__":
  try:
    __A :		Any      							=    int(input('''Enter Base : ''').strip())
    __A :		Any      							=    int(input('''Enter Power : ''').strip())
    __A :		List[Any]      							=    int(input('''Enter Modulo : ''').strip())
  except ValueError:
    print('''Invalid literal for integer''')

  print(bin_exp_mod(BASE, POWER, MODULO))




