
"""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		:					str    	=					int(1_00_00)  # Hardcoded max length to avoid infinite loop

a		:					Optional[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		:					List[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 						__magic_name__		(				UpperCamelCase    :		Tuple    )    ->	Tuple:
    np.random.seed(args.seed    )
    torch.manual_seed(args.seed    )
    if args.n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed    )



def 						__magic_name__		(				UpperCamelCase    :		Dict       ,		UpperCamelCase    :		Tuple       ,		UpperCamelCase    :		Union[str, Any]       ,		UpperCamelCase    :		Any    )    ->	Optional[Any]:
    if args.temperature > 0.7:
        logger.info('CTRL typically works better with lower temperatures (and lower top_k).'    )

    a__    = 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 						__magic_name__		(				UpperCamelCase    :		str       ,		UpperCamelCase    :		Any       ,		UpperCamelCase    :		Union[str, Any]       ,		UpperCamelCase    :		Tuple    )    ->	List[str]:
    # kwargs = {"language": None, "mask_token_id": None}

    # Set the language
    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(UpperCamelCase    )    ) + ' >>> '    )

        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 						__magic_name__		(				UpperCamelCase    :		List[str]       ,		UpperCamelCase    :		Optional[int]       ,		UpperCamelCase    :		str       ,		UpperCamelCase    :		Any    )    ->	List[str]:
    a__    = args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
    a__    = prefix + prompt_text
    return prompt_text



def 						__magic_name__		(				UpperCamelCase    :		Dict       ,		UpperCamelCase    :		Any       ,		UpperCamelCase    :		Optional[Any]       ,		UpperCamelCase    :		Dict    )    ->	Dict:
    a__    = args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
    a__    = prefix + prompt_text
    return prompt_text


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



def 						__magic_name__		(				UpperCamelCase    :		List[Any]       ,		UpperCamelCase    :		str    )    ->	int:
    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 						__magic_name__		(				UpperCamelCase    :		List[Any]    )    ->	Union[str, Any]:
    a__    = None
    if hasattr(UpperCamelCase       ,		'hidden_size'    ):
        a__    = model_config.hidden_size
    elif hasattr(UpperCamelCase       ,		'n_embed'    ):
        a__    = model_config.n_embed
    elif hasattr(UpperCamelCase       ,		'n_embd'    ):
        a__    = model_config.n_embd

    a__    = None
    if hasattr(UpperCamelCase       ,		'num_attention_heads'    ):
        a__    = model_config.num_attention_heads
    elif hasattr(UpperCamelCase       ,		'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(UpperCamelCase       ,		'n_layer'    ):
        a__    = model_config.n_layer
    elif hasattr(UpperCamelCase       ,		'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 						__magic_name__		(				UpperCamelCase    :		Union[str, Any]       ,		UpperCamelCase    :		Any       ,		UpperCamelCase    :		Any    )    ->	List[Any]:
    a__  ,    a__  ,    a__    = sparse_model_config(model.config    )
    if model.config.model_type == "bloom":
        a__    = 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:
        a__    = 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 						__magic_name__		(				UpperCamelCase    :		Tuple       ,		UpperCamelCase    :		int       ,		UpperCamelCase    :		List[str]    )    ->	List[str]:
    a__    = len(UpperCamelCase    )
    a__    = tokenizer.batch_encode_plus(UpperCamelCase       ,		return_tensors='pt'    )
    a__    = dummy_input.to(model.device    )
    if model.config.use_cache:
        a__    = generate_past_key_values(UpperCamelCase       ,		UpperCamelCase       ,		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 						lowercase(_lowercase					):
     __snake_case:  List[Any]  				=    ('_optimized', '_default')



     def __init__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    List[str]:







         """simple docstring"""



         a__    = optimized
         a__    = default



     def __call__(       self    ,							*__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE      )		->    Tuple:







         """simple docstring"""



         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'    ,							__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      )
         a__    = self._optimized(**__SCREAMING_SNAKE_CASE      )
         a__    = outputs[0]
         a__    = outputs[1]
         a__    = 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      )		->    List[str]:







         """simple docstring"""



         return getattr(self._default    ,							__SCREAMING_SNAKE_CASE      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE=None    ,							**__SCREAMING_SNAKE_CASE      )		->    List[str]:







         """simple docstring"""



         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  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    Tuple[Tuple[torch.Tensor]]:







         """simple docstring"""



         return self._default._reorder_cache(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )




def 						__magic_name__		(				)    ->	List[Any]:
    a__    = 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'    )
    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(UpperCamelCase    )

    # 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=UpperCamelCase    )
    logger.info(UpperCamelCase    )

    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(UpperCamelCase       ,		UpperCamelCase       ,		UpperCamelCase       ,		UpperCamelCase    )

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

        a__    = tokenizer.encode(
            UpperCamelCase       ,		add_special_tokens=UpperCamelCase       ,		return_tensors='pt'       ,		**UpperCamelCase    )
    else:
        a__    = args.prefix if args.prefix else args.padding_text
        a__    = tokenizer.encode(prefix + prompt_text       ,		add_special_tokens=UpperCamelCase       ,		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(UpperCamelCase       ,		UpperCamelCase       ,		UpperCamelCase    )
        torch._C._jit_set_texpr_fuser_enabled(UpperCamelCase    )
        a__    = False
        if hasattr(UpperCamelCase       ,		'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(UpperCamelCase       ,		UpperCamelCase    ) is not None    )
        a__    = torch.jit.trace(UpperCamelCase       ,		UpperCamelCase       ,		strict=UpperCamelCase    )
        a__    = torch.jit.freeze(traced_model.eval()    )
        traced_model(*UpperCamelCase    )
        traced_model(*UpperCamelCase    )

        a__    = _ModelFallbackWrapper(UpperCamelCase       ,		UpperCamelCase    )

    a__    = 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_()

    a__    = []

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

        # Decode text
        a__    = tokenizer.decode(UpperCamelCase       ,		clean_up_tokenization_spaces=UpperCamelCase    )

        # 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=UpperCamelCase    )    ) :]
        )

        generated_sequences.append(UpperCamelCase    )
        print(UpperCamelCase    )

    return generated_sequences


if __name__ == "__main__":
    main()




"""simple docstring"""




from itertools import combinations, product



def 						__magic_name__		(				UpperCamelCase    :		int       ,		UpperCamelCase    :		int       ,		UpperCamelCase    :		int       ,		UpperCamelCase    :		int    )    ->	bool:
    if xa == ya == 0 or xa == ya == 0:
        return False
    a__    = xa * xa + ya * ya
    a__    = xa * xa + ya * ya
    a__    = (xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
    return (
        a_square + b_square == c_square
        or a_square + c_square == b_square
        or b_square + c_square == a_square
    )



def 						__magic_name__		(				UpperCamelCase    :		int = 50    )    ->	int:
    return sum(
        1
        for pta, pta in combinations(product(range(limit + 1    )       ,		repeat=2    )       ,		2    )
        if is_right(*UpperCamelCase       ,		*UpperCamelCase    )    )


if __name__ == "__main__":
    print(F'''{solution() = }''')


