
"""simple docstring"""







import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


_SCREAMING_SNAKE_CASE	       =      """\

"""

_SCREAMING_SNAKE_CASE	       =      """
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.

For more information, see https://huggingface.co/docs/transformers/perplexity
"""

_SCREAMING_SNAKE_CASE	       =      """
Args:
    model_id (str): model used for calculating Perplexity
            NOTE: Perplexity can only be calculated for causal language models.
                    This includes models such as gpt2, causal variations of bert,
                    causal versions of t5, and more (the full list can be found
                    in the AutoModelForCausalLM documentation here:
                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )

    input_texts (list of str): input text, each separate text snippet
        is one list entry.
    batch_size (int): the batch size to run texts through the model. Defaults to 16.
    add_start_token (bool): whether to add the start token to the texts,
        so the perplexity can include the probability of the first word. Defaults to True.
    device (str): device to run on, defaults to \'cuda\' when available
Returns:
    perplexity: dictionary containing the perplexity scores for the texts
        in the input list, as well as the mean perplexity. If one of the input texts is
        longer than the max input length of the model, then it is truncated to the
        max length for the perplexity computation.
Examples:
    Example 1:
        >>> perplexity = datasets.load_metric(\"perplexity\")
        >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]
        >>> results = perplexity.compute(model_id=\'gpt2\',
        ...                              add_start_token=False,
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        [\'perplexities\', \'mean_perplexity\']
        >>> print(round(results[\"mean_perplexity\"], 2))
        78.22
        >>> print(round(results[\"perplexities\"][0], 2))
        11.11

    Example 2:
        >>> perplexity = datasets.load_metric(\"perplexity\")
        >>> input_texts = datasets.load_dataset(\"wikitext\",
        ...                                     \"wikitext-2-raw-v1\",
        ...                                     split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS
        [...]
        >>> input_texts = [s for s in input_texts if s!=\'\']
        >>> results = perplexity.compute(model_id=\'gpt2\',
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        [\'perplexities\', \'mean_perplexity\']
        >>> print(round(results[\"mean_perplexity\"], 2))
        60.35
        >>> print(round(results[\"perplexities\"][0], 2))
        81.12
"""







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,		_KWARGS_DESCRIPTION   )
class 							__magic_name__     (		datasets.Metric   ):





					def 							lowerCAmelCase      (				self   :							int    ):
												return datasets.MetricInfo(
												    description=_DESCRIPTION							,						citation=_CITATION							,						inputs_description=_KWARGS_DESCRIPTION							,						features=datasets.Features(
												        {
												            "input_texts": datasets.Value("string"    ),
												        }    )							,						reference_urls=["https://huggingface.co/docs/transformers/perplexity"]							,						)





					def 							lowerCAmelCase      (				self   :							Optional[Any]							,						snake_case_   :							Dict							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							List[Any] = 16							,						snake_case_   :							Optional[Any] = True							,						snake_case_   :							Any=None    ):
												if device is not None:
																			assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
																			if device == "gpu":
																										__snake_case     			=       "cuda"
												else:
																			__snake_case     			=       "cuda" if torch.cuda.is_available() else "cpu"

												__snake_case     			=       AutoModelForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE_    )
												__snake_case     			=       model.to(SCREAMING_SNAKE_CASE_    )

												__snake_case     			=       AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_    )

												# if batch_size > 1 (which generally leads to padding being required), and
												# if there is not an already assigned pad_token, assign an existing
												# special token to also be the padding token
												if tokenizer.pad_token is None and batch_size > 1:
																			__snake_case     			=       list(tokenizer.special_tokens_map_extended.values()    )
																			# check that the model already has at least one special token defined
																			assert (
																			    len(SCREAMING_SNAKE_CASE_    ) > 0
																			), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
																			# assign one of the special tokens to also be the pad token
																			tokenizer.add_special_tokens({"pad_token": existing_special_tokens[0]}    )

												if add_start_token:
																			# leave room for <BOS> token to be added:
																			assert (
																			    tokenizer.bos_token is not None
																			), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
																			__snake_case     			=       model.config.max_length - 1
												else:
																			__snake_case     			=       model.config.max_length

												__snake_case     			=       tokenizer(
												    SCREAMING_SNAKE_CASE_							,						add_special_tokens=SCREAMING_SNAKE_CASE_							,						padding=SCREAMING_SNAKE_CASE_							,						truncation=SCREAMING_SNAKE_CASE_							,						max_length=SCREAMING_SNAKE_CASE_							,						return_tensors="pt"							,						return_attention_mask=SCREAMING_SNAKE_CASE_							,						).to(SCREAMING_SNAKE_CASE_    )

												__snake_case     			=       encodings["input_ids"]
												__snake_case     			=       encodings["attention_mask"]

												# check that each input is long enough:
												if add_start_token:
																			assert torch.all(torch.ge(attn_masks.sum(1    )							,						1    )    ), "Each input text must be at least one token long."
												else:
																			assert torch.all(
																			    torch.ge(attn_masks.sum(1    )							,						2    )    ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

												__snake_case     			=       []
												__snake_case     			=       CrossEntropyLoss(reduction="none"    )

												for start_index in logging.tqdm(range(0							,						len(SCREAMING_SNAKE_CASE_    )							,						SCREAMING_SNAKE_CASE_    )    ):
																			__snake_case     			=       min(start_index + batch_size							,						len(SCREAMING_SNAKE_CASE_    )    )
																			__snake_case     			=       encoded_texts[start_index:end_index]
																			__snake_case     			=       attn_masks[start_index:end_index]

																			if add_start_token:
																										__snake_case     			=       torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0    )    ).to(SCREAMING_SNAKE_CASE_    )
																										__snake_case     			=       torch.cat([bos_tokens_tensor, encoded_batch]							,						dim=1    )
																										__snake_case     			=       torch.cat(
																										    [torch.ones(bos_tokens_tensor.size()							,						dtype=torch.intaa    ).to(SCREAMING_SNAKE_CASE_    ), attn_mask]							,						dim=1    )

																			__snake_case     			=       encoded_batch

																			with torch.no_grad():
																										__snake_case     			=       model(SCREAMING_SNAKE_CASE_							,						attention_mask=SCREAMING_SNAKE_CASE_    ).logits

																			__snake_case     			=       out_logits[..., :-1, :].contiguous()
																			__snake_case     			=       labels[..., 1:].contiguous()
																			__snake_case     			=       attn_mask[..., 1:].contiguous()

																			__snake_case     			=       torch.expa(
																			    (loss_fct(shift_logits.transpose(1							,						2    )							,						SCREAMING_SNAKE_CASE_    ) * shift_attention_mask_batch).sum(1    )
																			    / shift_attention_mask_batch.sum(1    )    )

																			ppls += perplexity_batch.tolist()

												return {"perplexities": ppls, "mean_perplexity": np.mean(SCREAMING_SNAKE_CASE_    )}


"""simple docstring"""







def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE = 10							,    SCREAMING_SNAKE_CASE = 10_00							,    SCREAMING_SNAKE_CASE = True )  ->     int:







							"""simple docstring"""


							assert (
							    isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							    and isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							    and isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							), "Invalid type of value(s) specified to function!"

							if min_val > max_val:
														raise ValueError("Invalid value for min_val or max_val (min_value < max_value)" )
							return min_val if option else max_val
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     int:







							"""simple docstring"""


							return int((number_a + number_a) / 2 )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     None:







							"""simple docstring"""


							assert (
							    isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ) and isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ) and isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							), 'argument values must be type of "int"'

							if lower > higher:
														raise ValueError("argument value for lower and higher must be(lower > higher)" )

							if not lower < to_guess < higher:
														raise ValueError(
														    "guess value must be within the range of lower and higher value" )

							def answer(SCREAMING_SNAKE_CASE ) -> str:
														if number > to_guess:
																					return "high"
														elif number < to_guess:
																					return "low"
														else:
																					return "same"

							print("started..." )

							__snake_case     			=       lower
							__snake_case     			=       higher

							__snake_case     			=       []

							while True:
														__snake_case     			=       get_avg(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
														last_numbers.append(SCREAMING_SNAKE_CASE )

														if answer(SCREAMING_SNAKE_CASE ) == "low":
																					__snake_case     			=       number
														elif answer(SCREAMING_SNAKE_CASE ) == "high":
																					__snake_case     			=       number
														else:
																					break

							print(F'''guess the number : {last_numbers[-1]}''' )
							print(F'''details : {last_numbers!s}''' )
def   __UpperCamelCase   (					)  ->     None:







							"""simple docstring"""


							__snake_case     			=       int(input("Enter lower value : " ).strip() )
							__snake_case     			=       int(input("Enter high value : " ).strip() )
							__snake_case     			=       int(input("Enter value to guess : " ).strip() )
							guess_the_number(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
		main()
