






'''simple docstring'''

from random import randint, random







def     lowercase__ (      __UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase = False		,						__UpperCamelCase = False		,						__UpperCamelCase = 5		,						)-> Union[str, Any]:
      UpperCamelCase				  =					[[-1] * number_of_cells]  # Create a highway without any car
      UpperCamelCase				  =					0
      UpperCamelCase				  =					max(__UpperCamelCase		,						0    )
      while i < number_of_cells:
            UpperCamelCase				  =					(
                randint(0		,						__UpperCamelCase    ) if random_speed else initial_speed
            )  # Place the cars
            i += (
                randint(1		,						max_speed * 2    ) if random_frequency else frequency
            )  # Arbitrary number, may need tuning
      return highway







def     lowercase__ (      __UpperCamelCase		,						__UpperCamelCase    )-> Any:
      UpperCamelCase				  =					0
      UpperCamelCase				  =					highway_now[car_index + 1 :]
      for cell in range(len(__UpperCamelCase    )    ):  # May need a better name for this
            if cells[cell] != -1:  # If the cell is not empty then
                  return distance  # we have the distance we wanted
            distance += 1
      # Here if the car is near the end of the highway
      return distance + get_distance(__UpperCamelCase		,						-1    )







def     lowercase__ (      __UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase    )-> Any:
      UpperCamelCase				  =					len(__UpperCamelCase    )
      # Beforce calculations, the highway is empty
      UpperCamelCase				  =					[-1] * number_of_cells

      for car_index in range(__UpperCamelCase    ):
            if highway_now[car_index] != -1:
                  # Add 1 to the current speed of the car and cap the speed
                  UpperCamelCase				  =					min(highway_now[car_index] + 1		,						__UpperCamelCase    )
                  # Number of empty cell before the next car
                  UpperCamelCase				  =					get_distance(__UpperCamelCase		,						__UpperCamelCase    ) - 1
                  # We can't have the car causing an accident
                  UpperCamelCase				  =					min(next_highway[car_index]		,						__UpperCamelCase    )
                  if random() < probability:
                        # Randomly, a driver will slow down
                        UpperCamelCase				  =					max(next_highway[car_index] - 1		,						0    )
      return next_highway







def     lowercase__ (      __UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase    )-> Union[str, Any]:
      UpperCamelCase				  =					len(highway[0]    )

      for i in range(__UpperCamelCase    ):
            UpperCamelCase				  =					update(highway[i]		,						__UpperCamelCase		,						__UpperCamelCase    )
            UpperCamelCase				  =					[-1] * number_of_cells

            for car_index in range(__UpperCamelCase    ):
                  UpperCamelCase				  =					next_speeds_calculated[car_index]
                  if speed != -1:
                        # Change the position based on the speed (with % to create the loop)
                        UpperCamelCase				  =					(car_index + speed) % number_of_cells
                        # Commit the change of position
                        UpperCamelCase				  =					speed
            highway.append(__UpperCamelCase    )

      return highway


if __name__ == "__main__":
     import doctest

     doctest.testmod()

"""simple docstring"""






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

import datasets
from datasets import logging


lowerCAmelCase__  :  List[Any]						     =	'\\n\n'

lowerCAmelCase__  :  Tuple						     =	'\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n'

lowerCAmelCase__  :  str						     =	'\nArgs:\n    model_id (str): model used for calculating Perplexity\n            NOTE: Perplexity can only be calculated for causal language models.\n                    This includes models such as gpt2, causal variations of bert,\n                    causal versions of t5, and more (the full list can be found\n                    in the AutoModelForCausalLM documentation here:\n                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n    input_texts (list of str): input text, each separate text snippet\n        is one list entry.\n    batch_size (int): the batch size to run texts through the model. Defaults to 16.\n    add_start_token (bool): whether to add the start token to the texts,\n        so the perplexity can include the probability of the first word. Defaults to True.\n    device (str): device to run on, defaults to \'cuda\' when available\nReturns:\n    perplexity: dictionary containing the perplexity scores for the texts\n        in the input list, as well as the mean perplexity. If one of the input texts is\n        longer than the max input length of the model, then it is truncated to the\n        max length for the perplexity computation.\nExamples:\n    Example 1:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              add_start_token=False,\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        78.22\n        >>> print(round(results["perplexities"][0], 2))\n        11.11\n\n    Example 2:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = datasets.load_dataset("wikitext",\n        ...                                     "wikitext-2-raw-v1",\n        ...                                     split="test")["text"][:50] # doctest:+ELLIPSIS\n        [...]\n        >>> input_texts = [s for s in input_texts if s!=\'\']\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        60.35\n        >>> print(round(results["perplexities"][0], 2))\n        81.12\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,    _KWARGS_DESCRIPTION				)
class 				snake_case       ( datasets.Metric				):




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	str			):
									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   :	Tuple    ,lowerCamelCase__   :	Optional[int]    ,lowerCamelCase__   :	List[str]    ,lowerCamelCase__   :	int = 16    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	List[str]=None			):
									if device is not None:
													assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
													if device == "gpu":
																	UpperCAmelCase__             =							'cuda'
									else:
													UpperCAmelCase__             =							'cuda' if torch.cuda.is_available() else 'cpu'

									UpperCAmelCase__             =							AutoModelForCausalLM.from_pretrained(lowerCamelCase__			)
									UpperCAmelCase__             =							model.to(lowerCamelCase__			)

									UpperCAmelCase__             =							AutoTokenizer.from_pretrained(lowerCamelCase__			)

									# 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:
													UpperCAmelCase__             =							list(tokenizer.special_tokens_map_extended.values()			)
													# check that the model already has at least one special token defined
													assert (
													    len(lowerCamelCase__			) > 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"
													UpperCAmelCase__             =							model.config.max_length - 1
									else:
													UpperCAmelCase__             =							model.config.max_length

									UpperCAmelCase__             =							tokenizer(
									    lowerCamelCase__    ,add_special_tokens=lowerCamelCase__    ,padding=lowerCamelCase__    ,truncation=lowerCamelCase__    ,max_length=lowerCamelCase__    ,return_tensors='pt'    ,return_attention_mask=lowerCamelCase__    ,).to(lowerCamelCase__			)

									UpperCAmelCase__             =							encodings['input_ids']
									UpperCAmelCase__             =							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."

									UpperCAmelCase__             =							[]
									UpperCAmelCase__             =							CrossEntropyLoss(reduction='none'			)

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

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

													UpperCAmelCase__             =							encoded_batch

													with torch.no_grad():
																	UpperCAmelCase__             =							model(lowerCamelCase__    ,attention_mask=lowerCamelCase__			).logits

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

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

													ppls += perplexity_batch.tolist()

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



