

'''simple docstring'''




def 						UpperCamelCase	(			a		,						a		,						a			)				->     bool:






     '''simple docstring'''
     return not any(
         neighbour == 1 and colored_vertices[i] == color
         for i, neighbour in enumerate(a			)			)




def 						UpperCamelCase	(			a		,						a		,						a		,						a			)				->     bool:






     '''simple docstring'''

     # Base Case
     if index == len(a			):
          return True

     # Recursive Step
     for i in range(a			):
          if valid_coloring(graph[index]		,						a		,						a			):
               # Color current vertex
               __magic_name__						       =    i
               # Validate coloring
               if util_color(a		,						a		,						a		,						index + 1			):
                    return True
               # Backtrack
               __magic_name__						       =    -1
     return False




def 						UpperCamelCase	(			a		,						a			)				->     list[int]:






     '''simple docstring'''
     __magic_name__						       =    [-1] * len(a			)

     if util_color(a		,						a		,						a		,						0			):
          return colored_vertices

     return []









'''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         =						"\\n\n"

_lowerCAmelCase         =						"\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         =						"\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   _SCREAMING_SNAKE_CASE   (   datasets.Metric     ):



 def 			snake_case__  (   self		:   Dict      ):
      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 			snake_case__  (   self		:   Optional[int]			,				a__		:   int			,				a__		:   Dict			,				a__		:   int = 16			,				a__		:   bool = True			,				a__		:   Any=None      ):
      if device is not None:
           assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
           if device == "gpu":
                __magic_name__						       =    '''cuda'''
      else:
           __magic_name__						       =    '''cuda''' if torch.cuda.is_available() else '''cpu'''

      __magic_name__						       =    AutoModelForCausalLM.from_pretrained(a__      )
      __magic_name__						       =    model.to(a__      )

      __magic_name__						       =    AutoTokenizer.from_pretrained(a__      )

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

      __magic_name__						       =    tokenizer(
          a__			,				add_special_tokens=a__			,				padding=a__			,				truncation=a__			,				max_length=a__			,				return_tensors='''pt'''			,				return_attention_mask=a__			,				).to(a__      )

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

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

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

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

           __magic_name__						       =    encoded_batch

           with torch.no_grad():
                __magic_name__						       =    model(a__			,				attention_mask=a__      ).logits

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

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

           ppls += perplexity_batch.tolist()

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






