

'''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 :      Tuple					 =     "\\n\n"

lowerCamelCase :      Dict					 =     "\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 							A__    (       datasets.Metric  ):





  def 						A  (		self      :	Optional[int] )	->    Any:





       '''simple docstring'''






       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 						A  (		self      :	str							,					_a      :	List[str]							,					_a      :	Optional[Any]							,					_a      :	int = 16							,					_a      :	bool = True							,					_a      :	str=None )	->    Optional[Any]:





       '''simple docstring'''






       if device is not None:
            assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
            if device == "gpu":
                 _SCREAMING_SNAKE_CASE							    ='cuda'
       else:
            _SCREAMING_SNAKE_CASE							    ='cuda' if torch.cuda.is_available() else 'cpu'

       _SCREAMING_SNAKE_CASE							    =AutoModelForCausalLM.from_pretrained(_a )
       _SCREAMING_SNAKE_CASE							    =model.to(_a )

       _SCREAMING_SNAKE_CASE							    =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:
            _SCREAMING_SNAKE_CASE							    =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"
            _SCREAMING_SNAKE_CASE							    =model.config.max_length - 1
       else:
            _SCREAMING_SNAKE_CASE							    =model.config.max_length

       _SCREAMING_SNAKE_CASE							    =tokenizer(
           _a							,					add_special_tokens=_a							,					padding=_a							,					truncation=_a							,					max_length=_a							,					return_tensors='pt'							,					return_attention_mask=_a							,					).to(_a )

       _SCREAMING_SNAKE_CASE							    =encodings['input_ids']
       _SCREAMING_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."

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

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

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

            _SCREAMING_SNAKE_CASE							    =encoded_batch

            with torch.no_grad():
                 _SCREAMING_SNAKE_CASE							    =model(_a							,					attention_mask=_a ).logits

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

            _SCREAMING_SNAKE_CASE							    =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 )}



'''simple docstring'''
import copy
import re







class 							A__    :
  A__		 =				'hp'
  A__		 =				{}
  A__		 =				None





  @classmethod
  def 						A  (		cls      :	Optional[Any]							,					_a      :	Optional[Any]							,					_a      :	Any )	->    Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =prefix
       _SCREAMING_SNAKE_CASE							    =defaults
       cls.build_naming_info()





  @staticmethod
  def 						A  (		_a      :	Optional[Any]							,					_a      :	List[Any] )	->    Any:





       '''simple docstring'''






       if len(_a ) == 0:
            return ""
       _SCREAMING_SNAKE_CASE							    =None
       if any(char.isdigit() for char in word ):
            raise Exception(f"Parameters should not contain numbers: '{word}' contains a number" )
       if word in info["short_word"]:
            return info["short_word"][word]
       for prefix_len in range(1							,					len(_a ) + 1 ):
            _SCREAMING_SNAKE_CASE							    =word[:prefix_len]
            if prefix in info["reverse_short_word"]:
                 continue
            else:
                 _SCREAMING_SNAKE_CASE							    =prefix
                 break

       if short_word is None:
            # Paranoid fallback
            def int_to_alphabetic(_a      :	str ):
                 _SCREAMING_SNAKE_CASE							    =''
                 while integer != 0:
                      _SCREAMING_SNAKE_CASE							    =chr(ord('A' ) + integer % 10 ) + s
                      integer //= 10
                 return s

            _SCREAMING_SNAKE_CASE							    =0
            while True:
                 _SCREAMING_SNAKE_CASE							    =word + '#' + int_to_alphabetic(_a )
                 if sword in info["reverse_short_word"]:
                      continue
                 else:
                      _SCREAMING_SNAKE_CASE							    =sword
                      break

       _SCREAMING_SNAKE_CASE							    =short_word
       _SCREAMING_SNAKE_CASE							    =word
       return short_word





  @staticmethod
  def 						A  (		_a      :	Optional[Any]							,					_a      :	int )	->    Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =param_name.split('_' )

       _SCREAMING_SNAKE_CASE							    =[TrialShortNamer.shortname_for_word(_a							,					_a ) for word in words]

       # We try to create a separatorless short name, but if there is a collision we have to fallback
       # to a separated short name
       _SCREAMING_SNAKE_CASE							    =['', '_']

       for separator in separators:
            _SCREAMING_SNAKE_CASE							    =separator.join(_a )
            if shortname not in info["reverse_short_param"]:
                 _SCREAMING_SNAKE_CASE							    =shortname
                 _SCREAMING_SNAKE_CASE							    =param_name
                 return shortname

       return param_name





  @staticmethod
  def 						A  (		_a      :	Dict							,					_a      :	int )	->    Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =TrialShortNamer.shortname_for_key(_a							,					_a )
       _SCREAMING_SNAKE_CASE							    =short_name
       _SCREAMING_SNAKE_CASE							    =param_name





  @classmethod
  def 						A  (		cls      :	Optional[int] )	->    Tuple:





       '''simple docstring'''






       if cls.NAMING_INFO is not None:
            return

       _SCREAMING_SNAKE_CASE							    ={
           'short_word': {},
           'reverse_short_word': {},
           'short_param': {},
           'reverse_short_param': {},
       }

       _SCREAMING_SNAKE_CASE							    =list(cls.DEFAULTS.keys() )

       for k in field_keys:
            cls.add_new_param_name(_a							,					_a )

       _SCREAMING_SNAKE_CASE							    =info





  @classmethod
  def 						A  (		cls      :	List[Any]							,					_a      :	int )	->    int:





       '''simple docstring'''






       cls.build_naming_info()
       assert cls.PREFIX is not None
       _SCREAMING_SNAKE_CASE							    =[copy.copy(cls.PREFIX )]

       for k, v in params.items():
            if k not in cls.DEFAULTS:
                 raise Exception(f"You should provide a default value for the param name {k} with value {v}" )
            if v == cls.DEFAULTS[k]:
                 # The default value is not added to the name
                 continue

            _SCREAMING_SNAKE_CASE							    =cls.NAMING_INFO['short_param'][k]

            if isinstance(_a							,					_a ):
                 _SCREAMING_SNAKE_CASE							    =1 if v else 0

            _SCREAMING_SNAKE_CASE							    ='' if isinstance(_a							,					(int, float) ) else '-'
            _SCREAMING_SNAKE_CASE							    =f"{key}{sep}{v}"
            name.append(_a )

       return "_".join(_a )





  @classmethod
  def 						A  (		cls      :	Optional[Any]							,					_a      :	List[Any] )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =repr[len(cls.PREFIX ) + 1 :]
       if repr == "":
            _SCREAMING_SNAKE_CASE							    =[]
       else:
            _SCREAMING_SNAKE_CASE							    =repr.split('_' )

       _SCREAMING_SNAKE_CASE							    ={}

       for value in values:
            if "-" in value:
                 _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =value.split('-' )
            else:
                 _SCREAMING_SNAKE_CASE							    =re.sub('[0-9.]'							,					''							,					_a )
                 _SCREAMING_SNAKE_CASE							    =float(re.sub('[^0-9.]'							,					''							,					_a ) )

            _SCREAMING_SNAKE_CASE							    =cls.NAMING_INFO['reverse_short_param'][p_k]

            _SCREAMING_SNAKE_CASE							    =p_v

       for k in cls.DEFAULTS:
            if k not in parameters:
                 _SCREAMING_SNAKE_CASE							    =cls.DEFAULTS[k]

       return parameters
