




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

import datasets
from datasets import logging


__magic_name__  :  List[str]					=       """\

"""

__magic_name__  :  List[Any]					=       """
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
"""

__magic_name__  :  List[str]					=       """
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     SCREAMING_SNAKE_CASE__		(datasets.Metric			):
     def        A__      (						self	:		Union[str, 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	:		Optional[int]       ,						__lowerCamelCase	:		Tuple       ,						__lowerCamelCase	:		Union[str, Any]       ,						__lowerCamelCase	:		int = 16       ,						__lowerCamelCase	:		bool = True       ,						__lowerCamelCase	:		Optional[int]=None						):



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

       lowerCAmelCase__										=      AutoModelForCausalLM.from_pretrained(__lowerCamelCase						)
       lowerCAmelCase__										=      model.to(__lowerCamelCase						)

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

       lowerCAmelCase__										=      tokenizer(
           __lowerCamelCase       ,						add_special_tokens=__lowerCamelCase       ,						padding=__lowerCamelCase       ,						truncation=__lowerCamelCase       ,						max_length=__lowerCamelCase       ,						return_tensors='''pt'''       ,						return_attention_mask=__lowerCamelCase       ,						).to(__lowerCamelCase						)

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

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

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

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

         lowerCAmelCase__										=      encoded_batch

         with torch.no_grad():
           lowerCAmelCase__										=      model(__lowerCamelCase       ,						attention_mask=__lowerCamelCase						).logits

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

         lowerCAmelCase__										=      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						)}







from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

__magic_name__  :  Any					=       TypeVar("""KEY""")
__magic_name__  :  Dict					=       TypeVar("""VAL""")





@dataclass(frozen=_a    ,					slots=_a			)
class     SCREAMING_SNAKE_CASE__		(Generic[KEY, VAL]			):
     lowercase_ :      KEY
     lowercase_ :      VAL





class     SCREAMING_SNAKE_CASE__		(_Item			):
     def __init__(						self	:		Tuple						):



       """simple docstring"""
       super().__init__(__lowerCamelCase       ,						__lowerCamelCase						)



     def __bool__(						self	:		Tuple						):



       """simple docstring"""
       return False



__magic_name__  :  str					=       _DeletedItem()





class     SCREAMING_SNAKE_CASE__		(MutableMapping[KEY, VAL]			):
     def __init__(						self	:		Any       ,						__lowerCamelCase	:		int = 8       ,						__lowerCamelCase	:		float = 0.75						):



       """simple docstring"""
       lowerCAmelCase__										=      initial_block_size
       lowerCAmelCase__										=      [None] * initial_block_size
       assert 0.0 < capacity_factor < 1.0
       lowerCAmelCase__										=      capacity_factor
       lowerCAmelCase__										=      0
     def        A__      (						self	:		Union[str, Any]       ,						__lowerCamelCase	:		KEY						):



       """simple docstring"""
       return hash(__lowerCamelCase						) % len(self._buckets						)
     def        A__      (						self	:		Tuple       ,						__lowerCamelCase	:		int						):



       """simple docstring"""
       return (ind + 1) % len(self._buckets						)
     def        A__      (						self	:		Dict       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		KEY       ,						__lowerCamelCase	:		VAL						):



       """simple docstring"""
       lowerCAmelCase__										=      self._buckets[ind]
       if not stored:
         lowerCAmelCase__										=      _Item(__lowerCamelCase       ,						__lowerCamelCase						)
         self._len += 1
         return True
       elif stored.key == key:
         lowerCAmelCase__										=      _Item(__lowerCamelCase       ,						__lowerCamelCase						)
         return True
       else:
         return False
     def        A__      (						self	:		Any						):



       """simple docstring"""
       lowerCAmelCase__										=      len(self._buckets						) * self._capacity_factor
       return len(self						) >= int(__lowerCamelCase						)
     def        A__      (						self	:		Tuple						):



       """simple docstring"""
       if len(self._buckets						) <= self._initial_block_size:
         return False
       lowerCAmelCase__										=      len(self._buckets						) * self._capacity_factor / 2
       return len(self						) < limit
     def        A__      (						self	:		List[Any]       ,						__lowerCamelCase	:		int						):



       """simple docstring"""
       lowerCAmelCase__										=      self._buckets
       lowerCAmelCase__										=      [None] * new_size
       lowerCAmelCase__										=      0
       for item in old_buckets:
         if item:
           self._add_item(item.key       ,						item.val						)
     def        A__      (						self	:		Any						):



       """simple docstring"""
       self._resize(len(self._buckets						) * 2						)
     def        A__      (						self	:		Dict						):



       """simple docstring"""
       self._resize(len(self._buckets						) // 2						)
     def        A__      (						self	:		List[str]       ,						__lowerCamelCase	:		KEY						):



       """simple docstring"""
       lowerCAmelCase__										=      self._get_bucket_index(__lowerCamelCase						)
       for _ in range(len(self._buckets						)						):
         yield ind
         lowerCAmelCase__										=      self._get_next_ind(__lowerCamelCase						)
     def        A__      (						self	:		Dict       ,						__lowerCamelCase	:		KEY       ,						__lowerCamelCase	:		VAL						):



       """simple docstring"""
       for ind in self._iterate_buckets(__lowerCamelCase						):
         if self._try_set(__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase						):
           break
     def __setitem__(						self	:		str       ,						__lowerCamelCase	:		KEY       ,						__lowerCamelCase	:		VAL						):



       """simple docstring"""
       if self._is_full():
         self._size_up()

       self._add_item(__lowerCamelCase       ,						__lowerCamelCase						)
     def __delitem__(						self	:		Tuple       ,						__lowerCamelCase	:		KEY						):



       """simple docstring"""
       for ind in self._iterate_buckets(__lowerCamelCase						):
         lowerCAmelCase__										=      self._buckets[ind]
         if item is None:
           raise KeyError(__lowerCamelCase						)
         if item is _deleted:
           continue
         if item.key == key:
           lowerCAmelCase__										=      _deleted
           self._len -= 1
           break
       if self._is_sparse():
         self._size_down()
     def __getitem__(						self	:		Optional[int]       ,						__lowerCamelCase	:		KEY						):



       """simple docstring"""
       for ind in self._iterate_buckets(__lowerCamelCase						):
         lowerCAmelCase__										=      self._buckets[ind]
         if item is None:
           break
         if item is _deleted:
           continue
         if item.key == key:
           return item.val
       raise KeyError(__lowerCamelCase						)
     def __len__(						self	:		int						):



       """simple docstring"""
       return self._len
     def __iter__(						self	:		Union[str, Any]						):



       """simple docstring"""
       yield from (item.key for item in self._buckets if item)



     def __repr__(						self	:		Union[str, Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      ''' ,'''.join(
           F"""{item.key}: {item.val}""" for item in self._buckets if item						)
       return F"""HashMap({val_string})"""

