






import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation

lowerCamelCase      :				Any							=logging.get_logger(__name__)


lowerCamelCase      :				Union[str, Any]							={
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

lowerCamelCase      :				Optional[Any]							={
    """vocab_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"""},
    """merges_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"""},
    """tokenizer_config_file""": {
        """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"""
    },
}

lowerCamelCase      :				Optional[int]							={"""facebook/blenderbot-3B""": 128}







@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 						SCREAMING_SNAKE_CASE				(   )					->      int:
     UpperCamelCase__     :  Dict										=    (
         list(range(ord("!"     )   ,       ord("~"     ) + 1     )     ) + list(range(ord("¡"     )   ,       ord("¬"     ) + 1     )     ) + list(range(ord("®"     )   ,       ord("ÿ"     ) + 1     )     )
     )
     UpperCamelCase__     :  Optional[int]										=    bs[:]
     UpperCamelCase__     :  int										=    0
     for b in range(2**8     ):
          if b not in bs:
               bs.append(__lowerCAmelCase     )
               cs.append(2**8 + n     )
               n += 1
     UpperCamelCase__     :  Any										=    [chr(__lowerCAmelCase     ) for n in cs]
     return dict(zip(__lowerCAmelCase   ,       __lowerCAmelCase     )     )







def 						SCREAMING_SNAKE_CASE				(   __lowerCAmelCase     )					->      Optional[Any]:
     UpperCamelCase__     :  Optional[Any]										=    set()
     UpperCamelCase__     :  List[str]										=    word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)     )
          UpperCamelCase__     :  Union[str, Any]										=    char
     return pairs





class      __a    (       SCREAMING_SNAKE_CASE_			):
 _lowerCAmelCase			:			Optional[int]								=					VOCAB_FILES_NAMES
 _lowerCAmelCase			:			Any								=					PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase			:			str								=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase			:			Dict								=					['input_ids', 'attention_mask']







 def __init__(						self   :  Dict				,  SCREAMING_SNAKE_CASE   :  Optional[int]				,  SCREAMING_SNAKE_CASE   :  Tuple				,  SCREAMING_SNAKE_CASE   :  Dict="replace"				,  SCREAMING_SNAKE_CASE   :  Optional[Any]="<s>"				,  SCREAMING_SNAKE_CASE   :  Dict="</s>"				,  SCREAMING_SNAKE_CASE   :  str="</s>"				,  SCREAMING_SNAKE_CASE   :  Tuple="<s>"				,  SCREAMING_SNAKE_CASE   :  List[Any]="<unk>"				,  SCREAMING_SNAKE_CASE   :  Dict="<pad>"				,  SCREAMING_SNAKE_CASE   :  int="<mask>"				,  SCREAMING_SNAKE_CASE   :  Any=False				,  **SCREAMING_SNAKE_CASE   :  str				,  ):


      '''simple docstring'''






      UpperCamelCase__     :  int										=    AddedToken(SCREAMING_SNAKE_CASE				,  lstrip=SCREAMING_SNAKE_CASE				,  rstrip=SCREAMING_SNAKE_CASE		) if isinstance(SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE		) else bos_token
      UpperCamelCase__     :  Union[str, Any]										=    AddedToken(SCREAMING_SNAKE_CASE				,  lstrip=SCREAMING_SNAKE_CASE				,  rstrip=SCREAMING_SNAKE_CASE		) if isinstance(SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE		) else eos_token
      UpperCamelCase__     :  int										=    AddedToken(SCREAMING_SNAKE_CASE				,  lstrip=SCREAMING_SNAKE_CASE				,  rstrip=SCREAMING_SNAKE_CASE		) if isinstance(SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE		) else sep_token
      UpperCamelCase__     :  Dict										=    AddedToken(SCREAMING_SNAKE_CASE				,  lstrip=SCREAMING_SNAKE_CASE				,  rstrip=SCREAMING_SNAKE_CASE		) if isinstance(SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE		) else cls_token
      UpperCamelCase__     :  Optional[Any]										=    AddedToken(SCREAMING_SNAKE_CASE				,  lstrip=SCREAMING_SNAKE_CASE				,  rstrip=SCREAMING_SNAKE_CASE		) if isinstance(SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE		) else unk_token
      UpperCamelCase__     :  List[Any]										=    AddedToken(SCREAMING_SNAKE_CASE				,  lstrip=SCREAMING_SNAKE_CASE				,  rstrip=SCREAMING_SNAKE_CASE		) if isinstance(SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE		) else pad_token

      # Mask token behave like a normal word, i.e. include the space before it
      UpperCamelCase__     :  str										=    AddedToken(SCREAMING_SNAKE_CASE				,  lstrip=SCREAMING_SNAKE_CASE				,  rstrip=SCREAMING_SNAKE_CASE		) if isinstance(SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE		) else mask_token

      super().__init__(
          errors=SCREAMING_SNAKE_CASE				,  bos_token=SCREAMING_SNAKE_CASE				,  eos_token=SCREAMING_SNAKE_CASE				,  unk_token=SCREAMING_SNAKE_CASE				,  sep_token=SCREAMING_SNAKE_CASE				,  cls_token=SCREAMING_SNAKE_CASE				,  pad_token=SCREAMING_SNAKE_CASE				,  mask_token=SCREAMING_SNAKE_CASE				,  add_prefix_space=SCREAMING_SNAKE_CASE				,  **SCREAMING_SNAKE_CASE				,  )

      with open(SCREAMING_SNAKE_CASE				,  encoding="utf-8"		) as vocab_handle:
           UpperCamelCase__     :  Union[str, Any]										=    json.load(SCREAMING_SNAKE_CASE		)
      UpperCamelCase__     :  int										=    {v: k for k, v in self.encoder.items()}
      UpperCamelCase__     :  Optional[int]										=    errors  # how to handle errors in decoding
      UpperCamelCase__     :  List[Any]										=    bytes_to_unicode()
      UpperCamelCase__     :  Union[str, Any]										=    {v: k for k, v in self.byte_encoder.items()}
      with open(SCREAMING_SNAKE_CASE				,  encoding="utf-8"		) as merges_handle:
           UpperCamelCase__     :  Union[str, Any]										=    merges_handle.read().split("\n"		)[1:-1]
      UpperCamelCase__     :  str										=    [tuple(merge.split()		) for merge in bpe_merges]
      UpperCamelCase__     :  List[str]										=    dict(zip(SCREAMING_SNAKE_CASE				,  range(len(SCREAMING_SNAKE_CASE		)		)		)		)
      UpperCamelCase__     :  int										=    {}
      UpperCamelCase__     :  str										=    add_prefix_space

      # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
      UpperCamelCase__     :  List[str]										=    re.compile(R"\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"		)







 @property
 # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
 def    __lowercase   (						self   :  str		):


      '''simple docstring'''






      return len(self.encoder		)







 def    __lowercase   (						self   :  Optional[Any]		):


      '''simple docstring'''






      return dict(self.encoder				,  **self.added_tokens_encoder		)







 def    __lowercase   (						self   :  List[str]				,  SCREAMING_SNAKE_CASE   :  int		):


      '''simple docstring'''






      if token in self.cache:
           return self.cache[token]
      UpperCamelCase__     :  int										=    tuple(SCREAMING_SNAKE_CASE		)
      UpperCamelCase__     :  Dict										=    get_pairs(SCREAMING_SNAKE_CASE		)

      if not pairs:
           return token

      while True:
           UpperCamelCase__     :  List[str]										=    min(SCREAMING_SNAKE_CASE				,  key=lambda SCREAMING_SNAKE_CASE		: self.bpe_ranks.get(SCREAMING_SNAKE_CASE				,  float("inf"		)		)		)
           if bigram not in self.bpe_ranks:
                break
           UpperCamelCase__     :  Dict										=    bigram
           UpperCamelCase__     :  Optional[int]										=    []
           UpperCamelCase__     :  Tuple										=    0
           while i < len(SCREAMING_SNAKE_CASE		):
                try:
                     UpperCamelCase__     :  List[str]										=    word.index(SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE		)
                except ValueError:
                     new_word.extend(word[i:]		)
                     break
                else:
                     new_word.extend(word[i:j]		)
                     UpperCamelCase__     :  Dict										=    j

                if word[i] == first and i < len(SCREAMING_SNAKE_CASE		) - 1 and word[i + 1] == second:
                     new_word.append(first + second		)
                     i += 2
                else:
                     new_word.append(word[i]		)
                     i += 1
           UpperCamelCase__     :  Any										=    tuple(SCREAMING_SNAKE_CASE		)
           UpperCamelCase__     :  Tuple										=    new_word
           if len(SCREAMING_SNAKE_CASE		) == 1:
                break
           else:
                UpperCamelCase__     :  Union[str, Any]										=    get_pairs(SCREAMING_SNAKE_CASE		)
      UpperCamelCase__     :  List[Any]										=    ' '.join(SCREAMING_SNAKE_CASE		)
      UpperCamelCase__     :  Dict										=    word
      return word







 def    __lowercase   (						self   :  Any				,  SCREAMING_SNAKE_CASE   :  Tuple		):


      '''simple docstring'''






      UpperCamelCase__     :  Tuple										=    []
      for token in re.findall(self.pat				,  SCREAMING_SNAKE_CASE		):
           UpperCamelCase__     :  Any										=    ''.join(
               self.byte_encoder[b] for b in token.encode("utf-8"		)		)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
           bpe_tokens.extend(bpe_token for bpe_token in self.bpe(SCREAMING_SNAKE_CASE		).split(" "		)		)
      return bpe_tokens







 def    __lowercase   (						self   :  Any				,  SCREAMING_SNAKE_CASE   :  int		):


      '''simple docstring'''






      return self.encoder.get(SCREAMING_SNAKE_CASE				,  self.encoder.get(self.unk_token		)		)







 def    __lowercase   (						self   :  List[Any]				,  SCREAMING_SNAKE_CASE   :  Dict		):


      '''simple docstring'''






      return self.decoder.get(SCREAMING_SNAKE_CASE		)







 def    __lowercase   (						self   :  List[Any]				,  SCREAMING_SNAKE_CASE   :  int		):


      '''simple docstring'''






      UpperCamelCase__     :  List[Any]										=    ''.join(SCREAMING_SNAKE_CASE		)
      UpperCamelCase__     :  Optional[Any]										=    bytearray([self.byte_decoder[c] for c in text]		).decode("utf-8"				,  errors=self.errors		)
      return text







 def    __lowercase   (						self   :  Union[str, Any]				,  SCREAMING_SNAKE_CASE   :  str				,  SCREAMING_SNAKE_CASE   :  Optional[str] = None		):


      '''simple docstring'''






      if not os.path.isdir(SCREAMING_SNAKE_CASE		):
           logger.error(F'Vocabulary path ({save_directory}) should be a directory'		)
           return
      UpperCamelCase__     :  List[Any]										=    os.path.join(
          SCREAMING_SNAKE_CASE				,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
      UpperCamelCase__     :  List[str]										=    os.path.join(
          SCREAMING_SNAKE_CASE				,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]		)

      with open(SCREAMING_SNAKE_CASE				,  "w"				,  encoding="utf-8"		) as f:
           f.write(json.dumps(self.encoder				,  indent=2				,  sort_keys=SCREAMING_SNAKE_CASE				,  ensure_ascii=SCREAMING_SNAKE_CASE		) + "\n"		)

      UpperCamelCase__     :  str										=    0
      with open(SCREAMING_SNAKE_CASE				,  "w"				,  encoding="utf-8"		) as writer:
           writer.write("#version: 0.2\n"		)
           for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				,  key=lambda SCREAMING_SNAKE_CASE		: kv[1]		):
                if index != token_index:
                     logger.warning(
                         F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                         " Please check that the tokenizer is not corrupted!"		)
                     UpperCamelCase__     :  List[str]										=    token_index
                writer.write(" ".join(SCREAMING_SNAKE_CASE		) + "\n"		)
                index += 1

      return vocab_file, merge_file







 def    __lowercase   (						self   :  Dict				,  SCREAMING_SNAKE_CASE   :  List[int]				,  SCREAMING_SNAKE_CASE   :  Optional[List[int]] = None				,  SCREAMING_SNAKE_CASE   :  bool = False		):


      '''simple docstring'''






      if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=SCREAMING_SNAKE_CASE				,  token_ids_a=SCREAMING_SNAKE_CASE				,  already_has_special_tokens=SCREAMING_SNAKE_CASE		)

      if token_ids_a is None:
           return [1] + ([0] * len(SCREAMING_SNAKE_CASE		)) + [1]
      return [1] + ([0] * len(SCREAMING_SNAKE_CASE		)) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE		)) + [1]







 def    __lowercase   (						self   :  int				,  SCREAMING_SNAKE_CASE   :  List[int]				,  SCREAMING_SNAKE_CASE   :  Optional[List[int]] = None		):


      '''simple docstring'''






      UpperCamelCase__     :  Dict										=    [self.sep_token_id]
      UpperCamelCase__     :  int										=    [self.cls_token_id]

      if token_ids_a is None:
           return len(cls + token_ids_a + sep		) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]







 def    __lowercase   (						self   :  Optional[Any]				,  SCREAMING_SNAKE_CASE   :  List[Any]				,  SCREAMING_SNAKE_CASE   :  Optional[Any]=False				,  **SCREAMING_SNAKE_CASE   :  Optional[int]		):


      '''simple docstring'''






      UpperCamelCase__     :  Optional[int]										=    kwargs.pop("add_prefix_space"				,  self.add_prefix_space		)
      if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE		) > 0 and not text[0].isspace()):
           UpperCamelCase__     :  Union[str, Any]										=    ' ' + text
      return (text, kwargs)







 def    __lowercase   (						self   :  List[str]				,  SCREAMING_SNAKE_CASE   :  List[int]				,  SCREAMING_SNAKE_CASE   :  Optional[List[int]] = None		):


      '''simple docstring'''






      return token_ids_a + [self.eos_token_id]







 def    __lowercase   (						self   :  List[str]				,  SCREAMING_SNAKE_CASE   :  "Conversation"		):


      '''simple docstring'''






      UpperCamelCase__     :  Tuple										=    []
      for is_user, text in conversation.iter_texts():
           if is_user:
                # We need to space prefix as it's being done within blenderbot
                inputs.append(" " + text		)
           else:
                # Generated responses should contain them already.
                inputs.append(SCREAMING_SNAKE_CASE		)

      UpperCamelCase__     :  Optional[Any]										=    '  '.join(SCREAMING_SNAKE_CASE		)
      UpperCamelCase__     :  str										=    self.encode(SCREAMING_SNAKE_CASE		)
      if len(SCREAMING_SNAKE_CASE		) > self.model_max_length:
           UpperCamelCase__     :  Optional[Any]										=    input_ids[-self.model_max_length :]
           logger.warning(F'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'		)
      return input_ids




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

import datasets
from datasets import logging


_lowerCamelCase    :		List[str]		      =    """\

"""

_lowerCamelCase    :		Optional[int]		      =    """
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
"""

_lowerCamelCase    :		List[Any]		      =    """
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 						lowercase    (							datasets.Metric):
						'''simple docstring'''


						def       lowerCamelCase_				(  self    :				List[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       lowerCamelCase_				(  self    :				List[Any]       ,	snake_case    :				Optional[int]       ,	snake_case    :				int       ,	snake_case    :				int = 16       ,	snake_case    :				bool = True       ,	snake_case    :				Dict=None	):



											'''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							:     Union[str, Any]						=				'cuda'
											else:
																SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				'cuda' if torch.cuda.is_available() else 'cpu'

											SCREAMING_SNAKE_CASE							:     Any						=				AutoModelForCausalLM.from_pretrained(snake_case	)
											SCREAMING_SNAKE_CASE							:     Dict						=				model.to(snake_case	)

											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				AutoTokenizer.from_pretrained(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:
																SCREAMING_SNAKE_CASE							:     str						=				list(tokenizer.special_tokens_map_extended.values()	)
																# check that the model already has at least one special token defined
																assert (
																    len(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"
																SCREAMING_SNAKE_CASE							:     List[str]						=				model.config.max_length - 1
											else:
																SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				model.config.max_length

											SCREAMING_SNAKE_CASE							:     Tuple						=				tokenizer(
											    snake_case       ,	add_special_tokens=snake_case       ,	padding=snake_case       ,	truncation=snake_case       ,	max_length=snake_case       ,	return_tensors='pt'       ,	return_attention_mask=snake_case       ,	).to(snake_case	)

											SCREAMING_SNAKE_CASE							:     List[Any]						=				encodings['input_ids']
											SCREAMING_SNAKE_CASE							:     Dict						=				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							:     Union[str, Any]						=				[]
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				CrossEntropyLoss(reduction='none'	)

											for start_index in logging.tqdm(range(0       ,	len(snake_case	)       ,	snake_case	)	):
																SCREAMING_SNAKE_CASE							:     Dict						=				min(start_index + batch_size       ,	len(snake_case	)	)
																SCREAMING_SNAKE_CASE							:     List[str]						=				encoded_texts[start_index:end_index]
																SCREAMING_SNAKE_CASE							:     Optional[int]						=				attn_masks[start_index:end_index]

																if add_start_token:
																					SCREAMING_SNAKE_CASE							:     List[Any]						=				torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0	)	).to(snake_case	)
																					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				torch.cat([bos_tokens_tensor, encoded_batch]       ,	dim=1	)
																					SCREAMING_SNAKE_CASE							:     int						=				torch.cat(
																					    [torch.ones(bos_tokens_tensor.size()       ,	dtype=torch.intaa	).to(snake_case	), attn_mask]       ,	dim=1	)

																SCREAMING_SNAKE_CASE							:     Optional[Any]						=				encoded_batch

																with torch.no_grad():
																					SCREAMING_SNAKE_CASE							:     str						=				model(snake_case       ,	attention_mask=snake_case	).logits

																SCREAMING_SNAKE_CASE							:     Optional[Any]						=				out_logits[..., :-1, :].contiguous()
																SCREAMING_SNAKE_CASE							:     Optional[int]						=				labels[..., 1:].contiguous()
																SCREAMING_SNAKE_CASE							:     Optional[int]						=				attn_mask[..., 1:].contiguous()

																SCREAMING_SNAKE_CASE							:     Optional[int]						=				torch.expa(
																    (loss_fct(shift_logits.transpose(1       ,	2	)       ,	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(snake_case	)}