





import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile

import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load


try:
    import torch

    lowercase      :       Optional[int]									=  True
except ImportError:
    lowercase      :       int									=  False


try:
    from torch.hub import _get_torch_home

    lowercase      :       List[Any]									=  _get_torch_home()
except ImportError:
    lowercase      :       Optional[int]									=  os.path.expanduser(
        os.getenv("""TORCH_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """torch"""))
    )

lowercase      :       List[str]									=  os.path.join(torch_cache_home, """transformers""")

lowercase      :       List[str]									=  """https://cdn.huggingface.co"""
lowercase      :       int									=  """https://s3.amazonaws.com/models.huggingface.co/bert"""
lowercase      :       int									=  """/""".join(str(Path(__file__).resolve()).split("""/""")[:-1])
lowercase      :       List[Any]									=  os.path.join(PATH, """config.yaml""")
lowercase      :       List[str]									=  os.path.join(PATH, """attributes.txt""")
lowercase      :       Optional[Any]									=  os.path.join(PATH, """objects.txt""")
lowercase      :       Tuple									=  os.getenv("""PYTORCH_PRETRAINED_BERT_CACHE""", default_cache_path)
lowercase      :       int									=  os.getenv("""PYTORCH_TRANSFORMERS_CACHE""", PYTORCH_PRETRAINED_BERT_CACHE)
lowercase      :       Optional[Any]									=  os.getenv("""TRANSFORMERS_CACHE""", PYTORCH_TRANSFORMERS_CACHE)
lowercase      :       Tuple									=  """pytorch_model.bin"""
lowercase      :       int									=  """config.yaml"""



def A_	(		A__=OBJECTS		,      A__=ATTRIBUTES				)  ->		Union[str, Any]:
       a__						:	Dict =				[]
       with open(A__				) as f:
              for object in f.readlines():
                     vg_classes.append(object.split(','				)[0].lower().strip()				)

       a__						:	Optional[int] =				[]
       with open(A__				) as f:
              for object in f.readlines():
                     vg_attrs.append(object.split(','				)[0].lower().strip()				)
       return vg_classes, vg_attrs



def A_	(		A__				)  ->		Union[str, Any]:
       a__						:	Optional[Any] =				OrderedDict()
       with open(A__		,      'rb'				) as f:
              a__						:	List[str] =				pkl.load(A__				)['model']
       for k in copy.deepcopy(list(ckp.keys()				)				):
              a__						:	Tuple =				ckp.pop(A__				)
              if isinstance(A__		,      np.ndarray				):
                     a__						:	str =				torch.tensor(A__				)
              else:
                     assert isinstance(A__		,      torch.tensor				), type(A__				)
              a__						:	Optional[int] =				v
       return r





class  A__    :




   """simple docstring"""



   __A   :				Optional[Any]         =  {}

   def __init__(					self					,  lowercase					,  lowercase = "root"					,  lowercase=0)			->						Union[str, Any]:







          '''simple docstring'''

          a__						:	List[str] =				name
          a__						:	Optional[Any] =				level
          a__						:	Tuple =				{}
          for k, v in dictionary.items():
                 if v is None:
                        raise ValueError()
                 a__						:	Dict =				copy.deepcopy(lowercase)
                 a__						:	Any =				copy.deepcopy(lowercase)
                 if isinstance(lowercase					,  lowercase):
                        a__						:	Tuple =				Config(lowercase					,  name=lowercase					,  level=level + 1)
                 a__						:	Dict =				v
                 setattr(self					,  lowercase					,  lowercase)

          a__						:	List[Any] =				d

   def __repr__(					self)			->						Optional[int]:







          '''simple docstring'''

          return str(list((self._pointer.keys())))

   def __setattr__(					self					,  lowercase					,  lowercase)			->						Optional[int]:







          '''simple docstring'''

          a__						:	str =				val
          a__						:	Tuple =				val
          a__						:	int =				key.split('.')
          a__						:	Tuple =				len(lowercase) - 1
          a__						:	int =				self._pointer
          if len(lowercase) > 1:
                 for i, l in enumerate(lowercase):
                        if hasattr(self					,  lowercase) and isinstance(getattr(self					,  lowercase)					,  lowercase):
                               setattr(getattr(self					,  lowercase)					,  '.'.join(levels[i:])					,  lowercase)
                        if l == last_level:
                               a__						:	List[str] =				val
                        else:
                               a__						:	Tuple =				pointer[l]

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          return self._pointer

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						str:







          '''simple docstring'''

          with open(F'{file_name}'					,  'w') as stream:
                 dump(lowercase					,  lowercase)

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						Optional[int]:







          '''simple docstring'''

          with open(F'{file_name}'					,  'w') as stream:
                 json.dump(lowercase					,  lowercase)

   @staticmethod
   def 							__lowercase		(					lowercase)			->						Optional[int]:







          '''simple docstring'''

          with open(lowercase) as stream:
                 a__						:	Tuple =				load(lowercase					,  Loader=lowercase)
          return data

   def __str__(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	Optional[int] =				'    '
          if self._name != "root":
                 a__						:	Any =				F'{t * (self._level-1)}{self._name}:\n'
          else:
                 a__						:	Dict =				''
          a__						:	Tuple =				self._level
          for i, (k, v) in enumerate(self._pointer.items()):
                 if isinstance(lowercase					,  lowercase):
                        r += F'{t * (self._level)}{v}\n'
                        self._level += 1
                 else:
                        r += F'{t * (self._level)}{k}: {v} ({type(lowercase).__name__})\n'
                 a__						:	Tuple =				level
          return r[:-1]

   @classmethod
   def 							__lowercase		(					cls					,  lowercase					,  **lowercase)			->						Tuple:







          '''simple docstring'''

          a__ ,							a__						:	Optional[Any] =				cls.get_config_dict(lowercase					,  **lowercase)
          return cls(lowercase)

   @classmethod
   def 							__lowercase		(					cls					,  lowercase					,  **lowercase)			->						Union[str, Any]:







          '''simple docstring'''

          a__						:	Union[str, Any] =				kwargs.pop('cache_dir'					,  lowercase)
          a__						:	Tuple =				kwargs.pop('force_download'					,  lowercase)
          a__						:	str =				kwargs.pop('resume_download'					,  lowercase)
          a__						:	Tuple =				kwargs.pop('proxies'					,  lowercase)
          a__						:	List[str] =				kwargs.pop('local_files_only'					,  lowercase)

          if os.path.isdir(lowercase):
                 a__						:	int =				os.path.join(lowercase					,  lowercase)
          elif os.path.isfile(lowercase) or is_remote_url(lowercase):
                 a__						:	Union[str, Any] =				pretrained_model_name_or_path
          else:
                 a__						:	Tuple =				hf_bucket_url(lowercase					,  filename=lowercase					,  use_cdn=lowercase)

          try:
                 # Load from URL or cache if already cached
                 a__						:	Optional[Any] =				cached_path(
                     lowercase					,  cache_dir=lowercase					,  force_download=lowercase					,  proxies=lowercase					,  resume_download=lowercase					,  local_files_only=lowercase					,  )
                 # Load config dict
                 if resolved_config_file is None:
                        raise EnvironmentError

                 a__						:	Optional[int] =				Config.load_yaml(lowercase)

          except EnvironmentError:
                 a__						:	List[str] =				'Can\'t load config for'
                 raise EnvironmentError(lowercase)

          if resolved_config_file == config_file:
                 print('loading configuration file from path')
          else:
                 print('loading configuration file cache')

          return Config.load_yaml(lowercase), kwargs



def A_	(		A__				)  ->		Union[str, Any]:
       a__						:	str =				torch.load('dump.pt'		,      map_location=in_tensor.device				)
       a__						:	Optional[Any] =				in_tensor.numpy()
       a__						:	List[Any] =				out_tensor.numpy()[0]
       print(na.shape		,      na[0, 0, :5]				)
       print(na.shape		,      na[0, 0, :5]				)
       assert np.allclose(A__		,      A__		,      rtol=0.01		,      atol=0.1				), (
           F'{sum([1 for x in np.isclose(A__		,      A__		,      rtol=0.01		,      atol=0.1				).flatten() if x is False]				)/len(na.flatten()				)*100:.4f} %'
           " element-wise mismatch"
       )
       raise Exception('tensors are all good'				)

       # Hugging face functions below



def A_	(		A__				)  ->		str:
       a__						:	Dict =				urlparse(A__				)
       return parsed.scheme in ("http", "https")



def A_	(		A__		,      A__		,      A__=True				)  ->		str:
       a__						:	int =				CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
       a__						:	Any =				'/' not in model_id
       if legacy_format:
              return F'{endpoint}/{model_id}-{filename}'
       else:
              return F'{endpoint}/{model_id}/{filename}'



def A_	(		A__		,      A__		,      A__=None		,      A__=0		,      A__=None		,      )  ->		Union[str, Any]:
       a__						:	int =				'python/{}'.format(sys.version.split()[0]				)
       if _torch_available:
              ua += "; torch/{}".format(torch.__version__				)
       if isinstance(A__		,      A__				):
              ua += "; " + "; ".join('{}/{}'.format(A__		,      A__				) for k, v in user_agent.items()				)
       elif isinstance(A__		,      A__				):
              ua += "; " + user_agent
       a__						:	Dict =				{'user-agent': ua}
       if resume_size > 0:
              a__						:	Optional[Any] =				'bytes=%d-' % (resume_size,)
       a__						:	Any =				requests.get(A__		,      stream=A__		,      proxies=A__		,      headers=A__				)
       if response.status_code == 416:  # Range not satisfiable
              return
       a__						:	List[Any] =				response.headers.get('Content-Length'				)
       a__						:	Optional[Any] =				resume_size + int(A__				) if content_length is not None else None
       a__						:	Any =				tqdm(
           unit='B'		,      unit_scale=A__		,      total=A__		,      initial=A__		,      desc='Downloading'		,      )
       for chunk in response.iter_content(chunk_size=1024				):
              if chunk:  # filter out keep-alive new chunks
                     progress.update(len(A__				)				)
                     temp_file.write(A__				)
       progress.close()



def A_	(		A__		,      A__=None		,      A__=False		,      A__=None		,      A__=10		,      A__=False		,      A__=None		,      A__=False		,      )  ->		List[str]:
       if cache_dir is None:
              a__						:	str =				TRANSFORMERS_CACHE
       if isinstance(A__		,      A__				):
              a__						:	Dict =				str(A__				)

       os.makedirs(A__		,      exist_ok=A__				)

       a__						:	Union[str, Any] =				None
       if not local_files_only:
              try:
                     a__						:	Union[str, Any] =				requests.head(A__		,      allow_redirects=A__		,      proxies=A__		,      timeout=A__				)
                     if response.status_code == 200:
                            a__						:	Union[str, Any] =				response.headers.get('ETag'				)
              except (EnvironmentError, requests.exceptions.Timeout):
                     # etag is already None
                     pass

       a__						:	Optional[Any] =				url_to_filename(A__		,      A__				)

       # get cache path to put the file
       a__						:	str =				os.path.join(A__		,      A__				)

       # etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
       # try to get the last downloaded one
       if etag is None:
              if os.path.exists(A__				):
                     return cache_path
              else:
                     a__						:	List[Any] =				[
                         file
                         for file in fnmatch.filter(os.listdir(A__				)		,      filename + '.*'				)
                         if not file.endswith('.json'				) and not file.endswith('.lock'				)
                     ]
                     if len(A__				) > 0:
                            return os.path.join(A__		,      matching_files[-1]				)
                     else:
                            # If files cannot be found and local_files_only=True,
                            # the models might've been found if local_files_only=False
                            # Notify the user about that
                            if local_files_only:
                                   raise ValueError(
                                       'Cannot find the requested files in the cached path and outgoing traffic has been'
                                       ' disabled. To enable model look-ups and downloads online, set \'local_files_only\''
                                       ' to False.'				)
                            return None

    # From now on, etag is not None.
       if os.path.exists(A__				) and not force_download:
              return cache_path

       # Prevent parallel downloads of the same file with a lock.
       a__						:	str =				cache_path + '.lock'
       with FileLock(A__				):
              # If the download just completed while the lock was activated.
              if os.path.exists(A__				) and not force_download:
                     # Even if returning early like here, the lock will be released.
                     return cache_path

              if resume_download:
                     a__						:	Any =				cache_path + '.incomplete'

                     @contextmanager
                     def _resumable_file_manager():
                            with open(A__		,      'a+b'				) as f:
                                   yield f

                     a__						:	Optional[Any] =				_resumable_file_manager
                     if os.path.exists(A__				):
                            a__						:	str =				os.stat(A__				).st_size
                     else:
                            a__						:	str =				0
              else:
                     a__						:	List[Any] =				partial(tempfile.NamedTemporaryFile		,      dir=A__		,      delete=A__				)
                     a__						:	Optional[Any] =				0

              # Download to temporary file, then copy to cache dir once finished.
              # Otherwise you get corrupt cache entries if the download gets interrupted.
              with temp_file_manager() as temp_file:
                     print(
                         '%s not found in cache or force_download set to True, downloading to %s'		,      A__		,      temp_file.name		,      )

                     http_get(
                         A__		,      A__		,      proxies=A__		,      resume_size=A__		,      user_agent=A__		,      )

              os.replace(temp_file.name		,      A__				)

              a__						:	Dict =				{'url': url, 'etag': etag}
              a__						:	str =				cache_path + '.json'
              with open(A__		,      'w'				) as meta_file:
                     json.dump(A__		,      A__				)

       return cache_path



def A_	(		A__		,      A__=None				)  ->		Union[str, Any]:
       a__						:	int =				url.encode('utf-8'				)
       a__						:	str =				shaaaa(A__				)
       a__						:	Union[str, Any] =				url_hash.hexdigest()

       if etag:
              a__						:	Optional[Any] =				etag.encode('utf-8'				)
              a__						:	Tuple =				shaaaa(A__				)
              filename += "." + etag_hash.hexdigest()

       if url.endswith('.h5'				):
              filename += ".h5"

       return filename



def A_	(		A__		,      A__=None		,      A__=False		,      A__=None		,      A__=False		,      A__=None		,      A__=False		,      A__=False		,      A__=False		,      )  ->		Any:
       if cache_dir is None:
              a__						:	Tuple =				TRANSFORMERS_CACHE
       if isinstance(A__		,      A__				):
              a__						:	Optional[Any] =				str(A__				)
       if isinstance(A__		,      A__				):
              a__						:	Optional[int] =				str(A__				)

       if is_remote_url(A__				):
              # URL, so get it from the cache (downloading if necessary)
              a__						:	Any =				get_from_cache(
                  A__		,      cache_dir=A__		,      force_download=A__		,      proxies=A__		,      resume_download=A__		,      user_agent=A__		,      local_files_only=A__		,      )
       elif os.path.exists(A__				):
              # File, and it exists.
              a__						:	Tuple =				url_or_filename
       elif urlparse(A__				).scheme == "":
              # File, but it doesn't exist.
              raise EnvironmentError('file {} not found'.format(A__				)				)
       else:
              # Something unknown
              raise ValueError('unable to parse {} as a URL or as a local path'.format(A__				)				)

       if extract_compressed_file:
              if not is_zipfile(A__				) and not tarfile.is_tarfile(A__				):
                     return output_path

              # Path where we extract compressed archives
              # We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
              a__ ,							a__						:	List[Any] =				os.path.split(A__				)
              a__						:	Dict =				output_file.replace('.'		,      '-'				) + '-extracted'
              a__						:	Union[str, Any] =				os.path.join(A__		,      A__				)

              if os.path.isdir(A__				) and os.listdir(A__				) and not force_extract:
                     return output_path_extracted

              # Prevent parallel extractions
              a__						:	List[Any] =				output_path + '.lock'
              with FileLock(A__				):
                     shutil.rmtree(A__		,      ignore_errors=A__				)
                     os.makedirs(A__				)
                     if is_zipfile(A__				):
                            with ZipFile(A__		,      'r'				) as zip_file:
                                   zip_file.extractall(A__				)
                                   zip_file.close()
                     elif tarfile.is_tarfile(A__				):
                            a__						:	List[Any] =				tarfile.open(A__				)
                            tar_file.extractall(A__				)
                            tar_file.close()
                     else:
                            raise EnvironmentError('Archive format of {} could not be identified'.format(A__				)				)

              return output_path_extracted

       return output_path



def A_	(		A__		,      A__=","				)  ->		Union[str, Any]:
       assert isinstance(A__		,      A__				)
       if os.path.isfile(A__				):
              with open(A__				) as f:
                     a__						:	List[Any] =				eval(f.read()				)
       else:
              a__						:	Optional[Any] =				requests.get(A__				)
              try:
                     a__						:	str =				requests.json()
              except Exception:
                     a__						:	str =				req.content.decode()
                     assert data is not None, "could not connect"
                     try:
                            a__						:	Tuple =				eval(A__				)
                     except Exception:
                            a__						:	List[Any] =				data.split('\n'				)
              req.close()
       return data



def A_	(		A__				)  ->		List[str]:
       a__						:	str =				requests.get(A__				)
       a__						:	str =				np.array(Image.open(BytesIO(response.content				)				)				)
       return img



def A_	(		A__				)  ->		Any:
       a__						:	List[str] =				url.split('/'				)[-1]
       if fn not in os.listdir(os.getcwd()				):
              wget.download(A__				)
       with open(A__		,      'rb'				) as stream:
              a__						:	Tuple =				pkl.load(A__				)
       a__						:	Optional[Any] =				weights.pop('model'				)
       a__						:	int =				{}
       for k, v in model.items():
              a__						:	List[str] =				torch.from_numpy(A__				)
              if "running_var" in k:
                     a__						:	str =				torch.tensor([0]				)
                     a__						:	List[Any] =				k.replace('running_var'		,      'num_batches_tracked'				)
                     a__						:	int =				zero
       return new



def A_	(		)  ->		List[Any]:
       print(F'{os.path.abspath(os.path.join(A__		,      os.pardir				)				)}/demo.ipynb'				)



def A_	(		A__		,      A__="RGB"				)  ->		List[Any]:
       assert isinstance(A__		,      A__				)
       if os.path.isfile(A__				):
              a__						:	Any =				cva.imread(A__				)
       else:
              a__						:	Dict =				get_image_from_url(A__				)
              assert img is not None, F'could not connect to: {im}'
       a__						:	Dict =				cva.cvtColor(A__		,      cva.COLOR_BGR2RGB				)
       if input_format == "RGB":
              a__						:	Optional[int] =				img[:, :, ::-1]
       return img



def A_	(		A__		,      A__=1				)  ->		Tuple:
       return (images[i : i + batch] for i in range(0		,      len(A__				)		,      A__				))



"""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__			)}



