



'''simple docstring'''


import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
       MODEL_MAPPING,
       LevitForImageClassification,
       LevitForImageClassificationWithTeacher,
       LevitModel,
   )
   from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import LevitImageProcessor

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    def  A       (  self      :					int ):


       """simple docstring"""







       UpperCamelCase					=  self.config_class(**self.inputs_dict )
       self.parent.assertTrue(hasattr(UpperCamelCase__  ,						'hidden_sizes' ) )
       self.parent.assertTrue(hasattr(UpperCamelCase__  ,						'num_attention_heads' ) )

class  SCREAMING_SNAKE_CASE :






    """simple docstring"""





    def __init__(  self      :					Optional[Any]  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					Any=1_3  ,						UpperCamelCase__      :					Dict=6_4  ,						UpperCamelCase__      :					Optional[int]=3  ,						UpperCamelCase__      :					List[Any]=3  ,						UpperCamelCase__      :					str=2  ,						UpperCamelCase__      :					Dict=1  ,						UpperCamelCase__      :					Dict=1_6  ,						UpperCamelCase__      :					Tuple=[1_2_8, 2_5_6, 3_8_4]  ,						UpperCamelCase__      :					Optional[Any]=[4, 6, 8]  ,						UpperCamelCase__      :					Dict=[2, 3, 4]  ,						UpperCamelCase__      :					List[Any]=[1_6, 1_6, 1_6]  ,						UpperCamelCase__      :					Optional[int]=0  ,						UpperCamelCase__      :					int=[2, 2, 2]  ,						UpperCamelCase__      :					Any=[2, 2, 2]  ,						UpperCamelCase__      :					List[str]=0.0_2  ,						UpperCamelCase__      :					Any=True  ,						UpperCamelCase__      :					List[Any]=True  ,						UpperCamelCase__      :					Optional[Any]=2  ,						):


       """simple docstring"""







       UpperCamelCase					=  parent
       UpperCamelCase					=  batch_size
       UpperCamelCase					=  image_size
       UpperCamelCase					=  num_channels
       UpperCamelCase					=  kernel_size
       UpperCamelCase					=  stride
       UpperCamelCase					=  padding
       UpperCamelCase					=  hidden_sizes
       UpperCamelCase					=  num_attention_heads
       UpperCamelCase					=  depths
       UpperCamelCase					=  key_dim
       UpperCamelCase					=  drop_path_rate
       UpperCamelCase					=  patch_size
       UpperCamelCase					=  attention_ratio
       UpperCamelCase					=  mlp_ratio
       UpperCamelCase					=  initializer_range
       UpperCamelCase					=  [
           ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
           ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
       ]
       UpperCamelCase					=  is_training
       UpperCamelCase					=  use_labels
       UpperCamelCase					=  num_labels
       UpperCamelCase					=  initializer_range






    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       UpperCamelCase					=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

       UpperCamelCase					=  None
       if self.use_labels:
          UpperCamelCase					=  ids_tensor([self.batch_size]  ,						self.num_labels )

       UpperCamelCase					=  self.get_config()
       return config, pixel_values, labels






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


       """simple docstring"""







       return LevitConfig(
           image_size=self.image_size  ,						num_channels=self.num_channels  ,						kernel_size=self.kernel_size  ,						stride=self.stride  ,						padding=self.padding  ,						patch_size=self.patch_size  ,						hidden_sizes=self.hidden_sizes  ,						num_attention_heads=self.num_attention_heads  ,						depths=self.depths  ,						key_dim=self.key_dim  ,						drop_path_rate=self.drop_path_rate  ,						mlp_ratio=self.mlp_ratio  ,						attention_ratio=self.attention_ratio  ,						initializer_range=self.initializer_range  ,						down_ops=self.down_ops  ,						)






    def  A       (  self      :					Any  ,						UpperCamelCase__      :					Optional[Any]  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase					=  LevitModel(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__ )
       UpperCamelCase					=  (self.image_size, self.image_size)
       UpperCamelCase				,      UpperCamelCase					=  image_size[0], image_size[1]
       for _ in range(4 ):
          UpperCamelCase					=  floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
          UpperCamelCase					=  floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
       self.parent.assertEqual(
           result.last_hidden_state.shape  ,						(self.batch_size, ceil(height / 4 ) * ceil(width / 4 ), self.hidden_sizes[-1])  ,						)






    def  A       (  self      :					List[Any]  ,						UpperCamelCase__      :					Optional[Any]  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					List[Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.num_labels
       UpperCamelCase					=  LevitForImageClassification(UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__  ,						labels=UpperCamelCase__ )
       self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_labels) )






    def  A       (  self      :					str ):


       """simple docstring"""







       UpperCamelCase					=  self.prepare_config_and_inputs()
       UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  config_and_inputs
       UpperCamelCase					=  {'pixel_values': pixel_values}
       return config, inputs_dict

@require_torch
class  SCREAMING_SNAKE_CASE (  _a   ,						_a   ,						unittest.TestCase		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  (
        (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
        if is_torch_available()
        else ()
    )
    _SCREAMING_SNAKE_CASE	  =  (
        {
            """feature-extraction""": LevitModel,
            """image-classification""": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
        }
        if is_torch_available()
        else {}
    )

    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False






    def  A       (  self      :					List[str] ):


       """simple docstring"""







       UpperCamelCase					=  LevitModelTester(self )
       UpperCamelCase					=  ConfigTester(self  ,						config_class=UpperCamelCase__  ,						has_text_modality=UpperCamelCase__  ,						hidden_size=3_7 )






    def  A       (  self      :					Optional[Any] ):


       """simple docstring"""







       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()






    def  A       (  self      :					Any ):


       """simple docstring"""







       return






    @unittest.skip(reason='Levit does not use inputs_embeds' )
    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       pass






    @unittest.skip(reason='Levit does not support input and output embeddings' )
    def  A       (  self      :					Tuple ):


       """simple docstring"""







       pass






    @unittest.skip(reason='Levit does not output attentions' )
    def  A       (  self      :					Optional[Any] ):


       """simple docstring"""







       pass






    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase				,      UpperCamelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          UpperCamelCase					=  model_class(UpperCamelCase__ )
          UpperCamelCase					=  inspect.signature(model.forward )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          UpperCamelCase					=  [*signature.parameters.keys()]

          UpperCamelCase					=  ['pixel_values']
          self.assertListEqual(arg_names[:1]  ,						UpperCamelCase__ )






    def  A       (  self      :					Dict ):


       """simple docstring"""







       def check_hidden_states_output(UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					List[Any] ):
          UpperCamelCase					=  model_class(UpperCamelCase__ )
          model.to(UpperCamelCase__ )
          model.eval()

          with torch.no_grad():
             UpperCamelCase					=  model(**self._prepare_for_class(UpperCamelCase__  ,						UpperCamelCase__ ) )

          UpperCamelCase					=  outputs.hidden_states

          UpperCamelCase					=  len(self.model_tester.depths ) + 1
          self.assertEqual(len(UpperCamelCase__ )  ,						UpperCamelCase__ )

          UpperCamelCase					=  (self.model_tester.image_size, self.model_tester.image_size)
          UpperCamelCase				,      UpperCamelCase					=  image_size[0], image_size[1]
          for _ in range(4 ):
             UpperCamelCase					=  floor(
                 (
                     (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                     / self.model_tester.stride
                 )
                 + 1 )
             UpperCamelCase					=  floor(
                 (
                     (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                     / self.model_tester.stride
                 )
                 + 1 )
          # verify the first hidden states (first block)
          self.assertListEqual(
              list(hidden_states[0].shape[-2:] )  ,						[
                  height * width,
                  self.model_tester.hidden_sizes[0],
              ]  ,						)

       UpperCamelCase				,      UpperCamelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          UpperCamelCase					=  True
          check_hidden_states_output(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          UpperCamelCase					=  True

          check_hidden_states_output(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )






    @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
    def  A       (  self      :					Dict ):


       """simple docstring"""







       pass






    def  A       (  self      :					List[Any]  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					int=False ):


       """simple docstring"""







       UpperCamelCase					=  super()._prepare_for_class(UpperCamelCase__  ,						UpperCamelCase__  ,						return_labels=UpperCamelCase__ )

       if return_labels:
          if model_class.__name__ == "LevitForImageClassificationWithTeacher":
             del inputs_dict["labels"]

       return inputs_dict






    def  A       (  self      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCamelCase__ )






    def  A       (  self      :					Dict ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__ )






    def  A       (  self      :					int ):


       """simple docstring"""







       if not self.model_tester.is_training:
          return

       UpperCamelCase				,      UpperCamelCase					=  self.model_tester.prepare_config_and_inputs_for_common()
       UpperCamelCase					=  True

       for model_class in self.all_model_classes:
          # LevitForImageClassificationWithTeacher supports inference-only
          if (
              model_class in get_values(UpperCamelCase__ )
              or model_class.__name__ == "LevitForImageClassificationWithTeacher"
          ):
             continue
          UpperCamelCase					=  model_class(UpperCamelCase__ )
          model.to(UpperCamelCase__ )
          model.train()
          UpperCamelCase					=  self._prepare_for_class(UpperCamelCase__  ,						UpperCamelCase__  ,						return_labels=UpperCamelCase__ )
          UpperCamelCase					=  model(**UpperCamelCase__ ).loss
          loss.backward()






    def  A       (  self      :					int ):


       """simple docstring"""







       UpperCamelCase				,      UpperCamelCase					=  self.model_tester.prepare_config_and_inputs_for_common()
       if not self.model_tester.is_training:
          return

       UpperCamelCase					=  False
       UpperCamelCase					=  True

       for model_class in self.all_model_classes:
          if model_class in get_values(UpperCamelCase__ ) or not model_class.supports_gradient_checkpointing:
             continue
          # LevitForImageClassificationWithTeacher supports inference-only
          if model_class.__name__ == "LevitForImageClassificationWithTeacher":
             continue
          UpperCamelCase					=  model_class(UpperCamelCase__ )
          model.gradient_checkpointing_enable()
          model.to(UpperCamelCase__ )
          model.train()
          UpperCamelCase					=  self._prepare_for_class(UpperCamelCase__  ,						UpperCamelCase__  ,						return_labels=UpperCamelCase__ )
          UpperCamelCase					=  model(**UpperCamelCase__ ).loss
          loss.backward()






    def  A       (  self      :					int ):


       """simple docstring"""







       UpperCamelCase				,      UpperCamelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

       UpperCamelCase					=  [
           {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
           {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
           {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
       ]

       for model_class in self.all_model_classes:
          if (
              model_class
              not in [
                  *get_values(UpperCamelCase__ ),
              ]
              or model_class.__name__ == "LevitForImageClassificationWithTeacher"
          ):
             continue

          for problem_type in problem_types:
             with self.subTest(msg=f"""Testing {model_class} with {problem_type['title']}""" ):
                UpperCamelCase					=  problem_type['title']
                UpperCamelCase					=  problem_type['num_labels']

                UpperCamelCase					=  model_class(UpperCamelCase__ )
                model.to(UpperCamelCase__ )
                model.train()

                UpperCamelCase					=  self._prepare_for_class(UpperCamelCase__  ,						UpperCamelCase__  ,						return_labels=UpperCamelCase__ )

                if problem_type["num_labels"] > 1:
                   UpperCamelCase					=  inputs['labels'].unsqueeze(1 ).repeat(1  ,						problem_type['num_labels'] )

                UpperCamelCase					=  inputs['labels'].to(problem_type['dtype'] )

                # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                # they have the same size." which is a symptom something in wrong for the regression problem.
                # See https://github.com/huggingface/transformers/issues/11780
                with warnings.catch_warnings(record=UpperCamelCase__ ) as warning_list:
                   UpperCamelCase					=  model(**UpperCamelCase__ ).loss
                for w in warning_list:
                   if "Using a target size that is different to the input size" in str(w.message ):
                      raise ValueError(
                          f"""Something is going wrong in the regression problem: intercepted {w.message}""" )

                loss.backward()






    @slow
    def  A       (  self      :					str ):


       """simple docstring"""







       for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          UpperCamelCase					=  LevitModel.from_pretrained(UpperCamelCase__ )
          self.assertIsNotNone(UpperCamelCase__ )







def 				__lowerCamelCase				(   )       ->	List[Any]:






   """simple docstring"""
   UpperCamelCase					=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
   return image

@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE (  unittest.TestCase		):






    """simple docstring"""





    @cached_property
    def  A       (  self      :					Dict ):


       """simple docstring"""







       return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )






    @slow
    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
           UpperCamelCase__ )

       UpperCamelCase					=  self.default_image_processor
       UpperCamelCase					=  prepare_img()
       UpperCamelCase					=  image_processor(images=UpperCamelCase__  ,						return_tensors='pt' ).to(UpperCamelCase__ )

       # forward pass
       with torch.no_grad():
          UpperCamelCase					=  model(**UpperCamelCase__ )

       # verify the logits
       UpperCamelCase					=  torch.Size((1, 1_0_0_0) )
       self.assertEqual(outputs.logits.shape  ,						UpperCamelCase__ )

       UpperCamelCase					=  torch.tensor([1.0_4_4_8, -0.3_7_4_5, -1.8_3_1_7] ).to(UpperCamelCase__ )

       self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,						UpperCamelCase__  ,						atol=1E-4 ) )






'''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	:       Union[str, Any]      	=	"\\n\n"

_lowerCamelCase	:       List[str]      	=	"\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	:       Dict      	=	"\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		):






    """simple docstring"""





    def  A       (  self      :					Tuple ):


       """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[Any]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Optional[Any]  ,						UpperCamelCase__      :					int = 1_6  ,						UpperCamelCase__      :					bool = True  ,						UpperCamelCase__      :					List[Any]=None ):


       """simple docstring"""







       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(UpperCamelCase__ )
       UpperCamelCase					=  model.to(UpperCamelCase__ )

       UpperCamelCase					=  AutoTokenizer.from_pretrained(UpperCamelCase__ )

       # 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(UpperCamelCase__ ) > 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(
           UpperCamelCase__  ,						add_special_tokens=UpperCamelCase__  ,						padding=UpperCamelCase__  ,						truncation=UpperCamelCase__  ,						max_length=UpperCamelCase__  ,						return_tensors='pt'  ,						return_attention_mask=UpperCamelCase__  ,						).to(UpperCamelCase__ )

       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(UpperCamelCase__ )  ,						UpperCamelCase__ ) ):
          UpperCamelCase					=  min(start_index + batch_size  ,						len(UpperCamelCase__ ) )
          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(UpperCamelCase__ )
             UpperCamelCase					=  torch.cat([bos_tokens_tensor, encoded_batch]  ,						dim=1 )
             UpperCamelCase					=  torch.cat(
                 [torch.ones(bos_tokens_tensor.size()  ,						dtype=torch.intaa ).to(UpperCamelCase__ ), attn_mask]  ,						dim=1 )

          UpperCamelCase					=  encoded_batch

          with torch.no_grad():
             UpperCamelCase					=  model(UpperCamelCase__  ,						attention_mask=UpperCamelCase__ ).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 )  ,						UpperCamelCase__ ) * shift_attention_mask_batch).sum(1 )
              / shift_attention_mask_batch.sum(1 ) )

          ppls += perplexity_batch.tolist()

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

