'''simple docstring'''


import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_						,     lowerCamelCase_						,     unittest.TestCase	):







  '''simple docstring'''






  lowerCamelCase_		:List[Any]						   =		AutoencoderKL
  lowerCamelCase_		:Dict						   =		'''sample'''
  lowerCamelCase_		:str						   =		1E-2


  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				4
       UpperCAmelCase_							:       Optional[Any]       =				3
       UpperCAmelCase_							:       List[Any]       =				(3_2, 3_2)

       UpperCAmelCase_							:       Any       =				floats_tensor((batch_size, num_channels) + sizes						).to(snake_case_						)

       return {"sample": image}


  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       return (3, 3_2, 3_2)


  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       return (3, 3_2, 3_2)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				{
           'block_out_channels': [3_2, 6_4],
           'in_channels': 3,
           'out_channels': 3,
           'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
           'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
           'latent_channels': 4,
       }
       UpperCAmelCase_							:       Optional[int]       =				self.dummy_input
       return init_dict, inputs_dict


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       pass


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       pass


  @unittest.skipIf(torch_device == 'mps'				,		'Gradient checkpointing skipped on MPS'						)
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				self.prepare_init_args_and_inputs_for_common()
       UpperCAmelCase_							:       str       =				self.model_class(**snake_case_						)
       model.to(snake_case_						)

       assert not model.is_gradient_checkpointing and model.training

       UpperCAmelCase_							:       Union[str, Any]       =				model(**snake_case_						).sample
       # run the backwards pass on the model. For backwards pass, for simplicity purpose,
       # we won't calculate the loss and rather backprop on out.sum()
       model.zero_grad()

       UpperCAmelCase_							:       List[Any]       =				torch.randn_like(snake_case_						)
       UpperCAmelCase_							:       Dict       =				(out - labels).mean()
       loss.backward()

       # re-instantiate the model now enabling gradient checkpointing
       UpperCAmelCase_							:       Tuple       =				self.model_class(**snake_case_						)
       # clone model
       model_a.load_state_dict(model.state_dict()						)
       model_a.to(snake_case_						)
       model_a.enable_gradient_checkpointing()

       assert model_a.is_gradient_checkpointing and model_a.training

       UpperCAmelCase_							:       Any       =				model_a(**snake_case_						).sample
       # run the backwards pass on the model. For backwards pass, for simplicity purpose,
       # we won't calculate the loss and rather backprop on out.sum()
       model_a.zero_grad()
       UpperCAmelCase_							:       Tuple       =				(out_a - labels).mean()
       loss_a.backward()

       # compare the output and parameters gradients
       self.assertTrue((loss - loss_a).abs() < 1E-5						)
       UpperCAmelCase_							:       Optional[int]       =				dict(model.named_parameters()						)
       UpperCAmelCase_							:       Any       =				dict(model_a.named_parameters()						)
       for name, param in named_params.items():
            self.assertTrue(torch_all_close(param.grad.data				,		named_params_a[name].grad.data				,		atol=5E-5						)						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'				,		output_loading_info=snake_case_						)
       self.assertIsNotNone(snake_case_						)
       self.assertEqual(len(loading_info['missing_keys']						)				,		0						)

       model.to(snake_case_						)
       UpperCAmelCase_							:       str       =				model(**self.dummy_input						)

       assert image is not None, "Make sure output is not None"






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'						)
       UpperCAmelCase_							:       Optional[int]       =				model.to(snake_case_						)
       model.eval()

       if torch_device == "mps":
            UpperCAmelCase_							:       Optional[Any]       =				torch.manual_seed(0						)
       else:
            UpperCAmelCase_							:       List[str]       =				torch.Generator(device=snake_case_						).manual_seed(0						)

       UpperCAmelCase_							:       List[str]       =				torch.randn(
           1				,		model.config.in_channels				,		model.config.sample_size				,		model.config.sample_size				,		generator=torch.manual_seed(0						)				,		)
       UpperCAmelCase_							:       int       =				image.to(snake_case_						)
       with torch.no_grad():
            UpperCAmelCase_							:       str       =				model(snake_case_				,		sample_posterior=snake_case_				,		generator=snake_case_						).sample

       UpperCAmelCase_							:       Union[str, Any]       =				output[0, -1, -3:, -3:].flatten().cpu()

       # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
       # the expected output slices are not the same for CPU and GPU.
       if torch_device == "mps":
            UpperCAmelCase_							:       Optional[int]       =				torch.tensor(
                [
                    -4.0_078E-01,
                    -3.8_323E-04,
                    -1.2_681E-01,
                    -1.1_462E-01,
                    2.0_095E-01,
                    1.0_893E-01,
                    -8.8_247E-02,
                    -3.0_361E-01,
                    -9.8_644E-03,
                ]						)
       elif torch_device == "cpu":
            UpperCAmelCase_							:       Tuple       =				torch.tensor(
                [-0.13_52, 0.08_78, 0.04_19, -0.08_18, -0.10_69, 0.06_88, -0.14_58, -0.44_46, -0.00_26]						)
       else:
            UpperCAmelCase_							:       str       =				torch.tensor(
                [-0.24_21, 0.46_42, 0.25_07, -0.04_38, 0.06_82, 0.31_60, -0.20_18, -0.07_27, 0.24_85]						)

       self.assertTrue(torch_all_close(snake_case_				,		snake_case_				,		rtol=1E-2						)						)







@slow
class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       return F'''gaussian_noise_s={seed}_shape={'_'.join([str(snake_case_						) for s in shape]						)}.npy'''


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()


  def _UpperCamelCase					(			self				,		snake_case_=0				,		snake_case_=(4, 3, 5_1_2, 5_1_2)				,		snake_case_=False						):




       '''simple docstring'''

       UpperCAmelCase_							:       Tuple       =				torch.floataa if fpaa else torch.floataa
       UpperCAmelCase_							:       Any       =				torch.from_numpy(load_hf_numpy(self.get_file_format(snake_case_				,		snake_case_						)						)						).to(snake_case_						).to(snake_case_						)
       return image


  def _UpperCamelCase					(			self				,		snake_case_="CompVis/stable-diffusion-v1-4"				,		snake_case_=False						):




       '''simple docstring'''

       UpperCAmelCase_							:       Tuple       =				'fp16' if fpaa else None
       UpperCAmelCase_							:       str       =				torch.floataa if fpaa else torch.floataa

       UpperCAmelCase_							:       List[Any]       =				AutoencoderKL.from_pretrained(
           snake_case_				,		subfolder='vae'				,		torch_dtype=snake_case_				,		revision=snake_case_				,		)
       model.to(snake_case_						).eval()

       return model


  def _UpperCamelCase					(			self				,		snake_case_=0						):




       '''simple docstring'''

       if torch_device == "mps":
            return torch.manual_seed(snake_case_						)
       return torch.Generator(device=snake_case_						).manual_seed(snake_case_						)


  @parameterized.expand(
      [
          # fmt: off
          [3_3, [-0.16_03, 0.98_78, -0.04_95, -0.07_90, -0.27_09, 0.83_75, -0.20_60, -0.08_24], [-0.23_95, 0.00_98, 0.01_02, -0.07_09, -0.28_40, -0.02_74, -0.07_18, -0.18_24]],
          [4_7, [-0.23_76, 0.11_68, 0.13_32, -0.48_40, -0.25_08, -0.07_91, -0.04_93, -0.40_89], [0.03_50, 0.08_47, 0.04_67, 0.03_44, -0.08_42, -0.05_47, -0.06_33, -0.11_31]],
          # fmt: on
      ]						)
  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				self.get_sd_vae_model()
       UpperCAmelCase_							:       List[str]       =				self.get_sd_image(snake_case_						)
       UpperCAmelCase_							:       List[Any]       =				self.get_generator(snake_case_						)

       with torch.no_grad():
            UpperCAmelCase_							:       Optional[Any]       =				model(snake_case_				,		generator=snake_case_				,		sample_posterior=snake_case_						).sample

       assert sample.shape == image.shape

       UpperCAmelCase_							:       Dict       =				sample[-1, -2:, -2:, :2].flatten().float().cpu()
       UpperCAmelCase_							:       int       =				torch.tensor(expected_slice_mps if torch_device == 'mps' else expected_slice						)

       assert torch_all_close(snake_case_				,		snake_case_				,		atol=3E-3						)


  @parameterized.expand(
      [
          # fmt: off
          [3_3, [-0.05_13, 0.02_89, 1.37_99, 0.21_66, -0.25_73, -0.08_71, 0.51_03, -0.09_99]],
          [4_7, [-0.41_28, -0.13_20, -0.37_04, 0.19_65, -0.41_16, -0.23_32, -0.33_40, 0.22_47]],
          # fmt: on
      ]						)
  @require_torch_gpu
  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				self.get_sd_vae_model(fpaa=snake_case_						)
       UpperCAmelCase_							:       int       =				self.get_sd_image(snake_case_				,		fpaa=snake_case_						)
       UpperCAmelCase_							:       Dict       =				self.get_generator(snake_case_						)

       with torch.no_grad():
            UpperCAmelCase_							:       Dict       =				model(snake_case_				,		generator=snake_case_				,		sample_posterior=snake_case_						).sample

       assert sample.shape == image.shape

       UpperCAmelCase_							:       Union[str, Any]       =				sample[-1, -2:, :2, -2:].flatten().float().cpu()
       UpperCAmelCase_							:       Any       =				torch.tensor(snake_case_						)

       assert torch_all_close(snake_case_				,		snake_case_				,		atol=1E-2						)


  @parameterized.expand(
      [
          # fmt: off
          [3_3, [-0.16_09, 0.98_66, -0.04_87, -0.07_77, -0.27_16, 0.83_68, -0.20_55, -0.08_14], [-0.23_95, 0.00_98, 0.01_02, -0.07_09, -0.28_40, -0.02_74, -0.07_18, -0.18_24]],
          [4_7, [-0.23_77, 0.11_47, 0.13_33, -0.48_41, -0.25_06, -0.08_05, -0.04_91, -0.40_85], [0.03_50, 0.08_47, 0.04_67, 0.03_44, -0.08_42, -0.05_47, -0.06_33, -0.11_31]],
          # fmt: on
      ]						)
  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				self.get_sd_vae_model()
       UpperCAmelCase_							:       Optional[int]       =				self.get_sd_image(snake_case_						)

       with torch.no_grad():
            UpperCAmelCase_							:       List[str]       =				model(snake_case_						).sample

       assert sample.shape == image.shape

       UpperCAmelCase_							:       str       =				sample[-1, -2:, -2:, :2].flatten().float().cpu()
       UpperCAmelCase_							:       int       =				torch.tensor(expected_slice_mps if torch_device == 'mps' else expected_slice						)

       assert torch_all_close(snake_case_				,		snake_case_				,		atol=3E-3						)


  @parameterized.expand(
      [
          # fmt: off
          [1_3, [-0.20_51, -0.18_03, -0.23_11, -0.21_14, -0.32_92, -0.35_74, -0.29_53, -0.33_23]],
          [3_7, [-0.26_32, -0.26_25, -0.21_99, -0.27_41, -0.45_39, -0.49_90, -0.37_20, -0.49_25]],
          # fmt: on
      ]						)
  @require_torch_gpu
  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				self.get_sd_vae_model()
       UpperCAmelCase_							:       List[str]       =				self.get_sd_image(snake_case_				,		shape=(3, 4, 6_4, 6_4)						)

       with torch.no_grad():
            UpperCAmelCase_							:       Optional[int]       =				model.decode(snake_case_						).sample

       assert list(sample.shape						) == [3, 3, 5_1_2, 5_1_2]

       UpperCAmelCase_							:       int       =				sample[-1, -2:, :2, -2:].flatten().cpu()
       UpperCAmelCase_							:       List[str]       =				torch.tensor(snake_case_						)

       assert torch_all_close(snake_case_				,		snake_case_				,		atol=1E-3						)


  @parameterized.expand(
      [
          # fmt: off
          [2_7, [-0.03_69, 0.02_07, -0.07_76, -0.06_82, -0.17_47, -0.19_30, -0.14_65, -0.20_39]],
          [1_6, [-0.16_28, -0.21_34, -0.27_47, -0.26_42, -0.37_74, -0.44_04, -0.36_87, -0.42_77]],
          # fmt: on
      ]						)
  @require_torch_gpu
  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				self.get_sd_vae_model(fpaa=snake_case_						)
       UpperCAmelCase_							:       List[Any]       =				self.get_sd_image(snake_case_				,		shape=(3, 4, 6_4, 6_4)				,		fpaa=snake_case_						)

       with torch.no_grad():
            UpperCAmelCase_							:       int       =				model.decode(snake_case_						).sample

       assert list(sample.shape						) == [3, 3, 5_1_2, 5_1_2]

       UpperCAmelCase_							:       List[str]       =				sample[-1, -2:, :2, -2:].flatten().float().cpu()
       UpperCAmelCase_							:       Dict       =				torch.tensor(snake_case_						)

       assert torch_all_close(snake_case_				,		snake_case_				,		atol=5E-3						)


  @parameterized.expand([(1_3,), (1_6,), (2_7,)]						)
  @require_torch_gpu
  @unittest.skipIf(not is_xformers_available()				,		reason='xformers is not required when using PyTorch 2.0.'						)
  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				self.get_sd_vae_model(fpaa=snake_case_						)
       UpperCAmelCase_							:       Dict       =				self.get_sd_image(snake_case_				,		shape=(3, 4, 6_4, 6_4)				,		fpaa=snake_case_						)

       with torch.no_grad():
            UpperCAmelCase_							:       List[str]       =				model.decode(snake_case_						).sample

       model.enable_xformers_memory_efficient_attention()
       with torch.no_grad():
            UpperCAmelCase_							:       Optional[int]       =				model.decode(snake_case_						).sample

       assert list(sample.shape						) == [3, 3, 5_1_2, 5_1_2]

       assert torch_all_close(snake_case_				,		snake_case_				,		atol=1E-1						)


  @parameterized.expand([(1_3,), (1_6,), (3_7,)]						)
  @require_torch_gpu
  @unittest.skipIf(not is_xformers_available()				,		reason='xformers is not required when using PyTorch 2.0.'						)
  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				self.get_sd_vae_model()
       UpperCAmelCase_							:       List[str]       =				self.get_sd_image(snake_case_				,		shape=(3, 4, 6_4, 6_4)						)

       with torch.no_grad():
            UpperCAmelCase_							:       List[Any]       =				model.decode(snake_case_						).sample

       model.enable_xformers_memory_efficient_attention()
       with torch.no_grad():
            UpperCAmelCase_							:       Optional[int]       =				model.decode(snake_case_						).sample

       assert list(sample.shape						) == [3, 3, 5_1_2, 5_1_2]

       assert torch_all_close(snake_case_				,		snake_case_				,		atol=1E-2						)






  @parameterized.expand(
      [
          # fmt: off
          [3_3, [-0.30_01, 0.09_18, -2.69_84, -3.97_20, -3.20_99, -5.03_53, 1.73_38, -0.20_65, 3.42_67]],
          [4_7, [-1.50_30, -4.38_71, -6.03_55, -9.11_57, -1.66_61, -2.78_53, 2.16_07, -5.08_23, 2.56_33]],
          # fmt: on
      ]						)
  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				self.get_sd_vae_model()
       UpperCAmelCase_							:       Dict       =				self.get_sd_image(snake_case_						)
       UpperCAmelCase_							:       Optional[Any]       =				self.get_generator(snake_case_						)

       with torch.no_grad():
            UpperCAmelCase_							:       Optional[Any]       =				model.encode(snake_case_						).latent_dist
            UpperCAmelCase_							:       List[Any]       =				dist.sample(generator=snake_case_						)

       assert list(sample.shape						) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

       UpperCAmelCase_							:       Optional[Any]       =				sample[0, -1, -3:, -3:].flatten().cpu()
       UpperCAmelCase_							:       Tuple       =				torch.tensor(snake_case_						)

       UpperCAmelCase_							:       Tuple       =				3E-3 if torch_device != 'mps' else 1E-2
       assert torch_all_close(snake_case_				,		snake_case_				,		atol=snake_case_						)







'''simple docstring'''


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

import datasets
from datasets import logging


snake_case__					:      str				      =  '''\

'''

snake_case__					:      Union[str, 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
'''

snake_case__					:      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	):







  '''simple docstring'''






  def _UpperCamelCase					(			self						):




       '''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 _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_ = 1_6				,		snake_case_ = True				,		snake_case_=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_							:       int       =				'cuda'
       else:
            UpperCAmelCase_							:       Union[str, Any]       =				'cuda' if torch.cuda.is_available() else 'cpu'

       UpperCAmelCase_							:       Tuple       =				AutoModelForCausalLM.from_pretrained(snake_case_						)
       UpperCAmelCase_							:       Dict       =				model.to(snake_case_						)

       UpperCAmelCase_							:       Dict       =				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:
            UpperCAmelCase_							:       Any       =				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"
            UpperCAmelCase_							:       Union[str, Any]       =				model.config.max_length - 1
       else:
            UpperCAmelCase_							:       int       =				model.config.max_length

       UpperCAmelCase_							:       List[str]       =				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_						)

       UpperCAmelCase_							:       Tuple       =				encodings['input_ids']
       UpperCAmelCase_							:       Optional[int]       =				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_							:       Union[str, Any]       =				[]
       UpperCAmelCase_							:       Optional[int]       =				CrossEntropyLoss(reduction='none'						)

       for start_index in logging.tqdm(range(0				,		len(snake_case_						)				,		snake_case_						)						):
            UpperCAmelCase_							:       Union[str, Any]       =				min(start_index + batch_size				,		len(snake_case_						)						)
            UpperCAmelCase_							:       List[Any]       =				encoded_texts[start_index:end_index]
            UpperCAmelCase_							:       Tuple       =				attn_masks[start_index:end_index]

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

            UpperCAmelCase_							:       List[Any]       =				encoded_batch

            with torch.no_grad():
                 UpperCAmelCase_							:       int       =				model(snake_case_				,		attention_mask=snake_case_						).logits

            UpperCAmelCase_							:       Any       =				out_logits[..., :-1, :].contiguous()
            UpperCAmelCase_							:       Dict       =				labels[..., 1:].contiguous()
            UpperCAmelCase_							:       Union[str, Any]       =				attn_mask[..., 1:].contiguous()

            UpperCAmelCase_							:       List[Any]       =				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_						)}






