






'''simple docstring'''

import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
     import torch

     from transformers import (
         MraForMaskedLM,
         MraForMultipleChoice,
         MraForQuestionAnswering,
         MraForSequenceClassification,
         MraForTokenClassification,
         MraModel,
     )
     from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST






class 				a_						:

  def __init__(      self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE=2			,		_SCREAMING_SNAKE_CASE=8			,		_SCREAMING_SNAKE_CASE=True			,		_SCREAMING_SNAKE_CASE=True			,		_SCREAMING_SNAKE_CASE=True			,		_SCREAMING_SNAKE_CASE=True			,		_SCREAMING_SNAKE_CASE=99			,		_SCREAMING_SNAKE_CASE=16			,		_SCREAMING_SNAKE_CASE=5			,		_SCREAMING_SNAKE_CASE=2			,		_SCREAMING_SNAKE_CASE=36			,		_SCREAMING_SNAKE_CASE="gelu"			,		_SCREAMING_SNAKE_CASE=0.0			,		_SCREAMING_SNAKE_CASE=0.0			,		_SCREAMING_SNAKE_CASE=512			,		_SCREAMING_SNAKE_CASE=16			,		_SCREAMING_SNAKE_CASE=2			,		_SCREAMING_SNAKE_CASE=0.0_2			,		_SCREAMING_SNAKE_CASE=3			,		_SCREAMING_SNAKE_CASE=4			,		_SCREAMING_SNAKE_CASE=None			,		)  ->      List[Any]:



        """simple docstring"""






        UpperCamelCase				  =					parent
        UpperCamelCase				  =					batch_size
        UpperCamelCase				  =					seq_length
        UpperCamelCase				  =					is_training
        UpperCamelCase				  =					use_input_mask
        UpperCamelCase				  =					use_token_type_ids
        UpperCamelCase				  =					use_labels
        UpperCamelCase				  =					vocab_size
        UpperCamelCase				  =					hidden_size
        UpperCamelCase				  =					num_hidden_layers
        UpperCamelCase				  =					num_attention_heads
        UpperCamelCase				  =					intermediate_size
        UpperCamelCase				  =					hidden_act
        UpperCamelCase				  =					hidden_dropout_prob
        UpperCamelCase				  =					attention_probs_dropout_prob
        UpperCamelCase				  =					max_position_embeddings
        UpperCamelCase				  =					type_vocab_size
        UpperCamelCase				  =					type_sequence_label_size
        UpperCamelCase				  =					initializer_range
        UpperCamelCase				  =					num_labels
        UpperCamelCase				  =					num_choices
        UpperCamelCase				  =					scope

  def   A__						(      self      )  ->      int:



        """simple docstring"""






        UpperCamelCase				  =					ids_tensor([self.batch_size, self.seq_length]			,		self.vocab_size      )

        UpperCamelCase				  =					None
        if self.use_input_mask:
              UpperCamelCase				  =					random_attention_mask([self.batch_size, self.seq_length]      )

        UpperCamelCase				  =					None
        if self.use_token_type_ids:
              UpperCamelCase				  =					ids_tensor([self.batch_size, self.seq_length]			,		self.type_vocab_size      )

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

        UpperCamelCase				  =					self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

  def   A__						(      self      )  ->      int:



        """simple docstring"""






        return MraConfig(
            vocab_size=self.vocab_size			,		hidden_size=self.hidden_size			,		num_hidden_layers=self.num_hidden_layers			,		num_attention_heads=self.num_attention_heads			,		intermediate_size=self.intermediate_size			,		hidden_act=self.hidden_act			,		hidden_dropout_prob=self.hidden_dropout_prob			,		attention_probs_dropout_prob=self.attention_probs_dropout_prob			,		max_position_embeddings=self.max_position_embeddings			,		type_vocab_size=self.type_vocab_size			,		is_decoder=lowerCamelCase__			,		initializer_range=self.initializer_range			,		)

  def   A__						(      self      )  ->      Union[str, Any]:



        """simple docstring"""






        UpperCamelCase				  =					self.get_config()
        UpperCamelCase				  =					300
        return config

  def   A__						(      self      )  ->      Optional[Any]:



        """simple docstring"""






        (
            (
            UpperCamelCase	
        )  ,(
            UpperCamelCase	
        )  ,(
            UpperCamelCase	
        )  ,(
            UpperCamelCase	
        )  ,(
            UpperCamelCase	
        )  ,(
            UpperCamelCase	
        )  ,(
            UpperCamelCase	
        )  ,	
        )				  =					self.prepare_config_and_inputs()

        UpperCamelCase				  =					True
        UpperCamelCase				  =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
        UpperCamelCase				  =					ids_tensor([self.batch_size, self.seq_length]			,		vocab_size=2      )

        return (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )

  def   A__						(      self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE      )  ->      Optional[int]:



        """simple docstring"""






        UpperCamelCase				  =					MraModel(config=lowerCamelCase__      )
        model.to(lowerCamelCase__      )
        model.eval()
        UpperCamelCase				  =					model(lowerCamelCase__			,		attention_mask=lowerCamelCase__			,		token_type_ids=lowerCamelCase__      )
        UpperCamelCase				  =					model(lowerCamelCase__			,		token_type_ids=lowerCamelCase__      )
        UpperCamelCase				  =					model(lowerCamelCase__      )
        self.parent.assertEqual(result.last_hidden_state.shape			,		(self.batch_size, self.seq_length, self.hidden_size)      )

  def   A__						(      self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		)  ->      List[Any]:



        """simple docstring"""






        UpperCamelCase				  =					True
        UpperCamelCase				  =					MraModel(lowerCamelCase__      )
        model.to(lowerCamelCase__      )
        model.eval()
        UpperCamelCase				  =					model(
            lowerCamelCase__			,		attention_mask=lowerCamelCase__			,		token_type_ids=lowerCamelCase__			,		encoder_hidden_states=lowerCamelCase__			,		encoder_attention_mask=lowerCamelCase__			,		)
        UpperCamelCase				  =					model(
            lowerCamelCase__			,		attention_mask=lowerCamelCase__			,		token_type_ids=lowerCamelCase__			,		encoder_hidden_states=lowerCamelCase__			,		)
        UpperCamelCase				  =					model(lowerCamelCase__			,		attention_mask=lowerCamelCase__			,		token_type_ids=lowerCamelCase__      )
        self.parent.assertEqual(result.last_hidden_state.shape			,		(self.batch_size, self.seq_length, self.hidden_size)      )

  def   A__						(      self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE      )  ->      int:



        """simple docstring"""






        UpperCamelCase				  =					MraForMaskedLM(config=lowerCamelCase__      )
        model.to(lowerCamelCase__      )
        model.eval()
        UpperCamelCase				  =					model(lowerCamelCase__			,		attention_mask=lowerCamelCase__			,		token_type_ids=lowerCamelCase__			,		labels=lowerCamelCase__      )
        self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.seq_length, self.vocab_size)      )

  def   A__						(      self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE      )  ->      Tuple:



        """simple docstring"""






        UpperCamelCase				  =					MraForQuestionAnswering(config=lowerCamelCase__      )
        model.to(lowerCamelCase__      )
        model.eval()
        UpperCamelCase				  =					model(
            lowerCamelCase__			,		attention_mask=lowerCamelCase__			,		token_type_ids=lowerCamelCase__			,		start_positions=lowerCamelCase__			,		end_positions=lowerCamelCase__			,		)
        self.parent.assertEqual(result.start_logits.shape			,		(self.batch_size, self.seq_length)      )
        self.parent.assertEqual(result.end_logits.shape			,		(self.batch_size, self.seq_length)      )

  def   A__						(      self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE      )  ->      List[Any]:



        """simple docstring"""






        UpperCamelCase				  =					self.num_labels
        UpperCamelCase				  =					MraForSequenceClassification(lowerCamelCase__      )
        model.to(lowerCamelCase__      )
        model.eval()
        UpperCamelCase				  =					model(lowerCamelCase__			,		attention_mask=lowerCamelCase__			,		token_type_ids=lowerCamelCase__			,		labels=lowerCamelCase__      )
        self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.num_labels)      )

  def   A__						(      self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE      )  ->      Dict:



        """simple docstring"""






        UpperCamelCase				  =					self.num_labels
        UpperCamelCase				  =					MraForTokenClassification(config=lowerCamelCase__      )
        model.to(lowerCamelCase__      )
        model.eval()
        UpperCamelCase				  =					model(lowerCamelCase__			,		attention_mask=lowerCamelCase__			,		token_type_ids=lowerCamelCase__			,		labels=lowerCamelCase__      )
        self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.seq_length, self.num_labels)      )

  def   A__						(      self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE      )  ->      List[str]:



        """simple docstring"""






        UpperCamelCase				  =					self.num_choices
        UpperCamelCase				  =					MraForMultipleChoice(config=lowerCamelCase__      )
        model.to(lowerCamelCase__      )
        model.eval()
        UpperCamelCase				  =					input_ids.unsqueeze(1      ).expand(-1			,		self.num_choices			,		-1      ).contiguous()
        UpperCamelCase				  =					token_type_ids.unsqueeze(1      ).expand(-1			,		self.num_choices			,		-1      ).contiguous()
        UpperCamelCase				  =					input_mask.unsqueeze(1      ).expand(-1			,		self.num_choices			,		-1      ).contiguous()
        UpperCamelCase				  =					model(
            lowerCamelCase__			,		attention_mask=lowerCamelCase__			,		token_type_ids=lowerCamelCase__			,		labels=lowerCamelCase__			,		)
        self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.num_choices)      )


  def   A__						(      self      )  ->      int:



        """simple docstring"""






        UpperCamelCase				  =					self.prepare_config_and_inputs()
        (
            (
            UpperCamelCase	
        )  ,(
            UpperCamelCase	
        )  ,(
            UpperCamelCase	
        )  ,(
            UpperCamelCase	
        )  ,(
            UpperCamelCase	
        )  ,(
            UpperCamelCase	
        )  ,(
            UpperCamelCase	
        )  ,	
        )				  =					config_and_inputs
        UpperCamelCase				  =					{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
        return config, inputs_dict






@require_torch
class 				a_						(			__UpperCAmelCase	,       unittest.TestCase				):
  lowercase     		=      (
      (
          MraModel,
          MraForMaskedLM,
          MraForMultipleChoice,
          MraForQuestionAnswering,
          MraForSequenceClassification,
          MraForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  lowercase     		=      False
  lowercase     		=      False
  lowercase     		=      False
  lowercase     		=      False

  lowercase     		=      ()

  def   A__						(      self      )  ->      List[str]:



        """simple docstring"""






        UpperCamelCase				  =					MraModelTester(self      )
        UpperCamelCase				  =					ConfigTester(self			,		config_class=lowerCamelCase__			,		hidden_size=37      )

  def   A__						(      self      )  ->      Optional[Any]:



        """simple docstring"""






        self.config_tester.run_common_tests()

  def   A__						(      self      )  ->      int:



        """simple docstring"""






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

  def   A__						(      self      )  ->      Optional[Any]:



        """simple docstring"""






        UpperCamelCase				  =					self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
              UpperCamelCase				  =					type
              self.model_tester.create_and_check_model(*lowerCamelCase__      )

  def   A__						(      self      )  ->      Any:



        """simple docstring"""






        UpperCamelCase				  =					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__      )

  def   A__						(      self      )  ->      List[Any]:



        """simple docstring"""






        UpperCamelCase				  =					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase__      )

  def   A__						(      self      )  ->      List[Any]:



        """simple docstring"""






        UpperCamelCase				  =					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__      )

  def   A__						(      self      )  ->      Any:



        """simple docstring"""






        UpperCamelCase				  =					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__      )

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



        """simple docstring"""






        UpperCamelCase				  =					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__      )

  @slow
  def   A__						(      self      )  ->      Union[str, Any]:



        """simple docstring"""






        for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCamelCase				  =					MraModel.from_pretrained(lowerCamelCase__      )
              self.assertIsNotNone(lowerCamelCase__      )


  @unittest.skip(reason="""MRA does not output attentions"""      )
  def   A__						(      self      )  ->      Dict:



        """simple docstring"""






        return






@require_torch
class 				a_						(			unittest.TestCase				):

  @slow
  def   A__						(      self      )  ->      str:



        """simple docstring"""






        UpperCamelCase				  =					MraModel.from_pretrained("""uw-madison/mra-base-512-4"""      )
        UpperCamelCase				  =					torch.arange(256      ).unsqueeze(0      )

        with torch.no_grad():
              UpperCamelCase				  =					model(lowerCamelCase__      )[0]

        UpperCamelCase				  =					torch.Size((1, 256, 768)      )
        self.assertEqual(output.shape			,		lowerCamelCase__      )

        UpperCamelCase				  =					torch.tensor(
            [[[-0.0_1_4_0, 0.0_8_3_0, -0.0_3_8_1], [0.1_5_4_6, 0.1_4_0_2, 0.0_2_2_0], [0.1_1_6_2, 0.0_8_5_1, 0.0_1_6_5]]]      )

        self.assertTrue(torch.allclose(output[:, :3, :3]			,		lowerCamelCase__			,		atol=1e-4      )      )

  @slow
  def   A__						(      self      )  ->      Tuple:



        """simple docstring"""






        UpperCamelCase				  =					MraForMaskedLM.from_pretrained("""uw-madison/mra-base-512-4"""      )
        UpperCamelCase				  =					torch.arange(256      ).unsqueeze(0      )

        with torch.no_grad():
              UpperCamelCase				  =					model(lowerCamelCase__      )[0]

        UpperCamelCase				  =					50265

        UpperCamelCase				  =					torch.Size((1, 256, vocab_size)      )
        self.assertEqual(output.shape			,		lowerCamelCase__      )

        UpperCamelCase				  =					torch.tensor(
            [[[9.2_5_9_5, -3.6_0_3_8, 11.8819], [9.3_8_6_9, -3.2_6_9_3, 11.0956], [11.8524, -3.4_9_3_8, 13.1210]]]      )

        self.assertTrue(torch.allclose(output[:, :3, :3]			,		lowerCamelCase__			,		atol=1e-4      )      )


  @slow
  def   A__						(      self      )  ->      str:



        """simple docstring"""






        UpperCamelCase				  =					MraForMaskedLM.from_pretrained("""uw-madison/mra-base-4096-8-d3"""      )
        UpperCamelCase				  =					torch.arange(4096      ).unsqueeze(0      )

        with torch.no_grad():
              UpperCamelCase				  =					model(lowerCamelCase__      )[0]

        UpperCamelCase				  =					50265

        UpperCamelCase				  =					torch.Size((1, 4096, vocab_size)      )
        self.assertEqual(output.shape			,		lowerCamelCase__      )

        UpperCamelCase				  =					torch.tensor(
            [[[5.4_7_8_9, -2.3_5_6_4, 7.5_0_6_4], [7.9_0_6_7, -1.3_3_6_9, 9.9_6_6_8], [9.0_7_1_2, -1.8_1_0_6, 7.0_3_8_0]]]      )

        self.assertTrue(torch.allclose(output[:, :3, :3]			,		lowerCamelCase__			,		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__  :  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__			)}



