


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

import datasets
from datasets import logging


__UpperCamelCase       :       str						       =				"\\n\n"

__UpperCamelCase       :       Optional[Any]						       =				"\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"

__UpperCamelCase       :       Optional[int]						       =				"\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       __magic_name__	(     datasets.Metric):


					def      UpperCAmelCase__	(      self							:     Tuple			)  ->      str:





						'''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							:     Union[str, Any]							,	lowerCamelCase__							:     List[Any]							,	lowerCamelCase__							:     List[str]							,	lowerCamelCase__							:     int = 16							,	lowerCamelCase__							:     bool = True							,	lowerCamelCase__							:     Optional[int]=None			)  ->      Dict:





						'''simple docstring'''






						if device is not None:
							assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
							if device == "gpu":
								UpperCamelCase__					:    List[str]          =   '''cuda'''
						else:
							UpperCamelCase__					:    Dict          =   '''cuda''' if torch.cuda.is_available() else '''cpu'''

						UpperCamelCase__					:    List[Any]          =   AutoModelForCausalLM.from_pretrained(lowerCamelCase__			)
						UpperCamelCase__					:    Tuple          =   model.to(lowerCamelCase__			)

						UpperCamelCase__					:    Optional[int]          =   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__					:    Union[str, Any]          =   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__					:    int          =   model.config.max_length - 1
						else:
							UpperCamelCase__					:    Dict          =   model.config.max_length

						UpperCamelCase__					:    Optional[int]          =   tokenizer(
						    lowerCamelCase__							,	add_special_tokens=lowerCamelCase__							,	padding=lowerCamelCase__							,	truncation=lowerCamelCase__							,	max_length=lowerCamelCase__							,	return_tensors='''pt'''							,	return_attention_mask=lowerCamelCase__							,	).to(lowerCamelCase__			)

						UpperCamelCase__					:    str          =   encodings['''input_ids''']
						UpperCamelCase__					:    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__					:    Tuple          =   []
						UpperCamelCase__					:    Optional[Any]          =   CrossEntropyLoss(reduction='''none'''			)

						for start_index in logging.tqdm(range(0							,	len(lowerCamelCase__			)							,	lowerCamelCase__			)			):
							UpperCamelCase__					:    Any          =   min(start_index + batch_size							,	len(lowerCamelCase__			)			)
							UpperCamelCase__					:    Optional[int]          =   encoded_texts[start_index:end_index]
							UpperCamelCase__					:    Union[str, Any]          =   attn_masks[start_index:end_index]

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

							UpperCamelCase__					:    Tuple          =   encoded_batch

							with torch.no_grad():
								UpperCamelCase__					:    Tuple          =   model(lowerCamelCase__							,	attention_mask=lowerCamelCase__			).logits

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

							UpperCamelCase__					:    List[Any]          =   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__			)}






import contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
       from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
       from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
       from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


__UpperCamelCase       :       Dict						       =				DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

__UpperCamelCase       :       Optional[int]						       =				"main"
# Default branch name
__UpperCamelCase       :       Optional[Any]						       =				"f2c752cfc5c0ab6f4bdec59acea69eefbee381c2"
# One particular commit (not the top of `main`)
__UpperCamelCase       :       str						       =				"aaaaaaa"
# This commit does not exist, so we should 404.

__UpperCamelCase       :       Optional[Any]						       =				"d9e9f15bc825e4b2c9249e9578f884bbcb5e3684"
# Sha-1 of config.json on the top of `main`, for checking purposes
__UpperCamelCase       :       Tuple						       =				"4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3"

@contextlib.contextmanager
def 			_a  (				):







 """simple docstring"""



 print('''Welcome!'''     )
 yield
 print('''Bye!'''     )

@contextlib.contextmanager
def 			_a  (				):







 """simple docstring"""



 print('''Bonjour!'''     )
 yield
 print('''Au revoir!'''     )

class       __magic_name__	(     unittest.TestCase):


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





      '''simple docstring'''






      assert transformers.__spec__ is not None
      assert importlib.util.find_spec('''transformers'''			) is not None



class       __magic_name__	(     unittest.TestCase):


     @unittest.mock.patch('''sys.stdout'''							,	new_callable=io.StringIO			)
     def      UpperCAmelCase__	(      self							:     Union[str, Any]							,	lowerCamelCase__							:     Union[str, Any]			)  ->      Optional[Any]:





      '''simple docstring'''






      with ContextManagers([]			):
       print('''Transformers are awesome!'''			)
      # The print statement adds a new line at the end of the output
      self.assertEqual(mock_stdout.getvalue()							,	'''Transformers are awesome!\n'''			)


     @unittest.mock.patch('''sys.stdout'''							,	new_callable=io.StringIO			)
     def      UpperCAmelCase__	(      self							:     Any							,	lowerCamelCase__							:     Union[str, Any]			)  ->      Dict:





      '''simple docstring'''






      with ContextManagers([context_en()]			):
       print('''Transformers are awesome!'''			)
      # The output should be wrapped with an English welcome and goodbye
      self.assertEqual(mock_stdout.getvalue()							,	'''Welcome!\nTransformers are awesome!\nBye!\n'''			)


     @unittest.mock.patch('''sys.stdout'''							,	new_callable=io.StringIO			)
     def      UpperCAmelCase__	(      self							:     List[Any]							,	lowerCamelCase__							:     List[str]			)  ->      Tuple:





      '''simple docstring'''






      with ContextManagers([context_fr(), context_en()]			):
       print('''Transformers are awesome!'''			)
      # The output should be wrapped with an English and French welcome and goodbye
      self.assertEqual(mock_stdout.getvalue()							,	'''Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n'''			)


     @require_torch
     def      UpperCAmelCase__	(      self							:     Tuple			)  ->      Optional[int]:





      '''simple docstring'''






      self.assertEqual(find_labels(lowerCamelCase__			)							,	['''labels''']			)
      self.assertEqual(find_labels(lowerCamelCase__			)							,	['''labels''', '''next_sentence_label''']			)
      self.assertEqual(find_labels(lowerCamelCase__			)							,	['''start_positions''', '''end_positions''']			)

      class       __magic_name__	(     __lowerCAmelCase):
           pass



      self.assertEqual(find_labels(lowerCamelCase__			)							,	['''labels''']			)


     @require_tf
     def      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      Tuple:





      '''simple docstring'''






      self.assertEqual(find_labels(lowerCamelCase__			)							,	['''labels''']			)
      self.assertEqual(find_labels(lowerCamelCase__			)							,	['''labels''', '''next_sentence_label''']			)
      self.assertEqual(find_labels(lowerCamelCase__			)							,	['''start_positions''', '''end_positions''']			)

      class       __magic_name__	(     __lowerCAmelCase):
           pass



      self.assertEqual(find_labels(lowerCamelCase__			)							,	['''labels''']			)


     @require_flax
     def      UpperCAmelCase__	(      self							:     str			)  ->      Optional[Any]:





      '''simple docstring'''






      self.assertEqual(find_labels(lowerCamelCase__			)							,	[]			)
      self.assertEqual(find_labels(lowerCamelCase__			)							,	[]			)
      self.assertEqual(find_labels(lowerCamelCase__			)							,	[]			)

      class       __magic_name__	(     __lowerCAmelCase):
           pass



      self.assertEqual(find_labels(lowerCamelCase__			)							,	[]			)


