



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						:Optional[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						:Tuple      		=     '''
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     _A       (	datasets.Metric    ):



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


           '''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 		_lowerCamelCase			(		self							:     str     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     int = 16     ,		__SCREAMING_SNAKE_CASE							:     bool = True     ,		__SCREAMING_SNAKE_CASE							:     int=None):


           '''simple docstring'''

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

           __a						       =				AutoModelForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE)
           __a						       =				model.to(__SCREAMING_SNAKE_CASE)

           __a						       =				AutoTokenizer.from_pretrained(__SCREAMING_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:
               __a						       =				list(tokenizer.special_tokens_map_extended.values())
               # check that the model already has at least one special token defined
               assert (
                   len(__SCREAMING_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"
               __a						       =				model.config.max_length - 1
           else:
               __a						       =				model.config.max_length

           __a						       =				tokenizer(
               __SCREAMING_SNAKE_CASE     ,		add_special_tokens=__SCREAMING_SNAKE_CASE     ,		padding=__SCREAMING_SNAKE_CASE     ,		truncation=__SCREAMING_SNAKE_CASE     ,		max_length=__SCREAMING_SNAKE_CASE     ,		return_tensors='''pt'''     ,		return_attention_mask=__SCREAMING_SNAKE_CASE     ,		).to(__SCREAMING_SNAKE_CASE)

           __a						       =				encodings['input_ids']
           __a						       =				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."

           __a						       =				[]
           __a						       =				CrossEntropyLoss(reduction='''none''')

           for start_index in logging.tqdm(range(0     ,		len(__SCREAMING_SNAKE_CASE)     ,		__SCREAMING_SNAKE_CASE)):
               __a						       =				min(start_index + batch_size     ,		len(__SCREAMING_SNAKE_CASE))
               __a						       =				encoded_texts[start_index:end_index]
               __a						       =				attn_masks[start_index:end_index]

               if add_start_token:
                   __a						       =				torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0)).to(__SCREAMING_SNAKE_CASE)
                   __a						       =				torch.cat([bos_tokens_tensor, encoded_batch]     ,		dim=1)
                   __a						       =				torch.cat(
                       [torch.ones(bos_tokens_tensor.size()     ,		dtype=torch.intaa).to(__SCREAMING_SNAKE_CASE), attn_mask]     ,		dim=1)

               __a						       =				encoded_batch

               with torch.no_grad():
                   __a						       =				model(__SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_SNAKE_CASE).logits

               __a						       =				out_logits[..., :-1, :].contiguous()
               __a						       =				labels[..., 1:].contiguous()
               __a						       =				attn_mask[..., 1:].contiguous()

               __a						       =				torch.expa(
                   (loss_fct(shift_logits.transpose(1     ,		2)     ,		__SCREAMING_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(__SCREAMING_SNAKE_CASE)}







import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase__          =		get_tests_dir("""fixtures/test_sentencepiece.model""")







@require_sentencepiece
@require_tokenizers
class      A__							(     __magic_name__						,   unittest.TestCase	):
				lowercase			    =  ReformerTokenizer
				lowercase			    =  ReformerTokenizerFast
				lowercase			    =  True
				lowercase			    =  False
				lowercase			    =  True






				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							super().setUp()

							lowerCAmelCase__      :      int         =		ReformerTokenizer(a    ,     keep_accents=a   )
							tokenizer.save_pretrained(self.tmpdirname   )






				def 							_lowerCamelCase		(						self					:      str   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		'<s>'
							lowerCAmelCase__      :      Optional[int]         =		1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(a   )    ,     a   )
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(a   )    ,     a   )






				def 							_lowerCamelCase		(						self					:      str   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		list(self.get_tokenizer().get_vocab().keys()   )

							self.assertEqual(vocab_keys[0]    ,     '<unk>'   )
							self.assertEqual(vocab_keys[1]    ,     '<s>'   )
							self.assertEqual(vocab_keys[-1]    ,     'j'   )
							self.assertEqual(len(a   )    ,     1_000   )






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






							'''simple docstring'''







							self.assertEqual(self.get_tokenizer().vocab_size    ,     1_000   )






				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							if not self.test_rust_tokenizer:
										return

							lowerCAmelCase__      :      Optional[int]         =		self.get_tokenizer()
							lowerCAmelCase__      :      Any         =		self.get_rust_tokenizer()

							lowerCAmelCase__      :      Optional[int]         =		'I was born in 92000, and this is falsé.'

							lowerCAmelCase__      :      List[Any]         =		tokenizer.tokenize(a   )
							lowerCAmelCase__      :      Optional[int]         =		rust_tokenizer.tokenize(a   )
							self.assertListEqual(a    ,     a   )

							lowerCAmelCase__      :      List[str]         =		tokenizer.encode(a    ,     add_special_tokens=a   )
							lowerCAmelCase__      :      Any         =		rust_tokenizer.encode(a    ,     add_special_tokens=a   )
							self.assertListEqual(a    ,     a   )

							lowerCAmelCase__      :      Any         =		self.get_rust_tokenizer()
							lowerCAmelCase__      :      List[str]         =		tokenizer.encode(a   )
							lowerCAmelCase__      :      Optional[int]         =		rust_tokenizer.encode(a   )
							self.assertListEqual(a    ,     a   )






				def 							_lowerCamelCase		(						self					:      Optional[Any]    ,     a					:      Union[str, Any]=15   ):






							'''simple docstring'''







							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''   ):
													lowerCAmelCase__      :      List[str]         =		self.rust_tokenizer_class.from_pretrained(a    ,     **a   )

													# Simple input
													lowerCAmelCase__      :      Any         =		'This is a simple input'
													lowerCAmelCase__      :      str         =		['This is a simple input 1', 'This is a simple input 2']
													lowerCAmelCase__      :      Optional[int]         =		('This is a simple input', 'This is a pair')
													lowerCAmelCase__      :      Optional[int]         =		[
													    ('This is a simple input 1', 'This is a simple input 2'),
													    ('This is a simple pair 1', 'This is a simple pair 2'),
													]

													# Simple input tests
													self.assertRaises(a    ,     tokenizer_r.encode    ,     a    ,     max_length=a    ,     padding='max_length'   )

													# Simple input
													self.assertRaises(a    ,     tokenizer_r.encode_plus    ,     a    ,     max_length=a    ,     padding='max_length'   )

													# Simple input
													self.assertRaises(
													    a    ,     tokenizer_r.batch_encode_plus    ,     a    ,     max_length=a    ,     padding='max_length'    ,     )

													# Pair input
													self.assertRaises(a    ,     tokenizer_r.encode    ,     a    ,     max_length=a    ,     padding='max_length'   )

													# Pair input
													self.assertRaises(a    ,     tokenizer_r.encode_plus    ,     a    ,     max_length=a    ,     padding='max_length'   )

													# Pair input
													self.assertRaises(
													    a    ,     tokenizer_r.batch_encode_plus    ,     a    ,     max_length=a    ,     padding='max_length'    ,     )






				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							pass






				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[int]         =		ReformerTokenizer(a    ,     keep_accents=a   )

							lowerCAmelCase__      :      Dict         =		tokenizer.tokenize('This is a test'   )
							self.assertListEqual(a    ,     ['▁This', '▁is', '▁a', '▁t', 'est']   )

							self.assertListEqual(
							    tokenizer.convert_tokens_to_ids(a   )    ,     [285, 46, 10, 170, 382]    ,     )

							lowerCAmelCase__      :      List[str]         =		tokenizer.tokenize('I was born in 92000, and this is falsé.'   )
							self.assertListEqual(
							    a    ,     [
							        SPIECE_UNDERLINE + 'I',
							        SPIECE_UNDERLINE + 'was',
							        SPIECE_UNDERLINE + 'b',
							        'or',
							        'n',
							        SPIECE_UNDERLINE + 'in',
							        SPIECE_UNDERLINE + '',
							        '9',
							        '2',
							        '0',
							        '0',
							        '0',
							        ',',
							        SPIECE_UNDERLINE + 'and',
							        SPIECE_UNDERLINE + 'this',
							        SPIECE_UNDERLINE + 'is',
							        SPIECE_UNDERLINE + 'f',
							        'al',
							        's',
							        'é',
							        '.',
							    ]    ,     )
							lowerCAmelCase__      :      int         =		tokenizer.convert_tokens_to_ids(a   )
							self.assertListEqual(
							    a    ,     [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]    ,     )

							lowerCAmelCase__      :      str         =		tokenizer.convert_ids_to_tokens(a   )
							self.assertListEqual(
							    a    ,     [
							        SPIECE_UNDERLINE + 'I',
							        SPIECE_UNDERLINE + 'was',
							        SPIECE_UNDERLINE + 'b',
							        'or',
							        'n',
							        SPIECE_UNDERLINE + 'in',
							        SPIECE_UNDERLINE + '',
							        '<unk>',
							        '2',
							        '0',
							        '0',
							        '0',
							        ',',
							        SPIECE_UNDERLINE + 'and',
							        SPIECE_UNDERLINE + 'this',
							        SPIECE_UNDERLINE + 'is',
							        SPIECE_UNDERLINE + 'f',
							        'al',
							        's',
							        '<unk>',
							        '.',
							    ]    ,     )






				@cached_property
				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							return ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment'   )






				@slow
				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		'Hello World!'
							lowerCAmelCase__      :      int         =		[126, 32, 262, 152, 38, 72, 287]

							self.assertListEqual(a    ,     self.big_tokenizer.encode(a   )   )






				@slow
				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		(
							    'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
							    ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
							)
							lowerCAmelCase__      :      int         =		[
							    108,
							    265,
							    24,
							    111,
							    4,
							    258,
							    156,
							    35,
							    28,
							    275,
							    3,
							    259,
							    297,
							    260,
							    84,
							    4,
							    35,
							    110,
							    44,
							    8,
							    259,
							    91,
							    268,
							    21,
							    11,
							    209,
							    274,
							    109,
							    266,
							    277,
							    117,
							    86,
							    93,
							    315,
							    258,
							    278,
							    258,
							    277,
							    258,
							    0,
							    258,
							    288,
							    258,
							    319,
							    258,
							    0,
							    258,
							    0,
							    258,
							    0,
							    258,
							    0,
							    258,
							    287,
							    258,
							    315,
							    258,
							    289,
							    258,
							    278,
							    99,
							    269,
							    266,
							    262,
							    8,
							    259,
							    241,
							    4,
							    217,
							    230,
							    268,
							    266,
							    55,
							    168,
							    106,
							    75,
							    193,
							    266,
							    223,
							    27,
							    49,
							    26,
							    282,
							    25,
							    264,
							    299,
							    19,
							    26,
							    0,
							    258,
							    277,
							    117,
							    86,
							    93,
							    176,
							    183,
							    270,
							    11,
							    262,
							    42,
							    61,
							    265,
							]

							self.assertListEqual(a    ,     self.big_tokenizer.encode(a   )   )






				@require_torch
				@slow
				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							import torch

							from transformers import ReformerConfig, ReformerModel

							# Build sequence
							lowerCAmelCase__      :      Dict         =		list(self.big_tokenizer.get_vocab().keys()   )[:10]
							lowerCAmelCase__      :      Optional[Any]         =		' '.join(a   )
							lowerCAmelCase__      :      Any         =		self.big_tokenizer.encode_plus(a    ,     return_tensors='pt'   )
							lowerCAmelCase__      :      Optional[int]         =		self.big_tokenizer.batch_encode_plus([sequence, sequence]    ,     return_tensors='pt'   )

							lowerCAmelCase__      :      List[Any]         =		ReformerConfig()
							# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
							lowerCAmelCase__      :      int         =		encoded_sequence['input_ids'].shape
							lowerCAmelCase__      :      Union[str, Any]         =		ReformerModel(a   )

							# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
							assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

							with torch.no_grad():
										model(**a   )
										model(**a   )






				@slow
				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		{'input_ids': [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
							# fmt: on

							# This tokenizer does not know some characters like ")".
							# That is the reason why we use very simple texts here.
							# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
							lowerCAmelCase__      :      List[str]         =		[
							    'This is a very simple sentence.',
							    'The quick brown fox jumps over the lazy dog.',
							]

							self.tokenizer_integration_test_util(
							    expected_encoding=a    ,     model_name='google/reformer-crime-and-punishment'    ,     revision='0e6c3decb8211d49bf881013425dc8b0448b3f5a'    ,     padding=a    ,     sequences=a    ,     )