





import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCAmelCase	:       Optional[int]    	=							get_tests_dir("fixtures/test_sentencepiece.model")

if is_torch_available():
      from transformers.models.mbart.modeling_mbart import shift_tokens_right

__UpperCAmelCase	:       Tuple    	=							250_004
__UpperCAmelCase	:       List[Any]    	=							250_020







@require_sentencepiece
@require_tokenizers
class    __snake_case	(							__lowerCAmelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					MBartaaTokenizer
       lowerCAmelCase__						=					MBartaaTokenizerFast
       lowerCAmelCase__						=					True
       lowerCAmelCase__						=					True




       def 							UpperCAmelCase__			(  self			:   str		):
         super().setUp()

         # We have a SentencePiece fixture for testing
         __snake_case:						Optional[Any]			  =						MBartaaTokenizer(lowerCAmelCase_							,    src_lang="""en_XX"""							,    tgt_lang="""ro_RO"""							,    keep_accents=lowerCAmelCase_		)
         tokenizer.save_pretrained(self.tmpdirname		)




       def 							UpperCAmelCase__			(  self			:   str		):
         __snake_case:						Optional[Any]			  =						"""<s>"""
         __snake_case:						Union[str, Any]			  =						0

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




       def 							UpperCAmelCase__			(  self			:   Any		):
         __snake_case:						List[Any]			  =						list(self.get_tokenizer().get_vocab().keys()		)

         self.assertEqual(vocab_keys[0]							,    """<s>"""		)
         self.assertEqual(vocab_keys[1]							,    """<pad>"""		)
         self.assertEqual(vocab_keys[-1]							,    """<mask>"""		)
         self.assertEqual(len(lowerCAmelCase_		)							,    1_054		)




       def 							UpperCAmelCase__			(  self			:   Dict		):
         self.assertEqual(self.get_tokenizer().vocab_size							,    1_054		)




       def 							UpperCAmelCase__			(  self			:   List[Any]		):
         __snake_case:						Optional[int]			  =						MBartaaTokenizer(lowerCAmelCase_							,    src_lang="""en_XX"""							,    tgt_lang="""ro_RO"""							,    keep_accents=lowerCAmelCase_		)

         __snake_case:						List[Any]			  =						tokenizer.tokenize("""This is a test"""		)
         self.assertListEqual(lowerCAmelCase_							,    ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]		)

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(lowerCAmelCase_		)							,    [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]							,    )

         __snake_case:						Tuple			  =						tokenizer.tokenize("""I was born in 92000, and this is falsé."""		)
         self.assertListEqual(
             lowerCAmelCase_							,    [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""", """."""]							,    )
         __snake_case:						Optional[Any]			  =						tokenizer.convert_tokens_to_ids(lowerCAmelCase_		)
         self.assertListEqual(
             lowerCAmelCase_							,    [
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
             ]							,    )

         __snake_case:						int			  =						tokenizer.convert_ids_to_tokens(lowerCAmelCase_		)
         self.assertListEqual(
             lowerCAmelCase_							,    [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>""", """."""]							,    )




       @slow
       def 							UpperCAmelCase__			(  self			:   Optional[int]		):
         # fmt: off
         __snake_case:						List[str]			  =						{"""input_ids""": [[250_004, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 5_428, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 12_399, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 2_789, 1_328, 4_589, 42, 122_009, 115_774, 23, 805, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [250_004, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 7, 5, 2, 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, 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], [250_004, 581, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 5, 2, 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, 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, 1, 1, 1, 1, 1, 1, 1]], """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, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=lowerCAmelCase_							,    model_name="""facebook/mbart-large-50"""							,    revision="""d3913889c59cd5c9e456b269c376325eabad57e2"""							,    )






       def 							UpperCAmelCase__			(  self			:   Union[str, Any]		):
         if not self.test_slow_tokenizer:
           # as we don't have a slow version, we can't compare the outputs between slow and fast versions
           return

         __snake_case:						Union[str, Any]			  =						(self.rust_tokenizer_class, """hf-internal-testing/tiny-random-mbart50""", {})
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
             __snake_case:						Optional[int]			  =						self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_							,    **lowerCAmelCase_		)
             __snake_case:						List[str]			  =						self.tokenizer_class.from_pretrained(lowerCAmelCase_							,    **lowerCAmelCase_		)

             __snake_case:						Optional[int]			  =						tempfile.mkdtemp()

             __snake_case:						Optional[Any]			  =						tokenizer_r.save_pretrained(lowerCAmelCase_		)
             __snake_case:						List[Any]			  =						tokenizer_p.save_pretrained(lowerCAmelCase_		)

             # Checks it save with the same files + the tokenizer.json file for the fast one
             self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files		)		)
             __snake_case:						Tuple			  =						tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f		)
             self.assertSequenceEqual(lowerCAmelCase_							,    lowerCAmelCase_		)

             # Checks everything loads correctly in the same way
             __snake_case:						Optional[Any]			  =						tokenizer_r.from_pretrained(lowerCAmelCase_		)
             __snake_case:						Dict			  =						tokenizer_p.from_pretrained(lowerCAmelCase_		)

             # Check special tokens are set accordingly on Rust and Python
             for key in tokenizer_pp.special_tokens_map:
               self.assertTrue(hasattr(lowerCAmelCase_							,    lowerCAmelCase_		)		)
               # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
               # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

             shutil.rmtree(lowerCAmelCase_		)

             # Save tokenizer rust, legacy_format=True
             __snake_case:						Union[str, Any]			  =						tempfile.mkdtemp()

             __snake_case:						Any			  =						tokenizer_r.save_pretrained(lowerCAmelCase_							,    legacy_format=lowerCAmelCase_		)
             __snake_case:						List[str]			  =						tokenizer_p.save_pretrained(lowerCAmelCase_		)

             # Checks it save with the same files
             self.assertSequenceEqual(lowerCAmelCase_							,    lowerCAmelCase_		)

             # Checks everything loads correctly in the same way
             __snake_case:						str			  =						tokenizer_r.from_pretrained(lowerCAmelCase_		)
             __snake_case:						Optional[Any]			  =						tokenizer_p.from_pretrained(lowerCAmelCase_		)

             # Check special tokens are set accordingly on Rust and Python
             for key in tokenizer_pp.special_tokens_map:
               self.assertTrue(hasattr(lowerCAmelCase_							,    lowerCAmelCase_		)		)

             shutil.rmtree(lowerCAmelCase_		)

             # Save tokenizer rust, legacy_format=False
             __snake_case:						Optional[Any]			  =						tempfile.mkdtemp()

             __snake_case:						str			  =						tokenizer_r.save_pretrained(lowerCAmelCase_							,    legacy_format=lowerCAmelCase_		)
             __snake_case:						int			  =						tokenizer_p.save_pretrained(lowerCAmelCase_		)

             # Checks it saved the tokenizer.json file
             self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files		)		)

             # Checks everything loads correctly in the same way
             __snake_case:						int			  =						tokenizer_r.from_pretrained(lowerCAmelCase_		)
             __snake_case:						int			  =						tokenizer_p.from_pretrained(lowerCAmelCase_		)

             # Check special tokens are set accordingly on Rust and Python
             for key in tokenizer_pp.special_tokens_map:
               self.assertTrue(hasattr(lowerCAmelCase_							,    lowerCAmelCase_		)		)

             shutil.rmtree(lowerCAmelCase_		)







@require_torch
@require_sentencepiece
@require_tokenizers
class    __snake_case	(							unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					'''facebook/mbart-large-50-one-to-many-mmt'''
       lowerCAmelCase__						=					[
           ''' UN Chief Says There Is No Military Solution in Syria''',
           ''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
       ]
       lowerCAmelCase__						=					[
           '''Şeful ONU declară că nu există o soluţie militară în Siria''',
           '''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
           ''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
           ''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
       ]
       lowerCAmelCase__						=					[EN_CODE, 82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2]




       @classmethod
       def 							UpperCAmelCase__			(  cls			:   List[str]		):
         __snake_case:						Tuple			  =						MBartaaTokenizer.from_pretrained(
             cls.checkpoint_name							,    src_lang="""en_XX"""							,    tgt_lang="""ro_RO"""		)
         __snake_case:						List[str]			  =						1
         return cls




       def 							UpperCAmelCase__			(  self			:   Optional[int]		):
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""]							,    250_001		)
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""]							,    250_004		)
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""]							,    250_020		)
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""mr_IN"""]							,    250_038		)




       def 							UpperCAmelCase__			(  self			:   List[str]		):
         __snake_case:						List[Any]			  =						self.tokenizer.batch_encode_plus(self.src_text		).input_ids[0]
         self.assertListEqual(self.expected_src_tokens							,    lowerCAmelCase_		)




       def 							UpperCAmelCase__			(  self			:   str		):
         self.assertIn(lowerCAmelCase_							,    self.tokenizer.all_special_ids		)
         __snake_case:						int			  =						[RO_CODE, 884, 9_019, 96, 9, 916, 86_792, 36, 18_743, 15_596, 5, 2]
         __snake_case:						Optional[Any]			  =						self.tokenizer.decode(lowerCAmelCase_							,    skip_special_tokens=lowerCAmelCase_		)
         __snake_case:						Optional[int]			  =						self.tokenizer.decode(generated_ids[1:]							,    skip_special_tokens=lowerCAmelCase_		)
         self.assertEqual(lowerCAmelCase_							,    lowerCAmelCase_		)
         self.assertNotIn(self.tokenizer.eos_token							,    lowerCAmelCase_		)




       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						Tuple			  =						["""this is gunna be a long sentence """ * 20]
         assert isinstance(src_text[0]							,    lowerCAmelCase_		)
         __snake_case:						List[Any]			  =						10
         __snake_case:						Optional[Any]			  =						self.tokenizer(lowerCAmelCase_							,    max_length=lowerCAmelCase_							,    truncation=lowerCAmelCase_		).input_ids[0]
         self.assertEqual(ids[0]							,    lowerCAmelCase_		)
         self.assertEqual(ids[-1]							,    2		)
         self.assertEqual(len(lowerCAmelCase_		)							,    lowerCAmelCase_		)




       def 							UpperCAmelCase__			(  self			:   Any		):
         self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]		)							,    [250_053, 250_001]		)




       def 							UpperCAmelCase__			(  self			:   str		):
         __snake_case:						Union[str, Any]			  =						tempfile.mkdtemp()
         __snake_case:						Optional[int]			  =						self.tokenizer.fairseq_tokens_to_ids
         self.tokenizer.save_pretrained(lowerCAmelCase_		)
         __snake_case:						str			  =						MBartaaTokenizer.from_pretrained(lowerCAmelCase_		)
         self.assertDictEqual(new_tok.fairseq_tokens_to_ids							,    lowerCAmelCase_		)




       @require_torch
       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						List[str]			  =						self.tokenizer(self.src_text							,    text_target=self.tgt_text							,    padding=lowerCAmelCase_							,    return_tensors="""pt"""		)
         __snake_case:						Tuple			  =						shift_tokens_right(batch["""labels"""]							,    self.tokenizer.pad_token_id		)

         # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
         assert batch.input_ids[1][0] == EN_CODE
         assert batch.input_ids[1][-1] == 2
         assert batch.labels[1][0] == RO_CODE
         assert batch.labels[1][-1] == 2
         assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]




       @require_torch
       def 							UpperCAmelCase__			(  self			:   Union[str, Any]		):
         __snake_case:						List[str]			  =						self.tokenizer(
             self.src_text							,    text_target=self.tgt_text							,    padding=lowerCAmelCase_							,    truncation=lowerCAmelCase_							,    max_length=len(self.expected_src_tokens		)							,    return_tensors="""pt"""							,    )
         __snake_case:						List[Any]			  =						shift_tokens_right(batch["""labels"""]							,    self.tokenizer.pad_token_id		)

         self.assertIsInstance(lowerCAmelCase_							,    lowerCAmelCase_		)

         self.assertEqual((2, 14)							,    batch.input_ids.shape		)
         self.assertEqual((2, 14)							,    batch.attention_mask.shape		)
         __snake_case:						int			  =						batch.input_ids.tolist()[0]
         self.assertListEqual(self.expected_src_tokens							,    lowerCAmelCase_		)
         self.assertEqual(2							,    batch.decoder_input_ids[0, 0]		)  # decoder_start_token_id
         # Test that special tokens are reset
         self.assertEqual(self.tokenizer.prefix_tokens							,    [EN_CODE]		)
         self.assertEqual(self.tokenizer.suffix_tokens							,    [self.tokenizer.eos_token_id]		)




       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						Optional[Any]			  =						self.tokenizer(self.src_text							,    padding=lowerCAmelCase_							,    truncation=lowerCAmelCase_							,    max_length=3							,    return_tensors="""pt"""		)
         __snake_case:						List[str]			  =						self.tokenizer(
             text_target=self.tgt_text							,    padding=lowerCAmelCase_							,    truncation=lowerCAmelCase_							,    max_length=10							,    return_tensors="""pt"""		)
         __snake_case:						int			  =						targets["""input_ids"""]
         __snake_case:						str			  =						shift_tokens_right(lowerCAmelCase_							,    self.tokenizer.pad_token_id		)

         self.assertEqual(batch.input_ids.shape[1]							,    3		)
         self.assertEqual(batch.decoder_input_ids.shape[1]							,    10		)






       @require_torch
       def 							UpperCAmelCase__			(  self			:   int		):
         __snake_case:						str			  =						self.tokenizer._build_translation_inputs(
             """A test"""							,    return_tensors="""pt"""							,    src_lang="""en_XX"""							,    tgt_lang="""ar_AR"""		)

         self.assertEqual(
             nested_simplify(lowerCAmelCase_		)							,    {
                 # en_XX, A, test, EOS
                 """input_ids""": [[250_004, 62, 3_034, 2]],
                 """attention_mask""": [[1, 1, 1, 1]],
                 # ar_AR
                 """forced_bos_token_id""": 250_001,
             }							,    )













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

import datasets
from datasets import logging


__UpperCAmelCase	:       Optional[int]    	=							"\\n\n"

__UpperCAmelCase	:       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"

__UpperCAmelCase	:       Tuple    	=							"\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 							UpperCAmelCase__			(  self			:   Tuple		):
         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			:   int							,    A			:   str							,    A			:   Optional[Any]							,    A			:   int = 16							,    A			:   bool = True							,    A			:   Optional[int]=None		):
         if device is not None:
           assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
           if device == "gpu":
             __snake_case:						Optional[Any]			  =						"""cuda"""
         else:
           __snake_case:						str			  =						"""cuda""" if torch.cuda.is_available() else """cpu"""

         __snake_case:						Dict			  =						AutoModelForCausalLM.from_pretrained(A		)
         __snake_case:						List[str]			  =						model.to(A		)

         __snake_case:						Optional[Any]			  =						AutoTokenizer.from_pretrained(A		)

         # 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:
           __snake_case:						Dict			  =						list(tokenizer.special_tokens_map_extended.values()		)
           # check that the model already has at least one special token defined
           assert (
               len(A		) > 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"
           __snake_case:						Tuple			  =						model.config.max_length - 1
         else:
           __snake_case:						Optional[Any]			  =						model.config.max_length

         __snake_case:						Optional[int]			  =						tokenizer(
             A							,    add_special_tokens=A							,    padding=A							,    truncation=A							,    max_length=A							,    return_tensors="""pt"""							,    return_attention_mask=A							,    ).to(A		)

         __snake_case:						Tuple			  =						encodings["""input_ids"""]
         __snake_case:						Any			  =						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."

         __snake_case:						Optional[int]			  =						[]
         __snake_case:						Optional[int]			  =						CrossEntropyLoss(reduction="""none"""		)

         for start_index in logging.tqdm(range(0							,    len(A		)							,    A		)		):
           __snake_case:						Dict			  =						min(start_index + batch_size							,    len(A		)		)
           __snake_case:						Optional[int]			  =						encoded_texts[start_index:end_index]
           __snake_case:						List[Any]			  =						attn_masks[start_index:end_index]

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

           __snake_case:						List[str]			  =						encoded_batch

           with torch.no_grad():
             __snake_case:						Union[str, Any]			  =						model(A							,    attention_mask=A		).logits

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

           __snake_case:						Optional[Any]			  =						torch.expa(
               (loss_fct(shift_logits.transpose(1							,    2		)							,    A		) * shift_attention_mask_batch).sum(1		)
               / shift_attention_mask_batch.sum(1		)		)

           ppls += perplexity_batch.tolist()

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






