

import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
     from transformers.models.rag.configuration_rag import RagConfig
     from transformers.models.rag.tokenization_rag import RagTokenizer


@require_faiss
@require_torch
class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    def 						snake_case__     (      self :       List[Any]		)->    int:





        '''simple docstring'''







        A__          =   tempfile.mkdtemp()
        A__          =   8

        # DPR tok
        A__          =   [
            '[UNK]',
            '[CLS]',
            '[SEP]',
            '[PAD]',
            '[MASK]',
            'want',
            '##want',
            '##ed',
            'wa',
            'un',
            'runn',
            '##ing',
            ',',
            'low',
            'lowest',
        ]
        A__          =   os.path.join(self.tmpdirname,'dpr_tokenizer'		)
        os.makedirs(lowercase_,exist_ok=lowercase_		)
        A__          =   os.path.join(lowercase_,DPR_VOCAB_FILES_NAMES['vocab_file']		)
        with open(self.vocab_file,'w',encoding='utf-8'		) as vocab_writer:
            vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]		)		)

        # BART tok
        A__          =   [
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            '\u0120',
            '\u0120l',
            '\u0120n',
            '\u0120lo',
            '\u0120low',
            'er',
            '\u0120lowest',
            '\u0120newer',
            '\u0120wider',
            '<unk>',
        ]
        A__          =   dict(zip(lowercase_,range(len(lowercase_		)		)		)		)
        A__          =   ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
        A__          =   {'unk_token': '<unk>'}

        A__          =   os.path.join(self.tmpdirname,'bart_tokenizer'		)
        os.makedirs(lowercase_,exist_ok=lowercase_		)
        A__          =   os.path.join(lowercase_,BART_VOCAB_FILES_NAMES['vocab_file']		)
        A__          =   os.path.join(lowercase_,BART_VOCAB_FILES_NAMES['merges_file']		)
        with open(self.vocab_file,'w',encoding='utf-8'		) as fp:
            fp.write(json.dumps(lowercase_		) + '\n'		)
        with open(self.merges_file,'w',encoding='utf-8'		) as fp:
            fp.write('\n'.join(lowercase_		)		)


    def 						snake_case__     (      self :       List[str]		)->    DPRQuestionEncoderTokenizer:





        '''simple docstring'''







        return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname,'dpr_tokenizer'		)		)


    def 						snake_case__     (      self :       List[Any]		)->    BartTokenizer:





        '''simple docstring'''







        return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname,'bart_tokenizer'		)		)


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





        '''simple docstring'''







        shutil.rmtree(self.tmpdirname		)


    @require_tokenizers
    def 						snake_case__     (      self :       str		)->    Any:





        '''simple docstring'''







        A__          =   os.path.join(self.tmpdirname,'rag_tokenizer'		)
        A__          =   RagConfig(question_encoder=DPRConfig().to_dict(),generator=BartConfig().to_dict()		)
        A__          =   RagTokenizer(question_encoder=self.get_dpr_tokenizer(),generator=self.get_bart_tokenizer()		)
        rag_config.save_pretrained(lowercase_		)
        rag_tokenizer.save_pretrained(lowercase_		)
        A__          =   RagTokenizer.from_pretrained(lowercase_,config=lowercase_		)
        self.assertIsInstance(new_rag_tokenizer.question_encoder,lowercase_		)
        self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab(),rag_tokenizer.question_encoder.get_vocab()		)
        self.assertIsInstance(new_rag_tokenizer.generator,lowercase_		)
        self.assertEqual(new_rag_tokenizer.generator.get_vocab(),rag_tokenizer.generator.get_vocab()		)


    @slow
    def 						snake_case__     (      self :       Optional[int]		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   RagTokenizer.from_pretrained('facebook/rag-token-nq'		)
        A__          =   [
            'who got the first nobel prize in physics',
            'when is the next deadpool movie being released',
            'which mode is used for short wave broadcast service',
            'who is the owner of reading football club',
            'when is the next scandal episode coming out',
            'when is the last time the philadelphia won the superbowl',
            'what is the most current adobe flash player version',
            'how many episodes are there in dragon ball z',
            'what is the first step in the evolution of the eye',
            'where is gall bladder situated in human body',
            'what is the main mineral in lithium batteries',
            'who is the president of usa right now',
            'where do the greasers live in the outsiders',
            'panda is a national animal of which country',
            'what is the name of manchester united stadium',
        ]
        A__          =   tokenizer(lowercase_		)
        self.assertIsNotNone(lowercase_		)


    @slow
    def 						snake_case__     (      self :       Optional[Any]		)->    Dict:





        '''simple docstring'''







        A__          =   RagTokenizer.from_pretrained('facebook/rag-sequence-nq'		)
        A__          =   [
            'who got the first nobel prize in physics',
            'when is the next deadpool movie being released',
            'which mode is used for short wave broadcast service',
            'who is the owner of reading football club',
            'when is the next scandal episode coming out',
            'when is the last time the philadelphia won the superbowl',
            'what is the most current adobe flash player version',
            'how many episodes are there in dragon ball z',
            'what is the first step in the evolution of the eye',
            'where is gall bladder situated in human body',
            'what is the main mineral in lithium batteries',
            'who is the president of usa right now',
            'where do the greasers live in the outsiders',
            'panda is a national animal of which country',
            'what is the name of manchester united stadium',
        ]
        A__          =   tokenizer(lowercase_		)
        self.assertIsNotNone(lowercase_		)




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


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


@require_sentencepiece
@require_tokenizers
class  A						(							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							ReformerTokenizer
    lowerCamelCase			=							ReformerTokenizerFast
    lowerCamelCase			=							True
    lowerCamelCase			=							False
    lowerCamelCase			=							True


    def 						snake_case__     (      self :       Any		)->    str:





        '''simple docstring'''







        super().setUp()

        A__          =   ReformerTokenizer(lowercase_,keep_accents=lowercase_		)
        tokenizer.save_pretrained(self.tmpdirname		)


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





        '''simple docstring'''







        A__          =   '<s>'
        A__          =   1

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


    def 						snake_case__     (      self :       str		)->    Tuple:





        '''simple docstring'''







        A__          =   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(lowercase_		),1_0_0_0		)


    def 						snake_case__     (      self :       Dict		)->    Dict:





        '''simple docstring'''







        self.assertEqual(self.get_tokenizer().vocab_size,1_0_0_0		)


    def 						snake_case__     (      self :       Dict		)->    List[str]:





        '''simple docstring'''







        if not self.test_rust_tokenizer:
            return

        A__          =   self.get_tokenizer()
        A__          =   self.get_rust_tokenizer()

        A__          =   'I was born in 92000, and this is falsé.'

        A__          =   tokenizer.tokenize(lowercase_		)
        A__          =   rust_tokenizer.tokenize(lowercase_		)
        self.assertListEqual(lowercase_,lowercase_		)

        A__          =   tokenizer.encode(lowercase_,add_special_tokens=lowercase_		)
        A__          =   rust_tokenizer.encode(lowercase_,add_special_tokens=lowercase_		)
        self.assertListEqual(lowercase_,lowercase_		)

        A__          =   self.get_rust_tokenizer()
        A__          =   tokenizer.encode(lowercase_		)
        A__          =   rust_tokenizer.encode(lowercase_		)
        self.assertListEqual(lowercase_,lowercase_		)


    def 						snake_case__     (      self :       int,lowercase_ :       Optional[int]=1_5		)->    Optional[Any]:





        '''simple docstring'''







        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'		):
                A__          =   self.rust_tokenizer_class.from_pretrained(lowercase_,**lowercase_		)

                # Simple input
                A__          =   'This is a simple input'
                A__          =   ['This is a simple input 1', 'This is a simple input 2']
                A__          =   ('This is a simple input', 'This is a pair')
                A__          =   [
                    ('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(lowercase_,tokenizer_r.encode,lowercase_,max_length=lowercase_,padding='max_length'		)

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

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

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

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

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


    def 						snake_case__     (      self :       List[Any]		)->    Tuple:





        '''simple docstring'''







        pass


    def 						snake_case__     (      self :       Dict		)->    str:





        '''simple docstring'''







        A__          =   ReformerTokenizer(lowercase_,keep_accents=lowercase_		)

        A__          =   tokenizer.tokenize('This is a test'		)
        self.assertListEqual(lowercase_,['▁This', '▁is', '▁a', '▁t', 'est']		)

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(lowercase_		),[2_8_5, 4_6, 1_0, 1_7_0, 3_8_2],)

        A__          =   tokenizer.tokenize('I was born in 92000, and this is falsé.'		)
        self.assertListEqual(
            lowercase_,[
                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',
                '.',
            ],)
        A__          =   tokenizer.convert_tokens_to_ids(lowercase_		)
        self.assertListEqual(
            lowercase_,[8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4],)

        A__          =   tokenizer.convert_ids_to_tokens(lowercase_		)
        self.assertListEqual(
            lowercase_,[
                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 						snake_case__     (      self :       Optional[int]		)->    Any:





        '''simple docstring'''







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


    @slow
    def 						snake_case__     (      self :       str		)->    Tuple:





        '''simple docstring'''







        A__          =   'Hello World!'
        A__          =   [1_2_6, 3_2, 2_6_2, 1_5_2, 3_8, 7_2, 2_8_7]

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


    @slow
    def 						snake_case__     (      self :       Optional[int]		)->    str:





        '''simple docstring'''







        A__          =   (
            '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'
        )
        A__          =   [
            1_0_8,
            2_6_5,
            2_4,
            1_1_1,
            4,
            2_5_8,
            1_5_6,
            3_5,
            2_8,
            2_7_5,
            3,
            2_5_9,
            2_9_7,
            2_6_0,
            8_4,
            4,
            3_5,
            1_1_0,
            4_4,
            8,
            2_5_9,
            9_1,
            2_6_8,
            2_1,
            1_1,
            2_0_9,
            2_7_4,
            1_0_9,
            2_6_6,
            2_7_7,
            1_1_7,
            8_6,
            9_3,
            3_1_5,
            2_5_8,
            2_7_8,
            2_5_8,
            2_7_7,
            2_5_8,
            0,
            2_5_8,
            2_8_8,
            2_5_8,
            3_1_9,
            2_5_8,
            0,
            2_5_8,
            0,
            2_5_8,
            0,
            2_5_8,
            0,
            2_5_8,
            2_8_7,
            2_5_8,
            3_1_5,
            2_5_8,
            2_8_9,
            2_5_8,
            2_7_8,
            9_9,
            2_6_9,
            2_6_6,
            2_6_2,
            8,
            2_5_9,
            2_4_1,
            4,
            2_1_7,
            2_3_0,
            2_6_8,
            2_6_6,
            5_5,
            1_6_8,
            1_0_6,
            7_5,
            1_9_3,
            2_6_6,
            2_2_3,
            2_7,
            4_9,
            2_6,
            2_8_2,
            2_5,
            2_6_4,
            2_9_9,
            1_9,
            2_6,
            0,
            2_5_8,
            2_7_7,
            1_1_7,
            8_6,
            9_3,
            1_7_6,
            1_8_3,
            2_7_0,
            1_1,
            2_6_2,
            4_2,
            6_1,
            2_6_5,
        ]

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


    @require_torch
    @slow
    def 						snake_case__     (      self :       int		)->    Any:





        '''simple docstring'''







        import torch

        from transformers import ReformerConfig, ReformerModel

        # Build sequence
        A__          =   list(self.big_tokenizer.get_vocab().keys()		)[:1_0]
        A__          =   ' '.join(lowercase_		)
        A__          =   self.big_tokenizer.encode_plus(lowercase_,return_tensors='pt'		)
        A__          =   self.big_tokenizer.batch_encode_plus([sequence, sequence],return_tensors='pt'		)

        A__          =   ReformerConfig()
        # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
        A__          =   encoded_sequence['input_ids'].shape
        A__          =   ReformerModel(lowercase_		)

        # 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(**lowercase_		)
            model(**lowercase_		)


    @slow
    def 						snake_case__     (      self :       int		)->    Tuple:





        '''simple docstring'''







        A__          =   {'input_ids': [[1_0_8, 2_6_5, 2_4, 1_1_1, 4, 2_5_8, 1_5_6, 7, 5_1, 2_7_9, 5_8, 7, 7_6, 2_5, 6_9, 2_7_8], [1_4_0, 2_4_3, 2_6_4, 1_3_4, 1_7, 2_6_7, 7_7, 2_6_3, 2_2, 2_6_2, 2_9_7, 2_5_8, 3_0_4, 1_7_7, 2_7_9, 2_6_6, 1_4, 8_9, 1_3, 3_5, 2_6_1, 2_9_9, 2_7_2, 1_3_7, 2_7_5, 2_7_8]], '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
        A__          =   [
            'This is a very simple sentence.',
            'The quick brown fox jumps over the lazy dog.',
        ]

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

