


import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class   A__ (   _a	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		LEDTokenizer
    __magic_name__        =		LEDTokenizerFast
    __magic_name__        =		True
    def 		a_      (	self   ):
         super().setUp()
         snake_case					=    [
             'l',
             'o',
             'w',
             'e',
             'r',
             's',
             't',
             'i',
             'd',
             'n',
             '\u0120',
             '\u0120l',
             '\u0120n',
             '\u0120lo',
             '\u0120low',
             'er',
             '\u0120lowest',
             '\u0120newer',
             '\u0120wider',
             '<unk>',
         ]
         snake_case					=    dict(zip(__snake_case      , range(len(__snake_case   )   )   )   )
         snake_case					=    ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
         snake_case					=    {'unk_token': '<unk>'}

         snake_case					=    os.path.join(self.tmpdirname      , VOCAB_FILES_NAMES['''vocab_file''']   )
         snake_case					=    os.path.join(self.tmpdirname      , VOCAB_FILES_NAMES['''merges_file''']   )
         with open(self.vocab_file      , '''w'''      , encoding='''utf-8'''   ) as fp:
              fp.write(json.dumps(__snake_case   ) + '''\n'''   )
         with open(self.merges_file      , '''w'''      , encoding='''utf-8'''   ) as fp:
              fp.write('''\n'''.join(__snake_case   )   )
    def 		a_      (	self      , **__snake_case   ):
         kwargs.update(self.special_tokens_map   )
         return self.tokenizer_class.from_pretrained(self.tmpdirname      , **__snake_case   )
    def 		a_      (	self      , **__snake_case   ):
         kwargs.update(self.special_tokens_map   )
         return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      , **__snake_case   )
    def 		a_      (	self      , __snake_case   ):
         return "lower newer", "lower newer"
    @cached_property
    def 		a_      (	self   ):
         return LEDTokenizer.from_pretrained('''allenai/led-base-16384'''   )
    @cached_property
    def 		a_      (	self   ):
         return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''   )
    @require_torch
    def 		a_      (	self   ):
         snake_case					=    ['A long paragraph for summarization.', 'Another paragraph for summarization.']
         snake_case					=    [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              snake_case					=    tokenizer(__snake_case      , max_length=len(__snake_case   )      , padding=__snake_case      , return_tensors='''pt'''   )
              self.assertIsInstance(__snake_case      , __snake_case   )

              self.assertEqual((2, 9)      , batch.input_ids.shape   )
              self.assertEqual((2, 9)      , batch.attention_mask.shape   )
              snake_case					=    batch.input_ids.tolist()[0]
              self.assertListEqual(__snake_case      , __snake_case   )
    @require_torch
    def 		a_      (	self   ):
         snake_case					=    ['A long paragraph for summarization.', 'Another paragraph for summarization.']
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              snake_case					=    tokenizer(__snake_case      , padding=__snake_case      , return_tensors='''pt'''   )
              self.assertIn('''input_ids'''      , __snake_case   )
              self.assertIn('''attention_mask'''      , __snake_case   )
              self.assertNotIn('''labels'''      , __snake_case   )
              self.assertNotIn('''decoder_attention_mask'''      , __snake_case   )
    @require_torch
    def 		a_      (	self   ):
         snake_case					=    [
             'Summary of the text.',
             'Another summary.',
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              snake_case					=    tokenizer(text_target=__snake_case      , max_length=3_2      , padding='''max_length'''      , return_tensors='''pt'''   )
              self.assertEqual(3_2      , targets['''input_ids'''].shape[1]   )
    @require_torch
    def 		a_      (	self   ):

         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              snake_case					=    tokenizer(
                  ['''I am a small frog''' * 1_0_2_4, '''I am a small frog''']      , padding=__snake_case      , truncation=__snake_case      , return_tensors='''pt'''   )
              self.assertIsInstance(__snake_case      , __snake_case   )
              self.assertEqual(batch.input_ids.shape      , (2, 5_1_2_2)   )
    @require_torch
    def 		a_      (	self   ):
         snake_case					=    ['A long paragraph for summarization.']
         snake_case					=    [
             'Summary of the text.',
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              snake_case					=    tokenizer(__snake_case      , return_tensors='''pt'''   )
              snake_case					=    tokenizer(text_target=__snake_case      , return_tensors='''pt'''   )
              snake_case					=    inputs['input_ids']
              snake_case					=    targets['input_ids']
              self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()   )
              self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()   )
              self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()   )
              self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()   )
    @require_torch
    def 		a_      (	self   ):

         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              snake_case					=    ['Summary of the text.', 'Another summary.']
              snake_case					=    [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

              snake_case					=    tokenizer(__snake_case      , padding=__snake_case   )
              snake_case					=    [[0] * len(__snake_case   ) for x in encoded_output['input_ids']]
              snake_case					=    tokenizer.pad(__snake_case   )
              self.assertSequenceEqual(outputs['''global_attention_mask''']      , __snake_case   )
    def 		a_      (	self   ):
         pass




    def 		a_      (	self   ):

         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''   ):
                   snake_case					=    self.rust_tokenizer_class.from_pretrained(__snake_case      , **__snake_case   )
                   snake_case					=    self.tokenizer_class.from_pretrained(__snake_case      , **__snake_case   )
                   snake_case					=    'A, <mask> AllenNLP sentence.'
                   snake_case					=    tokenizer_r.encode_plus(__snake_case      , add_special_tokens=__snake_case      , return_token_type_ids=__snake_case   )
                   snake_case					=    tokenizer_p.encode_plus(__snake_case      , add_special_tokens=__snake_case      , return_token_type_ids=__snake_case   )
                   self.assertEqual(sum(tokens_r['''token_type_ids''']   )      , sum(tokens_p['''token_type_ids''']   )   )
                   self.assertEqual(
                       sum(tokens_r['''attention_mask''']   ) / len(tokens_r['''attention_mask''']   )      , sum(tokens_p['''attention_mask''']   ) / len(tokens_p['''attention_mask''']   )      , )

                   snake_case					=    tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']   )
                   snake_case					=    tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']   )
                   self.assertSequenceEqual(tokens_p['''input_ids''']      , [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]   )
                   self.assertSequenceEqual(tokens_r['''input_ids''']      , [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]   )

                   self.assertSequenceEqual(
                       __snake_case      , ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']   )
                   self.assertSequenceEqual(
                       __snake_case      , ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']   )








import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


_SCREAMING_SNAKE_CASE      :			List[str]      				=   logging.get_logger(__name__)

class   A__ (   snake_case__     ):







    """simple docstring"""

    def __init__(	self      , *__snake_case      , **__snake_case   ):
         warnings.warn(
             '''The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
             ''' use BeitImageProcessor instead.'''      , __snake_case      , )
         super().__init__(*__snake_case      , **__snake_case   )




