import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta 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, filter_roberta_detectors



@require_tokenizers
class     _A   (    __UpperCamelCase	, unittest.TestCase							):
  SCREAMING_SNAKE_CASE_	:	Tuple						=MvpTokenizer
  SCREAMING_SNAKE_CASE_	:	List[Any]						=MvpTokenizerFast
  SCREAMING_SNAKE_CASE_	:	Optional[int]						=True
  SCREAMING_SNAKE_CASE_	:	List[Any]						=filter_roberta_detectors






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





       '''simple docstring'''





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

       UpperCamelCase__										=      os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''vocab_file''']	)
       UpperCamelCase__										=      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(SCREAMING_SNAKE_CASE_	) + '''\n'''	)
       with open(self.merges_file   ,				'''w'''   ,				encoding='''utf-8'''	) as fp:
            fp.write('''\n'''.join(SCREAMING_SNAKE_CASE_	)	)






  def 		_a					(self   ,				**SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





       kwargs.update(self.special_tokens_map	)
       return self.tokenizer_class.from_pretrained(self.tmpdirname   ,				**SCREAMING_SNAKE_CASE_	)






  def 		_a					(self   ,				**SCREAMING_SNAKE_CASE_	)		->      List[Any]:





       '''simple docstring'''





       kwargs.update(self.special_tokens_map	)
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,				**SCREAMING_SNAKE_CASE_	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[Any]:





       '''simple docstring'''





       return "lower newer", "lower newer"






  @cached_property
  def 		_a					(self	)		->      str:





       '''simple docstring'''





       return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''	)






  @cached_property
  def 		_a					(self	)		->      str:





       '''simple docstring'''





       return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''	)






  @require_torch
  def 		_a					(self	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       UpperCamelCase__										=      [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

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

            self.assertEqual((2, 9)   ,				batch.input_ids.shape	)
            self.assertEqual((2, 9)   ,				batch.attention_mask.shape	)
            UpperCamelCase__										=      batch.input_ids.tolist()[0]
            self.assertListEqual(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
            # Test that special tokens are reset






  @require_torch
  def 		_a					(self	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            UpperCamelCase__										=      tokenizer(SCREAMING_SNAKE_CASE_   ,				padding=SCREAMING_SNAKE_CASE_   ,				return_tensors='''pt'''	)
            # check if input_ids are returned and no labels
            self.assertIn('''input_ids'''   ,				SCREAMING_SNAKE_CASE_	)
            self.assertIn('''attention_mask'''   ,				SCREAMING_SNAKE_CASE_	)
            self.assertNotIn('''labels'''   ,				SCREAMING_SNAKE_CASE_	)
            self.assertNotIn('''decoder_attention_mask'''   ,				SCREAMING_SNAKE_CASE_	)






  @require_torch
  def 		_a					(self	)		->      Optional[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      [
           '''Summary of the text.''',
           '''Another summary.''',
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            UpperCamelCase__										=      tokenizer(text_target=SCREAMING_SNAKE_CASE_   ,				max_length=32   ,				padding='''max_length'''   ,				return_tensors='''pt'''	)
            self.assertEqual(32   ,				targets['''input_ids'''].shape[1]	)






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





       '''simple docstring'''





       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            UpperCamelCase__										=      tokenizer(
                ['''I am a small frog''' * 1024, '''I am a small frog''']   ,				padding=SCREAMING_SNAKE_CASE_   ,				truncation=SCREAMING_SNAKE_CASE_   ,				return_tensors='''pt'''	)
            self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
            self.assertEqual(batch.input_ids.shape   ,				(2, 1024)	)






  @require_torch
  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      ['''A long paragraph for summarization.''']
       UpperCamelCase__										=      [
           '''Summary of the text.''',
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            UpperCamelCase__										=      tokenizer(SCREAMING_SNAKE_CASE_   ,				text_target=SCREAMING_SNAKE_CASE_   ,				return_tensors='''pt'''	)
            UpperCamelCase__										=      inputs['''input_ids''']
            UpperCamelCase__										=      inputs['''labels''']
            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()	)






  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       pass






  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"	):
                 UpperCamelCase__										=      self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)
                 UpperCamelCase__										=      self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)
                 UpperCamelCase__										=      '''A, <mask> AllenNLP sentence.'''
                 UpperCamelCase__										=      tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE_   ,				add_special_tokens=SCREAMING_SNAKE_CASE_   ,				return_token_type_ids=SCREAMING_SNAKE_CASE_	)
                 UpperCamelCase__										=      tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE_   ,				add_special_tokens=SCREAMING_SNAKE_CASE_   ,				return_token_type_ids=SCREAMING_SNAKE_CASE_	)

                 # token_type_ids should put 0 everywhere
                 self.assertEqual(sum(tokens_r['''token_type_ids''']	)   ,				sum(tokens_p['''token_type_ids''']	)	)

                 # attention_mask should put 1 everywhere, so sum over length should be 1
                 self.assertEqual(
                     sum(tokens_r['''attention_mask''']	) / len(tokens_r['''attention_mask''']	)   ,				sum(tokens_p['''attention_mask''']	) / len(tokens_p['''attention_mask''']	)   ,				)

                 UpperCamelCase__										=      tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']	)
                 UpperCamelCase__										=      tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']	)

                 # Rust correctly handles the space before the mask while python doesnt
                 self.assertSequenceEqual(tokens_p['''input_ids''']   ,				[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]	)
                 self.assertSequenceEqual(tokens_r['''input_ids''']   ,				[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]	)

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




import warnings

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


__magic_name__								=logging.get_logger(__name__)



class     _A   (    __UpperCamelCase							):






  def __init__(self   ,				*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)		->      None:





       '''simple docstring'''





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



