
import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


_lowercase											=    logging.get_logger(__name__)


class 			lowercase_		(      A       ):


       def __init__(    self						,       *__A						,       **__A				)						->    None:
              warnings.warn(
                  '''The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
                  ''' Please use PerceiverImageProcessor instead.'''						,       __A						,       )
              super().__init__(*__A						,       **__A				)


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 			lowercase_		(      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						MvpTokenizer
       __lowerCamelCase				       =						MvpTokenizerFast
       __lowerCamelCase				       =						True
       __lowerCamelCase				       =						filter_roberta_detectors


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

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


       def 	_snake_case				(    self						,       **__A				)						->    int:
              kwargs.update(self.special_tokens_map				)
              return self.tokenizer_class.from_pretrained(self.tmpdirname						,       **__A				)


       def 	_snake_case				(    self						,       **__A				)						->    Any:
              kwargs.update(self.special_tokens_map				)
              return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,       **__A				)


       def 	_snake_case				(    self						,       __A				)						->    Any:
              return "lower newer", "lower newer"


       @cached_property
       def 	_snake_case				(    self				)						->    Union[str, Any]:
              return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''				)


       @cached_property
       def 	_snake_case				(    self				)						->    Optional[int]:
              return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''				)


       @require_torch
       def 	_snake_case				(    self				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				str		=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
              SCREAMING_SNAKE_CASE_							:				Tuple		=[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

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

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


       @require_torch
       def 	_snake_case				(    self				)						->    Optional[Any]:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
              for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                     SCREAMING_SNAKE_CASE_							:				Dict		=tokenizer(__A						,       padding=__A						,       return_tensors='''pt'''				)
                     # check if input_ids are returned and no labels
                     self.assertIn('''input_ids'''						,       __A				)
                     self.assertIn('''attention_mask'''						,       __A				)
                     self.assertNotIn('''labels'''						,       __A				)
                     self.assertNotIn('''decoder_attention_mask'''						,       __A				)


       @require_torch
       def 	_snake_case				(    self				)						->    Optional[Any]:
              SCREAMING_SNAKE_CASE_							:				Any		=[
                  '''Summary of the text.''',
                  '''Another summary.''',
              ]
              for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=tokenizer(text_target=__A						,       max_length=32						,       padding='''max_length'''						,       return_tensors='''pt'''				)
                     self.assertEqual(32						,       targets['''input_ids'''].shape[1]				)


       @require_torch
       def 	_snake_case				(    self				)						->    str:
              for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=tokenizer(
                         ['''I am a small frog''' * 1_024, '''I am a small frog''']						,       padding=__A						,       truncation=__A						,       return_tensors='''pt'''				)
                     self.assertIsInstance(__A						,       __A				)
                     self.assertEqual(batch.input_ids.shape						,       (2, 1_024)				)


       @require_torch
       def 	_snake_case				(    self				)						->    Tuple:
              SCREAMING_SNAKE_CASE_							:				str		=['''A long paragraph for summarization.''']
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=[
                  '''Summary of the text.''',
              ]
              for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=tokenizer(__A						,       text_target=__A						,       return_tensors='''pt'''				)
                     SCREAMING_SNAKE_CASE_							:				Tuple		=inputs['''input_ids''']
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=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 	_snake_case				(    self				)						->    Any:
              pass





       def 	_snake_case				(    self				)						->    Optional[Any]:
              for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                     with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
                            SCREAMING_SNAKE_CASE_							:				Dict		=self.rust_tokenizer_class.from_pretrained(__A						,       **__A				)
                            SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.tokenizer_class.from_pretrained(__A						,       **__A				)
                            SCREAMING_SNAKE_CASE_							:				List[Any]		='''A, <mask> AllenNLP sentence.'''
                            SCREAMING_SNAKE_CASE_							:				Dict		=tokenizer_r.encode_plus(__A						,       add_special_tokens=__A						,       return_token_type_ids=__A				)
                            SCREAMING_SNAKE_CASE_							:				Any		=tokenizer_p.encode_plus(__A						,       add_special_tokens=__A						,       return_token_type_ids=__A				)

                            # 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''']				)						,       )

                            SCREAMING_SNAKE_CASE_							:				Any		=tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']				)
                            SCREAMING_SNAKE_CASE_							:				List[str]		=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, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]				)
                            self.assertSequenceEqual(tokens_r['''input_ids''']						,       [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]				)

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