
'''simple docstring'''

import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
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 			UpperCAmelCase				(					a_  ,		unittest.TestCase ):





		"""simple docstring"""





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

				_UpperCamelCase       : Optional[int]    =      os.path.join(self.tmpdirname  ,					VOCAB_FILES_NAMES['''vocab_file''']     )
				_UpperCamelCase       : 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(_snake_case     ) + '''\n'''     )
				with open(self.merges_file  ,					'''w'''  ,					encoding='''utf-8'''     ) as fp:
						fp.write('''\n'''.join(_snake_case     )     )
		def _lowercase  (    self  ,					**_snake_case     )				->    Any:
				kwargs.update(self.special_tokens_map     )
				return self.tokenizer_class.from_pretrained(self.tmpdirname  ,					**_snake_case     )
		def _lowercase  (    self  ,					**_snake_case     )				->    Union[str, Any]:
				kwargs.update(self.special_tokens_map     )
				return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,					**_snake_case     )
		def _lowercase  (    self  ,					_snake_case     )				->    Any:
				return "lower newer", "lower newer"
		@cached_property
		def _lowercase  (    self     )				->    Dict:
				return BartTokenizer.from_pretrained('''facebook/bart-large'''     )
		@cached_property
		def _lowercase  (    self     )				->    str:
				return BartTokenizerFast.from_pretrained('''facebook/bart-large'''     )
		@require_torch
		def _lowercase  (    self     )				->    List[Any]:
				_UpperCamelCase       : Union[str, Any]    =      ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
				_UpperCamelCase       : Optional[Any]    =      [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						_UpperCamelCase       : Dict    =      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     )
						_UpperCamelCase       : List[Any]    =      batch.input_ids.tolist()[0]
						self.assertListEqual(_snake_case  ,					_snake_case     )
						# Test that special tokens are reset
		@require_torch
		def _lowercase  (    self     )				->    Dict:
				_UpperCamelCase       : Optional[int]    =      ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						_UpperCamelCase       : int    =      tokenizer(_snake_case  ,					padding=_snake_case  ,					return_tensors='''pt'''     )
						# check if input_ids are returned and no labels
						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 _lowercase  (    self     )				->    Any:
				_UpperCamelCase       : Tuple    =      [
				    '''Summary of the text.''',
				    '''Another summary.''',
				]
				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						_UpperCamelCase       : Dict    =      tokenizer(text_target=_snake_case  ,					max_length=32  ,					padding='''max_length'''  ,					return_tensors='''pt'''     )
						self.assertEqual(32  ,					targets['''input_ids'''].shape[1]     )
		@require_torch
		def _lowercase  (    self     )				->    str:
				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						_UpperCamelCase       : Union[str, Any]    =      tokenizer(
						    ['''I am a small frog''' * 1024, '''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, 1024)     )
		@require_torch
		def _lowercase  (    self     )				->    Optional[Any]:
				_UpperCamelCase       : List[str]    =      ['''A long paragraph for summarization.''']
				_UpperCamelCase       : Optional[Any]    =      [
				    '''Summary of the text.''',
				]
				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						_UpperCamelCase       : Any    =      tokenizer(_snake_case  ,					return_tensors='''pt'''     )
						_UpperCamelCase       : List[Any]    =      tokenizer(text_target=_snake_case  ,					return_tensors='''pt'''     )
						_UpperCamelCase       : List[str]    =      inputs['''input_ids''']
						_UpperCamelCase       : str    =      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()     )
		def _lowercase  (    self     )				->    List[str]:
				pass




		def _lowercase  (    self     )				->    int:
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
						with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
								_UpperCamelCase       : Optional[int]    =      self.rust_tokenizer_class.from_pretrained(_snake_case  ,					**_snake_case     )
								_UpperCamelCase       : List[str]    =      self.tokenizer_class.from_pretrained(_snake_case  ,					**_snake_case     )
								_UpperCamelCase       : Optional[int]    =      '''A, <mask> AllenNLP sentence.'''
								_UpperCamelCase       : str    =      tokenizer_r.encode_plus(_snake_case  ,					add_special_tokens=_snake_case  ,					return_token_type_ids=_snake_case     )
								_UpperCamelCase       : Dict    =      tokenizer_p.encode_plus(_snake_case  ,					add_special_tokens=_snake_case  ,					return_token_type_ids=_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       : Any    =      tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']     )
								_UpperCamelCase       : Any    =      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, 50264, 3823, 487, 21992, 3645, 4, 2]     )
								self.assertSequenceEqual(tokens_r['''input_ids''']  ,					[0, 250, 6, 50264, 3823, 487, 21992, 3645, 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>''']     )






'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


_UpperCAmelCase   :      Optional[int] 						=  logging.get_logger(__name__)

class 			UpperCAmelCase				(					a_ ):





		"""simple docstring"""





		def __init__(    self  ,					*_snake_case  ,					**_snake_case     )				->    None:
				warnings.warn(
				    '''The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
				    ''' Please use MobileNetV2ImageProcessor instead.'''  ,					_snake_case  ,					)
				super().__init__(*_snake_case  ,					**_snake_case     )





