


'''simple docstring'''



import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class  UpperCamelCase__(     lowerCAmelCase		,       unittest.TestCase ):
				__magic_name__       :   Tuple							= DebertaTokenizer
				__magic_name__       :   str							= True
				__magic_name__       :   Any							= DebertaTokenizerFast




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

										"""simple docstring"""




										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										UpperCAmelCase         =			[
										    '''l''',
										    '''o''',
										    '''w''',
										    '''e''',
										    '''r''',
										    '''s''',
										    '''t''',
										    '''i''',
										    '''d''',
										    '''n''',
										    '''\u0120''',
										    '''\u0120l''',
										    '''\u0120n''',
										    '''\u0120lo''',
										    '''\u0120low''',
										    '''er''',
										    '''\u0120lowest''',
										    '''\u0120newer''',
										    '''\u0120wider''',
										    '''[UNK]''',
										]
										UpperCAmelCase         =			dict(zip(lowerCAmelCase   ,					range(len(lowerCAmelCase   )   )   )   )
										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(lowerCAmelCase   ) + '''\n'''   )
										with open(self.merges_file   ,					'''w'''   ,					encoding='''utf-8'''   ) as fp:
																fp.write('''\n'''.join(lowerCAmelCase   )   )




				def   a__(	self    :				List[str]   ,					**lowerCAmelCase    :				Dict   )->  Tuple:

										"""simple docstring"""




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




				def   a__(	self    :				Any   ,					lowerCAmelCase    :				Any   )->  Dict:

										"""simple docstring"""




										UpperCAmelCase         =			'''lower newer'''
										UpperCAmelCase         =			'''lower newer'''
										return input_text, output_text




				def   a__(	self    :				Any   )->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			self.get_tokenizer()
										UpperCAmelCase         =			'''lower newer'''
										UpperCAmelCase         =			['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
										UpperCAmelCase         =			tokenizer.tokenize(lowerCAmelCase   )
										self.assertListEqual(lowerCAmelCase   ,					lowerCAmelCase   )

										UpperCAmelCase         =			tokens + [tokenizer.unk_token]
										UpperCAmelCase         =			[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase   )   ,					lowerCAmelCase   )




				def   a__(	self    :				Optional[Any]   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			self.get_tokenizer()
										UpperCAmelCase         =			tokenizer('''Hello'''   ,					'''World'''   )
										UpperCAmelCase         =			[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
										self.assertListEqual(tokd['''token_type_ids''']   ,					lowerCAmelCase   )




				@slow
				def   a__(	self    :				List[str]   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''   )

										UpperCAmelCase         =			tokenizer.encode('''sequence builders'''   ,					add_special_tokens=lowerCAmelCase   )
										UpperCAmelCase         =			tokenizer.encode('''multi-sequence build'''   ,					add_special_tokens=lowerCAmelCase   )

										UpperCAmelCase         =			tokenizer.encode(
										    '''sequence builders'''   ,					add_special_tokens=lowerCAmelCase   ,					add_prefix_space=lowerCAmelCase   )
										UpperCAmelCase         =			tokenizer.encode(
										    '''sequence builders'''   ,					'''multi-sequence build'''   ,					add_special_tokens=lowerCAmelCase   ,					add_prefix_space=lowerCAmelCase   )

										UpperCAmelCase         =			tokenizer.build_inputs_with_special_tokens(lowerCAmelCase   )
										UpperCAmelCase         =			tokenizer.build_inputs_with_special_tokens(lowerCAmelCase   ,					lowerCAmelCase   )

										assert encoded_sentence == encoded_text_from_decode
										assert encoded_pair == encoded_pair_from_decode





				@slow
				def   a__(	self    :				Tuple   )->  List[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			[self.tokenizer_class]
										if self.test_rust_tokenizer:
																tokenizer_classes.append(self.rust_tokenizer_class   )

										for tokenizer_class in tokenizer_classes:
																UpperCAmelCase         =			tokenizer_class.from_pretrained('''microsoft/deberta-base'''   )

																UpperCAmelCase         =			[
																    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
																    '''ALBERT incorporates two parameter reduction techniques''',
																    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
																    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
																    ''' vocabulary embedding.''',
																]

																UpperCAmelCase         =			tokenizer(lowerCAmelCase   ,					padding=lowerCAmelCase   )
																UpperCAmelCase         =			[tokenizer.decode(lowerCAmelCase   ,					skip_special_tokens=lowerCAmelCase   ) for seq in encoding['''input_ids''']]

																# fmt: off
																UpperCAmelCase         =			{
																    '''input_ids''': [
																        [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																        [1, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																        [1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
																    ],
																    '''token_type_ids''': [
																        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
																    ],
																    '''attention_mask''': [
																        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																        [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, 1, 1, 1]
																    ]
																}
																# fmt: on

																UpperCAmelCase         =			[
																    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
																    '''ALBERT incorporates two parameter reduction techniques''',
																    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
																    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
																    ''' vocabulary embedding.''',
																]

																self.assertDictEqual(encoding.data   ,					lowerCAmelCase   )

																for expected, decoded in zip(lowerCAmelCase   ,					lowerCAmelCase   ):
																						self.assertEqual(lowerCAmelCase   ,					lowerCAmelCase   )









'''simple docstring'''



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   Tuple							= ["image_processor", "tokenizer"]
				__magic_name__       :   Any							= "ViTImageProcessor"
				__magic_name__       :   str							= ("CLIPTokenizer", "CLIPTokenizerFast")




				def __init__(	self    :				List[str]   ,					lowerCAmelCase    :				List[Any]=None   ,					lowerCAmelCase    :				List[str]=None   ,					**lowerCAmelCase    :				Optional[int]   )->  List[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			None
										if "feature_extractor" in kwargs:
																warnings.warn(
																    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																    ''' instead.'''   ,					lowerCAmelCase   ,					)
																UpperCAmelCase         =			kwargs.pop('''feature_extractor'''   )

										UpperCAmelCase         =			image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
																raise ValueError('''You need to specify an `image_processor`.'''   )
										if tokenizer is None:
																raise ValueError('''You need to specify a `tokenizer`.'''   )

										super().__init__(lowerCAmelCase   ,					lowerCAmelCase   )




				def __call__(	self    :				str   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				Dict=None   ,					lowerCAmelCase    :				str=None   ,					**lowerCAmelCase    :				List[str]   )->  List[Any]:

										"""simple docstring"""




										if text is None and visual_prompt is None and images is None:
																raise ValueError('''You have to specify either text, visual prompt or images.'''   )

										if text is not None and visual_prompt is not None:
																raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''   )

										if text is not None:
																UpperCAmelCase         =			self.tokenizer(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if visual_prompt is not None:
																UpperCAmelCase         =			self.image_processor(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if images is not None:
																UpperCAmelCase         =			self.image_processor(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if visual_prompt is not None and images is not None:
																UpperCAmelCase         =			{
																    '''pixel_values''': image_features.pixel_values,
																    '''conditional_pixel_values''': prompt_features.pixel_values,
																}
																return encoding
										elif text is not None and images is not None:
																UpperCAmelCase         =			image_features.pixel_values
																return encoding
										elif text is not None:
																return encoding
										elif visual_prompt is not None:
																UpperCAmelCase         =			{
																    '''conditional_pixel_values''': prompt_features.pixel_values,
																}
																return encoding
										else:
																return BatchEncoding(data=dict(**lowerCAmelCase   )   ,					tensor_type=lowerCAmelCase   )




				def   a__(	self    :				List[str]   ,					*lowerCAmelCase    :				str   ,					**lowerCAmelCase    :				Optional[int]   )->  Union[str, Any]:

										"""simple docstring"""




										return self.tokenizer.batch_decode(*lowerCAmelCase   ,					**lowerCAmelCase   )




				def   a__(	self    :				Dict   ,					*lowerCAmelCase    :				Tuple   ,					**lowerCAmelCase    :				List[str]   )->  Optional[Any]:

										"""simple docstring"""




										return self.tokenizer.decode(*lowerCAmelCase   ,					**lowerCAmelCase   )




				@property
				def   a__(	self    :				List[Any]   )->  Optional[Any]:

										"""simple docstring"""




										warnings.warn(
										    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''   ,					lowerCAmelCase   ,					)
										return self.image_processor_class





				@property
				def   a__(	self    :				Any   )->  List[Any]:

										"""simple docstring"""




										warnings.warn(
										    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''   ,					lowerCAmelCase   ,					)
										return self.image_processor





