






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



class 						lowerCamelCase   (_snake_case    ):







		'''simple docstring'''



		_snake_case					:					str 		=						['''image_processor''', '''tokenizer''']
		_snake_case					:					int 		=						'''AutoImageProcessor'''
		_snake_case					:					Optional[Any] 		=						'''AutoTokenizer'''



		def __init__(							self       ,	_UpperCamelCase=None       ,	_UpperCamelCase=None       ,	**_UpperCamelCase     )			->							Optional[Any]:
							UpperCAmelCase_					:   Any   =		None
							if "feature_extractor" in kwargs:
												warnings.warn(
												    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
												    ' instead.'       ,	_UpperCamelCase       ,	)
												UpperCAmelCase_					:   Optional[int]   =		kwargs.pop('feature_extractor'     )

							UpperCAmelCase_					:   Any   =		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__(_UpperCamelCase       ,	_UpperCamelCase     )
							UpperCAmelCase_					:   Union[str, Any]   =		self.image_processor
							UpperCAmelCase_					:   Any   =		False



		def __call__(							self       ,	*_UpperCamelCase       ,	**_UpperCamelCase     )			->							Optional[int]:
							# For backward compatibility
							if self._in_target_context_manager:
												return self.current_processor(*_UpperCamelCase       ,	**_UpperCamelCase     )

							UpperCAmelCase_					:   int   =		kwargs.pop('images'       ,	_UpperCamelCase     )
							UpperCAmelCase_					:   List[Any]   =		kwargs.pop('text'       ,	_UpperCamelCase     )
							if len(_UpperCamelCase     ) > 0:
												UpperCAmelCase_					:   Dict   =		args[0]
												UpperCAmelCase_					:   List[Any]   =		args[1:]

							if images is None and text is None:
												raise ValueError('You need to specify either an `images` or `text` input to process.'     )

							if images is not None:
												UpperCAmelCase_					:   Optional[int]   =		self.image_processor(_UpperCamelCase       ,	*_UpperCamelCase       ,	**_UpperCamelCase     )
							if text is not None:
												UpperCAmelCase_					:   Optional[int]   =		self.tokenizer(_UpperCamelCase       ,	**_UpperCamelCase     )

							if text is None:
												return inputs
							elif images is None:
												return encodings
							else:
												UpperCAmelCase_					:   Any   =		encodings['input_ids']
												return inputs



		def       __UpperCAmelCase    (							self       ,	*_UpperCamelCase       ,	**_UpperCamelCase     )			->							List[Any]:
							return self.tokenizer.batch_decode(*_UpperCamelCase       ,	**_UpperCamelCase     )



		def       __UpperCAmelCase    (							self       ,	*_UpperCamelCase       ,	**_UpperCamelCase     )			->							List[str]:
							return self.tokenizer.decode(*_UpperCamelCase       ,	**_UpperCamelCase     )



		@contextmanager
		def       __UpperCAmelCase    (							self     )			->							List[Any]:
							warnings.warn(
							    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
							    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
							    'your images inputs, or in a separate call.'     )
							UpperCAmelCase_					:   List[Any]   =		True
							UpperCAmelCase_					:   Dict   =		self.tokenizer
							yield
							UpperCAmelCase_					:   List[Any]   =		self.image_processor
							UpperCAmelCase_					:   Dict   =		False



		@property
		def       __UpperCAmelCase    (							self     )			->							Union[str, Any]:
							warnings.warn(
							    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'       ,	_UpperCamelCase       ,	)
							return self.image_processor_class



		@property
		def       __UpperCAmelCase    (							self     )			->							List[Any]:
							warnings.warn(
							    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'       ,	_UpperCamelCase       ,	)
							return self.image_processor














import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class 						lowerCamelCase   (_snake_case				,						unittest.TestCase    ):







		'''simple docstring'''



		_snake_case					:					Tuple 		=						BertweetTokenizer
		_snake_case					:					List[str] 		=						False



		def       __UpperCAmelCase    (							self     )			->							Optional[int]:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							UpperCAmelCase_					:   Optional[int]   =		['I', 'm', 'V@@', 'R@@', 'r', 'e@@']
							UpperCAmelCase_					:   Dict   =		dict(zip(_UpperCamelCase       ,	range(len(_UpperCamelCase     )     )     )     )
							UpperCAmelCase_					:   List[Any]   =		['#version: 0.2', 'a m</w>']
							UpperCAmelCase_					:   Union[str, Any]   =		{'unk_token': '<unk>'}

							UpperCAmelCase_					:   Optional[Any]   =		os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES['vocab_file']     )
							UpperCAmelCase_					:   List[str]   =		os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES['merges_file']     )
							with open(self.vocab_file       ,	'w'       ,	encoding='utf-8'     ) as fp:
												for token in vocab_tokens:
																	fp.write(f"{token} {vocab_tokens[token]}\n"     )
							with open(self.merges_file       ,	'w'       ,	encoding='utf-8'     ) as fp:
												fp.write('\n'.join(_UpperCamelCase     )     )



		def       __UpperCAmelCase    (							self       ,	**_UpperCamelCase     )			->							List[Any]:
							kwargs.update(self.special_tokens_map     )
							return BertweetTokenizer.from_pretrained(self.tmpdirname       ,	**_UpperCamelCase     )



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase     )			->							List[Any]:
							UpperCAmelCase_					:   Any   =		'I am VinAI Research'
							UpperCAmelCase_					:   Any   =		'I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
							return input_text, output_text



		def       __UpperCAmelCase    (							self     )			->							Tuple:
							UpperCAmelCase_					:   Union[str, Any]   =		BertweetTokenizer(self.vocab_file       ,	self.merges_file       ,	**self.special_tokens_map     )
							UpperCAmelCase_					:   Optional[Any]   =		'I am VinAI Research'
							UpperCAmelCase_					:   str   =		'I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
							UpperCAmelCase_					:   Any   =		tokenizer.tokenize(_UpperCamelCase     )
							self.assertListEqual(_UpperCamelCase       ,	_UpperCamelCase     )

							UpperCAmelCase_					:   Optional[int]   =		tokens + [tokenizer.unk_token]

							UpperCAmelCase_					:   List[str]   =		[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase     )       ,	_UpperCamelCase     )






