import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		A_						 =      ['''image_processor''', '''tokenizer''']
		A_						 =      '''AutoImageProcessor'''
		A_						 =      '''AutoTokenizer'''



		def __init__(			self    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=None  ,					__SCREAMING_SNAKE_CASE    :				List[str]=None  ,					**__SCREAMING_SNAKE_CASE    :				List[str]   ):






					"""simple docstring"""
					_snake_case              =      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  ,					)
								_snake_case              =      kwargs.pop("feature_extractor"   )

					_snake_case              =      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   )
					_snake_case              =      self.image_processor
					_snake_case              =      False



		def __call__(			self    :				str  ,					*__SCREAMING_SNAKE_CASE    :				Optional[int]  ,					**__SCREAMING_SNAKE_CASE    :				str   ):






					"""simple docstring"""
					# For backward compatibility
					if self._in_target_context_manager:
								return self.current_processor(*__lowerCamelCase  ,					**__lowerCamelCase   )

					_snake_case              =      kwargs.pop("images"  ,					__lowerCamelCase   )
					_snake_case              =      kwargs.pop("text"  ,					__lowerCamelCase   )
					if len(__lowerCamelCase   ) > 0:
								_snake_case              =      args[0]
								_snake_case              =      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:
								_snake_case              =      self.image_processor(__lowerCamelCase  ,					*__lowerCamelCase  ,					**__lowerCamelCase   )
					if text is not None:
								_snake_case              =      self.tokenizer(__lowerCamelCase  ,					**__lowerCamelCase   )

					if text is None:
								return inputs
					elif images is None:
								return encodings
					else:
								_snake_case              =      encodings["input_ids"]
								return inputs



		def 				_a				(			self    :				str  ,					*__SCREAMING_SNAKE_CASE    :				List[Any]  ,					**__SCREAMING_SNAKE_CASE    :				Dict   ):






					"""simple docstring"""
					return self.tokenizer.batch_decode(*__lowerCamelCase  ,					**__lowerCamelCase   )



		def 				_a				(			self    :				str  ,					*__SCREAMING_SNAKE_CASE    :				Optional[int]  ,					**__SCREAMING_SNAKE_CASE    :				str   ):






					"""simple docstring"""
					return self.tokenizer.decode(*__lowerCamelCase  ,					**__lowerCamelCase   )



		@contextmanager
		def 				_a				(			self    :				Dict   ):






					"""simple docstring"""
					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."   )
					_snake_case              =      True
					_snake_case              =      self.tokenizer
					yield
					_snake_case              =      self.image_processor
					_snake_case              =      False



		@property
		def 				_a				(			self    :				List[str]   ):






					"""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    :				int   ):






					"""simple docstring"""
					warnings.warn(
					    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."  ,					__lowerCamelCase  ,					)
					return self.image_processor





import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property

class 	_SCREAMING_SNAKE_CASE							(     unittest.TestCase							):






		"""simple docstring"""







		@cached_property
		def 				_a				(			self    :				Tuple   ):






					"""simple docstring"""
					return BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"   )



		@cached_property
		def 				_a				(			self    :				Tuple   ):






					"""simple docstring"""
					return BlenderbotTokenizerFast.from_pretrained("facebook/blenderbot-3B"   )



		def 				_a				(			self    :				Optional[Any]   ):






					"""simple docstring"""
					_snake_case              =      self.tokenizer_ab
					_snake_case              =      " I am a small frog."
					_snake_case              =      tok([src_text]  ,					padding=__SCREAMING_SNAKE_CASE  ,					truncation=__SCREAMING_SNAKE_CASE   )["input_ids"]
					_snake_case              =      tok.batch_decode(__SCREAMING_SNAKE_CASE  ,					skip_special_tokens=__SCREAMING_SNAKE_CASE  ,					clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )[0]
					assert src_text == decoded



		def 				_a				(			self    :				str   ):






					"""simple docstring"""
					_snake_case              =      self.rust_tokenizer_ab
					_snake_case              =      " I am a small frog."
					_snake_case              =      tok([src_text]  ,					padding=__SCREAMING_SNAKE_CASE  ,					truncation=__SCREAMING_SNAKE_CASE   )["input_ids"]
					_snake_case              =      tok.batch_decode(__SCREAMING_SNAKE_CASE  ,					skip_special_tokens=__SCREAMING_SNAKE_CASE  ,					clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )[0]
					assert src_text == decoded



		def 				_a				(			self    :				Optional[Any]   ):






					"""simple docstring"""
					assert self.tokenizer_ab.add_prefix_space
					assert self.tokenizer_ab([" Sam", "Sam"]   ).input_ids == [[5_502, 2], [5_502, 2]]







		def 				_a				(			self    :				str   ):






					"""simple docstring"""
					assert self.rust_tokenizer_ab.add_prefix_space
					assert self.rust_tokenizer_ab([" Sam", "Sam"]   ).input_ids == [[5_502, 2], [5_502, 2]]

