


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class 					lowerCAmelCase__	(    a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			["image_processor", "tokenizer"]
     __SCREAMING_SNAKE_CASE        =			"AutoImageProcessor"
     __SCREAMING_SNAKE_CASE        =			"AutoTokenizer"


     def __init__(						self			,						__lowerCamelCase=None			,						__lowerCamelCase=None			,						**__lowerCamelCase)      ->    Dict:
            _A				:	Optional[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."			,						__lowerCamelCase			,						)
                   _A				:	Any       =			kwargs.pop("feature_extractor")

            _A				:	str       =			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)
            _A				:	List[str]       =			self.image_processor
            _A				:	Optional[int]       =			False


     def __call__(						self			,						*__lowerCamelCase			,						**__lowerCamelCase)      ->    Tuple:
            # For backward compatibility
            if self._in_target_context_manager:
                   return self.current_processor(*__lowerCamelCase			,						**__lowerCamelCase)

            _A				:	str       =			kwargs.pop("images"			,						__lowerCamelCase)
            _A				:	Optional[int]       =			kwargs.pop("text"			,						__lowerCamelCase)
            if len(__lowerCamelCase) > 0:
                   _A				:	Tuple       =			args[0]
                   _A				:	Optional[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:
                   _A				:	Union[str, Any]       =			self.image_processor(__lowerCamelCase			,						*__lowerCamelCase			,						**__lowerCamelCase)
            if text is not None:
                   _A				:	Tuple       =			self.tokenizer(__lowerCamelCase			,						**__lowerCamelCase)

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


     def 			_lowerCamelCase			(						self			,						*__lowerCamelCase			,						**__lowerCamelCase)      ->    Any:
            return self.tokenizer.batch_decode(*__lowerCamelCase			,						**__lowerCamelCase)


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


     @contextmanager
     def 			_lowerCamelCase			(						self)      ->    Dict:
            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.")
            _A				:	List[Any]       =			True
            _A				:	Tuple       =			self.tokenizer
            yield
            _A				:	Dict       =			self.image_processor
            _A				:	List[Any]       =			False


     @property
     def 			_lowerCamelCase			(						self)      ->    Tuple:
            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 			_lowerCamelCase			(						self)      ->    Dict:
            warnings.warn(
                "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,						__lowerCamelCase			,						)
            return self.image_processor




from __future__ import annotations

from typing import Any







class 					lowerCAmelCase__	:




     '''simple docstring'''


     def __init__(						self			,						__lowerCamelCase			,						__lowerCamelCase)      ->    None:  # noqa: A002
            _A				:	List[str]       =			item
            _A				:	List[Any]       =			next







class 					lowerCAmelCase__	:




     '''simple docstring'''


     def __init__(						self)      ->    None:
            _A				:	Node | None       =			None
            _A				:	List[Any]       =			0


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    None:
            _A				:	Union[str, Any]       =			Node(__lowerCamelCase			,						self.head)
            self.size += 1


     def 			_lowerCamelCase			(						self)      ->    Any:
            # Switched 'self.is_empty()' to 'self.head is None'
            # because mypy was considering the possibility that 'self.head'
            # can be None in below else part and giving error
            if self.head is None:
                   return None
            else:
                   _A				:	str       =			self.head.item
                   _A				:	List[Any]       =			self.head.next
                   self.size -= 1
                   return item


     def 			_lowerCamelCase			(						self)      ->    bool:
            return self.head is None


     def __str__(						self)      ->    str:
            if self.is_empty():
                   return ""
            else:
                   _A				:	List[str]       =			self.head
                   _A				:	Optional[int]       =			""
                   _A				:	list[str]       =			[]
                   while iterate:
                          item_list.append(str(iterate.item))
                          _A				:	Optional[Any]       =			iterate.next

                   _A				:	List[str]       =			" --> ".join(__lowerCamelCase)

                   return item_str


     def __len__(						self)      ->    int:
            return self.size
