





"""simple docstring"""




from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
						from PIL import Image
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):
						__magic_name__:     List[Any]	 =    "dandelin/vilt-b32-finetuned-vqa"
						__magic_name__:     str	 =    (
						    "This is a tool that answers a question about an image. It takes an input named `image` which should be the "
						    "image containing the information, as well as a `question` which should be the question in English. It "
						    "returns a text that is the answer to the question."
						)
						__magic_name__:     Optional[Any]	 =    "image_qa"
						__magic_name__:     Any	 =    AutoProcessor
						__magic_name__:     Optional[int]	 =    AutoModelForVisualQuestionAnswering

						__magic_name__:     Tuple	 =    ["image", "text"]
						__magic_name__:     int	 =    ["text"]






						def __init__( self : Any						,				*_A : Optional[Any]						,				**_A : Optional[Any]		)							->      int:







							"""simple docstring"""


							requires_backends(self						,				['vision']		)
							super().__init__(*__a						,				**__a		)






						def UpperCAmelCase_			( self : Dict						,				_A : int						,				_A : int		)							->      Dict:







							"""simple docstring"""


							return self.pre_processor(__a						,				__a						,				return_tensors='pt'		)






						def UpperCAmelCase_			( self : Tuple						,				_A : List[str]		)							->      Tuple:







							"""simple docstring"""


							with torch.no_grad():
								return self.model(**__a		).logits






						def UpperCAmelCase_			( self : List[Any]						,				_A : List[Any]		)							->      Optional[Any]:







							"""simple docstring"""


							snake_case_						:     int     =		outputs.argmax(-1		).item()
							return self.model.config.idalabel[idx]








import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):
						__magic_name__:     Optional[Any]	 =    ["image_processor", "tokenizer"]
						__magic_name__:     Optional[Any]	 =    "LayoutLMv3ImageProcessor"
						__magic_name__:     str	 =    ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")






						def __init__( self : int						,				_A : List[str]=None						,				_A : Dict=None						,				**_A : int		)							->      List[str]:







							"""simple docstring"""


							snake_case_						:     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.'						,				_A						,				)
								snake_case_						:     Any     =		kwargs.pop('feature_extractor'		)

							snake_case_						:     Optional[int]     =		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__(_A						,				_A		)






						def __call__( self : List[str]						,				_A : Optional[Any]						,				_A : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,				_A : Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None						,				_A : Union[List[List[int]], List[List[List[int]]]] = None						,				_A : Optional[Union[List[int], List[List[int]]]] = None						,				_A : bool = True						,				_A : Union[bool, str, PaddingStrategy] = False						,				_A : Union[bool, str, TruncationStrategy] = None						,				_A : Optional[int] = None						,				_A : int = 0						,				_A : Optional[int] = None						,				_A : Optional[bool] = None						,				_A : Optional[bool] = None						,				_A : bool = False						,				_A : bool = False						,				_A : bool = False						,				_A : bool = False						,				_A : bool = True						,				_A : Optional[Union[str, TensorType]] = None						,				**_A : str						,				)							->      BatchEncoding:







							"""simple docstring"""


							if self.image_processor.apply_ocr and (boxes is not None):
								raise ValueError(
								    'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'		)

							if self.image_processor.apply_ocr and (word_labels is not None):
								raise ValueError(
								    'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'		)

							# first, apply the image processor
							snake_case_						:     str     =		self.image_processor(images=_A						,				return_tensors=_A		)

							# second, apply the tokenizer
							if text is not None and self.image_processor.apply_ocr and text_pair is None:
								if isinstance(_A						,				_A		):
									snake_case_						:     List[Any]     =		[text]  # add batch dimension (as the image processor always adds a batch dimension)
								snake_case_						:     str     =		features['words']

							snake_case_						:     Optional[int]     =		self.tokenizer(
							    text=text if text is not None else features['words']						,				text_pair=text_pair if text_pair is not None else None						,				boxes=boxes if boxes is not None else features['boxes']						,				word_labels=_A						,				add_special_tokens=_A						,				padding=_A						,				truncation=_A						,				max_length=_A						,				stride=_A						,				pad_to_multiple_of=_A						,				return_token_type_ids=_A						,				return_attention_mask=_A						,				return_overflowing_tokens=_A						,				return_special_tokens_mask=_A						,				return_offsets_mapping=_A						,				return_length=_A						,				verbose=_A						,				return_tensors=_A						,				**_A						,				)

							# add pixel values
							snake_case_						:     List[str]     =		features.pop('pixel_values'		)
							if return_overflowing_tokens is True:
								snake_case_						:     Dict     =		self.get_overflowing_images(_A						,				encoded_inputs['overflow_to_sample_mapping']		)
							snake_case_						:     Optional[Any]     =		images

							return encoded_inputs






						def UpperCAmelCase_			( self : Dict						,				_A : Tuple						,				_A : Dict		)							->      Union[str, Any]:







							"""simple docstring"""


							snake_case_						:     List[str]     =		[]
							for sample_idx in overflow_to_sample_mapping:
								images_with_overflow.append(images[sample_idx]		)

							if len(_A		) != len(_A		):
								raise ValueError(
								    'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
								    F""" {len(_A		)} and {len(_A		)}"""		)

							return images_with_overflow






						def UpperCAmelCase_			( self : Optional[Any]						,				*_A : Optional[Any]						,				**_A : List[Any]		)							->      List[str]:







							"""simple docstring"""


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






						def UpperCAmelCase_			( self : Union[str, Any]						,				*_A : Dict						,				**_A : str		)							->      Any:







							"""simple docstring"""


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






						@property
						def UpperCAmelCase_			( self : Optional[int]		)							->      int:







							"""simple docstring"""


							return ["input_ids", "bbox", "attention_mask", "pixel_values"]






						@property
						def UpperCAmelCase_			( self : Any		)							->      Any:







							"""simple docstring"""


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






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







							"""simple docstring"""


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

