


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 				a		(lowerCAmelCase__      ):




							"""simple docstring"""


							__UpperCAmelCase    :							Dict       =  """dandelin/vilt-b32-finetuned-vqa"""
							__UpperCAmelCase    :							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."""
							)
							__UpperCAmelCase    :							List[str]       =  """image_qa"""
							__UpperCAmelCase    :							Any       =  AutoProcessor
							__UpperCAmelCase    :							int       =  AutoModelForVisualQuestionAnswering

							__UpperCAmelCase    :							str       =  ["""image""", """text"""]
							__UpperCAmelCase    :							Dict       =  ["""text"""]

							def __init__(							self       :  Any    ,	*lowerCamelCase       :  str    ,	**lowerCamelCase       :  Optional[int]				)	->    Tuple:
														requires_backends(self    ,	["vision"]				)
														super().__init__(*__a    ,	**__a				)

							def     __snake_case					(							self       :  List[str]    ,	lowerCamelCase       :  List[str]    ,	lowerCamelCase       :  Tuple				)	->    Optional[Any]:
														return self.pre_processor(__a    ,	__a    ,	return_tensors="pt"				)

							def     __snake_case					(							self       :  Any    ,	lowerCamelCase       :  List[str]				)	->    List[str]:
														with torch.no_grad():
																					return self.model(**__a				).logits






							def     __snake_case					(							self       :  Any    ,	lowerCamelCase       :  Union[str, Any]				)	->    Tuple:
														__snake_case		:			Dict							 =   outputs.argmax(-1				).item()
														return self.model.config.idalabel[idx]





"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A     :   Optional[int]			    =			{
    "configuration_altclip": [
        "ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "AltCLIPConfig",
        "AltCLIPTextConfig",
        "AltCLIPVisionConfig",
    ],
    "processing_altclip": ["AltCLIPProcessor"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   Tuple			    =			[
	    "ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "AltCLIPPreTrainedModel",
	    "AltCLIPModel",
	    "AltCLIPTextModel",
	    "AltCLIPVisionModel",
	]


if TYPE_CHECKING:
	from .configuration_altclip import (
	    ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    AltCLIPConfig,
	    AltCLIPTextConfig,
	    AltCLIPVisionConfig,
	)
	from .processing_altclip import AltCLIPProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_altclip import (
		    ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AltCLIPModel,
		    AltCLIPPreTrainedModel,
		    AltCLIPTextModel,
		    AltCLIPVisionModel,
		)


else:
	import sys

	A     :   int			    =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




