






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 							_lowerCAmelCase(			_lowercase   ):





       """simple docstring"""







       a							:						Optional[Any]										='''dandelin/vilt-b32-finetuned-vqa'''
       a							:						int										=(
           '''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.'''
       )
       a							:						str										='''image_qa'''
       a							:						int										=AutoProcessor
       a							:						Any										=AutoModelForVisualQuestionAnswering

       a							:						List[Any]										=['''image''', '''text''']
       a							:						Union[str, Any]										=['''text''']





       def __init__(   self    ,     *_lowerCamelCase    ,     **_lowerCamelCase				):
         requires_backends(self    ,     ['vision']				)
         super().__init__(*A_    ,     **A_				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         return self.pre_processor(A_    ,     A_    ,     return_tensors='pt'				)





       def      _a       (   self    ,     _lowerCamelCase				):
         with torch.no_grad():
           return self.model(**A_				).logits





       def      _a       (   self    ,     _lowerCamelCase				):
         UpperCamelCase_:   Optional[int]        =							outputs.argmax(-1				).item()
         return self.model.config.idalabel[idx]

"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase				 =					logging.get_logger(__name__)

_lowercase				 =					{
    '''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[Any]   =     '''mobilenet_v1'''


						def __init__(			self  :   Optional[int]	,A_  :   Optional[int]=3	,A_  :   Any=224	,A_  :   List[Any]=1.0	,A_  :   Union[str, Any]=8	,A_  :   Union[str, Any]="relu6"	,A_  :   Optional[Any]=True	,A_  :   List[str]=0.9_99	,A_  :   int=0.02	,A_  :   int=0.0_01	,**A_  :   Union[str, Any]	,)  -> Dict:
							super().__init__(**A_   )

							if depth_multiplier <= 0:
								raise ValueError('depth_multiplier must be greater than zero.'   )

							A     = num_channels
							A     = image_size
							A     = depth_multiplier
							A     = min_depth
							A     = hidden_act
							A     = tf_padding
							A     = classifier_dropout_prob
							A     = initializer_range
							A     = layer_norm_eps







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[int]   =     version.parse('''1.11'''     )


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Mapping[str, Mapping[int, str]]:
							return OrderedDict([('pixel_values', {0: 'batch'})]   )


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Mapping[str, Mapping[int, str]]:
							if self.task == "image-classification":
								return OrderedDict([('logits', {0: 'batch'})]   )
							else:
								return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]   )




						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> float:
							return 1e-4