






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase__							=     logging.get_logger(__name__)

lowerCamelCase__							=     {
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}




class 			snake_case__   (							lowercase_	,    lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				Optional[Any] 				= "bit"
       lowerCamelCase							:				Optional[int] 				= ["preactivation", "bottleneck"]
       lowerCamelCase							:				str 				= ["SAME", "VALID"]



       def __init__(							self			,						a__=3			,						a__=64			,						a__=[2_56, 5_12, 10_24, 20_48]			,						a__=[3, 4, 6, 3]			,						a__="preactivation"			,						a__="relu"			,						a__=None			,						a__=32			,						a__=0.0			,						a__=False			,						a__=32			,						a__=1			,						a__=None			,						a__=None			,						**a__			,						) ->      List[Any]:






            '''simple docstring'''





            super().__init__(**__UpperCamelCase    )
            if layer_type not in self.layer_types:
                 raise ValueError(F'''layer_type={layer_type} is not one of {','.join(self.layer_types    )}'''    )
            if global_padding is not None:
                 if global_padding.upper() in self.supported_padding:
                      __snake_case			:List[str]          =						global_padding.upper()
                 else:
                      raise ValueError(F'''Padding strategy {global_padding} not supported'''    )
            __snake_case			:int          =						num_channels
            __snake_case			:Optional[int]          =						embedding_size
            __snake_case			:str          =						hidden_sizes
            __snake_case			:str          =						depths
            __snake_case			:Any          =						layer_type
            __snake_case			:str          =						hidden_act
            __snake_case			:int          =						global_padding
            __snake_case			:Any          =						num_groups
            __snake_case			:List[Any]          =						drop_path_rate
            __snake_case			:Dict          =						embedding_dynamic_padding
            __snake_case			:str          =						output_stride
            __snake_case			:List[Any]          =						width_factor

            __snake_case			:Any          =						["""stem"""] + [F'''stage{idx}''' for idx in range(1			,						len(__UpperCamelCase    ) + 1    )]
            __snake_case       , __snake_case			:Tuple          =						get_aligned_output_features_output_indices(
                out_features=__UpperCamelCase			,						out_indices=__UpperCamelCase			,						stage_names=self.stage_names    )










# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				Tuple 				= "dandelin/vilt-b32-finetuned-vqa"
       lowerCamelCase							:				List[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."
       )
       lowerCamelCase							:				Optional[Any] 				= "image_qa"
       lowerCamelCase							:				str 				= AutoProcessor
       lowerCamelCase							:				Union[str, Any] 				= AutoModelForVisualQuestionAnswering

       lowerCamelCase							:				Any 				= ["image", "text"]
       lowerCamelCase							:				Dict 				= ["text"]



       def __init__(							self			,						*a__			,						**a__    ) ->      Any:






            '''simple docstring'''





            requires_backends(self			,						["""vision"""]    )
            super().__init__(*a__			,						**a__    )



       def 					__lowercase						(							self			,						a__			,						a__    ) ->      int:






            '''simple docstring'''





            return self.pre_processor(a__			,						a__			,						return_tensors="""pt"""    )



       def 					__lowercase						(							self			,						a__    ) ->      Union[str, Any]:






            '''simple docstring'''





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



       def 					__lowercase						(							self			,						a__    ) ->      Tuple:






            '''simple docstring'''





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


