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#
# 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 				lowerCAmelCase_		(  __A							):
       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__				:					Any        =  "image_qa"
       UpperCAmelCase__				:					str        =  AutoProcessor
       UpperCAmelCase__				:					Optional[int]        =  AutoModelForVisualQuestionAnswering

       UpperCAmelCase__				:					Optional[int]        =  ["image", "text"]
       UpperCAmelCase__				:					List[Any]        =  ["text"]
       def __init__(			self,			*SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )     ->		List[Any]:
          requires_backends(self,			['vision']       )
          super().__init__(*_UpperCamelCase,			**_UpperCamelCase       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		Union[str, Any]:
          return self.pre_processor(_UpperCamelCase,			_UpperCamelCase,			return_tensors='pt'       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		int:
          with torch.no_grad():
             return self.model(**_UpperCamelCase       ).logits






       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		Tuple:
          UpperCamelCase    :  Tuple							=	outputs.argmax(-1       ).item()
          return self.model.config.idalabel[idx]






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_       	=	logging.get_logger(__name__)

lowerCAmelCase_       	=	{
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   List[Any]									=				"vit_msn"







			def __init__(     self :				Dict							,   _UpperCamelCase :				Optional[int]=7_6_8							,   _UpperCamelCase :				Optional[Any]=1_2							,   _UpperCamelCase :				Union[str, Any]=1_2							,   _UpperCamelCase :				str=3_0_7_2							,   _UpperCamelCase :				Tuple="gelu"							,   _UpperCamelCase :				Dict=0.0							,   _UpperCamelCase :				Dict=0.0							,   _UpperCamelCase :				List[str]=0.02							,   _UpperCamelCase :				List[Any]=1e-06							,   _UpperCamelCase :				Any=2_2_4							,   _UpperCamelCase :				Optional[Any]=1_6							,   _UpperCamelCase :				Any=3							,   _UpperCamelCase :				str=True							,   **_UpperCamelCase :				Any							,   )      ->int:
									super().__init__(**_UpperCamelCase							)

									snake_case_   =	hidden_size
									snake_case_   =	num_hidden_layers
									snake_case_   =	num_attention_heads
									snake_case_   =	intermediate_size
									snake_case_   =	hidden_act
									snake_case_   =	hidden_dropout_prob
									snake_case_   =	attention_probs_dropout_prob
									snake_case_   =	initializer_range
									snake_case_   =	layer_norm_eps
									snake_case_   =	image_size
									snake_case_   =	patch_size
									snake_case_   =	num_channels
									snake_case_   =	qkv_bias