
'''simple docstring'''





# 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 						a   (				__magic_name__    ):
				_snake_case		    = '''dandelin/vilt-b32-finetuned-vqa'''
				_snake_case		    = (
				    '''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.'''
				)
				_snake_case		    = '''image_qa'''
				_snake_case		    = AutoProcessor
				_snake_case		    = AutoModelForVisualQuestionAnswering

				_snake_case		    = ['''image''', '''text''']
				_snake_case		    = ['''text''']




				def __init__(							self	:				Optional[int],    *SCREAMING_SNAKE_CASE_	:				Tuple,    **SCREAMING_SNAKE_CASE_	:				Optional[Any]      ):
										requires_backends(self,    ['''vision''']      )
										super().__init__(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				int,    SCREAMING_SNAKE_CASE_	:				"Image",    SCREAMING_SNAKE_CASE_	:				str      ):
										return self.pre_processor(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    return_tensors='''pt'''      )




				def 	__snake_case							(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				str      ):
										with torch.no_grad():
																return self.model(**SCREAMING_SNAKE_CASE_      ).logits




				def 	__snake_case							(							self	:				str,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]      ):
										snake_case       :     Optional[Any] =    outputs.argmax(-1      ).item()
										return self.model.config.idalabel[idx]



'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase    					=   {
    "configuration_swiftformer": [
        "SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SwiftFormerConfig",
        "SwiftFormerOnnxConfig",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase    					=   [
						    "SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "SwiftFormerForImageClassification",
						    "SwiftFormerModel",
						    "SwiftFormerPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_swiftformer import (
						    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    SwiftFormerConfig,
						    SwiftFormerOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_swiftformer import (
												    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SwiftFormerForImageClassification,
												    SwiftFormerModel,
												    SwiftFormerPreTrainedModel,
												)

else:
						import sys

						UpperCAmelCase    					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

