'''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 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = '''dandelin/vilt-b32-finetuned-vqa'''
			UpperCamelCase__       = (
			    '''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__       = '''image_qa'''
			UpperCamelCase__       = AutoProcessor
			UpperCamelCase__       = AutoModelForVisualQuestionAnswering

			UpperCamelCase__       = ['''image''', '''text''']
			UpperCamelCase__       = ['''text''']







			def __init__(			self       :						Optional[Any]       ,    *lowercase_       :						Tuple       ,    **lowercase_       :						List[Any]					):
										requires_backends(self       ,    ["""vision"""]					)
										super().__init__(*lowercase_       ,    **lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict       ,    lowercase_       :						"Image"       ,    lowercase_       :						str					):
										return self.pre_processor(lowercase_       ,    lowercase_       ,    return_tensors="""pt"""					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]       ,    lowercase_       :						Optional[int]					):
										with torch.no_grad():
																	return self.model(**lowercase_					).logits







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






'''simple docstring'''




from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase		:				List[Any]	       =		{"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:				int	       =		[
						    "FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "FocalNetForImageClassification",
						    "FocalNetForMaskedImageModeling",
						    "FocalNetBackbone",
						    "FocalNetModel",
						    "FocalNetPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_focalnet import (
												    FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FocalNetBackbone,
												    FocalNetForImageClassification,
												    FocalNetForMaskedImageModeling,
												    FocalNetModel,
												    FocalNetPreTrainedModel,
												)

else:
						import sys

						_lowercase		:				Union[str, Any]	       =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





