




'''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      (			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[int]    , *__snake_case   :Dict    , **__snake_case   :List[Any] ):
										'''simple docstring'''

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

						def     A__						(		self   :Tuple    , __snake_case   :"Image"    , __snake_case   :str ):
										'''simple docstring'''

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

						def     A__						(		self   :List[Any]    , __snake_case   :int ):
										'''simple docstring'''

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


						def     A__						(		self   :Any    , __snake_case   :Any ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=outputs.argmax(-1 ).item()
										return self.model.config.idalabel[idx]










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_		:				Dict									=     {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_		:				Optional[int]									=     [
							    "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "WavLMForAudioFrameClassification",
							    "WavLMForCTC",
							    "WavLMForSequenceClassification",
							    "WavLMForXVector",
							    "WavLMModel",
							    "WavLMPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_wavlm import (
														    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    WavLMForAudioFrameClassification,
														    WavLMForCTC,
														    WavLMForSequenceClassification,
														    WavLMForXVector,
														    WavLMModel,
														    WavLMPreTrainedModel,
														)

else:
							import sys

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




