
"""simple docstring"""





import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment






@require_huggingface_suite
class    _UpperCAmelCase(   unittest.TestCase     ):



		def 						UpperCAmelCase							( self)	->	List[str]:






							'''simple docstring'''
							_UpperCamelCase									=  inspect.getfile(accelerate.test_utils)
							_UpperCamelCase									=  os.path.sep.join(
							    mod_file.split(os.path.sep)[:-1] + ['''scripts''', '''external_deps''', '''test_metrics.py'''])

							from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

							_UpperCamelCase									=  test_metrics



		@require_cpu
		def 						UpperCAmelCase							( self)	->	Optional[int]:






							'''simple docstring'''
							debug_launcher(self.test_metrics.main    ,		num_processes=1)



		@require_cpu
		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							debug_launcher(self.test_metrics.main)



		@require_single_gpu
		def 						UpperCAmelCase							( self)	->	Union[str, Any]:






							'''simple docstring'''
							self.test_metrics.main()




		@require_multi_gpu
		def 						UpperCAmelCase							( self)	->	Optional[int]:






							'''simple docstring'''
							print(F'''Found {torch.cuda.device_count()} devices.''')
							_UpperCamelCase									=  ["torchrun", F'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
							with patch_environment(omp_num_threads=1):
												execute_subprocess_async(UpperCAmelCase_    ,		env=os.environ.copy())








"""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    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'dandelin/vilt-b32-finetuned-vqa'
		lowercase__      =				(
		    '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.'
		)
		lowercase__      =				'image_qa'
		lowercase__      =				AutoProcessor
		lowercase__      =				AutoModelForVisualQuestionAnswering

		lowercase__      =				['image', 'text']
		lowercase__      =				['text']



		def __init__( self    ,		*__a    ,		**__a)	->	int:






							'''simple docstring'''
							requires_backends(self    ,		['''vision'''])
							super().__init__(*__a    ,		**__a)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	Dict:






							'''simple docstring'''
							return self.pre_processor(__a    ,		__a    ,		return_tensors='''pt''')



		def 						UpperCAmelCase							( self    ,		__a)	->	Tuple:






							'''simple docstring'''
							with torch.no_grad():
												return self.model(**__a).logits




		def 						UpperCAmelCase							( self    ,		__a)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  outputs.argmax(-1).item()
							return self.model.config.idalabel[idx]






