
"""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__	(     _lowerCamelCase):
		A_						:	Tuple           =     'dandelin/vilt-b32-finetuned-vqa'
		A_						:	Dict           =     (
		    '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.'
		)
		A_						:	Optional[Any]           =     'image_qa'
		A_						:	Tuple           =     AutoProcessor
		A_						:	Tuple           =     AutoModelForVisualQuestionAnswering

		A_						:	List[str]           =     ['image', 'text']
		A_						:	Dict           =     ['text']

		def __init__(				self	,		*_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			):
			requires_backends(self	,		['vision']			)
			super().__init__(*_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			return self.pre_processor(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		return_tensors='pt'			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			with torch.no_grad():
				return self.model(**_SCREAMING_SNAKE_CASE			).logits







		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Any						      =				outputs.argmax(-1			).item()
			return self.model.config.idalabel[idx]

"""simple docstring"""







import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('dataset_size'	,  [None, 400 * 2**20, 600 * 2**20]		)
@pytest.mark.parametrize('input_in_memory_max_size'	,  ['default', 0, 100 * 2**20, 900 * 2**20]		)
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
	if input_in_memory_max_size != "default":
		monkeypatch.setattr(datasets.config	,  'IN_MEMORY_MAX_SIZE'	,  _UpperCamelCase		)
	__lowerCAmelCase      :						Union[str, Any]						      =				datasets.config.IN_MEMORY_MAX_SIZE
	if input_in_memory_max_size == "default":
		assert in_memory_max_size == 0
	else:
		assert in_memory_max_size == input_in_memory_max_size
	if dataset_size and in_memory_max_size:
		__lowerCAmelCase      :						Tuple						      =				dataset_size < in_memory_max_size
	else:
		__lowerCAmelCase      :						str						      =				False
	__lowerCAmelCase      :						Optional[int]						      =				is_small_dataset(_UpperCamelCase		)
	assert result == expected