






'''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_	(a_							):
							"""simple docstring"""




							a__   		=	'''dandelin/vilt-b32-finetuned-vqa'''
							a__   		=	(
							    '''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__   		=	'''image_qa'''
							a__   		=	AutoProcessor
							a__   		=	AutoModelForVisualQuestionAnswering

							a__   		=	['''image''', '''text''']
							a__   		=	['''text''']

							def __init__(		self			:int       ,							*lowerCAmelCase__			:Tuple       ,							**lowerCAmelCase__			:Union[str, Any]				)				->  Union[str, Any]:


										'''simple docstring'''




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

							def 							_A	(		self			:int       ,							lowerCAmelCase__			:"Image"       ,							lowerCAmelCase__			:str				)				->  List[Any]:


										'''simple docstring'''




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

							def 							_A	(		self			:Union[str, Any]       ,							lowerCAmelCase__			:Optional[int]				)				->  Dict:


										'''simple docstring'''




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






							def 							_A	(		self			:int       ,							lowerCAmelCase__			:Optional[Any]				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					Any   =       outputs.argmax(-1				).item()
										return self.model.config.idalabel[idx]












'''simple docstring'''




from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters

def 	__UpperCAmelCase       ( )->    int:



			"""simple docstring"""



			snake_case_				:					Any   =       {
			    "repo_name": ["test_repo1", "test_repo2", "test_repo3"],
			    "path": ["test_1.py", "test_2.py", "unit_test.py"],
			    "content": ["a " * 20, "a " * 30, "b " * 7],
			}
			snake_case_				:					int   =       Dataset.from_dict(__magic_name__						)
			return dataset







class    A_	(a_							):
							"""simple docstring"""




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


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       get_dataset()
										snake_case_				:					Optional[int]   =       make_duplicate_clusters(lowerCAmelCase__       ,							0.8_5				)
										self.assertEqual(len(duplicate_clusters[0]				)       ,							2				)






							def 							_A	(		self			:Union[str, Any]				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       get_dataset()
										snake_case_,	snake_case_				:					List[Any]   =       deduplicate_dataset(lowerCAmelCase__				)
										self.assertEqual(len(lowerCAmelCase__				)       ,							2				)
										print(lowerCAmelCase__				)
										self.assertEqual(duplicate_clusters[0][0]["copies"]       ,							2				)
										self.assertEqual(duplicate_clusters[0][0]["is_extreme"]       ,							lowerCAmelCase__				)




