
import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
			from PIL import Image
else:





			class       _lowerCAmelCase   :




							@staticmethod
							def 	__a		(   *_UpperCamelCase							,							**_UpperCamelCase	)							-> Optional[Any]:
										pass





@is_pipeline_test
@require_torch
@require_vision
class       _lowerCAmelCase   (      unittest.TestCase			):
				_lowercase     						=MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> List[Any]:
							lowerCAmelCase_         =      pipeline("visual-question-answering"							,							model="hf-internal-testing/tiny-vilt-random-vqa"	)
							lowerCAmelCase_         =      [
							    {
							        "image": Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	),
							        "question": "How many cats are there?",
							    },
							    {
							        "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
							        "question": "How many cats are there?",
							    },
							]
							return vqa_pipeline, examples




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase	)							-> Optional[Any]:
							lowerCAmelCase_         =      vqa_pipeline(_UpperCamelCase							,							top_k=1	)
							self.assertEqual(
							    _UpperCamelCase							,							[
							        [{"score": ANY(_UpperCamelCase	), "answer": ANY(_UpperCamelCase	)}],
							        [{"score": ANY(_UpperCamelCase	), "answer": ANY(_UpperCamelCase	)}],
							    ]							,							)




				@require_torch
				def 	__a		(   self	)							-> Tuple:
							lowerCAmelCase_         =      pipeline("visual-question-answering"							,							model="hf-internal-testing/tiny-vilt-random-vqa"	)
							lowerCAmelCase_         =      "./tests/fixtures/tests_samples/COCO/000000039769.png"
							lowerCAmelCase_         =      "How many cats are there?"

							lowerCAmelCase_         =      vqa_pipeline(image=_UpperCamelCase							,							question="How many cats are there?"							,							top_k=2	)
							self.assertEqual(
							    _UpperCamelCase							,							[{"score": ANY(_UpperCamelCase	), "answer": ANY(_UpperCamelCase	)}, {"score": ANY(_UpperCamelCase	), "answer": ANY(_UpperCamelCase	)}]	)

							lowerCAmelCase_         =      vqa_pipeline({"image": image, "question": question}							,							top_k=2	)
							self.assertEqual(
							    _UpperCamelCase							,							[{"score": ANY(_UpperCamelCase	), "answer": ANY(_UpperCamelCase	)}, {"score": ANY(_UpperCamelCase	), "answer": ANY(_UpperCamelCase	)}]	)




				@slow
				@require_torch
				def 	__a		(   self	)							-> str:
							lowerCAmelCase_         =      pipeline("visual-question-answering"							,							model="dandelin/vilt-b32-finetuned-vqa"	)
							lowerCAmelCase_         =      "./tests/fixtures/tests_samples/COCO/000000039769.png"
							lowerCAmelCase_         =      "How many cats are there?"

							lowerCAmelCase_         =      vqa_pipeline(image=_UpperCamelCase							,							question=_UpperCamelCase							,							top_k=2	)
							self.assertEqual(
							    nested_simplify(_UpperCamelCase							,							decimals=4	)							,							[{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}]	)

							lowerCAmelCase_         =      vqa_pipeline({"image": image, "question": question}							,							top_k=2	)
							self.assertEqual(
							    nested_simplify(_UpperCamelCase							,							decimals=4	)							,							[{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}]	)

							lowerCAmelCase_         =      vqa_pipeline(
							    [{"image": image, "question": question}, {"image": image, "question": question}]							,							top_k=2	)
							self.assertEqual(
							    nested_simplify(_UpperCamelCase							,							decimals=4	)							,							[[{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}]] * 2							,							)




				@require_tf
				@unittest.skip("Visual question answering not implemented in TF"	)
				def 	__a		(   self	)							-> Union[str, Any]:
							pass





from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available





@dataclass
class       _lowerCAmelCase   (      __a			):
				_lowercase     						=42
				_lowercase     						=42


if is_transformers_available() and is_torch_available():
			from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline



