





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    _a     :






											"""simple docstring"""

											@staticmethod
											def      A_  (      *a				:     Dict							,	**a				:     Dict    )	->Any:
																	pass



@is_pipeline_test
@require_torch
@require_vision
class    _a     (				unittest.TestCase							):






							"""simple docstring"""

							snake_case_							 =						MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

							def      A_  (      self				:     Tuple							,	a				:     Optional[int]							,	a				:     List[Any]							,	a				:     Any    )	->Any:
													SCREAMING_SNAKE_CASE__     :				int   =					pipeline("visual-question-answering"							,	model="hf-internal-testing/tiny-vilt-random-vqa"    )
													SCREAMING_SNAKE_CASE__     :				str   =					[
													    {
													        "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				:     Dict							,	a				:     Union[str, Any]							,	a				:     Optional[int]    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__     :				Dict   =					vqa_pipeline(a							,	top_k=1    )
													self.assertEqual(
													    a							,	[
													        [{"score": ANY(a    ), "answer": ANY(a    )}],
													        [{"score": ANY(a    ), "answer": ANY(a    )}],
													    ]							,	)

							@require_torch
							def      A_  (      self				:     Any    )	->List[Any]:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					pipeline("visual-question-answering"							,	model="hf-internal-testing/tiny-vilt-random-vqa"    )
													SCREAMING_SNAKE_CASE__     :				Dict   =					"./tests/fixtures/tests_samples/COCO/000000039769.png"
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					"How many cats are there?"

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

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

							@slow
							@require_torch
							def      A_  (      self				:     Optional[Any]    )	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					pipeline("visual-question-answering"							,	model="dandelin/vilt-b32-finetuned-vqa"    )
													SCREAMING_SNAKE_CASE__     :				int   =					"./tests/fixtures/tests_samples/COCO/000000039769.png"
													SCREAMING_SNAKE_CASE__     :				List[str]   =					"How many cats are there?"

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

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

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					vqa_pipeline(
													    [{"image": image, "question": question}, {"image": image, "question": question}]							,	top_k=2    )
													self.assertEqual(
													    nested_simplify(a							,	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				:     Tuple    )	->Optional[Any]:
													pass






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase       :str       =  {
    "configuration_upernet": ["UperNetConfig"],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowercase       :Union[str, Any]       =  [
				    "UperNetForSemanticSegmentation",
				    "UperNetPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_upernet import UperNetConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
				import sys

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