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__							:



				@staticmethod
				def __SCREAMING_SNAKE_CASE							(						*UpperCAmelCase				,						**UpperCAmelCase   )			->   List[Any]:
									pass


@is_pipeline_test
@require_torch
@require_vision
class 						a__							(       unittest.TestCase		):
			A__    :       Tuple        =      MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )			->   List[str]:
								__a		 =       pipeline('visual-question-answering'				,						model='hf-internal-testing/tiny-vilt-random-vqa'   )
								__a		 =       [
								    {
								        '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 __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase   )			->   Optional[Any]:
								__a		 =       vqa_pipeline(UpperCAmelCase				,						top_k=1   )
								self.assertEqual(
								    UpperCAmelCase				,						[
								        [{'score': ANY(UpperCAmelCase   ), 'answer': ANY(UpperCAmelCase   )}],
								        [{'score': ANY(UpperCAmelCase   ), 'answer': ANY(UpperCAmelCase   )}],
								    ]				,						)



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

								__a		 =       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   )}]   )

								__a		 =       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 __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								__a		 =       pipeline('visual-question-answering'				,						model='dandelin/vilt-b32-finetuned-vqa'   )
								__a		 =       './tests/fixtures/tests_samples/COCO/000000039769.png'
								__a		 =       'How many cats are there?'

								__a		 =       vqa_pipeline(image=UpperCAmelCase				,						question=UpperCAmelCase				,						top_k=2   )
								self.assertEqual(
								    nested_simplify(UpperCAmelCase				,						decimals=4   )				,						[{'score': 0.8_799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]   )

								__a		 =       vqa_pipeline({'image': image, 'question': question}				,						top_k=2   )
								self.assertEqual(
								    nested_simplify(UpperCAmelCase				,						decimals=4   )				,						[{'score': 0.8_799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]   )

								__a		 =       vqa_pipeline(
								    [{'image': image, 'question': question}, {'image': image, 'question': question}]				,						top_k=2   )
								self.assertEqual(
								    nested_simplify(UpperCAmelCase				,						decimals=4   )				,						[[{'score': 0.8_799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]] * 2				,						)







			@require_tf
			@unittest.skip('Visual question answering not implemented in TF'   )
			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								pass





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


lowerCamelCase_    :		Dict							=	{"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Optional[Any]							=	["""SpeechEncoderDecoderModel"""]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Dict							=	["""FlaxSpeechEncoderDecoderModel"""]

if TYPE_CHECKING:
	from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
	import sys

	lowerCamelCase_    :		Optional[int]							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




