


"""simple docstring"""




import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image
else:
	class    lowerCAmelCase      :




			'''simple docstring'''







			@staticmethod
			def 				__A		(      *lowerCAmelCase__		,			**lowerCAmelCase__							)	->				str:
									pass


def        lowercase     (SCREAMING_SNAKE_CASE_						:							Image    )  ->		List[Any]:
						SCREAMING_SNAKE_CASE						=   hashlib.mda(image.tobytes()    )
						return m.hexdigest()


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class    lowerCAmelCase      (   unittest.TestCase       ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_		:	Any														=	MODEL_FOR_DEPTH_ESTIMATION_MAPPING







		def 				__A		(      self		,			lowerCAmelCase__		,			lowerCAmelCase__		,			lowerCAmelCase__							)	->				str:
								SCREAMING_SNAKE_CASE						=   DepthEstimationPipeline(model=_UpperCamelCase		,			image_processor=_UpperCamelCase							)
								return depth_estimator, [
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]







		def 				__A		(      self		,			lowerCAmelCase__		,			lowerCAmelCase__							)	->				Dict:
								SCREAMING_SNAKE_CASE						=   depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
								self.assertEqual({'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)}		,			_UpperCamelCase							)
								import datasets

								SCREAMING_SNAKE_CASE						=   datasets.load_dataset('hf-internal-testing/fixtures_image_utils'		,			'image'		,			split='test'							)
								SCREAMING_SNAKE_CASE						=   depth_estimator(
								    [
								        Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							),
								        'http://images.cocodataset.org/val2017/000000039769.jpg',
								        # RGBA
								        dataset[0]['file'],
								        # LA
								        dataset[1]['file'],
								        # L
								        dataset[2]['file'],
								    ]							)
								self.assertEqual(
								    [
								        {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
								        {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
								        {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
								        {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
								        {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
								    ]		,			_UpperCamelCase		,			)







		@require_tf
		@unittest.skip('Depth estimation is not implemented in TF'							)
		def 				__A		(      self							)	->				Union[str, Any]:
								pass







		@slow
		@require_torch
		def 				__A		(      self							)	->				Any:
								SCREAMING_SNAKE_CASE						=   'Intel/dpt-large'
								SCREAMING_SNAKE_CASE						=   pipeline('depth-estimation'		,			model=_UpperCamelCase							)
								SCREAMING_SNAKE_CASE						=   depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'							)
								SCREAMING_SNAKE_CASE						=   hashimage(outputs['depth']							)

								# This seems flaky.
								# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
								self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()							)		,			29.3_04							)
								self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()							)		,			2.6_62							)







		@require_torch
		def 				__A		(      self							)	->				Optional[int]:
								# This is highly irregular to have no small tests.
								self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'							)




from typing import List

import datasets
from datasets.tasks import AudioClassification

from ..folder_based_builder import folder_based_builder


_A  =	datasets.utils.logging.get_logger(__name__)





class       _lowerCAmelCase   (      folder_based_builder.FolderBasedBuilderConfig			):
				_lowercase     						=None
				_lowercase     						=None





class       _lowerCAmelCase   (      folder_based_builder.FolderBasedBuilder			):
				_lowercase     						=datasets.Audio()
				_lowercase     						='''audio'''
				_lowercase     						=AudioFolderConfig
				_lowercase     						=42  # definition at the bottom of the script
				_lowercase     						=AudioClassification(audio_column='''audio'''   , label_column='''label'''			)


_A  =	[
    ".aiff",
    ".au",
    ".avr",
    ".caf",
    ".flac",
    ".htk",
    ".svx",
    ".mat4",
    ".mat5",
    ".mpc2k",
    ".ogg",
    ".paf",
    ".pvf",
    ".raw",
    ".rf64",
    ".sd2",
    ".sds",
    ".ircam",
    ".voc",
    ".w64",
    ".wav",
    ".nist",
    ".wavex",
    ".wve",
    ".xi",
    ".mp3",
    ".opus",
]
_A  =	AUDIO_EXTENSIONS



