


'''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        lowercase     :

				"""simple docstring"""

				@staticmethod
				def    lowerCAmelCase__    (		*UpperCamelCase_   ,       **UpperCamelCase_							):

									'''simple docstring'''


									pass


def       a				(      __a       )  ->     str:



					'''simple docstring'''






					UpperCamelCase__       :Dict							 =			hashlib.mda(image.tobytes()       )
					return m.hexdigest()







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

		"""simple docstring"""

		_a   			=    MODEL_FOR_DEPTH_ESTIMATION_MAPPING




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :List[str]							 =			DepthEstimationPipeline(model=lowerCAmelCase_   ,       image_processor=lowerCAmelCase_							)
							return depth_estimator, [
							    "./tests/fixtures/tests_samples/COCO/000000039769.png",
							    "./tests/fixtures/tests_samples/COCO/000000039769.png",
							]




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Union[str, Any]							 =			depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
							self.assertEqual({'''predicted_depth''': ANY(torch.Tensor							), '''depth''': ANY(Image.Image							)}   ,       lowerCAmelCase_							)
							import datasets

							UpperCamelCase__       :Optional[int]							 =			datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''   ,       '''image'''   ,       split='''test'''							)
							UpperCamelCase__       :List[str]							 =			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							)},
							    ]   ,       lowerCAmelCase_   ,       )




		@require_tf
		@unittest.skip('''Depth estimation is not implemented in TF'''							)
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							pass




		@slow
		@require_torch
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			'''Intel/dpt-large'''
							UpperCamelCase__       :Optional[Any]							 =			pipeline('''depth-estimation'''   ,       model=lowerCAmelCase_							)
							UpperCamelCase__       :Tuple							 =			depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''							)
							UpperCamelCase__       :Optional[Any]							 =			hashimage(outputs['''depth''']							)

							# This seems flaky.
							# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
							self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()							)   ,       29.304							)
							self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()							)   ,       2.662							)




		@require_torch
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''							)



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import _LazyModule


__snake_case											=					{'''tokenization_wav2vec2_phoneme''': ['''Wav2Vec2PhonemeCTCTokenizer''']}


if TYPE_CHECKING:
		from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
		import sys

		__snake_case											=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)