



'''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      __magic_name__						:







														@staticmethod
														def 							__lowercase      (							*_UpperCAmelCase :	int     ,**_UpperCAmelCase :	str		):
																pass



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				str:
		_a					:				Tuple				=							hashlib.mda(image.tobytes()  )
		return m.hexdigest()

@is_pipeline_test
@require_vision
@require_timm
@require_torch
class      __magic_name__						(   unittest.TestCase       ):
							lowerCAmelCase	:   List[Any]           = MODEL_FOR_DEPTH_ESTIMATION_MAPPING







							def 							__lowercase      (							self :	List[Any]     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	List[str]     ,_UpperCAmelCase :	Optional[Any]		):
									_a					:				int				=							DepthEstimationPipeline(model=_UpperCAmelCase     ,image_processor=_UpperCAmelCase		)
									return depth_estimator, [
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									]







							def 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	Dict     ,_UpperCAmelCase :	str		):
									_a					:				List[str]				=							depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
									self.assertEqual({'predicted_depth': ANY(torch.Tensor		), 'depth': ANY(Image.Image		)}     ,_UpperCAmelCase		)
									import datasets

									_a					:				Optional[int]				=							datasets.load_dataset('hf-internal-testing/fixtures_image_utils'     ,'image'     ,split='test'		)
									_a					:				Dict				=							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 							__lowercase      (							self :	List[str]		):
									pass







							@slow
							@require_torch
							def 							__lowercase      (							self :	Optional[Any]		):
									_a					:				List[str]				=							'Intel/dpt-large'
									_a					:				Optional[int]				=							pipeline('depth-estimation'     ,model=_UpperCAmelCase		)
									_a					:				Optional[int]				=							depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'		)
									_a					:				Any				=							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 							__lowercase      (							self :	Tuple		):
									# This is highly irregular to have no small tests.
									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 OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__lowerCAmelCase						      =       {
    '''configuration_data2vec_audio''': ['''DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Data2VecAudioConfig'''],
    '''configuration_data2vec_text''': [
        '''DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecTextConfig''',
        '''Data2VecTextOnnxConfig''',
    ],
    '''configuration_data2vec_vision''': [
        '''DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecVisionConfig''',
        '''Data2VecVisionOnnxConfig''',
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase						      =       [
							    '''DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''Data2VecAudioForAudioFrameClassification''',
							    '''Data2VecAudioForCTC''',
							    '''Data2VecAudioForSequenceClassification''',
							    '''Data2VecAudioForXVector''',
							    '''Data2VecAudioModel''',
							    '''Data2VecAudioPreTrainedModel''',
							]
							__lowerCAmelCase						      =       [
							    '''DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''Data2VecTextForCausalLM''',
							    '''Data2VecTextForMaskedLM''',
							    '''Data2VecTextForMultipleChoice''',
							    '''Data2VecTextForQuestionAnswering''',
							    '''Data2VecTextForSequenceClassification''',
							    '''Data2VecTextForTokenClassification''',
							    '''Data2VecTextModel''',
							    '''Data2VecTextPreTrainedModel''',
							]
							__lowerCAmelCase						      =       [
							    '''DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''Data2VecVisionForImageClassification''',
							    '''Data2VecVisionForMaskedImageModeling''',
							    '''Data2VecVisionForSemanticSegmentation''',
							    '''Data2VecVisionModel''',
							    '''Data2VecVisionPreTrainedModel''',
							]

if is_tf_available():
							__lowerCAmelCase						      =       [
							    '''TFData2VecVisionForImageClassification''',
							    '''TFData2VecVisionForSemanticSegmentation''',
							    '''TFData2VecVisionModel''',
							    '''TFData2VecVisionPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
							from .configuration_dataavec_text import (
							    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DataaVecTextConfig,
							    DataaVecTextOnnxConfig,
							)
							from .configuration_dataavec_vision import (
							    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DataaVecVisionConfig,
							    DataaVecVisionOnnxConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_dataavec_audio import (
														    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DataaVecAudioForAudioFrameClassification,
														    DataaVecAudioForCTC,
														    DataaVecAudioForSequenceClassification,
														    DataaVecAudioForXVector,
														    DataaVecAudioModel,
														    DataaVecAudioPreTrainedModel,
														)
														from .modeling_dataavec_text import (
														    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DataaVecTextForCausalLM,
														    DataaVecTextForMaskedLM,
														    DataaVecTextForMultipleChoice,
														    DataaVecTextForQuestionAnswering,
														    DataaVecTextForSequenceClassification,
														    DataaVecTextForTokenClassification,
														    DataaVecTextModel,
														    DataaVecTextPreTrainedModel,
														)
														from .modeling_dataavec_vision import (
														    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DataaVecVisionForImageClassification,
														    DataaVecVisionForMaskedImageModeling,
														    DataaVecVisionForSemanticSegmentation,
														    DataaVecVisionModel,
														    DataaVecVisionPreTrainedModel,
														)
							if is_tf_available():
														from .modeling_tf_dataavec_vision import (
														    TFDataaVecVisionForImageClassification,
														    TFDataaVecVisionForSemanticSegmentation,
														    TFDataaVecVisionModel,
														    TFDataaVecVisionPreTrainedModel,
														)

else:
							import sys

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




