
import logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


lowercase        =  {
    '''debug''': logging.DEBUG,
    '''info''': logging.INFO,
    '''warning''': logging.WARNING,
    '''error''': logging.ERROR,
    '''critical''': logging.CRITICAL,
}

lowercase        =  logging.WARNING



def      __lowerCAmelCase						(							)  ->    str:
							lowerCamelCase_							  =					os.getenv("""DATASETS_VERBOSITY"""			,				UpperCAmelCase__       )
							if env_level_str:
														if env_level_str in log_levels:
																					return log_levels[env_level_str]
														else:
																					logging.getLogger().warning(
																					    F'''Unknown option DATASETS_VERBOSITY={env_level_str}, '''
																					    F'''has to be one of: { ', '.join(log_levels.keys()       ) }'''       )
							return _default_log_level



def      __lowerCAmelCase						(							)  ->    str:
							return __name__.split("""."""       )[0]



def      __lowerCAmelCase						(							)  ->    logging.Logger:
							return logging.getLogger(_get_library_name()       )



def      __lowerCAmelCase						(							)  ->    None:
							# Apply our default configuration to the library root logger.
							lowerCamelCase_							  =					_get_library_root_logger()
							library_root_logger.setLevel(_get_default_logging_level()       )



def      __lowerCAmelCase						(							)  ->    None:
							lowerCamelCase_							  =					_get_library_root_logger()
							library_root_logger.setLevel(logging.NOTSET       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Optional[str] = None       )  ->    logging.Logger:
							if name is None:
														lowerCamelCase_							  =					_get_library_name()
							return logging.getLogger(UpperCAmelCase__       )



def      __lowerCAmelCase						(							)  ->    int:
							return _get_library_root_logger().getEffectiveLevel()



def      __lowerCAmelCase						(							UpperCAmelCase__				:      int       )  ->    None:
							_get_library_root_logger().setLevel(UpperCAmelCase__       )



def      __lowerCAmelCase						(							)  ->    Dict:
							return set_verbosity(UpperCAmelCase__       )



def      __lowerCAmelCase						(							)  ->    Tuple:
							return set_verbosity(UpperCAmelCase__       )



def      __lowerCAmelCase						(							)  ->    Dict:
							return set_verbosity(UpperCAmelCase__       )



def      __lowerCAmelCase						(							)  ->    Optional[int]:
							return set_verbosity(UpperCAmelCase__       )



def      __lowerCAmelCase						(							)  ->    None:
							lowerCamelCase_							  =					False



def      __lowerCAmelCase						(							)  ->    None:
							lowerCamelCase_							  =					True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()





class       __A:

					def __init__(	self					:     Union[str, Any]   ,					*__UpperCamelCase					:     Any   ,					**__UpperCamelCase					:     int   ):  # pylint: disable=unused-argument
												lowerCamelCase_							  =					args[0] if args else None

					def __iter__(	self					:     Tuple   ):
												return iter(self._iterator   )

					def __getattr__(	self					:     str   ,					__UpperCamelCase					:     List[str]   ):

												def empty_fn(*__UpperCamelCase					:     List[str]   ,					**__UpperCamelCase					:     str   ):  # pylint: disable=unused-argument
																			return

												return empty_fn

					def __enter__(	self					:     Union[str, Any]   ):
												return self



					def __exit__(	self					:     Optional[Any]   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Dict   ):
												return
lowercase        =  True





class       __A:

					def __call__(	self					:     Any   ,					*__UpperCamelCase					:     str   ,					__UpperCamelCase					:     str=False   ,					**__UpperCamelCase					:     Optional[Any]   ):
												if _tqdm_active and not disable:
																			return tqdm_lib.tqdm(*__UpperCamelCase   ,					**__UpperCamelCase   )
												else:
																			return EmptyTqdm(*__UpperCamelCase   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     Optional[Any]   ,					*__UpperCamelCase					:     List[Any]   ,					**__UpperCamelCase					:     List[Any]   ):
												lowerCamelCase_							  =					None
												if _tqdm_active:
																			return tqdm_lib.tqdm.set_lock(*__UpperCamelCase   ,					**__UpperCamelCase   )



					def    lowercase__      (	self					:     List[str]   ):
												if _tqdm_active:
																			return tqdm_lib.tqdm.get_lock()
lowercase        =  _tqdm_cls()



def      __lowerCAmelCase						(							)  ->    bool:
							global _tqdm_active
							return bool(_tqdm_active       )



def      __lowerCAmelCase						(							)  ->    Any:
							global _tqdm_active
							lowerCamelCase_							  =					True



def      __lowerCAmelCase						(							)  ->    Optional[Any]:
							global _tqdm_active
							lowerCamelCase_							  =					False







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       __A:

							@staticmethod
							def    lowercase__      (	*__UpperCamelCase					:     str   ,					**__UpperCamelCase					:     Dict   ):
														pass



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Image       )  ->    str:
							lowerCamelCase_							  =					hashlib.mda(image.tobytes()       )
							return m.hexdigest()





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class       __A(				unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =    MODEL_FOR_DEPTH_ESTIMATION_MAPPING

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     int   ):
												lowerCamelCase_							  =					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					:     List[str]   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					depth_estimator("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
												self.assertEqual({"""predicted_depth""": ANY(torch.Tensor   ), """depth""": ANY(Image.Image   )}   ,					__UpperCamelCase   )
												import datasets

												lowerCamelCase_							  =					datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""   ,					"""image"""   ,					split="""test"""   )
												lowerCamelCase_							  =					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					:     Optional[int]   ):
												pass

					@slow
					@require_torch
					def    lowercase__      (	self					:     List[Any]   ):
												lowerCamelCase_							  =					"""Intel/dpt-large"""
												lowerCamelCase_							  =					pipeline("""depth-estimation"""   ,					model=__UpperCamelCase   )
												lowerCamelCase_							  =					depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""   )
												lowerCamelCase_							  =					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    lowercase__      (	self					:     Dict   ):
												# This is highly irregular to have no small tests.
												self.skipTest("""There is not hf-internal-testing tiny model for either GLPN nor DPT"""   )





