

import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict


@pytest.mark.parametrize(
    'files'		,					[
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]		,					)
def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							tmp_path_factory.mktemp('dset_infos_dir'      )
					if "full:README.md" in files:
										with open(dataset_infos_dir / 'README.md'		,					'w'      ) as f:
															f.write('---\ndataset_info:\n  dataset_size: 42\n---'      )
					if "empty:README.md" in files:
										with open(dataset_infos_dir / 'README.md'		,					'w'      ) as f:
															f.write(''      )
										# we want to support dataset_infos.json for backward compatibility
					if "dataset_infos.json" in files:
										with open(dataset_infos_dir / 'dataset_infos.json'		,					'w'      ) as f:
															f.write('{\"default\": {\"dataset_size\": 42}}'      )
					__lowerCAmelCase					=							DatasetInfosDict.from_directory(snake_case__      )
					assert dataset_infos
					assert dataset_infos["default"].dataset_size == 42


@pytest.mark.parametrize(
    'dataset_info'		,					[
        DatasetInfo(),
        DatasetInfo(
            description='foo'		,					features=Features({'a': Value('int32'      )}      )		,					builder_name='builder'		,					config_name='config'		,					version='1.0.0'		,					splits=[{'name': 'train'}]		,					download_size=42		,					),
    ]		,					)
def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							str(snake_case__      )
					dataset_info.write_to_directory(snake_case__      )
					__lowerCAmelCase					=							DatasetInfo.from_directory(snake_case__      )
					assert dataset_info == reloaded
					assert os.path.exists(os.path.join(snake_case__		,					'dataset_info.json'      )      )


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							DatasetInfo(
					    description='foo'		,					citation='bar'		,					homepage='https://foo.bar'		,					license='CC0'		,					features=Features({'a': Value('int32'      )}      )		,					post_processed={}		,					supervised_keys=()		,					task_templates=[]		,					builder_name='builder'		,					config_name='config'		,					version='1.0.0'		,					splits=[{'name': 'train', 'num_examples': 42}]		,					download_checksums={}		,					download_size=1_337		,					post_processing_size=442		,					dataset_size=1_234		,					size_in_bytes=1_337 + 442 + 1_234		,					)
					__lowerCAmelCase					=							dataset_info._to_yaml_dict()
					assert sorted(snake_case__      ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML      )
					for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
										assert key in dataset_info_yaml_dict
										assert isinstance(dataset_info_yaml_dict[key]		,					(list, dict, int, str)      )
					__lowerCAmelCase					=							yaml.safe_dump(snake_case__      )
					__lowerCAmelCase					=							yaml.safe_load(snake_case__      )
					assert dataset_info_yaml_dict == reloaded


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							DatasetInfo()
					__lowerCAmelCase					=							dataset_info._to_yaml_dict()
					assert dataset_info_yaml_dict == {}


@pytest.mark.parametrize(
    'dataset_infos_dict'		,					[
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()}      ),
        DatasetInfosDict({'my_config_name': DatasetInfo()}      ),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'		,					features=Features({'a': Value('int32'      )}      )		,					builder_name='builder'		,					config_name='config'		,					version='1.0.0'		,					splits=[{'name': 'train'}]		,					download_size=42		,					)
            }      ),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=42      ),
                'v2': DatasetInfo(dataset_size=1_337      ),
            }      ),
    ]		,					)
def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							str(snake_case__      )
					dataset_infos_dict.write_to_directory(snake_case__      )
					__lowerCAmelCase					=							DatasetInfosDict.from_directory(snake_case__      )

					# the config_name of the dataset_infos_dict take over the attribute
					for config_name, dataset_info in dataset_infos_dict.items():
										__lowerCAmelCase					=							config_name
										# the yaml representation doesn't include fields like description or citation
										# so we just test that we can recover what we can from the yaml
										__lowerCAmelCase					=							DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()      )
					assert dataset_infos_dict == reloaded

					if dataset_infos_dict:
										assert os.path.exists(os.path.join(snake_case__		,					'README.md'      )      )









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     snake_case_			:


									"""simple docstring"""





									@staticmethod
									def        A__  ( *_A					,						**_A							):
														pass


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							hashlib.mda(image.tobytes()      )
					return m.hexdigest()





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


							"""simple docstring"""





							__UpperCAmelCase       =MODEL_FOR_DEPTH_ESTIMATION_MAPPING




							def        A__  ( self					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							DepthEstimationPipeline(model=_A					,						image_processor=_A							)
												return depth_estimator, [
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												]




							def        A__  ( self					,						_A					,						_A							):
												__lowerCAmelCase					=							depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
												self.assertEqual({'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)}					,						_A							)
												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							)},
												    ]					,						_A					,						)




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




							@slow
							@require_torch
							def        A__  ( self							):
												__lowerCAmelCase					=							'Intel/dpt-large'
												__lowerCAmelCase					=							pipeline('depth-estimation'					,						model=_A							)
												__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        A__  ( self							):
												# This is highly irregular to have no small tests.
												self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'							)






