

import importlib
import os

import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry

from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem

from .utils import require_lza, require_zstandard


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					assert "mock" in _fsspec_registry
					assert "bz2" in _fsspec_registry


def        __lowercase    (  ):

					"""simple docstring"""






					assert "mock" not in _fsspec_registry
					assert "bz2" in _fsspec_registry


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							"mock-s3-bucket"
					__lowerCAmelCase					=							F"""s3://{mock_bucket}"""
					__lowerCAmelCase					=							extract_path_from_uri(_lowerCamelCase      )
					assert dataset_path.startswith('s3://'      ) is False

					__lowerCAmelCase					=							"./local/path"
					__lowerCAmelCase					=							extract_path_from_uri(_lowerCamelCase      )
					assert dataset_path == new_dataset_path


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							is_remote_filesystem(_lowerCamelCase      )
					assert is_remote is True

					__lowerCAmelCase					=							fsspec.filesystem('file'      )

					__lowerCAmelCase					=							is_remote_filesystem(_lowerCamelCase      )
					assert is_remote is False


@pytest.mark.parametrize('compression_fs_class'		,					_lowerCamelCase      )
def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							{"gzip": gz_file, "xz": xz_file, "zstd": zstd_file, "bz2": bza_file, "lz4": lza_file}
					__lowerCAmelCase					=							input_paths[compression_fs_class.protocol]
					if input_path is None:
										__lowerCAmelCase					=							F"""for \'{compression_fs_class.protocol}\' compression protocol, """
										if compression_fs_class.protocol == "lz4":
															reason += require_lza.kwargs["reason"]
										elif compression_fs_class.protocol == "zstd":
															reason += require_zstandard.kwargs["reason"]
										pytest.skip(_lowerCamelCase      )
					__lowerCAmelCase					=							fsspec.filesystem(compression_fs_class.protocol		,					fo=_lowerCamelCase      )
					assert isinstance(_lowerCamelCase		,					_lowerCamelCase      )
					__lowerCAmelCase					=							os.path.basename(_lowerCamelCase      )
					__lowerCAmelCase					=							expected_filename[: expected_filename.rindex('.'      )]
					assert fs.glob('*'      ) == [expected_filename]
					with fs.open(_lowerCamelCase		,					'r'		,					encoding='utf-8'      ) as f, open(_lowerCamelCase		,					encoding='utf-8'      ) as expected_file:
										assert f.read() == expected_file.read()


@pytest.mark.parametrize('protocol'		,					['zip', 'gzip']      )
def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							{"zip": zip_jsonl_path, "gzip": jsonl_gz_path}
					__lowerCAmelCase					=							compressed_file_paths[protocol]
					__lowerCAmelCase					=							"dataset.jsonl"
					__lowerCAmelCase					=							F"""{protocol}://{member_file_path}::{compressed_file_path}"""
					__lowerCAmelCase					=							fsspec.get_fs_token_paths(_lowerCamelCase      )
					assert fs.isfile(_lowerCamelCase      )
					assert not fs.isfile('non_existing_' + member_file_path      )


@pytest.mark.integration
def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							hf_api.dataset_info(_lowerCamelCase		,					token=_lowerCamelCase      )
					__lowerCAmelCase					=							HfFileSystem(repo_info=_lowerCamelCase		,					token=_lowerCamelCase      )
					assert sorted(hffs.glob('*'      )      ) == [".gitattributes", "data"]
					assert hffs.isdir('data'      )
					assert hffs.isfile('.gitattributes'      ) and hffs.isfile('data/text_data.txt'      )
					with open(_lowerCamelCase      ) as f:
										assert hffs.open('data/text_data.txt'		,					'r'      ).read() == f.read()


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							"bz2"

					# Import module
					import datasets.filesystems

					# Overwrite protocol and reload
					register_implementation(_lowerCamelCase		,					_lowerCamelCase		,					clobber=_lowerCamelCase      )
					with pytest.warns(_lowerCamelCase      ) as warning_info:
										importlib.reload(datasets.filesystems      )

					assert len(_lowerCamelCase      ) == 1
					assert (
					    str(warning_info[0].message      )
					    == F"""A filesystem protocol was already set for {protocol} and will be overwritten."""
					)









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'							)






