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        __UpperCAmelCase    (       UpperCAmelCase       )->       int:



			"""simple docstring"""







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






def        __UpperCAmelCase    (       )->       Optional[Any]:



			"""simple docstring"""







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






def        __UpperCAmelCase    (       )->       List[Any]:



			"""simple docstring"""







			lowercase							=		'''mock-s3-bucket'''
			lowercase							=		f's3://{mock_bucket}'
			lowercase							=		extract_path_from_uri(UpperCAmelCase       )
			assert dataset_path.startswith('''s3://'''       ) is False

			lowercase							=		'''./local/path'''
			lowercase							=		extract_path_from_uri(UpperCAmelCase       )
			assert dataset_path == new_dataset_path






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Any:



			"""simple docstring"""







			lowercase							=		is_remote_filesystem(UpperCAmelCase       )
			assert is_remote is True

			lowercase							=		fsspec.filesystem('''file'''       )

			lowercase							=		is_remote_filesystem(UpperCAmelCase       )
			assert is_remote is False






@pytest.mark.parametrize('''compression_fs_class''',		UpperCAmelCase       )
def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       Any:



			"""simple docstring"""







			lowercase							=		{'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_file, '''bz2''': bza_file, '''lz4''': lza_file}
			lowercase							=		input_paths[compression_fs_class.protocol]
			if input_path is None:
						lowercase							=		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(UpperCAmelCase       )
			lowercase							=		fsspec.filesystem(compression_fs_class.protocol,		fo=UpperCAmelCase       )
			assert isinstance(UpperCAmelCase,		UpperCAmelCase       )
			lowercase							=		os.path.basename(UpperCAmelCase       )
			lowercase							=		expected_filename[: expected_filename.rindex('''.'''       )]
			assert fs.glob('''*'''       ) == [expected_filename]
			with fs.open(UpperCAmelCase,		'''r''',		encoding='''utf-8'''       ) as f, open(UpperCAmelCase,		encoding='''utf-8'''       ) as expected_file:
						assert f.read() == expected_file.read()






@pytest.mark.parametrize('''protocol''',		['''zip''', '''gzip''']       )
def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       List[str]:



			"""simple docstring"""







			lowercase							=		{'''zip''': zip_jsonl_path, '''gzip''': jsonl_gz_path}
			lowercase							=		compressed_file_paths[protocol]
			lowercase							=		'''dataset.jsonl'''
			lowercase							=		f'{protocol}://{member_file_path}::{compressed_file_path}'
			lowercase		,*lowercase							=		fsspec.get_fs_token_paths(UpperCAmelCase       )
			assert fs.isfile(UpperCAmelCase       )
			assert not fs.isfile('''non_existing_''' + member_file_path       )






@pytest.mark.integration
def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       Union[str, Any]:



			"""simple docstring"""







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






def        __UpperCAmelCase    (       )->       List[str]:



			"""simple docstring"""







			lowercase							=		'''bz2'''

			# Import module
			import datasets.filesystems

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

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






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import MobileViTImageProcessor






class      __lowercase					(    unittest.TestCase    ):

						def __init__(	self				:      int							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      int=7							,							__lowerCamelCase				:      Dict=3							,							__lowerCamelCase				:      Optional[int]=18							,							__lowerCamelCase				:      List[Any]=30							,							__lowerCamelCase				:      Union[str, Any]=4_00							,							__lowerCamelCase				:      Optional[int]=True							,							__lowerCamelCase				:      List[str]=None							,							__lowerCamelCase				:      Dict=True							,							__lowerCamelCase				:      Dict=None							,							__lowerCamelCase				:      Tuple=True							,							)							->		Tuple:


									'''simple docstring'''

									lowercase							=		size if size is not None else {'''shortest_edge''': 20}
									lowercase							=		crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
									lowercase							=		parent
									lowercase							=		batch_size
									lowercase							=		num_channels
									lowercase							=		image_size
									lowercase							=		min_resolution
									lowercase							=		max_resolution
									lowercase							=		do_resize
									lowercase							=		size
									lowercase							=		do_center_crop
									lowercase							=		crop_size
									lowercase							=		do_flip_channel_order



						def        __a		(	self				:      Optional[int]   )							->		Any:


									'''simple docstring'''

									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_center_crop": self.do_center_crop,
									    "crop_size": self.crop_size,
									    "do_flip_channel_order": self.do_flip_channel_order,
									}






@require_torch
@require_vision
class      __lowercase					(    _A       ,     unittest.TestCase    ):
						lowercase					 =     MobileViTImageProcessor if is_vision_available() else None

						def        __a		(	self				:      Dict   )							->		Dict:


									'''simple docstring'''

									lowercase							=		MobileViTImageProcessingTester(self   )

						@property
						def        __a		(	self				:      Tuple   )							->		Dict:


									'''simple docstring'''

									return self.image_processor_tester.prepare_image_processor_dict()

						def        __a		(	self				:      Dict   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		self.image_processing_class(**self.image_processor_dict   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''do_resize'''   )   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''size'''   )   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''do_center_crop'''   )   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''center_crop'''   )   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''do_flip_channel_order'''   )   )

						def        __a		(	self				:      List[str]   )							->		str:


									'''simple docstring'''

									lowercase							=		self.image_processing_class.from_dict(self.image_processor_dict   )
									self.assertEqual(image_processor.size							,							{'''shortest_edge''': 20}   )
									self.assertEqual(image_processor.crop_size							,							{'''height''': 18, '''width''': 18}   )

									lowercase							=		self.image_processing_class.from_dict(self.image_processor_dict							,							size=42							,							crop_size=84   )
									self.assertEqual(image_processor.size							,							{'''shortest_edge''': 42}   )
									self.assertEqual(image_processor.crop_size							,							{'''height''': 84, '''width''': 84}   )

						def        __a		(	self				:      Any   )							->		Optional[Any]:


									'''simple docstring'''

									pass

						def        __a		(	self				:      Any   )							->		List[str]:


									'''simple docstring'''

									lowercase							=		self.image_processing_class(**self.image_processor_dict   )
									# create random PIL images
									lowercase							=		prepare_image_inputs(self.image_processor_tester							,							equal_resolution=__lowerCamelCase   )
									for image in image_inputs:
												self.assertIsInstance(__lowerCamelCase							,							Image.Image   )

									# Test not batched input
									lowercase							=		image_processing(image_inputs[0]							,							return_tensors='''pt'''   ).pixel_values
									self.assertEqual(
									    encoded_images.shape							,							(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )							,							)

									# Test batched
									lowercase							=		image_processing(__lowerCamelCase							,							return_tensors='''pt'''   ).pixel_values
									self.assertEqual(
									    encoded_images.shape							,							(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )							,							)

						def        __a		(	self				:      int   )							->		str:


									'''simple docstring'''

									lowercase							=		self.image_processing_class(**self.image_processor_dict   )
									# create random numpy tensors
									lowercase							=		prepare_image_inputs(self.image_processor_tester							,							equal_resolution=__lowerCamelCase							,							numpify=__lowerCamelCase   )
									for image in image_inputs:
												self.assertIsInstance(__lowerCamelCase							,							np.ndarray   )

									# Test not batched input
									lowercase							=		image_processing(image_inputs[0]							,							return_tensors='''pt'''   ).pixel_values
									self.assertEqual(
									    encoded_images.shape							,							(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )							,							)

									# Test batched
									lowercase							=		image_processing(__lowerCamelCase							,							return_tensors='''pt'''   ).pixel_values
									self.assertEqual(
									    encoded_images.shape							,							(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )							,							)



						def        __a		(	self				:      Dict   )							->		Any:


									'''simple docstring'''

									lowercase							=		self.image_processing_class(**self.image_processor_dict   )
									# create random PyTorch tensors
									lowercase							=		prepare_image_inputs(self.image_processor_tester							,							equal_resolution=__lowerCamelCase							,							torchify=__lowerCamelCase   )
									for image in image_inputs:
												self.assertIsInstance(__lowerCamelCase							,							torch.Tensor   )

									# Test not batched input
									lowercase							=		image_processing(image_inputs[0]							,							return_tensors='''pt'''   ).pixel_values
									self.assertEqual(
									    encoded_images.shape							,							(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )							,							)

									# Test batched
									lowercase							=		image_processing(__lowerCamelCase							,							return_tensors='''pt'''   ).pixel_values
									self.assertEqual(
									    encoded_images.shape							,							(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )							,							)





