"""simple docstring"""




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     UpperCamelCase__(			unittest.TestCase ):





						def __init__(       self		,__UpperCAmelCase		,__UpperCAmelCase=7		,__UpperCAmelCase=3		,__UpperCAmelCase=18		,__UpperCAmelCase=30		,__UpperCAmelCase=4_00		,__UpperCAmelCase=True		,__UpperCAmelCase=None		,__UpperCAmelCase=True		,__UpperCAmelCase=None		,__UpperCAmelCase=True		,)					->				str:
													A__	     =							size if size is not None else {'shortest_edge': 20}
													A__	     =							crop_size if crop_size is not None else {'height': 18, 'width': 18}
													A__	     =							parent
													A__	     =							batch_size
													A__	     =							num_channels
													A__	     =							image_size
													A__	     =							min_resolution
													A__	     =							max_resolution
													A__	     =							do_resize
													A__	     =							size
													A__	     =							do_center_crop
													A__	     =							crop_size
													A__	     =							do_flip_channel_order





						def 				snake_case__	(       self     )					->				List[str]:
													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     UpperCamelCase__(			__A		,						unittest.TestCase ):
						lowerCAmelCase__  :						Union[str, Any]													=		MobileViTImageProcessor if is_vision_available() else None





						def 				snake_case__	(       self     )					->				List[str]:
													A__	     =							MobileViTImageProcessingTester(self     )





						@property
						def 				snake_case__	(       self     )					->				Dict:
													return self.image_processor_tester.prepare_image_processor_dict()





						def 				snake_case__	(       self     )					->				Optional[Any]:
													A__	     =							self.image_processing_class(**self.image_processor_dict     )
													self.assertTrue(hasattr(UpperCamelCase__		,'do_resize'     )     )
													self.assertTrue(hasattr(UpperCamelCase__		,'size'     )     )
													self.assertTrue(hasattr(UpperCamelCase__		,'do_center_crop'     )     )
													self.assertTrue(hasattr(UpperCamelCase__		,'center_crop'     )     )
													self.assertTrue(hasattr(UpperCamelCase__		,'do_flip_channel_order'     )     )





						def 				snake_case__	(       self     )					->				Optional[Any]:
													A__	     =							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}     )

													A__	     =							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 				snake_case__	(       self     )					->				Optional[Any]:
													pass





						def 				snake_case__	(       self     )					->				Optional[int]:
													A__	     =							self.image_processing_class(**self.image_processor_dict     )
													# create random PIL images
													A__	     =							prepare_image_inputs(self.image_processor_tester		,equal_resolution=UpperCamelCase__     )
													for image in image_inputs:
																				self.assertIsInstance(UpperCamelCase__		,Image.Image     )

													# Test not batched input
													A__	     =							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
													A__	     =							image_processing(UpperCamelCase__		,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 				snake_case__	(       self     )					->				Optional[int]:
													A__	     =							self.image_processing_class(**self.image_processor_dict     )
													# create random numpy tensors
													A__	     =							prepare_image_inputs(self.image_processor_tester		,equal_resolution=UpperCamelCase__		,numpify=UpperCamelCase__     )
													for image in image_inputs:
																				self.assertIsInstance(UpperCamelCase__		,np.ndarray     )

													# Test not batched input
													A__	     =							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
													A__	     =							image_processing(UpperCamelCase__		,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 				snake_case__	(       self     )					->				List[str]:
													A__	     =							self.image_processing_class(**self.image_processor_dict     )
													# create random PyTorch tensors
													A__	     =							prepare_image_inputs(self.image_processor_tester		,equal_resolution=UpperCamelCase__		,torchify=UpperCamelCase__     )
													for image in image_inputs:
																				self.assertIsInstance(UpperCamelCase__		,torch.Tensor     )

													# Test not batched input
													A__	     =							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
													A__	     =							image_processing(UpperCamelCase__		,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'],
													    )		,)






"""simple docstring"""




import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


__lowerCamelCase											=  importlib.util.find_spec("s3fs") is not None

if _has_safs:
				from .safilesystem import SaFileSystem  # noqa: F401

__lowerCamelCase											=  [
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
				if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
								warnings.warn(F'''A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.''')
				fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							if "://" in dataset_path:
														A__	     =							dataset_path.split('://'				)[1]
							return dataset_path






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							if fs is not None and fs.protocol != "file":
														return True
							else:
														return False






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							not is_remote_filesystem(UpperCamelCase__				)
							if is_local:
														# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
														shutil.move(fs._strip_protocol(UpperCamelCase__				)       ,    fs._strip_protocol(UpperCamelCase__				)				)
							else:
														fs.mv(UpperCamelCase__       ,    UpperCamelCase__       ,    recursive=UpperCamelCase__				)






def   UpperCAmelCase					(	):






							"""simple docstring"""





							if hasattr(fsspec.asyn       ,    'reset_lock'				):
														# for future fsspec>2022.05.0
														fsspec.asyn.reset_lock()
							else:
														A__	     =							None
														A__	     =							None
														A__	     =							threading.Lock()





