


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

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


snake_case							:	List[str]					      =				importlib.util.find_spec('''s3fs''') is not None

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

snake_case							:	List[compression.BaseCompressedFileFileSystem]					      =				[
    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 							__lowercase       (			__lowerCAmelCase							:    str       ):
							if "://" in dataset_path:
														a__					=					dataset_path.split('://'       )[1]
							return dataset_path






def 							__lowercase       (			__lowerCAmelCase							:    fsspec.AbstractFileSystem       ):
							if fs is not None and fs.protocol != "file":
														return True
							else:
														return False






def 							__lowercase       (			__lowerCAmelCase							:    fsspec.AbstractFileSystem ,   __lowerCAmelCase							:    str ,   __lowerCAmelCase							:    str       ):
							a__					=					not is_remote_filesystem(__lowerCAmelCase       )
							if is_local:
														# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
														shutil.move(fs._strip_protocol(__lowerCAmelCase       ) ,   fs._strip_protocol(__lowerCAmelCase       )       )
							else:
														fs.mv(__lowerCAmelCase ,   __lowerCAmelCase ,   recursive=__lowerCAmelCase       )






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








from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


snake_case							:	str					      =				logging.get_logger(__name__)






def 							__lowercase       (			__lowerCAmelCase							:    Union[str, Any] ,   __lowerCAmelCase							:    Optional[Any]       ):
							a__					=					b.T
							a__					=					np.sum(np.square(__lowerCAmelCase       ) ,   axis=1       )
							a__					=					np.sum(np.square(__lowerCAmelCase       ) ,   axis=0       )
							a__					=					np.matmul(__lowerCAmelCase ,   __lowerCAmelCase       )
							a__					=					aa[:, None] - 2 * ab + ba[None, :]
							return d






def 							__lowercase       (			__lowerCAmelCase							:    List[Any] ,   __lowerCAmelCase							:    str       ):
							a__					=					x.reshape(-1 ,   3       )
							a__					=					squared_euclidean_distance(__lowerCAmelCase ,   __lowerCAmelCase       )
							return np.argmin(__lowerCAmelCase ,   axis=1       )







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   Optional[Any]   				=      ['''pixel_values''']
					def __init__(				self       :Dict      ,__snake_case       :Optional[Union[List[List[int]], np.ndarray]] = None      ,__snake_case       :bool = True      ,__snake_case       :Dict[str, int] = None      ,__snake_case       :PILImageResampling = PILImageResampling.BILINEAR      ,__snake_case       :bool = True      ,__snake_case       :bool = True      ,**__snake_case       :Optional[int]      ,) ->   None:
												super().__init__(**__snake_case				)
												a__					=					size if size is not None else {'height': 2_56, 'width': 2_56}
												a__					=					get_size_dict(__snake_case				)
												a__					=					np.array(__snake_case				) if clusters is not None else None
												a__					=					do_resize
												a__					=					size
												a__					=					resample
												a__					=					do_normalize
												a__					=					do_color_quantize
					def 			lowerCamelCase__(				self       :Union[str, Any]      ,__snake_case       :np.ndarray      ,__snake_case       :Dict[str, int]      ,__snake_case       :PILImageResampling = PILImageResampling.BILINEAR      ,__snake_case       :Optional[Union[str, ChannelDimension]] = None      ,**__snake_case       :Any      ,) ->   np.ndarray:
												a__					=					get_size_dict(__snake_case				)
												if "height" not in size or "width" not in size:
																			raise ValueError(F'Size dictionary must contain both height and width keys. Got {size.keys()}'				)
												return resize(
												    __snake_case      ,size=(size['height'], size['width'])      ,resample=__snake_case      ,data_format=__snake_case      ,**__snake_case				)
					def 			lowerCamelCase__(				self       :List[Any]      ,__snake_case       :np.ndarray      ,__snake_case       :Optional[Union[str, ChannelDimension]] = None      ,) ->   np.ndarray:
												a__					=					rescale(image=__snake_case      ,scale=1 / 1_27.5      ,data_format=__snake_case				)
												a__					=					image - 1
												return image



					def 			lowerCamelCase__(				self       :Optional[Any]      ,__snake_case       :ImageInput      ,__snake_case       :bool = None      ,__snake_case       :Dict[str, int] = None      ,__snake_case       :PILImageResampling = None      ,__snake_case       :bool = None      ,__snake_case       :Optional[bool] = None      ,__snake_case       :Optional[Union[List[List[int]], np.ndarray]] = None      ,__snake_case       :Optional[Union[str, TensorType]] = None      ,__snake_case       :Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST      ,**__snake_case       :List[str]      ,) ->   PIL.Image.Image:
												a__					=					do_resize if do_resize is not None else self.do_resize
												a__					=					size if size is not None else self.size
												a__					=					get_size_dict(__snake_case				)
												a__					=					resample if resample is not None else self.resample
												a__					=					do_normalize if do_normalize is not None else self.do_normalize
												a__					=					do_color_quantize if do_color_quantize is not None else self.do_color_quantize
												a__					=					clusters if clusters is not None else self.clusters
												a__					=					np.array(__snake_case				)

												a__					=					make_list_of_images(__snake_case				)

												if not valid_images(__snake_case				):
																			raise ValueError(
																			    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																			    'torch.Tensor, tf.Tensor or jax.ndarray.'				)

												if do_resize and size is None or resample is None:
																			raise ValueError('Size and resample must be specified if do_resize is True.'				)

												if do_color_quantize and clusters is None:
																			raise ValueError('Clusters must be specified if do_color_quantize is True.'				)

												# All transformations expect numpy arrays.
												a__					=					[to_numpy_array(__snake_case				) for image in images]

												if do_resize:
																			a__					=					[self.resize(image=__snake_case      ,size=__snake_case      ,resample=__snake_case				) for image in images]

												if do_normalize:
																			a__					=					[self.normalize(image=__snake_case				) for image in images]

												if do_color_quantize:
																			a__					=					[to_channel_dimension_format(__snake_case      ,ChannelDimension.LAST				) for image in images]
																			# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
																			a__					=					np.array(__snake_case				)
																			a__					=					color_quantize(__snake_case      ,__snake_case				).reshape(images.shape[:-1]				)

																			# flatten to (batch_size, height*width)
																			a__					=					images.shape[0]
																			a__					=					images.reshape(__snake_case      ,-1				)

																			# We need to convert back to a list of images to keep consistent behaviour across processors.
																			a__					=					list(__snake_case				)
												else:
																			a__					=					[to_channel_dimension_format(__snake_case      ,__snake_case				) for image in images]

												a__					=					{'input_ids': images}
												return BatchFeature(data=__snake_case      ,tensor_type=__snake_case				)




