"""simple docstring"""




from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
				import torch

if is_tf_available():
				import tensorflow as tf
class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						List[str]													=		['image_processor']
						lowerCAmelCase__  :						List[Any]													=		'SamImageProcessor'





						def __init__(       self		,__UpperCAmelCase     )					->				Optional[int]:
													super().__init__(__UpperCAmelCase     )
													A__	     =							self.image_processor
													A__	     =							-10
													A__	     =							self.image_processor.size['longest_edge']





						def __call__(       self		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase = None		,**__UpperCAmelCase		,)					->				BatchEncoding:
													A__	     =							self.image_processor(
													    __UpperCAmelCase		,return_tensors=__UpperCAmelCase		,**__UpperCAmelCase		,)

													# pop arguments that are not used in the foward but used nevertheless
													A__	     =							encoding_image_processor['original_sizes']

													if hasattr(__UpperCAmelCase		,'numpy'     ):  # Checks if Torch or TF tensor
																				A__	     =							original_sizes.numpy()

													A__     ,  A__     ,  A__	     =							self._check_and_preprocess_points(
													    input_points=__UpperCAmelCase		,input_labels=__UpperCAmelCase		,input_boxes=__UpperCAmelCase		,)

													A__	     =							self._normalize_and_convert(
													    __UpperCAmelCase		,__UpperCAmelCase		,input_points=__UpperCAmelCase		,input_labels=__UpperCAmelCase		,input_boxes=__UpperCAmelCase		,return_tensors=__UpperCAmelCase		,)

													return encoding_image_processor





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase="pt"		,)					->				Optional[Any]:
													if input_points is not None:
																				if len(__UpperCAmelCase     ) != len(__UpperCAmelCase     ):
																											A__	     =							[
																											    self._normalize_coordinates(self.target_size		,__UpperCAmelCase		,original_sizes[0]     ) for point in input_points
																											]
																				else:
																											A__	     =							[
																											    self._normalize_coordinates(self.target_size		,__UpperCAmelCase		,__UpperCAmelCase     )
																											    for point, original_size in zip(__UpperCAmelCase		,__UpperCAmelCase     )
																											]
																				# check that all arrays have the same shape
																				if not all(point.shape == input_points[0].shape for point in input_points     ):
																											if input_labels is not None:
																																		A__     ,  A__	     =							self._pad_points_and_labels(__UpperCAmelCase		,__UpperCAmelCase     )

																				A__	     =							np.array(__UpperCAmelCase     )

													if input_labels is not None:
																				A__	     =							np.array(__UpperCAmelCase     )

													if input_boxes is not None:
																				if len(__UpperCAmelCase     ) != len(__UpperCAmelCase     ):
																											A__	     =							[
																											    self._normalize_coordinates(self.target_size		,__UpperCAmelCase		,original_sizes[0]		,is_bounding_box=__UpperCAmelCase     )
																											    for box in input_boxes
																											]
																				else:
																											A__	     =							[
																											    self._normalize_coordinates(self.target_size		,__UpperCAmelCase		,__UpperCAmelCase		,is_bounding_box=__UpperCAmelCase     )
																											    for box, original_size in zip(__UpperCAmelCase		,__UpperCAmelCase     )
																											]
																				A__	     =							np.array(__UpperCAmelCase     )

													if input_boxes is not None:
																				if return_tensors == "pt":
																											A__	     =							torch.from_numpy(__UpperCAmelCase     )
																											# boxes batch size of 1 by default
																											A__	     =							input_boxes.unsqueeze(1     ) if len(input_boxes.shape     ) != 3 else input_boxes
																				elif return_tensors == "tf":
																											A__	     =							tf.convert_to_tensor(__UpperCAmelCase     )
																											# boxes batch size of 1 by default
																											A__	     =							tf.expand_dims(__UpperCAmelCase		,1     ) if len(input_boxes.shape     ) != 3 else input_boxes
																				encoding_image_processor.update({'input_boxes': input_boxes}     )
													if input_points is not None:
																				if return_tensors == "pt":
																											A__	     =							torch.from_numpy(__UpperCAmelCase     )
																											# point batch size of 1 by default
																											A__	     =							input_points.unsqueeze(1     ) if len(input_points.shape     ) != 4 else input_points
																				elif return_tensors == "tf":
																											A__	     =							tf.convert_to_tensor(__UpperCAmelCase     )
																											# point batch size of 1 by default
																											A__	     =							tf.expand_dims(__UpperCAmelCase		,1     ) if len(input_points.shape     ) != 4 else input_points
																				encoding_image_processor.update({'input_points': input_points}     )
													if input_labels is not None:
																				if return_tensors == "pt":
																											A__	     =							torch.from_numpy(__UpperCAmelCase     )
																											# point batch size of 1 by default
																											A__	     =							input_labels.unsqueeze(1     ) if len(input_labels.shape     ) != 3 else input_labels
																				elif return_tensors == "tf":
																											A__	     =							tf.convert_to_tensor(__UpperCAmelCase     )
																											# point batch size of 1 by default
																											A__	     =							tf.expand_dims(__UpperCAmelCase		,1     ) if len(input_labels.shape     ) != 3 else input_labels
																				encoding_image_processor.update({'input_labels': input_labels}     )

													return encoding_image_processor





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				str:
													A__	     =							max([point.shape[0] for point in input_points]     )
													A__	     =							[]
													for i, point in enumerate(__UpperCAmelCase     ):
																				if point.shape[0] != expected_nb_points:
																											A__	     =							np.concatenate(
																											    [point, np.zeros((expected_nb_points - point.shape[0], 2)     ) + self.point_pad_value]		,axis=0     )
																											A__	     =							np.append(input_labels[i]		,[self.point_pad_value]     )
																				processed_input_points.append(__UpperCAmelCase     )
													A__	     =							processed_input_points
													return input_points, input_labels





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase=False     )					->				np.ndarray:
													A__     ,  A__	     =							original_size
													A__     ,  A__	     =							self.image_processor._get_preprocess_shape(__UpperCAmelCase		,longest_edge=__UpperCAmelCase     )
													A__	     =							deepcopy(__UpperCAmelCase     ).astype(__UpperCAmelCase     )

													if is_bounding_box:
																				A__	     =							coords.reshape(-1		,2		,2     )

													A__	     =							coords[..., 0] * (new_w / old_w)
													A__	     =							coords[..., 1] * (new_h / old_h)

													if is_bounding_box:
																				A__	     =							coords.reshape(-1		,4     )

													return coords





						def 				snake_case__	(       self		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,)					->				Tuple:
													if input_points is not None:
																				if hasattr(__UpperCAmelCase		,'numpy'     ):  # Checks for TF or Torch tensor
																											A__	     =							input_points.numpy().tolist()

																				if not isinstance(__UpperCAmelCase		,__UpperCAmelCase     ) or not isinstance(input_points[0]		,__UpperCAmelCase     ):
																											raise ValueError('Input points must be a list of list of floating points.'     )
																				A__	     =							[np.array(__UpperCAmelCase     ) for input_point in input_points]
													else:
																				A__	     =							None

													if input_labels is not None:
																				if hasattr(__UpperCAmelCase		,'numpy'     ):
																											A__	     =							input_labels.numpy().tolist()

																				if not isinstance(__UpperCAmelCase		,__UpperCAmelCase     ) or not isinstance(input_labels[0]		,__UpperCAmelCase     ):
																											raise ValueError('Input labels must be a list of list integers.'     )
																				A__	     =							[np.array(__UpperCAmelCase     ) for label in input_labels]
													else:
																				A__	     =							None

													if input_boxes is not None:
																				if hasattr(__UpperCAmelCase		,'numpy'     ):
																											A__	     =							input_boxes.numpy().tolist()

																				if (
																				    not isinstance(__UpperCAmelCase		,__UpperCAmelCase     )
																				    or not isinstance(input_boxes[0]		,__UpperCAmelCase     )
																				    or not isinstance(input_boxes[0][0]		,__UpperCAmelCase     )
																				):
																											raise ValueError('Input boxes must be a list of list of list of floating points.'     )
																				A__	     =							[np.array(__UpperCAmelCase     ).astype(np.floataa     ) for box in input_boxes]
													else:
																				A__	     =							None

													return input_points, input_labels, input_boxes





						@property
						def 				snake_case__	(       self     )					->				Union[str, Any]:
													A__	     =							self.image_processor.model_input_names
													return list(dict.fromkeys(__UpperCAmelCase     )     )





						def 				snake_case__	(       self		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				Optional[Any]:
													return self.image_processor.post_process_masks(*__UpperCAmelCase		,**__UpperCAmelCase     )






"""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()





