






from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
		import torch





class __lowerCamelCase					( snake_case__):





	"""simple docstring"""






	UpperCamelCase__								= ["image_processor", "tokenizer"]
	UpperCamelCase__								= "OneFormerImageProcessor"
	UpperCamelCase__								= ("CLIPTokenizer", "CLIPTokenizerFast")

	def __init__(				self			,       UpperCAmelCase=None			,       UpperCAmelCase=None			,       UpperCAmelCase = 77			,       UpperCAmelCase = 77			,       **UpperCAmelCase       ):
							"""simple docstring"""







							if image_processor is None:
													raise ValueError('You need to specify an `image_processor`.'       )
							if tokenizer is None:
													raise ValueError('You need to specify a `tokenizer`.'       )

							_UpperCAmelCase							   =     max_seq_length
							_UpperCAmelCase							   =     task_seq_length

							super().__init__(UpperCAmelCase			,       UpperCAmelCase       )

	def 			UpperCamelCase       (				self			,       UpperCAmelCase=None			,       UpperCAmelCase=77       ):
							"""simple docstring"""







							if text_list is None:
													raise ValueError('tokens cannot be None.'       )

							_UpperCAmelCase							   =     self.tokenizer(UpperCAmelCase			,       padding='max_length'			,       max_length=UpperCAmelCase			,       truncation=UpperCAmelCase       )

							_UpperCAmelCase   ,					_UpperCAmelCase							   =     tokens['attention_mask'], tokens['input_ids']

							_UpperCAmelCase							   =     []
							for attn_mask, input_id in zip(UpperCAmelCase			,       UpperCAmelCase       ):
													_UpperCAmelCase							   =     torch.tensor(UpperCAmelCase       ) * torch.tensor(UpperCAmelCase       )
													token_inputs.append(token.unsqueeze(0       )       )

							_UpperCAmelCase							   =     torch.cat(UpperCAmelCase			,       dim=0       )
							return token_inputs

	def __call__(				self			,       UpperCAmelCase=None			,       UpperCAmelCase=None			,       UpperCAmelCase=None			,       **UpperCAmelCase       ):
							"""simple docstring"""







							if task_inputs is None:
													raise ValueError('You have to specify the task_input. Found None.'       )
							elif images is None:
													raise ValueError('You have to specify the image. Found None.'       )

							if not all(task in ['semantic', 'instance', 'panoptic'] for task in task_inputs       ):
													raise ValueError('task_inputs must be semantic, instance, or panoptic.'       )

							_UpperCAmelCase							   =     self.image_processor(UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase			,       **UpperCAmelCase       )

							if isinstance(UpperCAmelCase			,       UpperCAmelCase       ):
													_UpperCAmelCase							   =     [task_inputs]

							if isinstance(UpperCAmelCase			,       UpperCAmelCase       ) and all(isinstance(UpperCAmelCase			,       UpperCAmelCase       ) for task_input in task_inputs       ):
													_UpperCAmelCase							   =     []
													for task in task_inputs:
																			_UpperCAmelCase							   =     F"""the task is {task}"""
																			task_token_inputs.append(UpperCAmelCase       )
													_UpperCAmelCase							   =     self._preprocess_text(UpperCAmelCase			,       max_length=self.task_seq_length       )
							else:
													raise TypeError('Task Inputs should be a string or a list of strings.'       )

							if hasattr(UpperCAmelCase			,       'text_inputs'       ):
													_UpperCAmelCase							   =     encoded_inputs.text_inputs

													_UpperCAmelCase							   =     []
													for texts in texts_list:
																			_UpperCAmelCase							   =     self._preprocess_text(UpperCAmelCase			,       max_length=self.max_seq_length       )
																			text_inputs.append(text_input_list.unsqueeze(0       )       )

													_UpperCAmelCase							   =     torch.cat(UpperCAmelCase			,       dim=0       )

							return encoded_inputs

	def 			UpperCamelCase       (				self			,       UpperCAmelCase=None			,       UpperCAmelCase=None			,       UpperCAmelCase=None			,       **UpperCAmelCase       ):
							"""simple docstring"""







							if task_inputs is None:
													raise ValueError('You have to specify the task_input. Found None.'       )
							elif images is None:
													raise ValueError('You have to specify the image. Found None.'       )

							if not all(task in ['semantic', 'instance', 'panoptic'] for task in task_inputs       ):
													raise ValueError('task_inputs must be semantic, instance, or panoptic.'       )

							_UpperCAmelCase							   =     self.image_processor.encode_inputs(UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase			,       **UpperCAmelCase       )

							if isinstance(UpperCAmelCase			,       UpperCAmelCase       ):
													_UpperCAmelCase							   =     [task_inputs]

							if isinstance(UpperCAmelCase			,       UpperCAmelCase       ) and all(isinstance(UpperCAmelCase			,       UpperCAmelCase       ) for task_input in task_inputs       ):
													_UpperCAmelCase							   =     []
													for task in task_inputs:
																			_UpperCAmelCase							   =     F"""the task is {task}"""
																			task_token_inputs.append(UpperCAmelCase       )
													_UpperCAmelCase							   =     self._preprocess_text(UpperCAmelCase			,       max_length=self.task_seq_length       )
							else:
													raise TypeError('Task Inputs should be a string or a list of strings.'       )

							if hasattr(UpperCAmelCase			,       'text_inputs'       ):
													_UpperCAmelCase							   =     encoded_inputs.text_inputs

													_UpperCAmelCase							   =     []
													for texts in texts_list:
																			_UpperCAmelCase							   =     self._preprocess_text(UpperCAmelCase			,       max_length=self.max_seq_length       )
																			text_inputs.append(text_input_list.unsqueeze(0       )       )

													_UpperCAmelCase							   =     torch.cat(UpperCAmelCase			,       dim=0       )

							return encoded_inputs

	def 			UpperCamelCase       (				self			,       *UpperCAmelCase			,       **UpperCAmelCase       ):
							"""simple docstring"""







							return self.image_processor.post_process_semantic_segmentation(*UpperCAmelCase			,       **UpperCAmelCase       )

	def 			UpperCamelCase       (				self			,       *UpperCAmelCase			,       **UpperCAmelCase       ):
							"""simple docstring"""







							return self.image_processor.post_process_instance_segmentation(*UpperCAmelCase			,       **UpperCAmelCase       )

	def 			UpperCamelCase       (				self			,       *UpperCAmelCase			,       **UpperCAmelCase       ):
							"""simple docstring"""







							return self.image_processor.post_process_panoptic_segmentation(*UpperCAmelCase			,       **UpperCAmelCase       )








import numpy as np


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )->    str:





						"""simple docstring"""



						_UpperCAmelCase							   =     n_draws * class_counts / class_counts.sum()
						# floored means we don't overshoot n_samples, but probably undershoot
						_UpperCAmelCase							   =     np.floor(__lowerCAmelCase      )
						# we add samples according to how much "left over" probability
						# they had, until we arrive at n_samples
						_UpperCAmelCase							   =     int(n_draws - floored.sum()      )
						if need_to_add > 0:
												_UpperCAmelCase							   =     continuous - floored
												_UpperCAmelCase							   =     np.sort(np.unique(__lowerCAmelCase      )      )[::-1]
												# add according to remainder, but break ties
												# randomly to avoid biases
												for value in values:
																		((_UpperCAmelCase)   ,					)							   =     np.where(remainder == value      )
																		# if we need_to_add less than what's in inds
																		# we draw randomly from them.
																		# if we need to add more, we add them all and
																		# go to the next value
																		_UpperCAmelCase							   =     min(len(__lowerCAmelCase      )					,						__lowerCAmelCase      )
																		_UpperCAmelCase							   =     rng.choice(__lowerCAmelCase					,						size=__lowerCAmelCase					,						replace=__lowerCAmelCase      )
																		floored[inds] += 1
																		need_to_add -= add_now
																		if need_to_add == 0:
																								break
						return floored.astype(np.intaa      )


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase=10      )->    int:





						"""simple docstring"""



						_UpperCAmelCase   ,					_UpperCAmelCase							   =     np.unique(__lowerCAmelCase					,						return_inverse=__lowerCAmelCase      )
						_UpperCAmelCase							   =     classes.shape[0]
						_UpperCAmelCase							   =     np.bincount(__lowerCAmelCase      )
						if np.min(__lowerCAmelCase      ) < 2:
												raise ValueError('Minimum class count error'      )
						if n_train < n_classes:
												raise ValueError(
												    'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)      )
						if n_test < n_classes:
												raise ValueError(
												    'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)      )
						_UpperCAmelCase							   =     np.split(np.argsort(__lowerCAmelCase					,						kind='mergesort'      )					,						np.cumsum(__lowerCAmelCase      )[:-1]      )
						for _ in range(__lowerCAmelCase      ):
												_UpperCAmelCase							   =     approximate_mode(__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )
												_UpperCAmelCase							   =     class_counts - n_i
												_UpperCAmelCase							   =     approximate_mode(__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )

												_UpperCAmelCase							   =     []
												_UpperCAmelCase							   =     []

												for i in range(__lowerCAmelCase      ):
																		_UpperCAmelCase							   =     rng.permutation(class_counts[i]      )
																		_UpperCAmelCase							   =     class_indices[i].take(__lowerCAmelCase					,						mode='clip'      )
																		train.extend(perm_indices_class_i[: n_i[i]]      )
																		test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]      )
												_UpperCAmelCase							   =     rng.permutation(__lowerCAmelCase      )
												_UpperCAmelCase							   =     rng.permutation(__lowerCAmelCase      )

												yield train, test
