
"""simple docstring"""




import warnings
from typing import List

import numpy as np

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





class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   ['''image_processor''', '''tokenizer''']
							UpperCAmelCase__      =   '''OwlViTImageProcessor'''
							UpperCAmelCase__      =   ('''CLIPTokenizer''', '''CLIPTokenizerFast''')

							def __init__(self							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			None
									if "feature_extractor" in kwargs:
											warnings.warn(
											    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
											    ''' instead.'''							,							SCREAMING_SNAKE_CASE_							,							)
											SCREAMING_SNAKE_CASE_    			=			kwargs.pop('''feature_extractor'''     )

									SCREAMING_SNAKE_CASE_    			=			image_processor if image_processor is not None else feature_extractor
									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`.'''     )

									super().__init__(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

							def __call__(self							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_="max_length"							,							SCREAMING_SNAKE_CASE_="np"							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									if text is None and query_images is None and images is None:
											raise ValueError(
											    '''You have to specify at least one text or query image or image. All three cannot be none.'''     )

									if text is not None:
											if isinstance(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ) or (isinstance(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ) and not isinstance(text[0]							,							SCREAMING_SNAKE_CASE_     )):
													SCREAMING_SNAKE_CASE_    			=			[self.tokenizer(SCREAMING_SNAKE_CASE_							,							padding=SCREAMING_SNAKE_CASE_							,							return_tensors=SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )]

											elif isinstance(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ) and isinstance(text[0]							,							SCREAMING_SNAKE_CASE_     ):
													SCREAMING_SNAKE_CASE_    			=			[]

													# Maximum number of queries across batch
													SCREAMING_SNAKE_CASE_    			=			max([len(SCREAMING_SNAKE_CASE_     ) for t in text]     )

													# Pad all batch samples to max number of text queries
													for t in text:
															if len(SCREAMING_SNAKE_CASE_     ) != max_num_queries:
																	SCREAMING_SNAKE_CASE_    			=			t + [''' '''] * (max_num_queries - len(SCREAMING_SNAKE_CASE_     ))

															SCREAMING_SNAKE_CASE_    			=			self.tokenizer(SCREAMING_SNAKE_CASE_							,							padding=SCREAMING_SNAKE_CASE_							,							return_tensors=SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )
															encodings.append(SCREAMING_SNAKE_CASE_     )
											else:
													raise TypeError('''Input text should be a string, a list of strings or a nested list of strings'''     )

											if return_tensors == "np":
													SCREAMING_SNAKE_CASE_    			=			np.concatenate([encoding['''input_ids'''] for encoding in encodings]							,							axis=0     )
													SCREAMING_SNAKE_CASE_    			=			np.concatenate([encoding['''attention_mask'''] for encoding in encodings]							,							axis=0     )

											elif return_tensors == "jax" and is_flax_available():
													import jax.numpy as jnp

													SCREAMING_SNAKE_CASE_    			=			jnp.concatenate([encoding['''input_ids'''] for encoding in encodings]							,							axis=0     )
													SCREAMING_SNAKE_CASE_    			=			jnp.concatenate([encoding['''attention_mask'''] for encoding in encodings]							,							axis=0     )

											elif return_tensors == "pt" and is_torch_available():
													import torch

													SCREAMING_SNAKE_CASE_    			=			torch.cat([encoding['''input_ids'''] for encoding in encodings]							,							dim=0     )
													SCREAMING_SNAKE_CASE_    			=			torch.cat([encoding['''attention_mask'''] for encoding in encodings]							,							dim=0     )

											elif return_tensors == "tf" and is_tf_available():
													import tensorflow as tf

													SCREAMING_SNAKE_CASE_    			=			tf.stack([encoding['''input_ids'''] for encoding in encodings]							,							axis=0     )
													SCREAMING_SNAKE_CASE_    			=			tf.stack([encoding['''attention_mask'''] for encoding in encodings]							,							axis=0     )

											else:
													raise ValueError('''Target return tensor type could not be returned'''     )

											SCREAMING_SNAKE_CASE_    			=			BatchEncoding()
											SCREAMING_SNAKE_CASE_    			=			input_ids
											SCREAMING_SNAKE_CASE_    			=			attention_mask

									if query_images is not None:
											SCREAMING_SNAKE_CASE_    			=			BatchEncoding()
											SCREAMING_SNAKE_CASE_    			=			self.image_processor(
											    SCREAMING_SNAKE_CASE_							,							return_tensors=SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ).pixel_values
											SCREAMING_SNAKE_CASE_    			=			query_pixel_values

									if images is not None:
											SCREAMING_SNAKE_CASE_    			=			self.image_processor(SCREAMING_SNAKE_CASE_							,							return_tensors=SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )

									if text is not None and images is not None:
											SCREAMING_SNAKE_CASE_    			=			image_features.pixel_values
											return encoding
									elif query_images is not None and images is not None:
											SCREAMING_SNAKE_CASE_    			=			image_features.pixel_values
											return encoding
									elif text is not None or query_images is not None:
											return encoding
									else:
											return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_     )							,							tensor_type=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self							,							*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									return self.image_processor.post_process(*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self							,							*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									return self.image_processor.post_process_object_detection(*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self							,							*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									return self.image_processor.post_process_image_guided_detection(*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self							,							*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self							,							*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )

							@property
							def _lowercase       (self     ):

									"""simple docstring"""







									warnings.warn(
									    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							,							SCREAMING_SNAKE_CASE_							,							)
									return self.image_processor_class







							@property
							def _lowercase       (self     ):

									"""simple docstring"""







									warnings.warn(
									    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''							,							SCREAMING_SNAKE_CASE_							,							)
									return self.image_processor

"""simple docstring"""




from __future__ import annotations



def 							_lowerCamelCase			(			__a,				__a = None     ):
		SCREAMING_SNAKE_CASE_    			=			word_bank or []
		# create a table
		SCREAMING_SNAKE_CASE_    			=			len(__a     ) + 1

		SCREAMING_SNAKE_CASE_    			=			[]
		for _ in range(__a     ):
				table.append([]     )
		# seed value
		SCREAMING_SNAKE_CASE_    			=			[[]]  # because empty string has empty combination

		# iterate through the indices
		for i in range(__a     ):
				# condition
				if table[i] != []:
						for word in word_bank:
								# slice condition
								if target[i : i + len(__a     )] == word:
										SCREAMING_SNAKE_CASE_    			=			[
										    [word, *way] for way in table[i]
										]
										# adds the word to every combination the current position holds
										# now,push that combination to the table[i+len(word)]
										table[i + len(__a     )] += new_combinations

    # combinations are in reverse order so reverse for better output
		for combination in table[len(__a     )]:
				combination.reverse()

		return table[len(__a     )]


if __name__ == "__main__":
						print(all_construct('jwajalapa', ['jwa', 'j', 'w', 'a', 'la', 'lapa']))
						print(all_construct('rajamati', ['s', 'raj', 'amat', 'raja', 'ma', 'i', 't']))
						print(
						    all_construct(
						        'hexagonosaurus',
						        ['h', 'ex', 'hex', 'ag', 'ago', 'ru', 'auru', 'rus', 'go', 'no', 'o', 's'],
						    )
						)