"""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 (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				["image_processor", "tokenizer"]
					snake_case__       					=				"OwlViTImageProcessor"
					snake_case__       					=				("CLIPTokenizer", "CLIPTokenizerFast")







					def __init__(       self     :			Tuple					,lowerCamelCase__     :			Dict=None					,lowerCamelCase__     :			List[Any]=None					,**lowerCamelCase__     :			Tuple    ):
									UpperCAmelCase__    =							None
									if "feature_extractor" in kwargs:
													warnings.warn(
													    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
													    ' instead.'					,lowerCamelCase__					,)
													UpperCAmelCase__    =							kwargs.pop('feature_extractor'    )

									UpperCAmelCase__    =							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__(lowerCamelCase__					,lowerCamelCase__    )







					def __call__(       self     :			str					,lowerCamelCase__     :			Optional[Any]=None					,lowerCamelCase__     :			str=None					,lowerCamelCase__     :			Dict=None					,lowerCamelCase__     :			Union[str, Any]="max_length"					,lowerCamelCase__     :			List[Any]="np"					,**lowerCamelCase__     :			int    ):

									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(lowerCamelCase__					,lowerCamelCase__    ) or (isinstance(lowerCamelCase__					,lowerCamelCase__    ) and not isinstance(text[0]					,lowerCamelCase__    )):
																	UpperCAmelCase__    =							[self.tokenizer(lowerCamelCase__					,padding=lowerCamelCase__					,return_tensors=lowerCamelCase__					,**lowerCamelCase__    )]

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

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

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

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

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

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

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

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

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

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

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

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

													UpperCAmelCase__    =							BatchEncoding()
													UpperCAmelCase__    =							input_ids
													UpperCAmelCase__    =							attention_mask

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

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

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







					def 	__lowerCAmelCase		(       self     :			int					,*lowerCamelCase__     :			List[str]					,**lowerCamelCase__     :			str    ):
									return self.image_processor.post_process(*lowerCamelCase__					,**lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,*lowerCamelCase__     :			List[str]					,**lowerCamelCase__     :			List[str]    ):
									return self.image_processor.post_process_object_detection(*lowerCamelCase__					,**lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,*lowerCamelCase__     :			int					,**lowerCamelCase__     :			List[Any]    ):
									return self.image_processor.post_process_image_guided_detection(*lowerCamelCase__					,**lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Tuple					,*lowerCamelCase__     :			Any					,**lowerCamelCase__     :			Union[str, Any]    ):
									return self.tokenizer.batch_decode(*lowerCamelCase__					,**lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			List[Any]					,*lowerCamelCase__     :			Optional[int]					,**lowerCamelCase__     :			Union[str, Any]    ):
									return self.tokenizer.decode(*lowerCamelCase__					,**lowerCamelCase__    )







					@property
					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									warnings.warn(
									    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'					,lowerCamelCase__					,)
									return self.image_processor_class







					@property
					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									warnings.warn(
									    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'					,lowerCamelCase__					,)
									return self.image_processor




"""simple docstring"""






lowerCAmelCase__						:						Tuple		  =			range(2, 20 + 1)
lowerCAmelCase__						:						Optional[Any]		  =			[10**k for k in range(ks[-1] + 1)]
lowerCAmelCase__						:						dict[int, dict[int, list[list[int]]]]		  =			{}



def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							sum(a_i[j] for j in range(lowerCamelCase      ,    len(lowerCamelCase ) ) )
				UpperCAmelCase__    =							sum(a_i[j] * base[j] for j in range(min(len(lowerCamelCase )      ,    lowerCamelCase ) ) )

				UpperCAmelCase__   ,			UpperCAmelCase__    =							0, 0
				UpperCAmelCase__    =							n - i

				UpperCAmelCase__    =							memo.get(lowerCamelCase )

				if sub_memo is not None:
								UpperCAmelCase__    =							sub_memo.get(lowerCamelCase )

								if jumps is not None and len(lowerCamelCase ) > 0:
												# find and make the largest jump without going over
												UpperCAmelCase__    =							-1
												for _k in range(len(lowerCamelCase ) - 1      ,    -1      ,    -1 ):
																if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
																				UpperCAmelCase__    =							_k
																				break

												if max_jump >= 0:
																UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__    =							jumps[max_jump]
																# since the difference between jumps is cached, add c
																UpperCAmelCase__    =							diff + c
																for j in range(min(lowerCamelCase      ,    len(lowerCamelCase ) ) ):
																				UpperCAmelCase__   ,			UpperCAmelCase__    =							divmod(lowerCamelCase      ,    1_0 )
																if new_c > 0:
																				add(lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase )

								else:
												UpperCAmelCase__    =							[]
				else:
								UpperCAmelCase__    =							{c: []}
								UpperCAmelCase__    =							sub_memo

				if dn >= max_dn or c + diff >= base[k]:
								return diff, dn

				if k > ks[0]:
								while True:
												# keep doing smaller jumps
												UpperCAmelCase__   ,			UpperCAmelCase__    =							next_term(lowerCamelCase      ,    k - 1      ,    i + dn      ,    lowerCamelCase )
												diff += _diff
												dn += terms_jumped

												if dn >= max_dn or c + diff >= base[k]:
																break
				else:
								# would be too small a jump, just compute sequential terms instead
								UpperCAmelCase__   ,			UpperCAmelCase__    =							compute(lowerCamelCase      ,    lowerCamelCase      ,    i + dn      ,    lowerCamelCase )
								diff += _diff
								dn += terms_jumped

				UpperCAmelCase__    =							sub_memo[c]

				# keep jumps sorted by # of terms skipped
				UpperCAmelCase__    =							0
				while j < len(lowerCamelCase ):
								if jumps[j][1] > dn:
												break
								j += 1

				# cache the jump for this value digitsum(b) and c
				sub_memo[c].insert(lowerCamelCase      ,    (diff, dn, k) )
				return (diff, dn)



def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				if i >= n:
								return 0, i
				if k > len(lowerCamelCase ):
								a_i.extend([0 for _ in range(k - len(lowerCamelCase ) )] )

				# note: a_i -> b * 10^k + c
				# ds_b -> digitsum(b)
				# ds_c -> digitsum(c)
				UpperCAmelCase__    =							i
				UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__    =							0, 0, 0
				for j in range(len(lowerCamelCase ) ):
								if j >= k:
												ds_b += a_i[j]
								else:
												ds_c += a_i[j]

				while i < n:
								i += 1
								UpperCAmelCase__    =							ds_c + ds_b
								diff += addend
								UpperCAmelCase__    =							0
								for j in range(lowerCamelCase ):
												UpperCAmelCase__    =							a_i[j] + addend
												UpperCAmelCase__   ,			UpperCAmelCase__    =							divmod(lowerCamelCase      ,    1_0 )

												ds_c += a_i[j]

								if addend > 0:
												break

				if addend > 0:
								add(lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase )
				return diff, i - start_i



def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				for j in range(lowerCamelCase      ,    len(lowerCamelCase ) ):
								UpperCAmelCase__    =							digits[j] + addend
								if s >= 1_0:
												UpperCAmelCase__   ,			UpperCAmelCase__    =							divmod(lowerCamelCase      ,    1_0 )
												UpperCAmelCase__    =							addend // 1_0 + quotient
								else:
												UpperCAmelCase__    =							s
												UpperCAmelCase__    =							addend // 1_0

								if addend == 0:
												break

				while addend > 0:
								UpperCAmelCase__   ,			UpperCAmelCase__    =							divmod(lowerCamelCase      ,    1_0 )
								digits.append(lowerCamelCase )



def 						a_		(      lowerCamelCase = 1_0**1_5 ):
				UpperCAmelCase__    =							[1]
				UpperCAmelCase__    =							1
				UpperCAmelCase__    =							0
				while True:
								UpperCAmelCase__   ,			UpperCAmelCase__    =							next_term(lowerCamelCase      ,    2_0      ,    i + dn      ,    lowerCamelCase )
								dn += terms_jumped
								if dn == n - i:
												break

				UpperCAmelCase__    =							0
				for j in range(len(lowerCamelCase ) ):
								a_n += digits[j] * 1_0**j
				return a_n


if __name__ == "__main__":
						print(F"""{solution() = }""")



