"""simple docstring"""






import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
						from ... import PreTrainedTokenizerBase, TensorType

lowerCAmelCase__						:						Optional[int]        =			logging.get_logger(__name__)
class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				"vision-encoder-decoder"
					snake_case__       					=				True







					def __init__(       self     :			List[str]					,**lowerCamelCase__     :			Optional[Any]    ):
									super().__init__(**UpperCamelCase_    )
									if "encoder" not in kwargs or "decoder" not in kwargs:
													raise ValueError(
													    f'''A configuraton of type {self.model_type} cannot be instantiated because '''
													    f'''not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'''    )

									UpperCAmelCase__    =							kwargs.pop('encoder'    )
									UpperCAmelCase__    =							encoder_config.pop('model_type'    )
									UpperCAmelCase__    =							kwargs.pop('decoder'    )
									UpperCAmelCase__    =							decoder_config.pop('model_type'    )

									UpperCAmelCase__    =							AutoConfig.for_model(UpperCamelCase_					,**UpperCamelCase_    )
									UpperCAmelCase__    =							AutoConfig.for_model(UpperCamelCase_					,**UpperCamelCase_    )
									UpperCAmelCase__    =							True







					@classmethod
					def 	__lowerCAmelCase		(       cls     :			Any					,lowerCamelCase__     :			str					,lowerCamelCase__     :			Dict					,**lowerCamelCase__     :			str    ):
									logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'    )
									UpperCAmelCase__    =							True
									UpperCAmelCase__    =							True

									return cls(encoder=encoder_config.to_dict()					,decoder=decoder_config.to_dict()					,**UpperCamelCase_    )







					def 	__lowerCAmelCase		(       self     :			str    ):
									UpperCAmelCase__    =							copy.deepcopy(self.__dict__    )
									UpperCAmelCase__    =							self.encoder.to_dict()
									UpperCAmelCase__    =							self.decoder.to_dict()
									UpperCAmelCase__    =							self.__class__.model_type
									return output






class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				version.parse("1.11"       )







					@property
					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									return OrderedDict(
									    [
									        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
									    ]    )







					@property
					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									return 1e-4







					@property
					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}}    )






class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					@property
					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									UpperCAmelCase__    =							OrderedDict()
									UpperCAmelCase__    =							{0: "batch", 1: "past_decoder_sequence + sequence"}
									UpperCAmelCase__    =							{0: "batch", 1: "past_decoder_sequence + sequence"}
									UpperCAmelCase__    =							{0: "batch", 1: "encoder_sequence"}

									return common_inputs







					def 	__lowerCAmelCase		(       self     :			List[Any]					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			Dict = -1					,lowerCamelCase__     :			Dict = -1					,lowerCamelCase__     :			Dict = False					,lowerCamelCase__     :			int = None					,):
									import torch

									UpperCAmelCase__    =							OrderedDict()

									UpperCAmelCase__    =							super().generate_dummy_inputs(
									    UpperCamelCase_					,batch_size=UpperCamelCase_					,seq_length=UpperCamelCase_					,is_pair=UpperCamelCase_					,framework=UpperCamelCase_    )

									UpperCAmelCase__    =							dummy_input["input_ids"].shape
									UpperCAmelCase__    =							(batch, encoder_sequence, self._config.encoder_hidden_size)
									UpperCAmelCase__    =							dummy_input.pop('input_ids'    )
									UpperCAmelCase__    =							dummy_input.pop('attention_mask'    )
									UpperCAmelCase__    =							torch.zeros(UpperCamelCase_    )

									return common_inputs






class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					@property
					def 	__lowerCAmelCase		(       self     :			int    ):
									pass







					def 	__lowerCAmelCase		(       self     :			List[str]					,lowerCamelCase__     :			int    ):
									return VisionEncoderDecoderEncoderOnnxConfig(UpperCamelCase_    )







					def 	__lowerCAmelCase		(       self     :			Tuple					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			Union[str, Any] = "default"    ):
									UpperCAmelCase__    =							encoder_config.hidden_size
									return VisionEncoderDecoderDecoderOnnxConfig(UpperCamelCase_					,UpperCamelCase_    )




"""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() = }""")



