

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

a		=logging.get_logger(__name__)






class        A_		(       SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase    :  Tuple						      =      '''vision-encoder-decoder'''
							_UpperCAmelCase    :  Union[str, Any]						      =      True
							def __init__(		self		:			Union[str, Any]   ,**SCREAMING_SNAKE_CASE__		:			Optional[int]):
								super().__init__(**SCREAMING_SNAKE_CASE__)
								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}")

								__lowerCamelCase				:				Optional[int]					       = kwargs.pop('encoder')
								__lowerCamelCase				:				List[str]					       = encoder_config.pop('model_type')
								__lowerCamelCase				:				Union[str, Any]					       = kwargs.pop('decoder')
								__lowerCamelCase				:				Optional[Any]					       = decoder_config.pop('model_type')

								__lowerCamelCase				:				str					       = AutoConfig.for_model(SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Tuple					       = AutoConfig.for_model(SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Optional[Any]					       = True
							@classmethod
							def     lowerCAmelCase     (		cls		:			str   ,SCREAMING_SNAKE_CASE__		:			PretrainedConfig   ,SCREAMING_SNAKE_CASE__		:			PretrainedConfig   ,**SCREAMING_SNAKE_CASE__		:			List[str]):
								logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config')
								__lowerCamelCase				:				List[str]					       = True
								__lowerCamelCase				:				Tuple					       = True

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


							def     lowerCAmelCase     (		self		:			Tuple):
								__lowerCamelCase				:				str					       = copy.deepcopy(self.__dict__)
								__lowerCamelCase				:				Optional[Any]					       = self.encoder.to_dict()
								__lowerCamelCase				:				Optional[Any]					       = self.decoder.to_dict()
								__lowerCamelCase				:				Tuple					       = self.__class__.model_type
								return output






class        A_		(       SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase    :  List[Any]						      =      version.parse('''1.11'''				)
							@property
							def     lowerCAmelCase     (		self		:			Dict):
								return OrderedDict(
								    [
								        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
								    ])
							@property
							def     lowerCAmelCase     (		self		:			Tuple):
								return 1E-4


							@property
							def     lowerCAmelCase     (		self		:			List[Any]):
								return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}})






class        A_		(       SCREAMING_SNAKE_CASE				):
							@property
							def     lowerCAmelCase     (		self		:			str):
								__lowerCamelCase				:				Union[str, Any]					       = OrderedDict()
								__lowerCamelCase				:				List[str]					       = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
								__lowerCamelCase				:				Tuple					       = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
								__lowerCamelCase				:				Tuple					       = {0: 'batch', 1: 'encoder_sequence'}

								return common_inputs


							def     lowerCAmelCase     (		self		:			Any   ,SCREAMING_SNAKE_CASE__		:			"PreTrainedTokenizerBase"   ,SCREAMING_SNAKE_CASE__		:			int = -1   ,SCREAMING_SNAKE_CASE__		:			int = -1   ,SCREAMING_SNAKE_CASE__		:			bool = False   ,SCREAMING_SNAKE_CASE__		:			Optional["TensorType"] = None   ,):
								import torch

								__lowerCamelCase				:				int					       = OrderedDict()

								__lowerCamelCase				:				Any					       = super().generate_dummy_inputs(
								    SCREAMING_SNAKE_CASE__   ,batch_size=SCREAMING_SNAKE_CASE__   ,seq_length=SCREAMING_SNAKE_CASE__   ,is_pair=SCREAMING_SNAKE_CASE__   ,framework=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase       , __lowerCamelCase				:				Any					       = dummy_input['input_ids'].shape
								__lowerCamelCase				:				Optional[Any]					       = (batch, encoder_sequence, self._config.encoder_hidden_size)
								__lowerCamelCase				:				str					       = dummy_input.pop('input_ids')
								__lowerCamelCase				:				Any					       = dummy_input.pop('attention_mask')
								__lowerCamelCase				:				Dict					       = torch.zeros(SCREAMING_SNAKE_CASE__)

								return common_inputs






class        A_		(       SCREAMING_SNAKE_CASE				):
							@property
							def     lowerCAmelCase     (		self		:			Any):
								pass
							def     lowerCAmelCase     (		self		:			int   ,SCREAMING_SNAKE_CASE__		:			PretrainedConfig):
								return VisionEncoderDecoderEncoderOnnxConfig(SCREAMING_SNAKE_CASE__)


							def     lowerCAmelCase     (		self		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			PretrainedConfig   ,SCREAMING_SNAKE_CASE__		:			PretrainedConfig   ,SCREAMING_SNAKE_CASE__		:			str = "default"):
								__lowerCamelCase				:				Optional[Any]					       = encoder_config.hidden_size
								return VisionEncoderDecoderDecoderOnnxConfig(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__)









"""simple docstring"""






from __future__ import annotations






def      snake_case__   (		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int	):
			"""simple docstring"""
			if b == 0:
						return (1, 0)
			((lowerCamelCase__)	,   (lowerCamelCase__))		:		Any			=extended_euclid(__lowerCamelCase							,		a % b	)
			lowerCamelCase__		:		Optional[Any]			=a // b
			return (y, x - k * y)






def      snake_case__   (		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int	):
			"""simple docstring"""
			((lowerCamelCase__)	,   (lowerCamelCase__))		:		Any			=extended_euclid(__lowerCamelCase							,		__lowerCamelCase	)
			lowerCamelCase__		:		List[Any]			=na * na
			lowerCamelCase__		:		Union[str, Any]			=ra * x * na + ra * y * na
			return (n % m + m) % m






def      snake_case__   (		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int	):
			"""simple docstring"""
			((lowerCamelCase__)	,   (lowerCamelCase__))		:		int			=extended_euclid(__lowerCamelCase							,		__lowerCamelCase	)
			if b < 0:
						lowerCamelCase__		:		Any			=(b % n + n) % n
			return b






def      snake_case__   (		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int	):
			"""simple docstring"""
			lowerCamelCase__	,   lowerCamelCase__		:		Any			=invert_modulo(__lowerCamelCase							,		__lowerCamelCase	), invert_modulo(__lowerCamelCase							,		__lowerCamelCase	)
			lowerCamelCase__		:		Tuple			=na * na
			lowerCamelCase__		:		Optional[Any]			=ra * x * na + ra * y * na
			return (n % m + m) % m


if __name__ == "__main__":
		from doctest import testmod

		testmod(name="chinese_remainder_theorem", verbose=True)
		testmod(name="chinese_remainder_theorem2", verbose=True)
		testmod(name="invert_modulo", verbose=True)
		testmod(name="extended_euclid", verbose=True)




