

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__								=		logging.get_logger(__name__)







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "vision-encoder-decoder"
		a__					       =  True


		def __init__(				self	,       **__snake_case):
			super().__init__(**__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}''')

			_UpperCamelCase   :		Tuple			=						kwargs.pop('encoder')
			_UpperCamelCase   :		Optional[Any]			=						encoder_config.pop('model_type')
			_UpperCamelCase   :		List[str]			=						kwargs.pop('decoder')
			_UpperCamelCase   :		List[str]			=						decoder_config.pop('model_type')

			_UpperCamelCase   :		Optional[int]			=						AutoConfig.for_model(__snake_case	,       **__snake_case)
			_UpperCamelCase   :		Union[str, Any]			=						AutoConfig.for_model(__snake_case	,       **__snake_case)
			_UpperCamelCase   :		List[Any]			=						True


		@classmethod
		def 		A__					(				cls	,       __snake_case	,       __snake_case	,       **__snake_case):
			logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config')
			_UpperCamelCase   :		Tuple			=						True
			_UpperCamelCase   :		int			=						True

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






		def 		A__					(				self):
			_UpperCamelCase   :		str			=						copy.deepcopy(self.__dict__)
			_UpperCamelCase   :		List[Any]			=						self.encoder.to_dict()
			_UpperCamelCase   :		Dict			=						self.decoder.to_dict()
			_UpperCamelCase   :		Dict			=						self.__class__.model_type
			return output







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  version.parse("1.11"							)


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


		@property
		def 		A__					(				self):
			return 1e-4






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







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		@property
		def 		A__					(				self):
			_UpperCamelCase   :		str			=						OrderedDict()
			_UpperCamelCase   :		Dict			=						{0: 'batch', 1: 'past_decoder_sequence + sequence'}
			_UpperCamelCase   :		str			=						{0: 'batch', 1: 'past_decoder_sequence + sequence'}
			_UpperCamelCase   :		Optional[int]			=						{0: 'batch', 1: 'encoder_sequence'}

			return common_inputs






		def 		A__					(				self	,       __snake_case	,       __snake_case = -1	,       __snake_case = -1	,       __snake_case = False	,       __snake_case = None	,       ):
			import torch

			_UpperCamelCase   :		Tuple			=						OrderedDict()

			_UpperCamelCase   :		str			=						super().generate_dummy_inputs(
			    __snake_case	,       batch_size=__snake_case	,       seq_length=__snake_case	,       is_pair=__snake_case	,       framework=__snake_case)

			_UpperCamelCase	,		_UpperCamelCase   :		List[Any]			=						dummy_input['input_ids'].shape
			_UpperCamelCase   :		str			=						(batch, encoder_sequence, self._config.encoder_hidden_size)
			_UpperCamelCase   :		Optional[int]			=						dummy_input.pop('input_ids')
			_UpperCamelCase   :		Optional[Any]			=						dummy_input.pop('attention_mask')
			_UpperCamelCase   :		Optional[int]			=						torch.zeros(__snake_case)

			return common_inputs







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		@property
		def 		A__					(				self):
			pass


		def 		A__					(				self	,       __snake_case):
			return VisionEncoderDecoderEncoderOnnxConfig(__snake_case)






		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case = "default"):
			_UpperCamelCase   :		int			=						encoder_config.hidden_size
			return VisionEncoderDecoderDecoderOnnxConfig(__snake_case	,       __snake_case)







def 					lowerCamelCase_     (							UpperCAmelCase_							:     list	)						->				list:




	'''simple docstring'''




	if len(UpperCAmelCase_	) <= 1:
		return [tuple(UpperCAmelCase_	)]

	_UpperCamelCase   :		List[Any]			=						[]

	def generate(UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     list	):
		_UpperCamelCase   :		Optional[int]			=						[0] * n
		res.append(tuple(UpperCAmelCase_	)	)

		_UpperCamelCase   :		List[Any]			=						0
		while i < n:
			if c[i] < i:
				if i % 2 == 0:
					_UpperCamelCase	,		_UpperCamelCase   :		List[str]			=						arr[i], arr[0]
				else:
					_UpperCamelCase	,		_UpperCamelCase   :		List[str]			=						arr[i], arr[c[i]]
				res.append(tuple(UpperCAmelCase_	)	)
				c[i] += 1
				_UpperCamelCase   :		Tuple			=						0
			else:
				_UpperCamelCase   :		Tuple			=						0
				i += 1

	generate(len(UpperCAmelCase_	)							,       UpperCAmelCase_	)
	return res


if __name__ == "__main__":
							lowerCAmelCase__								=		input("""Enter numbers separated by a comma:\n""").strip()
							lowerCAmelCase__								=		[int(item) for item in user_input.split(""",""")]
							print(heaps(arr))




