
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

UpperCAmelCase__     = logging.get_logger(__name__)







class     __lowerCAmelCase					(    __SCREAMING_SNAKE_CASE		):
			UpperCamelCase           =  'vision-encoder-decoder'
			UpperCamelCase           =  True



			def __init__(		self     :						Optional[int]				,	**A     :						Optional[Any])     ->					Any:



						"""simple docstring"""






						super().__init__(**_a)
						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(_a				,	**_a)
						_UpperCAmelCase	 =     AutoConfig.for_model(_a				,	**_a)
						_UpperCAmelCase	 =     True



			@classmethod
			def 					_lowerCamelCase (		cls     :						Optional[int]				,	A     :						Dict				,	A     :						List[str]				,	**A     :						List[str])     ->					List[Any]:



						"""simple docstring"""






						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()				,	**_a)



			def 					_lowerCamelCase (		self     :						str)     ->					List[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     copy.deepcopy(self.__dict__)
						_UpperCAmelCase	 =     self.encoder.to_dict()
						_UpperCAmelCase	 =     self.decoder.to_dict()
						_UpperCAmelCase	 =     self.__class__.model_type
						return output







class     __lowerCAmelCase					(    __SCREAMING_SNAKE_CASE		):
			UpperCamelCase           =  version.parse('''1.11'''		)



			@property
			def 					_lowerCamelCase (		self     :						Optional[Any])     ->					Dict:



						"""simple docstring"""






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



			@property
			def 					_lowerCamelCase (		self     :						str)     ->					str:



						"""simple docstring"""






						return 1E-4



			@property
			def 					_lowerCamelCase (		self     :						List[str])     ->					Union[str, Any]:



						"""simple docstring"""






						return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}})







class     __lowerCAmelCase					(    __SCREAMING_SNAKE_CASE		):



			@property
			def 					_lowerCamelCase (		self     :						Dict)     ->					Any:



						"""simple docstring"""






						_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     :						Tuple				,	A     :						Optional[int]				,	A     :						int = -1				,	A     :						List[str] = -1				,	A     :						str = False				,	A     :						int = None				,	)     ->					Tuple:



						"""simple docstring"""






						import torch

						_UpperCAmelCase	 =     OrderedDict()

						_UpperCAmelCase	 =     super().generate_dummy_inputs(
						    _a				,	batch_size=_a				,	seq_length=_a				,	is_pair=_a				,	framework=_a)

						_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(_a)

						return common_inputs







class     __lowerCAmelCase					(    __SCREAMING_SNAKE_CASE		):



			@property
			def 					_lowerCamelCase (		self     :						str)     ->					str:



						"""simple docstring"""






						pass



			def 					_lowerCamelCase (		self     :						List[Any]				,	A     :						Any)     ->					str:



						"""simple docstring"""






						return VisionEncoderDecoderEncoderOnnxConfig(_a)



			def 					_lowerCamelCase (		self     :						int				,	A     :						List[str]				,	A     :						List[str]				,	A     :						int = "default")     ->					List[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     encoder_config.hidden_size
						return VisionEncoderDecoderDecoderOnnxConfig(_a				,	_a)





def 							A			(  _UpperCAmelCase       :     list					)       ->				list:







			'''simple docstring'''
			if len(_UpperCAmelCase					) <= 1:
						return lst

			_UpperCAmelCase	 =     1

			while i < len(_UpperCAmelCase					):
						if lst[i - 1] <= lst[i]:
									i += 1
						else:
									_UpperCAmelCase ,  _UpperCAmelCase	 =     lst[i], lst[i - 1]
									i -= 1
									if i == 0:
												_UpperCAmelCase	 =     1

			return lst


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