



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

lowercase_	       =			logging.get_logger(__name__)






class 						__a (     SCREAMING_SNAKE_CASE				):
	SCREAMING_SNAKE_CASE       =       "vision-encoder-decoder"
	SCREAMING_SNAKE_CASE       =       True



	def __init__(						self							:    str				,	**snake_case_							:    str)->							Tuple:
						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}""")

						__lowerCAmelCase	   =kwargs.pop("""encoder""")
						__lowerCAmelCase	   =encoder_config.pop("""model_type""")
						__lowerCAmelCase	   =kwargs.pop("""decoder""")
						__lowerCAmelCase	   =decoder_config.pop("""model_type""")

						__lowerCAmelCase	   =AutoConfig.for_model(snake_case_				,	**snake_case_)
						__lowerCAmelCase	   =AutoConfig.for_model(snake_case_				,	**snake_case_)
						__lowerCAmelCase	   =True



	@classmethod
	def 		UpperCamelCase							(						cls							:    Union[str, Any]				,	snake_case_							:    PretrainedConfig				,	snake_case_							:    PretrainedConfig				,	**snake_case_							:    Optional[int])->							PretrainedConfig:
						logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config""")
						__lowerCAmelCase	   =True
						__lowerCAmelCase	   =True

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



	def 		UpperCamelCase							(						self							:    Any)->							int:
						__lowerCAmelCase	   =copy.deepcopy(self.__dict__)
						__lowerCAmelCase	   =self.encoder.to_dict()
						__lowerCAmelCase	   =self.decoder.to_dict()
						__lowerCAmelCase	   =self.__class__.model_type
						return output






class 						__a (     SCREAMING_SNAKE_CASE				):
	SCREAMING_SNAKE_CASE       =       version.parse("1.11"				)



	@property
	def 		UpperCamelCase							(						self							:    Optional[Any])->							Mapping[str, Mapping[int, str]]:
						return OrderedDict(
						    [
						        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
						    ])



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



	@property
	def 		UpperCamelCase							(						self							:    Optional[int])->							Mapping[str, Mapping[int, str]]:
						return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}})






class 						__a (     SCREAMING_SNAKE_CASE				):



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

						return common_inputs



	def 		UpperCamelCase							(						self							:    Optional[int]				,	snake_case_							:    "PreTrainedTokenizerBase"				,	snake_case_							:    int = -1				,	snake_case_							:    int = -1				,	snake_case_							:    bool = False				,	snake_case_							:    Optional["TensorType"] = None				,	)->							Mapping[str, Any]:
						import torch

						__lowerCAmelCase	   =OrderedDict()

						__lowerCAmelCase	   =super().generate_dummy_inputs(
						    snake_case_				,	batch_size=snake_case_				,	seq_length=snake_case_				,	is_pair=snake_case_				,	framework=snake_case_)

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

						return common_inputs






class 						__a (     SCREAMING_SNAKE_CASE				):



	@property
	def 		UpperCamelCase							(						self							:    Optional[Any])->							None:
						pass



	def 		UpperCamelCase							(						self							:    Optional[Any]				,	snake_case_							:    PretrainedConfig)->							OnnxConfig:
						return VisionEncoderDecoderEncoderOnnxConfig(snake_case_)



	def 		UpperCamelCase							(						self							:    Optional[Any]				,	snake_case_							:    PretrainedConfig				,	snake_case_							:    PretrainedConfig				,	snake_case_							:    str = "default")->							OnnxConfig:
						__lowerCAmelCase	   =encoder_config.hidden_size
						return VisionEncoderDecoderDecoderOnnxConfig(snake_case_				,	snake_case_)









def 					__lowerCAmelCase		(  __lowerCamelCase	:    List[Any]	)  ->						Any:
					__lowerCAmelCase	   =[]
					__lowerCAmelCase	   =set({"""(""", """[""", """{"""}	)
					__lowerCAmelCase	   =set({""")""", """]""", """}"""}	)
					__lowerCAmelCase	   ={"""{""": """}""", """[""": """]""", """(""": """)"""}

					for i in range(len(__lowerCamelCase	)	):
										if s[i] in open_brackets:
															stack.append(s[i]	)

										elif s[i] in closed_brackets and (
										    len(__lowerCamelCase	) == 0 or (len(__lowerCamelCase	) > 0 and open_to_closed[stack.pop()] != s[i])
										):
															return False

					return len(__lowerCamelCase	) == 0






def 					__lowerCAmelCase		(  )  ->						List[str]:
					__lowerCAmelCase	   =input("""Enter sequence of brackets: """	)
					if is_balanced(__lowerCamelCase	):
										print(__lowerCamelCase				,   """is balanced"""	)
					else:
										print(__lowerCamelCase				,   """is not balanced"""	)


if __name__ == "__main__":
				main()




