


"""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

UpperCAmelCase				=						logging.get_logger(__name__)




class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					'''vision-encoder-decoder'''
   __UpperCAmelCase							       =					True





   def __init__(							self      ,   **SCREAMING_SNAKE_CASE)  ->  Optional[Any]:
      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	:					int   =		kwargs.pop("""encoder""")
      _lowerCamelCase	:					List[Any]   =		encoder_config.pop("""model_type""")
      _lowerCamelCase	:					Tuple   =		kwargs.pop("""decoder""")
      _lowerCamelCase	:					Tuple   =		decoder_config.pop("""model_type""")

      _lowerCamelCase	:					int   =		AutoConfig.for_model(SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Optional[Any]   =		AutoConfig.for_model(SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Optional[Any]   =		True





   @classmethod
   def 		UpperCamelCase_  (							cls      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)  ->  PretrainedConfig:
      logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config""")
      _lowerCamelCase	:					List[Any]   =		True
      _lowerCamelCase	:					Tuple   =		True

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





   def 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase	:					Optional[Any]   =		copy.deepcopy(self.__dict__)
      _lowerCamelCase	:					Any   =		self.encoder.to_dict()
      _lowerCamelCase	:					Optional[int]   =		self.decoder.to_dict()
      _lowerCamelCase	:					List[Any]   =		self.__class__.model_type
      return output







class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					version.parse('''1.11'''				)





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





   @property
   def 		UpperCamelCase_  (							self)  ->  float:
      return 1e-4





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







class 	lowercase__				(					A_				):





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

      return common_inputs





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = -1      ,   SCREAMING_SNAKE_CASE = -1      ,   SCREAMING_SNAKE_CASE = False      ,   SCREAMING_SNAKE_CASE = None      ,   )  ->  Mapping[str, Any]:
      import torch

      _lowerCamelCase	:					Optional[Any]   =		OrderedDict()

      _lowerCamelCase	:					List[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	:					List[Any]   =		dummy_input["""input_ids"""].shape
      _lowerCamelCase	:					List[str]   =		(batch, encoder_sequence, self._config.encoder_hidden_size)
      _lowerCamelCase	:					List[str]   =		dummy_input.pop("""input_ids""")
      _lowerCamelCase	:					Tuple   =		dummy_input.pop("""attention_mask""")
      _lowerCamelCase	:					Dict   =		torch.zeros(SCREAMING_SNAKE_CASE)

      return common_inputs







class 	lowercase__				(					A_				):





   @property
   def 		UpperCamelCase_  (							self)  ->  None:
      pass





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  OnnxConfig:
      return VisionEncoderDecoderEncoderOnnxConfig(SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = "default")  ->  OnnxConfig:
      _lowerCamelCase	:					List[Any]   =		encoder_config.hidden_size
      return VisionEncoderDecoderDecoderOnnxConfig(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)




import math






class      lowerCamelCase_      :




	def __init__(     self						:     Union[str, Any]					,      __A						:     List[str]=0							):  # a graph with Node 0,1,...,N-1
		__A :			List[str]		    =			n
		__A :			List[str]		    =			[
		    [math.inf for j in range(0					,      __A							)] for i in range(0					,      __A							)
		]  # adjacency matrix for weight
		__A :			str		    =			[
		    [math.inf for j in range(0					,      __A							)] for i in range(0					,      __A							)
		]  # dp[i][j] stores minimum distance from i to j




	def        lowerCAmelCase_       (     self						:     str					,      __A						:     Union[str, Any]					,      __A						:     Any					,      __A						:     Optional[int]							):
		__A :			List[Any]		    =			w




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		for k in range(0					,      self.n							):
			for i in range(0					,      self.n							):
				for j in range(0					,      self.n							):
					__A :			List[Any]		    =			min(self.dp[i][j]					,      self.dp[i][k] + self.dp[k][j]							)




	def        lowerCAmelCase_       (     self						:     int					,      __A						:     List[str]					,      __A						:     List[str]							):
		return self.dp[u][v]


if __name__ == "__main__":
			UpperCAmelCase_							:					Tuple		 =   Graph(5)
			graph.add_edge(0, 2, 9)
			graph.add_edge(0, 4, 10)
			graph.add_edge(1, 3, 5)
			graph.add_edge(2, 3, 7)
			graph.add_edge(3, 0, 10)
			graph.add_edge(3, 1, 2)
			graph.add_edge(3, 2, 1)
			graph.add_edge(3, 4, 6)
			graph.add_edge(4, 1, 3)
			graph.add_edge(4, 2, 4)
			graph.add_edge(4, 3, 9)
			graph.floyd_warshall()
			graph.show_min(1, 4)
			graph.show_min(0, 3)





