




def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     Tuple			=						[]
      _A							:     Union[str, Any]			=						[]
      _A							:     Optional[int]			=						{
          """^""": 3,
          """*""": 2,
          """/""": 2,
          """%""": 2,
          """+""": 1,
          """-""": 1,
      }  # Priority of each operator
      _A							:     Dict			=						len(snake_case_     ) if (len(snake_case_     ) > 7) else 7

      # Print table header for output
      print(
          """Symbol""".center(8     ),"""Stack""".center(snake_case_     ),"""Postfix""".center(snake_case_     ),sep=""" | """,)
      print("""-""" * (print_width * 3 + 7)     )

      for x in infix:
            if x.isalpha() or x.isdigit():
                  post_fix.append(snake_case_     )  # if x is Alphabet / Digit, add it to Postfix
            elif x == "(":
                  stack.append(snake_case_     )  # if x is "(" push to Stack
            elif x == ")":  # if x is ")" pop stack until "(" is encountered
                  while stack[-1] != "(":
                        post_fix.append(stack.pop()     )  # Pop stack & add the content to Postfix
                  stack.pop()
            else:
                  if len(snake_case_     ) == 0:
                        stack.append(snake_case_     )  # If stack is empty, push x to stack
                  else:  # while priority of x is not > priority of element in the stack
                        while len(snake_case_     ) > 0 and priority[x] <= priority[stack[-1]]:
                              post_fix.append(stack.pop()     )  # pop stack & add to Postfix
                        stack.append(snake_case_     )  # push x to stack

            print(
                x.center(8     ),("""""".join(snake_case_     )).ljust(snake_case_     ),("""""".join(snake_case_     )).ljust(snake_case_     ),sep=""" | """,)  # Output in tabular format

      while len(snake_case_     ) > 0:  # while stack is not empty
            post_fix.append(stack.pop()     )  # pop stack & add to Postfix
            print(
                """ """.center(8     ),("""""".join(snake_case_     )).ljust(snake_case_     ),("""""".join(snake_case_     )).ljust(snake_case_     ),sep=""" | """,)  # Output in tabular format

      return "".join(snake_case_     )  # return Postfix as str







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     str			=						list(infix[::-1]     )  # reverse the infix equation

      for i in range(len(snake_case_     )     ):
            if infix[i] == "(":
                  _A							:     Any			=						""")"""  # change "(" to ")"
            elif infix[i] == ")":
                  _A							:     int			=						"""("""  # change ")" to "("

      return (infix_2_postfix("""""".join(snake_case_     )     ))[
          ::-1
      ]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
  _snake_case										=       input("\nEnter an Infix Equation = ")  # Input an Infix equation
  _snake_case										=       "".join(Infix.split())  # Remove spaces from the input
  print("\n\t", Infix, "(Infix) -> ", infix_2_prefix(Infix), "(Prefix)")



'''simple docstring'''




import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD





class  A				(							nn.Module						):
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		0.0
     __magic_name__						=		1
     __magic_name__						=		1
     __magic_name__						=		True
     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		jnp.floataa





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         A       :   Union[str, Any]					=						[]
         A       :   Union[str, Any]					=						[]

         for i in range(self.num_layers	):
             A       :   Any					=						self.in_channels if i == 0 else self.out_channels

             A       :   Optional[Any]					=						FlaxResnetBlockaD(
                 in_channels=SCREAMING_SNAKE_CASE						,			out_channels=self.out_channels						,			dropout_prob=self.dropout						,			dtype=self.dtype						,			)
             resnets.append(SCREAMING_SNAKE_CASE	)

             A       :   Optional[int]					=						FlaxTransformeraDModel(
                 in_channels=self.out_channels						,			n_heads=self.num_attention_heads						,			d_head=self.out_channels // self.num_attention_heads						,			depth=1						,			use_linear_projection=self.use_linear_projection						,			only_cross_attention=self.only_cross_attention						,			use_memory_efficient_attention=self.use_memory_efficient_attention						,			dtype=self.dtype						,			)
             attentions.append(SCREAMING_SNAKE_CASE	)

         A       :   Union[str, Any]					=						resnets
         A       :   Union[str, Any]					=						attentions

         if self.add_downsample:
             A       :   int					=						FlaxDownsampleaD(self.out_channels						,			dtype=self.dtype	)





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=True	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   Optional[Any]					=						()

         for resnet, attn in zip(self.resnets						,			self.attentions	):
             A       :   int					=						resnet(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)
             A       :   Dict					=						attn(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)
             output_states += (hidden_states,)

         if self.add_downsample:
             A       :   Optional[Any]					=						self.downsamplers_a(SCREAMING_SNAKE_CASE	)
             output_states += (hidden_states,)

         return hidden_states, output_states





class  A				(							nn.Module						):
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		0.0
     __magic_name__						=		1
     __magic_name__						=		True
     __magic_name__						=		jnp.floataa





     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






         A       :   Optional[Any]					=						[]

         for i in range(self.num_layers	):
             A       :   Optional[Any]					=						self.in_channels if i == 0 else self.out_channels

             A       :   List[str]					=						FlaxResnetBlockaD(
                 in_channels=SCREAMING_SNAKE_CASE						,			out_channels=self.out_channels						,			dropout_prob=self.dropout						,			dtype=self.dtype						,			)
             resnets.append(SCREAMING_SNAKE_CASE	)
         A       :   Dict					=						resnets

         if self.add_downsample:
             A       :   Dict					=						FlaxDownsampleaD(self.out_channels						,			dtype=self.dtype	)





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=True	) ->				Optional[Any]:






         """simple docstring"""






         A       :   str					=						()

         for resnet in self.resnets:
             A       :   Optional[int]					=						resnet(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)
             output_states += (hidden_states,)

         if self.add_downsample:
             A       :   Optional[int]					=						self.downsamplers_a(SCREAMING_SNAKE_CASE	)
             output_states += (hidden_states,)

         return hidden_states, output_states





class  A				(							nn.Module						):
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		0.0
     __magic_name__						=		1
     __magic_name__						=		1
     __magic_name__						=		True
     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		jnp.floataa





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         A       :   Optional[Any]					=						[]
         A       :   Optional[int]					=						[]

         for i in range(self.num_layers	):
             A       :   str					=						self.in_channels if (i == self.num_layers - 1) else self.out_channels
             A       :   Dict					=						self.prev_output_channel if i == 0 else self.out_channels

             A       :   List[str]					=						FlaxResnetBlockaD(
                 in_channels=resnet_in_channels + res_skip_channels						,			out_channels=self.out_channels						,			dropout_prob=self.dropout						,			dtype=self.dtype						,			)
             resnets.append(SCREAMING_SNAKE_CASE	)

             A       :   int					=						FlaxTransformeraDModel(
                 in_channels=self.out_channels						,			n_heads=self.num_attention_heads						,			d_head=self.out_channels // self.num_attention_heads						,			depth=1						,			use_linear_projection=self.use_linear_projection						,			only_cross_attention=self.only_cross_attention						,			use_memory_efficient_attention=self.use_memory_efficient_attention						,			dtype=self.dtype						,			)
             attentions.append(SCREAMING_SNAKE_CASE	)

         A       :   Dict					=						resnets
         A       :   Optional[Any]					=						attentions

         if self.add_upsample:
             A       :   Optional[int]					=						FlaxUpsampleaD(self.out_channels						,			dtype=self.dtype	)





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=True	) ->				Optional[int]:






         """simple docstring"""






         for resnet, attn in zip(self.resnets						,			self.attentions	):
             # pop res hidden states
             A       :   List[str]					=						res_hidden_states_tuple[-1]
             A       :   int					=						res_hidden_states_tuple[:-1]
             A       :   List[str]					=						jnp.concatenate((hidden_states, res_hidden_states)						,			axis=-1	)

             A       :   Union[str, Any]					=						resnet(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)
             A       :   Tuple					=						attn(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)

         if self.add_upsample:
             A       :   Dict					=						self.upsamplers_a(SCREAMING_SNAKE_CASE	)

         return hidden_states





class  A				(							nn.Module						):
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		0.0
     __magic_name__						=		1
     __magic_name__						=		True
     __magic_name__						=		jnp.floataa





     def 				__lowerCAmelCase							(							self	) ->				Dict:






         """simple docstring"""






         A       :   int					=						[]

         for i in range(self.num_layers	):
             A       :   List[Any]					=						self.in_channels if (i == self.num_layers - 1) else self.out_channels
             A       :   List[str]					=						self.prev_output_channel if i == 0 else self.out_channels

             A       :   str					=						FlaxResnetBlockaD(
                 in_channels=resnet_in_channels + res_skip_channels						,			out_channels=self.out_channels						,			dropout_prob=self.dropout						,			dtype=self.dtype						,			)
             resnets.append(SCREAMING_SNAKE_CASE	)

         A       :   List[Any]					=						resnets

         if self.add_upsample:
             A       :   Optional[Any]					=						FlaxUpsampleaD(self.out_channels						,			dtype=self.dtype	)





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=True	) ->				Tuple:






         """simple docstring"""






         for resnet in self.resnets:
             # pop res hidden states
             A       :   Optional[int]					=						res_hidden_states_tuple[-1]
             A       :   Optional[Any]					=						res_hidden_states_tuple[:-1]
             A       :   List[Any]					=						jnp.concatenate((hidden_states, res_hidden_states)						,			axis=-1	)

             A       :   Optional[Any]					=						resnet(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)

         if self.add_upsample:
             A       :   List[str]					=						self.upsamplers_a(SCREAMING_SNAKE_CASE	)

         return hidden_states





class  A				(							nn.Module						):
     __magic_name__						=		42
     __magic_name__						=		0.0
     __magic_name__						=		1
     __magic_name__						=		1
     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		jnp.floataa





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         A       :   str					=						[
             FlaxResnetBlockaD(
                 in_channels=self.in_channels						,			out_channels=self.in_channels						,			dropout_prob=self.dropout						,			dtype=self.dtype						,			)
         ]

         A       :   List[Any]					=						[]

         for _ in range(self.num_layers	):
             A       :   int					=						FlaxTransformeraDModel(
                 in_channels=self.in_channels						,			n_heads=self.num_attention_heads						,			d_head=self.in_channels // self.num_attention_heads						,			depth=1						,			use_linear_projection=self.use_linear_projection						,			use_memory_efficient_attention=self.use_memory_efficient_attention						,			dtype=self.dtype						,			)
             attentions.append(SCREAMING_SNAKE_CASE	)

             A       :   Union[str, Any]					=						FlaxResnetBlockaD(
                 in_channels=self.in_channels						,			out_channels=self.in_channels						,			dropout_prob=self.dropout						,			dtype=self.dtype						,			)
             resnets.append(SCREAMING_SNAKE_CASE	)

         A       :   List[str]					=						resnets
         A       :   List[str]					=						attentions





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=True	) ->				Dict:






         """simple docstring"""






         A       :   Optional[Any]					=						self.resnets[0](SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
         for attn, resnet in zip(self.attentions						,			self.resnets[1:]	):
             A       :   Optional[int]					=						attn(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)
             A       :   Union[str, Any]					=						resnet(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)

         return hidden_states




