




"""simple docstring"""





def        _UpperCamelCase     (      UpperCamelCase				)      ->      str:


			"""simple docstring"""
			__UpperCAmelCase						:			int												=       []
			__UpperCAmelCase						:			Optional[int]												=       []
			__UpperCAmelCase						:			Union[str, Any]												=       {
			    "^": 3,
			    "*": 2,
			    "/": 2,
			    "%": 2,
			    "+": 1,
			    "-": 1,
			}  # Priority of each operator
			__UpperCAmelCase						:			List[Any]												=       len(UpperCamelCase				) if (len(UpperCamelCase				) > 7) else 7

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

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

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

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

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

def        _UpperCamelCase     (      UpperCamelCase				)      ->      Optional[Any]:


			"""simple docstring"""
			__UpperCAmelCase						:			Any												=       list(infix[::-1]				)  # reverse the infix equation

			for i in range(len(UpperCamelCase				)				):
						if infix[i] == "(":
									__UpperCAmelCase						:			Optional[int]												=       ")"  # change "(" to ")"
						elif infix[i] == ")":
									__UpperCAmelCase						:			int												=       "("  # change ")" to "("

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


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





import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class __UpperCamelCase     (				_a					,_a		):






					'''simple docstring'''



					@register_to_config
					def __init__(				self				,      *,
					    lowerCamelCase__ = 4				,      lowerCamelCase__ = 7_6_8				,      lowerCamelCase__				,      lowerCamelCase__				,      ):
						super().__init__()

						UpperCAmelCase__:	int									= nn.Parameter(torch.zeros(lowerCamelCase__						)						)

						# parameters for additional clip time embeddings
						UpperCAmelCase__:	Optional[Any]									= nn.Linear(lowerCamelCase__				,      lowerCamelCase__						)
						UpperCAmelCase__:	str									= nn.Linear(lowerCamelCase__				,      lowerCamelCase__						)

						# parameters for encoder hidden states
						UpperCAmelCase__:	Tuple									= clip_extra_context_tokens
						UpperCAmelCase__:	List[str]									= nn.Linear(
						    lowerCamelCase__				,      self.clip_extra_context_tokens * cross_attention_dim						)
						UpperCAmelCase__:	Union[str, Any]									= nn.Linear(lowerCamelCase__				,      lowerCamelCase__						)
						UpperCAmelCase__:	str									= nn.LayerNorm(lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      *, lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						if do_classifier_free_guidance:
							# Add the classifier free guidance embeddings to the image embeddings
							UpperCAmelCase__:	Any									= image_embeddings.shape[0]
							UpperCAmelCase__:	Optional[Any]									= self.learned_classifier_free_guidance_embeddings.unsqueeze(0						)
							UpperCAmelCase__:	Union[str, Any]									= classifier_free_guidance_embeddings.expand(
							    lowerCamelCase__				,      -1						)
							UpperCAmelCase__:	Tuple									= torch.cat([classifier_free_guidance_embeddings, image_embeddings]				,      dim=0						)

						# The image embeddings batch size and the text embeddings batch size are equal
						assert image_embeddings.shape[0] == prompt_embeds.shape[0]

						UpperCAmelCase__:	str									= prompt_embeds.shape[0]

						# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
						# adding CLIP embeddings to the existing timestep embedding, ...
						UpperCAmelCase__:	List[str]									= self.embedding_proj(lowerCamelCase__						)
						UpperCAmelCase__:	Optional[int]									= self.clip_image_embeddings_project_to_time_embeddings(lowerCamelCase__						)
						UpperCAmelCase__:	Any									= time_projected_image_embeddings + time_projected_prompt_embeds

						# ... and by projecting CLIP embeddings into four
						# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
						UpperCAmelCase__:	Dict									= self.clip_extra_context_tokens_proj(lowerCamelCase__						)
						UpperCAmelCase__:	Optional[Any]									= clip_extra_context_tokens.reshape(lowerCamelCase__				,      -1				,      self.clip_extra_context_tokens						)
						UpperCAmelCase__:	Dict									= clip_extra_context_tokens.permute(0				,      2				,      1						)

						UpperCAmelCase__:	Any									= self.encoder_hidden_states_proj(lowerCamelCase__						)
						UpperCAmelCase__:	List[str]									= self.text_encoder_hidden_states_norm(lowerCamelCase__						)
						UpperCAmelCase__:	Optional[int]									= torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]				,      dim=1						)

						return text_encoder_hidden_states, additive_clip_time_embeddings