






import operator as op

def _UpperCAmelCase   (	a						:	int ):
							snake_case__	  =       []
							snake_case__	  =       lambda a  ,					a : int(x / y )  # noqa: E731 integer division operation
							snake_case__	  =       {
							    """^""": op.pow,
							    """*""": op.mul,
							    """/""": div,
							    """+""": op.add,
							    """-""": op.sub,
							}  # operators & their respective operation

							# print table header
							print("""Symbol""".center(8 )  ,					"""Action""".center(12 )  ,					"""Stack"""  ,					sep=""" | """ )
							print("""-""" * (30 + len(a )) )

							for x in post_fix:
														if x.isdigit():  # if x in digit
																					stack.append(a )  # append x to stack
																					# output in tabular format
																					print(x.rjust(8 )  ,					("""push(""" + x + """)""").ljust(12 )  ,					""",""".join(a )  ,					sep=""" | """ )
														else:
																					snake_case__	  =       stack.pop()  # pop stack
																					# output in tabular format
																					print("""""".rjust(8 )  ,					("""pop(""" + b + """)""").ljust(12 )  ,					""",""".join(a )  ,					sep=""" | """ )

																					snake_case__	  =       stack.pop()  # pop stack
																					# output in tabular format
																					print("""""".rjust(8 )  ,					("""pop(""" + a + """)""").ljust(12 )  ,					""",""".join(a )  ,					sep=""" | """ )

																					stack.append(
																					    str(opr[x](int(a )  ,					int(a ) ) ) )  # evaluate the 2 values popped from stack & push result to stack
																					# output in tabular format
																					print(
																					    x.rjust(8 )  ,					("""push(""" + a + x + b + """)""").ljust(12 )  ,					""",""".join(a )  ,					sep=""" | """  ,					)

							return int(stack[0] )


if __name__ == "__main__":
	a__       =     input("""\n\nEnter a Postfix Equation (space separated) = """).split(""" """)
	print("""\n\tResult = """, solve(Postfix))








import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
	from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


a__       =     logging.get_logger(__name__)  # pylint: disable=invalid-name

a__       =     2_5_6


class    _lowerCAmelCase			(		lowercase_      ):



				"""simple docstring"""



				_lowercase				:   str      =  ['''melgan''']



				def __init__(      self    :    int			,		UpperCamelCase__    :    SpectrogramNotesEncoder			,		UpperCamelCase__    :    SpectrogramContEncoder			,		UpperCamelCase__    :    TaFilmDecoder			,		UpperCamelCase__    :    DDPMScheduler			,		UpperCamelCase__    :    OnnxRuntimeModel if is_onnx_available() else Any			,		):






											'''simple docstring'''







											super().__init__()

											# From MELGAN
											snake_case__	  =       math.log(1E-5)  # Matches MelGAN training.
											snake_case__	  =       4.0  # Largest value for most examples
											snake_case__	  =       1_2_8

											self.register_modules(
											    notes_encoder=UpperCamelCase__			,		continuous_encoder=UpperCamelCase__			,		decoder=UpperCamelCase__			,		scheduler=UpperCamelCase__			,		melgan=UpperCamelCase__			,		)



				def 					__magic_name__							(      self    :    Tuple			,		UpperCamelCase__    :    Any			,		UpperCamelCase__    :    int=(-1.0, 1.0)			,		UpperCamelCase__    :    Tuple=False):






											'''simple docstring'''







											snake_case__  , snake_case__	  =       output_range
											if clip:
																		snake_case__	  =       torch.clip(UpperCamelCase__			,		self.min_value			,		self.max_value)
											# Scale to [0, 1].
											snake_case__	  =       (features - self.min_value) / (self.max_value - self.min_value)
											# Scale to [min_out, max_out].
											return zero_one * (max_out - min_out) + min_out



				def 					__magic_name__							(      self    :    Optional[Any]			,		UpperCamelCase__    :    str			,		UpperCamelCase__    :    List[str]=(-1.0, 1.0)			,		UpperCamelCase__    :    Optional[Any]=False):






											'''simple docstring'''







											snake_case__  , snake_case__	  =       input_range
											snake_case__	  =       torch.clip(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__) if clip else outputs
											# Scale to [0, 1].
											snake_case__	  =       (outputs - min_out) / (max_out - min_out)
											# Scale to [self.min_value, self.max_value].
											return zero_one * (self.max_value - self.min_value) + self.min_value



				def 					__magic_name__							(      self    :    Optional[int]			,		UpperCamelCase__    :    Union[str, Any]			,		UpperCamelCase__    :    Optional[int]			,		UpperCamelCase__    :    Any):






											'''simple docstring'''







											snake_case__	  =       input_tokens > 0
											snake_case__  , snake_case__	  =       self.notes_encoder(
											    encoder_input_tokens=UpperCamelCase__			,		encoder_inputs_mask=UpperCamelCase__)

											snake_case__  , snake_case__	  =       self.continuous_encoder(
											    encoder_inputs=UpperCamelCase__			,		encoder_inputs_mask=UpperCamelCase__)

											return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]



				def 					__magic_name__							(      self    :    List[Any]			,		UpperCamelCase__    :    Dict			,		UpperCamelCase__    :    Any			,		UpperCamelCase__    :    int):






											'''simple docstring'''







											snake_case__	  =       noise_time
											if not torch.is_tensor(UpperCamelCase__):
																		snake_case__	  =       torch.tensor([timesteps]			,		dtype=torch.long			,		device=input_tokens.device)
											elif torch.is_tensor(UpperCamelCase__) and len(timesteps.shape) == 0:
																		snake_case__	  =       timesteps[None].to(input_tokens.device)

											# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
											snake_case__	  =       timesteps * torch.ones(input_tokens.shape[0]			,		dtype=timesteps.dtype			,		device=timesteps.device)

											snake_case__	  =       self.decoder(
											    encodings_and_masks=UpperCamelCase__			,		decoder_input_tokens=UpperCamelCase__			,		decoder_noise_time=UpperCamelCase__)
											return logits



				@torch.no_grad()
				def __call__(      self    :    Dict			,		UpperCamelCase__    :    List[List[int]]			,		UpperCamelCase__    :    Optional[torch.Generator] = None			,		UpperCamelCase__    :    int = 1_0_0			,		UpperCamelCase__    :    bool = True			,		UpperCamelCase__    :    str = "numpy"			,		UpperCamelCase__    :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,		UpperCamelCase__    :    int = 1			,		):






											'''simple docstring'''







											if (callback_steps is None) or (
											    callback_steps is not None and (not isinstance(UpperCamelCase__			,		UpperCamelCase__) or callback_steps <= 0)
											):
																		raise ValueError(
																		    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
																		    F''' {type(UpperCamelCase__)}.''')

											snake_case__	  =       np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]			,		dtype=np.floataa)
											snake_case__	  =       np.zeros([1, 0, self.n_dims]			,		np.floataa)
											snake_case__	  =       torch.ones((1, TARGET_FEATURE_LENGTH)			,		dtype=UpperCamelCase__			,		device=self.device)

											for i, encoder_input_tokens in enumerate(UpperCamelCase__):
																		if i == 0:
																									snake_case__	  =       torch.from_numpy(pred_mel[:1].copy()).to(
																									    device=self.device			,		dtype=self.decoder.dtype)
																									# The first chunk has no previous context.
																									snake_case__	  =       torch.zeros((1, TARGET_FEATURE_LENGTH)			,		dtype=UpperCamelCase__			,		device=self.device)
																		else:
																									# The full song pipeline does not feed in a context feature, so the mask
																									# will be all 0s after the feature converter. Because we know we're
																									# feeding in a full context chunk from the previous prediction, set it
																									# to all 1s.
																									snake_case__	  =       ones

																		snake_case__	  =       self.scale_features(
																		    UpperCamelCase__			,		output_range=[-1.0, 1.0]			,		clip=UpperCamelCase__)

																		snake_case__	  =       self.encode(
																		    input_tokens=torch.IntTensor([encoder_input_tokens]).to(device=self.device)			,		continuous_inputs=UpperCamelCase__			,		continuous_mask=UpperCamelCase__			,		)

																		# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
																		snake_case__	  =       randn_tensor(
																		    shape=encoder_continuous_inputs.shape			,		generator=UpperCamelCase__			,		device=self.device			,		dtype=self.decoder.dtype			,		)

																		# set step values
																		self.scheduler.set_timesteps(UpperCamelCase__)

																		# Denoising diffusion loop
																		for j, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
																									snake_case__	  =       self.decode(
																									    encodings_and_masks=UpperCamelCase__			,		input_tokens=UpperCamelCase__			,		noise_time=t / self.scheduler.config.num_train_timesteps			,		)

																									# Compute previous output: x_t -> x_t-1
																									snake_case__	  =       self.scheduler.step(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		generator=UpperCamelCase__).prev_sample

																		snake_case__	  =       self.scale_to_features(UpperCamelCase__			,		input_range=[-1.0, 1.0])
																		snake_case__	  =       mel[:1]
																		snake_case__	  =       mel.cpu().float().numpy()

																		snake_case__	  =       np.concatenate([full_pred_mel, pred_mel[:1]]			,		axis=1)

																		# call the callback, if provided
																		if callback is not None and i % callback_steps == 0:
																									callback(UpperCamelCase__			,		UpperCamelCase__)

																		logger.info("""Generated segment"""			,		UpperCamelCase__)

											if output_type == "numpy" and not is_onnx_available():
																		raise ValueError(
																		    """Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'.""")
											elif output_type == "numpy" and self.melgan is None:
																		raise ValueError(
																		    """Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'.""")

											if output_type == "numpy":
																		snake_case__	  =       self.melgan(input_features=full_pred_mel.astype(np.floataa))
											else:
																		snake_case__	  =       full_pred_mel

											if not return_dict:
																		return (output,)

											return AudioPipelineOutput(audios=UpperCamelCase__)
