




'''simple docstring'''

import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np






class 		a							:


				def     A_					(						self       :   Tuple   ):
									torch.manual_seed(0   )
									snake_case_		     =							TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''   )

									torch.manual_seed(0   )
									snake_case_		     =							AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''   )

									torch.manual_seed(0   )
									snake_case_		     =							UNetaDConditionModel(
									    sample_size=32			,							layers_per_block=1			,							block_out_channels=[32, 64]			,							down_block_types=[
									        '''ResnetDownsampleBlock2D''',
									        '''SimpleCrossAttnDownBlock2D''',
									    ]			,							mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''			,							up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']			,							in_channels=3			,							out_channels=6			,							cross_attention_dim=32			,							encoder_hid_dim=32			,							attention_head_dim=8			,							addition_embed_type='''text'''			,							addition_embed_type_num_heads=2			,							cross_attention_norm='''group_norm'''			,							resnet_time_scale_shift='''scale_shift'''			,							act_fn='''gelu'''			,							)
									unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

									torch.manual_seed(0   )
									snake_case_		     =							DDPMScheduler(
									    num_train_timesteps=1000			,							beta_schedule='''squaredcos_cap_v2'''			,							beta_start=0.0001			,							beta_end=0.02			,							thresholding=lowercase_			,							dynamic_thresholding_ratio=0.95			,							sample_max_value=1.0			,							prediction_type='''epsilon'''			,							variance_type='''learned_range'''			,							)

									torch.manual_seed(0   )
									snake_case_		     =							IFWatermarker()

									return {
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "unet": unet,
									    "scheduler": scheduler,
									    "watermarker": watermarker,
									    "safety_checker": None,
									    "feature_extractor": None,
									}


				def     A_					(						self       :   Union[str, Any]   ):
									torch.manual_seed(0   )
									snake_case_		     =							TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''   )

									torch.manual_seed(0   )
									snake_case_		     =							AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''   )

									torch.manual_seed(0   )
									snake_case_		     =							UNetaDConditionModel(
									    sample_size=32			,							layers_per_block=[1, 2]			,							block_out_channels=[32, 64]			,							down_block_types=[
									        '''ResnetDownsampleBlock2D''',
									        '''SimpleCrossAttnDownBlock2D''',
									    ]			,							mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''			,							up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']			,							in_channels=6			,							out_channels=6			,							cross_attention_dim=32			,							encoder_hid_dim=32			,							attention_head_dim=8			,							addition_embed_type='''text'''			,							addition_embed_type_num_heads=2			,							cross_attention_norm='''group_norm'''			,							resnet_time_scale_shift='''scale_shift'''			,							act_fn='''gelu'''			,							class_embed_type='''timestep'''			,							mid_block_scale_factor=1.414			,							time_embedding_act_fn='''gelu'''			,							time_embedding_dim=32			,							)
									unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

									torch.manual_seed(0   )
									snake_case_		     =							DDPMScheduler(
									    num_train_timesteps=1000			,							beta_schedule='''squaredcos_cap_v2'''			,							beta_start=0.0001			,							beta_end=0.02			,							thresholding=lowercase_			,							dynamic_thresholding_ratio=0.95			,							sample_max_value=1.0			,							prediction_type='''epsilon'''			,							variance_type='''learned_range'''			,							)

									torch.manual_seed(0   )
									snake_case_		     =							DDPMScheduler(
									    num_train_timesteps=1000			,							beta_schedule='''squaredcos_cap_v2'''			,							beta_start=0.0001			,							beta_end=0.02			,							)

									torch.manual_seed(0   )
									snake_case_		     =							IFWatermarker()

									return {
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "unet": unet,
									    "scheduler": scheduler,
									    "image_noising_scheduler": image_noising_scheduler,
									    "watermarker": watermarker,
									    "safety_checker": None,
									    "feature_extractor": None,
									}


				def     A_					(						self       :   Dict   ):
									snake_case_		     =							self.get_dummy_components()
									snake_case_		     =							self.pipeline_class(**lowercase_   )
									pipe.to(lowercase_   )
									pipe.set_progress_bar_config(disable=lowercase_   )

									snake_case_		     =							self.get_dummy_inputs(lowercase_   )

									snake_case_		     =							inputs['''prompt''']
									snake_case_		     =							inputs['''generator''']
									snake_case_		     =							inputs['''num_inference_steps''']
									snake_case_		     =							inputs['''output_type''']

									if "image" in inputs:
														snake_case_		     =							inputs['''image''']
									else:
														snake_case_		     =							None

									if "mask_image" in inputs:
														snake_case_		     =							inputs['''mask_image''']
									else:
														snake_case_		     =							None

									if "original_image" in inputs:
														snake_case_		     =							inputs['''original_image''']
									else:
														snake_case_		     =							None

									snake_case_     ,snake_case_		     =							pipe.encode_prompt(lowercase_   )

									# inputs with prompt converted to embeddings
									snake_case_		     =							{
									    '''prompt_embeds''': prompt_embeds,
									    '''negative_prompt_embeds''': negative_prompt_embeds,
									    '''generator''': generator,
									    '''num_inference_steps''': num_inference_steps,
									    '''output_type''': output_type,
									}

									if image is not None:
														snake_case_		     =							image

									if mask_image is not None:
														snake_case_		     =							mask_image

									if original_image is not None:
														snake_case_		     =							original_image

									# set all optional components to None
									for optional_component in pipe._optional_components:
														setattr(lowercase_			,							lowercase_			,							lowercase_   )

									snake_case_		     =							pipe(**lowercase_   )[0]

									with tempfile.TemporaryDirectory() as tmpdir:
														pipe.save_pretrained(lowercase_   )
														snake_case_		     =							self.pipeline_class.from_pretrained(lowercase_   )
														pipe_loaded.to(lowercase_   )
														pipe_loaded.set_progress_bar_config(disable=lowercase_   )

									pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

									for optional_component in pipe._optional_components:
														self.assertTrue(
														    getattr(lowercase_			,							lowercase_   ) is None			,							F"`{optional_component}` did not stay set to None after loading."			,							)

									snake_case_		     =							self.get_dummy_inputs(lowercase_   )

									snake_case_		     =							inputs['''generator''']
									snake_case_		     =							inputs['''num_inference_steps''']
									snake_case_		     =							inputs['''output_type''']

									# inputs with prompt converted to embeddings
									snake_case_		     =							{
									    '''prompt_embeds''': prompt_embeds,
									    '''negative_prompt_embeds''': negative_prompt_embeds,
									    '''generator''': generator,
									    '''num_inference_steps''': num_inference_steps,
									    '''output_type''': output_type,
									}

									if image is not None:
														snake_case_		     =							image

									if mask_image is not None:
														snake_case_		     =							mask_image

									if original_image is not None:
														snake_case_		     =							original_image

									snake_case_		     =							pipe_loaded(**lowercase_   )[0]

									snake_case_		     =							np.abs(to_np(lowercase_   ) - to_np(lowercase_   )   ).max()
									self.assertLess(lowercase_			,							1e-4   )


				def     A_					(						self       :   int   ):
									snake_case_		     =							self.get_dummy_components()
									snake_case_		     =							self.pipeline_class(**lowercase_   )
									pipe.to(lowercase_   )
									pipe.set_progress_bar_config(disable=lowercase_   )

									snake_case_		     =							self.get_dummy_inputs(lowercase_   )
									snake_case_		     =							pipe(**lowercase_   )[0]

									with tempfile.TemporaryDirectory() as tmpdir:
														pipe.save_pretrained(lowercase_   )
														snake_case_		     =							self.pipeline_class.from_pretrained(lowercase_   )
														pipe_loaded.to(lowercase_   )
														pipe_loaded.set_progress_bar_config(disable=lowercase_   )

									pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

									snake_case_		     =							self.get_dummy_inputs(lowercase_   )
									snake_case_		     =							pipe_loaded(**lowercase_   )[0]

									snake_case_		     =							np.abs(to_np(lowercase_   ) - to_np(lowercase_   )   ).max()
									self.assertLess(lowercase_			,							1e-4   )







'''simple docstring'''

import operator as op


def 						__magic_name__       (      __UpperCAmelCase      )						->   Dict:

					'''simple docstring'''

					snake_case_		     =							[]
					snake_case_		     =							lambda __UpperCAmelCase, __UpperCAmelCase      : 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(__UpperCAmelCase      ))      )

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

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

															stack.append(
															    str(opr[x](int(__UpperCAmelCase      ), int(__UpperCAmelCase      )      )      )      )  # 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(__UpperCAmelCase      ), sep=''' | ''', )

					return int(stack[0]      )


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

