

import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


class SCREAMING_SNAKE_CASE  (		__snake_case							,						unittest.TestCase							):






			"""simple docstring"""




			__A						  =  ShapEImgaImgPipeline
			__A						  =  ["""image"""]
			__A						  =  ["""image"""]
			__A						  =  [
			    """num_images_per_prompt""",
			    """num_inference_steps""",
			    """generator""",
			    """latents""",
			    """guidance_scale""",
			    """frame_size""",
			    """output_type""",
			    """return_dict""",
			]
			__A						  =  False


			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return 32


			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return 32


			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return self.time_input_dim * 4


			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return 8


			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				torch.manual_seed(0							)
				snake_case_      		= CLIPVisionConfig(
				    hidden_size=self.text_embedder_hidden_size ,				image_size=64 ,				projection_dim=self.text_embedder_hidden_size ,				intermediate_size=37 ,				num_attention_heads=4 ,				num_channels=3 ,				num_hidden_layers=5 ,				patch_size=1 ,				)

				snake_case_      		= CLIPVisionModel(__UpperCamelCase							)
				return model


			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= CLIPImageProcessor(
				    crop_size=2_24 ,				do_center_crop=__UpperCamelCase ,				do_normalize=__UpperCamelCase ,				do_resize=__UpperCamelCase ,				image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073] ,				image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711] ,				resample=3 ,				size=2_24 ,				)

				return image_processor


			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				torch.manual_seed(0							)

				snake_case_      		= {
				    'num_attention_heads': 2,
				    'attention_head_dim': 16,
				    'embedding_dim': self.time_input_dim,
				    'num_embeddings': 32,
				    'embedding_proj_dim': self.text_embedder_hidden_size,
				    'time_embed_dim': self.time_embed_dim,
				    'num_layers': 1,
				    'clip_embed_dim': self.time_input_dim * 2,
				    'additional_embeddings': 0,
				    'time_embed_act_fn': 'gelu',
				    'norm_in_type': 'layer',
				    'embedding_proj_norm_type': 'layer',
				    'encoder_hid_proj_type': None,
				    'added_emb_type': None,
				}

				snake_case_      		= PriorTransformer(**__UpperCamelCase							)
				return model


			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				torch.manual_seed(0							)

				snake_case_      		= {
				    'param_shapes': (
				        (self.renderer_dim, 93),
				        (self.renderer_dim, 8),
				        (self.renderer_dim, 8),
				        (self.renderer_dim, 8),
				    ),
				    'd_latent': self.time_input_dim,
				    'd_hidden': self.renderer_dim,
				    'n_output': 12,
				    'background': (
				        0.1,
				        0.1,
				        0.1,
				    ),
				}
				snake_case_      		= ShapERenderer(**__UpperCamelCase							)
				return model


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.dummy_prior
				snake_case_      		= self.dummy_image_encoder
				snake_case_      		= self.dummy_image_processor
				snake_case_      		= self.dummy_renderer

				snake_case_      		= HeunDiscreteScheduler(
				    beta_schedule='exp' ,				num_train_timesteps=10_24 ,				prediction_type='sample' ,				use_karras_sigmas=__UpperCamelCase ,				clip_sample=__UpperCamelCase ,				clip_sample_range=1.0 ,				)
				snake_case_      		= {
				    'prior': prior,
				    'image_encoder': image_encoder,
				    'image_processor': image_processor,
				    'renderer': renderer,
				    'scheduler': scheduler,
				}

				return components


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase=0							):

				"""simple docstring"""


				snake_case_      		= floats_tensor((1, 3, 64, 64) ,				rng=random.Random(__UpperCamelCase							)							).to(__UpperCamelCase							)

				if str(__UpperCamelCase							).startswith('mps'							):
					snake_case_      		= torch.manual_seed(__UpperCamelCase							)
				else:
					snake_case_      		= torch.Generator(device=__UpperCamelCase							).manual_seed(__UpperCamelCase							)
				snake_case_      		= {
				    'image': input_image,
				    'generator': generator,
				    'num_inference_steps': 1,
				    'frame_size': 32,
				    'output_type': 'np',
				}
				return inputs


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= 'cpu'

				snake_case_      		= self.get_dummy_components()

				snake_case_      		= self.pipeline_class(**__UpperCamelCase							)
				snake_case_      		= pipe.to(__UpperCamelCase							)

				pipe.set_progress_bar_config(disable=__UpperCamelCase							)

				snake_case_      		= pipe(**self.get_dummy_inputs(__UpperCamelCase							)							)
				snake_case_      		= output.images[0]
				snake_case_      		= image[0, -3:, -3:, -1]

				assert image.shape == (20, 32, 32, 3)

				snake_case_      		= np.array(
				    [
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				    ]							)

				assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				self._test_inference_batch_consistent(batch_sizes=[1, 2]							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= torch_device == 'cpu'
				snake_case_      		= True
				self._test_inference_batch_single_identical(
				    batch_size=2 ,				test_max_difference=__UpperCamelCase ,				relax_max_difference=__UpperCamelCase ,				)







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.get_dummy_components()
				snake_case_      		= self.pipeline_class(**__UpperCamelCase							)
				snake_case_      		= pipe.to(__UpperCamelCase							)
				pipe.set_progress_bar_config(disable=__UpperCamelCase							)

				snake_case_      		= 1
				snake_case_      		= 2

				snake_case_      		= self.get_dummy_inputs(__UpperCamelCase							)

				for key in inputs.keys():
					if key in self.batch_params:
						snake_case_      		= batch_size * [inputs[key]]

				snake_case_      		= pipe(**__UpperCamelCase ,				num_images_per_prompt=__UpperCamelCase							)[0]

				assert images.shape[0] == batch_size * num_images_per_prompt







@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png'							)
				snake_case_      		= load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/shap_e/test_shap_e_img2img_out.npy'							)
				snake_case_      		= ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img'							)
				snake_case_      		= pipe.to(__UpperCamelCase							)
				pipe.set_progress_bar_config(disable=__UpperCamelCase							)

				snake_case_      		= torch.Generator(device=__UpperCamelCase							).manual_seed(0							)

				snake_case_      		= pipe(
				    __UpperCamelCase ,				generator=__UpperCamelCase ,				guidance_scale=3.0 ,				num_inference_steps=64 ,				frame_size=64 ,				output_type='np' ,				).images[0]

				assert images.shape == (20, 64, 64, 3)

				assert_mean_pixel_difference(__UpperCamelCase ,				__UpperCamelCase							)



import operator as op





def a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= []
	snake_case_      		= lambda lowercase__			, lowercase__	: 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(lowercase__	))	)

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

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

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

	return int(stack[0]	)


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