






import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()





class 					A__     (     unittest.TestCase ):

     '''simple docstring'''






     snake_case__					=					StableDiffusionLDMaDPipeline
     snake_case__					=					TEXT_TO_IMAGE_PARAMS
     snake_case__					=					TEXT_TO_IMAGE_BATCH_PARAMS
     snake_case__					=					TEXT_TO_IMAGE_IMAGE_PARAMS







     def   _SCREAMING_SNAKE_CASE     ( self      :    Any ):






           """simple docstring"""




           torch.manual_seed(0 )
           UpperCamelCase					       =  UNetaDConditionModel(
               block_out_channels=(32, 64)   ,						layers_per_block=2   ,						sample_size=32   ,						in_channels=4   ,						out_channels=4   ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')   ,						up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')   ,						cross_attention_dim=32   ,						)
           UpperCamelCase					       =  DDIMScheduler(
               beta_start=0.0_0_0_8_5   ,						beta_end=0.0_1_2   ,						beta_schedule='scaled_linear'   ,						clip_sample=_SCREAMING_SNAKE_CASE   ,						set_alpha_to_one=_SCREAMING_SNAKE_CASE   ,						)
           torch.manual_seed(0 )
           UpperCamelCase					       =  AutoencoderKL(
               block_out_channels=[32, 64]   ,						in_channels=6   ,						out_channels=6   ,						down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,						up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,						latent_channels=4   ,						)
           torch.manual_seed(0 )
           UpperCamelCase					       =  CLIPTextConfig(
               bos_token_id=0   ,						eos_token_id=2   ,						hidden_size=32   ,						intermediate_size=37   ,						layer_norm_eps=1E-0_5   ,						num_attention_heads=4   ,						num_hidden_layers=5   ,						pad_token_id=1   ,						vocab_size=1000   ,						)
           UpperCamelCase					       =  CLIPTextModel(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

           UpperCamelCase					       =  {
               'unet': unet,
               'scheduler': scheduler,
               'vae': vae,
               'text_encoder': text_encoder,
               'tokenizer': tokenizer,
               'safety_checker': None,
               'feature_extractor': None,
           }
           return components







     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    str   ,						_SCREAMING_SNAKE_CASE      :    int=0 ):






           """simple docstring"""




           if str(_SCREAMING_SNAKE_CASE ).startswith('mps' ):
                 UpperCamelCase					       =  torch.manual_seed(_SCREAMING_SNAKE_CASE )
           else:
                 UpperCamelCase					       =  torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  {
               'prompt': 'A painting of a squirrel eating a burger',
               'generator': generator,
               'num_inference_steps': 2,
               'guidance_scale': 6.0,
               'output_type': 'numpy',
           }
           return inputs







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  'cpu'  # ensure determinism for the device-dependent torch.Generator

           UpperCamelCase					       =  self.get_dummy_components()
           UpperCamelCase					       =  StableDiffusionLDMaDPipeline(**_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  ldmad_pipe.to(_SCREAMING_SNAKE_CASE )
           ldmad_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  self.get_dummy_inputs(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  ldmad_pipe(**_SCREAMING_SNAKE_CASE )
           UpperCamelCase       ,  UpperCamelCase					       =  output.rgb, output.depth

           UpperCamelCase					       =  rgb[0, -3:, -3:, -1]
           UpperCamelCase					       =  depth[0, -3:, -1]

           assert rgb.shape == (1, 64, 64, 3)
           assert depth.shape == (1, 64, 64)

           UpperCamelCase					       =  np.array(
               [0.3_7_3_3_8_1_7_6, 0.7_0_2_4_7, 0.7_4_2_0_3_1_9_3, 0.5_1_6_4_3_6_0_4, 0.5_8_2_5_6_7_9_3, 0.6_0_9_3_2_1_3_6, 0.4_1_8_1_0_9_5, 0.4_8_3_5_5_8_7_7, 0.4_6_5_3_5_2_6_2] )
           UpperCamelCase					       =  np.array([1_0_3.4_6_7_2_7, 8_5.8_1_2_0_0_4, 8_7.8_4_9_2_3_6] )

           assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb ).max() < 1E-2
           assert np.abs(image_slice_depth.flatten() - expected_slice_depth ).max() < 1E-2







     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




           UpperCamelCase					       =  self.get_dummy_components()
           UpperCamelCase					       =  StableDiffusionLDMaDPipeline(**_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  ldmad_pipe.to(_SCREAMING_SNAKE_CASE )
           ldmad_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  self.get_dummy_inputs(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  3 * [inputs['prompt']]

           # forward
           UpperCamelCase					       =  ldmad_pipe(**_SCREAMING_SNAKE_CASE )
           UpperCamelCase       ,  UpperCamelCase					       =  output.rgb, output.depth
           UpperCamelCase					       =  rgb_slice_a[0, -3:, -3:, -1]
           UpperCamelCase					       =  depth_slice_a[0, -3:, -1]

           UpperCamelCase					       =  self.get_dummy_inputs(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  3 * [inputs.pop('prompt' )]

           UpperCamelCase					       =  ldmad_pipe.tokenizer(
               _SCREAMING_SNAKE_CASE   ,						padding='max_length'   ,						max_length=ldmad_pipe.tokenizer.model_max_length   ,						truncation=_SCREAMING_SNAKE_CASE   ,						return_tensors='pt'   ,						)
           UpperCamelCase					       =  text_inputs['input_ids'].to(_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  ldmad_pipe.text_encoder(_SCREAMING_SNAKE_CASE )[0]

           UpperCamelCase					       =  prompt_embeds

           # forward
           UpperCamelCase					       =  ldmad_pipe(**_SCREAMING_SNAKE_CASE )
           UpperCamelCase       ,  UpperCamelCase					       =  output.rgb, output.depth
           UpperCamelCase					       =  rgb_slice_a[0, -3:, -3:, -1]
           UpperCamelCase					       =  depth_slice_a[0, -3:, -1]

           assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten() ).max() < 1E-4
           assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten() ).max() < 1E-4







     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




           UpperCamelCase					       =  'cpu'  # ensure determinism for the device-dependent torch.Generator
           UpperCamelCase					       =  self.get_dummy_components()
           UpperCamelCase					       =  PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  StableDiffusionLDMaDPipeline(**_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  ldmad_pipe.to(_SCREAMING_SNAKE_CASE )
           ldmad_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  self.get_dummy_inputs(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  'french fries'
           UpperCamelCase					       =  ldmad_pipe(**_SCREAMING_SNAKE_CASE   ,						negative_prompt=_SCREAMING_SNAKE_CASE )

           UpperCamelCase       ,  UpperCamelCase					       =  output.rgb, output.depth
           UpperCamelCase					       =  rgb[0, -3:, -3:, -1]
           UpperCamelCase					       =  depth[0, -3:, -1]

           assert rgb.shape == (1, 64, 64, 3)
           assert depth.shape == (1, 64, 64)

           UpperCamelCase					       =  np.array(
               [0.3_7_0_4_4, 0.7_1_8_1_1_5_0_3, 0.7_2_2_3_2_5_1, 0.4_8_6_0_3_6_7_5, 0.5_6_3_8_3_9_1, 0.6_3_6_4_9_4_8, 0.4_2_8_3_3_7_0_4, 0.4_9_0_1_3_1_5, 0.4_7_9_2_6_2_1_7] )
           UpperCamelCase					       =  np.array([1_0_7.8_4_7_3_8, 8_4.6_2_8_0_2, 8_9.9_6_2_1_3_5] )
           assert np.abs(rgb_slice.flatten() - expected_slice_rgb ).max() < 1E-2
           assert np.abs(depth_slice.flatten() - expected_slice_depth ).max() < 1E-2





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

     '''simple docstring'''






     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




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







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[str]   ,						_SCREAMING_SNAKE_CASE      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    str="cpu"   ,						_SCREAMING_SNAKE_CASE      :    Tuple=torch.floataa   ,						_SCREAMING_SNAKE_CASE      :    Any=0 ):






           """simple docstring"""




           UpperCamelCase					       =  torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  np.random.RandomState(_SCREAMING_SNAKE_CASE ).standard_normal((1, 4, 64, 64) )
           UpperCamelCase					       =  torch.from_numpy(_SCREAMING_SNAKE_CASE ).to(device=_SCREAMING_SNAKE_CASE   ,						dtype=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  {
               'prompt': 'a photograph of an astronaut riding a horse',
               'latents': latents,
               'generator': generator,
               'num_inference_steps': 3,
               'guidance_scale': 7.5,
               'output_type': 'numpy',
           }
           return inputs







     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




           UpperCamelCase					       =  StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d' )
           UpperCamelCase					       =  ldmad_pipe.to(_SCREAMING_SNAKE_CASE )
           ldmad_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  self.get_inputs(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  ldmad_pipe(**_SCREAMING_SNAKE_CASE )
           UpperCamelCase       ,  UpperCamelCase					       =  output.rgb, output.depth
           UpperCamelCase					       =  rgb[0, -3:, -3:, -1].flatten()
           UpperCamelCase					       =  rgb[0, -3:, -1].flatten()

           assert rgb.shape == (1, 512, 512, 3)
           assert depth.shape == (1, 512, 512)

           UpperCamelCase					       =  np.array(
               [0.5_3_8_0_5_4_6_5, 0.5_6_7_0_7_3_0_5, 0.5_4_8_6_5_1_5, 0.5_7_0_1_2_2_3_6, 0.5_8_1_4_5_1_1, 0.5_6_2_5_3_4_8_7, 0.5_4_8_4_3_0_1_4, 0.5_5_0_9_2_2_6_3, 0.6_4_5_9_7_0_6] )
           UpperCamelCase					       =  np.array(
               [0.9_2_6_3_7_8_1, 0.6_6_7_8_6_7_2, 0.5_4_8_6_5_1_5, 0.9_2_2_0_2_1_4_5, 0.6_7_8_3_1_1_3_5, 0.5_6_2_5_3_4_8_7, 0.9_2_4_1_6_9_4, 0.7_5_5_1_4_7_8, 0.6_4_5_9_7_0_6] )
           assert np.abs(rgb_slice - expected_slice_rgb ).max() < 3E-3
           assert np.abs(depth_slice - expected_slice_depth ).max() < 3E-3





@nightly
@require_torch_gpu
class 					A__     (     unittest.TestCase ):

     '''simple docstring'''






     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




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







     def   _SCREAMING_SNAKE_CASE     ( self      :    str   ,						_SCREAMING_SNAKE_CASE      :    str   ,						_SCREAMING_SNAKE_CASE      :    Tuple="cpu"   ,						_SCREAMING_SNAKE_CASE      :    Dict=torch.floataa   ,						_SCREAMING_SNAKE_CASE      :    List[str]=0 ):






           """simple docstring"""




           UpperCamelCase					       =  torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  np.random.RandomState(_SCREAMING_SNAKE_CASE ).standard_normal((1, 4, 64, 64) )
           UpperCamelCase					       =  torch.from_numpy(_SCREAMING_SNAKE_CASE ).to(device=_SCREAMING_SNAKE_CASE   ,						dtype=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  {
               'prompt': 'a photograph of an astronaut riding a horse',
               'latents': latents,
               'generator': generator,
               'num_inference_steps': 50,
               'guidance_scale': 7.5,
               'output_type': 'numpy',
           }
           return inputs







     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           UpperCamelCase					       =  StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d' ).to(_SCREAMING_SNAKE_CASE )
           ldmad_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  self.get_inputs(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  ldmad_pipe(**_SCREAMING_SNAKE_CASE )
           UpperCamelCase       ,  UpperCamelCase					       =  output.rgb, output.depth

           UpperCamelCase					       =  0.4_9_5_5_8_6
           UpperCamelCase					       =  0.3_3_7_9_5_5_1_5
           UpperCamelCase					       =  1_1_2.4_8_5_1_8
           UpperCamelCase					       =  9_8.4_8_9_7_4_6
           assert np.abs(expected_rgb_mean - rgb.mean() ) < 1E-3
           assert np.abs(expected_rgb_std - rgb.std() ) < 1E-3
           assert np.abs(expected_depth_mean - depth.mean() ) < 1E-3
           assert np.abs(expected_depth_std - depth.std() ) < 1E-3







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d-4c' ).to(_SCREAMING_SNAKE_CASE )
           ldmad_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  self.get_inputs(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  ldmad_pipe(**_SCREAMING_SNAKE_CASE )
           UpperCamelCase       ,  UpperCamelCase					       =  output.rgb, output.depth

           UpperCamelCase					       =  0.4_1_9_4_1_2_7
           UpperCamelCase					       =  0.3_5_3_7_5_5_8_6
           UpperCamelCase					       =  0.5_6_3_8_5_0_2
           UpperCamelCase					       =  0.3_4_6_8_6_1_0_3

           assert rgb.shape == (1, 512, 512, 3)
           assert depth.shape == (1, 512, 512, 1)
           assert np.abs(expected_rgb_mean - rgb.mean() ) < 1E-3
           assert np.abs(expected_rgb_std - rgb.std() ) < 1E-3
           assert np.abs(expected_depth_mean - depth.mean() ) < 1E-3
           assert np.abs(expected_depth_std - depth.std() ) < 1E-3











def 		lowercase__  (				_UpperCamelCase) ->			Any:
      """simple docstring"""

      UpperCamelCase					       =  []
      UpperCamelCase					       =  []
      UpperCamelCase					       =  {
          '^': 3,
          '*': 2,
          '/': 2,
          '%': 2,
          '+': 1,
          '-': 1,
      }  # Priority of each operator
      UpperCamelCase					       =  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 		lowercase__  (				_UpperCamelCase) ->			Optional[Any]:
      """simple docstring"""

      UpperCamelCase					       =  list(infix[::-1])  # reverse the infix equation

      for i in range(len(_UpperCamelCase)):
            if infix[i] == "(":
                  UpperCamelCase					       =  ')'  # change "(" to ")"
            elif infix[i] == ")":
                  UpperCamelCase					       =  '('  # change ")" to "("

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


if __name__ == "__main__":
       __magic_name__   :  int        =							input('''\nEnter an Infix Equation = ''')  # Input an Infix equation
       __magic_name__   :  Optional[Any]        =							''''''.join(Infix.split())  # Remove spaces from the input
       print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')



