

'''simple docstring'''




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   (   __a		,unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:Any      	=					ShapEImgaImgPipeline
 __SCREAMING_SNAKE_CASE					:Dict      	=					["""image"""]
 __SCREAMING_SNAKE_CASE					:Tuple      	=					["""image"""]
 __SCREAMING_SNAKE_CASE					:List[str]      	=					[
     """num_images_per_prompt""",
     """num_inference_steps""",
     """generator""",
     """latents""",
     """guidance_scale""",
     """frame_size""",
     """output_type""",
     """return_dict""",
 ]
 __SCREAMING_SNAKE_CASE					:Any      	=					False



 @property
 def 			snake_case__  (   self		:   Optional[int]      ):
      return 32



 @property
 def 			snake_case__  (   self		:   int      ):
      return 32



 @property
 def 			snake_case__  (   self		:   int      ):
      return self.time_input_dim * 4



 @property
 def 			snake_case__  (   self		:   Tuple      ):
      return 8



 @property
 def 			snake_case__  (   self		:   List[str]      ):
      torch.manual_seed(0      )
      __magic_name__						       =    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			,				)

      __magic_name__						       =    CLIPVisionModel(a__      )
      return model



 @property
 def 			snake_case__  (   self		:   Union[str, Any]      ):
      __magic_name__						       =    CLIPImageProcessor(
          crop_size=224			,				do_center_crop=a__			,				do_normalize=a__			,				do_resize=a__			,				image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]			,				image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]			,				resample=3			,				size=224			,				)

      return image_processor



 @property
 def 			snake_case__  (   self		:   List[Any]      ):
      torch.manual_seed(0      )

      __magic_name__						       =    {
          '''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,
      }

      __magic_name__						       =    PriorTransformer(**a__      )
      return model



 @property
 def 			snake_case__  (   self		:   List[str]      ):
      torch.manual_seed(0      )

      __magic_name__						       =    {
          '''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,
          ),
      }
      __magic_name__						       =    ShapERenderer(**a__      )
      return model



 def 			snake_case__  (   self		:   Optional[Any]      ):
      __magic_name__						       =    self.dummy_prior
      __magic_name__						       =    self.dummy_image_encoder
      __magic_name__						       =    self.dummy_image_processor
      __magic_name__						       =    self.dummy_renderer

      __magic_name__						       =    HeunDiscreteScheduler(
          beta_schedule='''exp'''			,				num_train_timesteps=1024			,				prediction_type='''sample'''			,				use_karras_sigmas=a__			,				clip_sample=a__			,				clip_sample_range=1.0			,				)
      __magic_name__						       =    {
          '''prior''': prior,
          '''image_encoder''': image_encoder,
          '''image_processor''': image_processor,
          '''renderer''': renderer,
          '''scheduler''': scheduler,
      }

      return components



 def 			snake_case__  (   self		:   Optional[Any]			,				a__		:   List[Any]			,				a__		:   Union[str, Any]=0      ):
      __magic_name__						       =    floats_tensor((1, 3, 64, 64)			,				rng=random.Random(a__      )      ).to(a__      )

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



 def 			snake_case__  (   self		:   Dict      ):
      __magic_name__						       =    '''cpu'''

      __magic_name__						       =    self.get_dummy_components()

      __magic_name__						       =    self.pipeline_class(**a__      )
      __magic_name__						       =    pipe.to(a__      )

      pipe.set_progress_bar_config(disable=a__      )

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

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

      __magic_name__						       =    np.array(
          [
              0.00_039_216,
              0.00_039_216,
              0.00_039_216,
              0.00_039_216,
              0.00_039_216,
              0.00_039_216,
              0.00_039_216,
              0.00_039_216,
              0.00_039_216,
          ]      )

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



 def 			snake_case__  (   self		:   Optional[int]      ):
      # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
      self._test_inference_batch_consistent(batch_sizes=[1, 2]      )



 def 			snake_case__  (   self		:   Any      ):
      __magic_name__						       =    torch_device == '''cpu'''
      __magic_name__						       =    True
      self._test_inference_batch_single_identical(
          batch_size=2			,				test_max_difference=a__			,				relax_max_difference=a__			,				)



 def 			snake_case__  (   self		:   List[str]      ):
      __magic_name__						       =    self.get_dummy_components()
      __magic_name__						       =    self.pipeline_class(**a__      )
      __magic_name__						       =    pipe.to(a__      )
      pipe.set_progress_bar_config(disable=a__      )

      __magic_name__						       =    1
      __magic_name__						       =    2

      __magic_name__						       =    self.get_dummy_inputs(a__      )

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

      __magic_name__						       =    pipe(**a__			,				num_images_per_prompt=a__      )[0]

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






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



 def 			snake_case__  (   self		:   Tuple      ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()



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

      __magic_name__						       =    torch.Generator(device=a__      ).manual_seed(0      )

      __magic_name__						       =    pipe(
          a__			,				generator=a__			,				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(a__			,				a__      )









'''simple docstring'''




from __future__ import annotations

import math
import random
from typing import Any






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   Union[str, Any]      ):
      __magic_name__						       =    []
      __magic_name__						       =    0
      __magic_name__						       =    0



 def 			snake_case__  (   self		:   int      ):
      return self.head == self.tail



 def 			snake_case__  (   self		:   int			,				a__		:   Any      ):
      self.data.append(a__      )
      __magic_name__						       =    self.tail + 1



 def 			snake_case__  (   self		:   Tuple      ):
      __magic_name__						       =    self.data[self.head]
      __magic_name__						       =    self.head + 1
      return ret



 def 			snake_case__  (   self		:   Optional[Any]      ):
      return self.tail - self.head



 def 			snake_case__  (   self		:   List[Any]      ):
      print(self.data      )
      print('''**************'''      )
      print(self.data[self.head : self.tail]      )






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   List[str]			,				a__		:   Any      ):
      __magic_name__						       =    data
      __magic_name__						       =    None
      __magic_name__						       =    None
      __magic_name__						       =    1



 def 			snake_case__  (   self		:   Optional[int]      ):
      return self.data



 def 			snake_case__  (   self		:   List[Any]      ):
      return self.left



 def 			snake_case__  (   self		:   Tuple      ):
      return self.right



 def 			snake_case__  (   self		:   Any      ):
      return self.height



 def 			snake_case__  (   self		:   Optional[Any]			,				a__		:   Any      ):
      __magic_name__						       =    data



 def 			snake_case__  (   self		:   int			,				a__		:   MyNode | None      ):
      __magic_name__						       =    node



 def 			snake_case__  (   self		:   Tuple			,				a__		:   MyNode | None      ):
      __magic_name__						       =    node



 def 			snake_case__  (   self		:   List[str]			,				a__		:   int      ):
      __magic_name__						       =    height




def 						UpperCamelCase	(			a			)				->     int:






     '''simple docstring'''
     if node is None:
          return 0
     return node.get_height()




def 						UpperCamelCase	(			a		,						a			)				->     int:






     '''simple docstring'''
     if a > b:
          return a
     return b




def 						UpperCamelCase	(			a			)				->     MyNode:






     '''simple docstring'''
     print('''left rotation node:'''		,						node.get_data()			)
     __magic_name__						       =    node.get_left()
     assert ret is not None
     node.set_left(ret.get_right()			)
     ret.set_right(a			)
     __magic_name__						       =    my_max(get_height(node.get_right()			)		,						get_height(node.get_left()			)			) + 1
     node.set_height(a			)
     __magic_name__						       =    my_max(get_height(ret.get_right()			)		,						get_height(ret.get_left()			)			) + 1
     ret.set_height(a			)
     return ret




def 						UpperCamelCase	(			a			)				->     MyNode:






     '''simple docstring'''
     print('''right rotation node:'''		,						node.get_data()			)
     __magic_name__						       =    node.get_right()
     assert ret is not None
     node.set_right(ret.get_left()			)
     ret.set_left(a			)
     __magic_name__						       =    my_max(get_height(node.get_right()			)		,						get_height(node.get_left()			)			) + 1
     node.set_height(a			)
     __magic_name__						       =    my_max(get_height(ret.get_right()			)		,						get_height(ret.get_left()			)			) + 1
     ret.set_height(a			)
     return ret




def 						UpperCamelCase	(			a			)				->     MyNode:






     '''simple docstring'''
     __magic_name__						       =    node.get_left()
     assert left_child is not None
     node.set_left(left_rotation(a			)			)
     return right_rotation(a			)




def 						UpperCamelCase	(			a			)				->     MyNode:






     '''simple docstring'''
     __magic_name__						       =    node.get_right()
     assert right_child is not None
     node.set_right(right_rotation(a			)			)
     return left_rotation(a			)




def 						UpperCamelCase	(			a		,						a			)				->     MyNode | None:






     '''simple docstring'''
     if node is None:
          return MyNode(a			)
     if data < node.get_data():
          node.set_left(insert_node(node.get_left()		,						a			)			)
          if (
              get_height(node.get_left()			) - get_height(node.get_right()			) == 2
          ):  # an unbalance detected
               __magic_name__						       =    node.get_left()
               assert left_child is not None
               if (
                   data < left_child.get_data()
               ):  # new node is the left child of the left child
                    __magic_name__						       =    right_rotation(a			)
               else:
                    __magic_name__						       =    lr_rotation(a			)
     else:
          node.set_right(insert_node(node.get_right()		,						a			)			)
          if get_height(node.get_right()			) - get_height(node.get_left()			) == 2:
               __magic_name__						       =    node.get_right()
               assert right_child is not None
               if data < right_child.get_data():
                    __magic_name__						       =    rl_rotation(a			)
               else:
                    __magic_name__						       =    left_rotation(a			)
     __magic_name__						       =    my_max(get_height(node.get_right()			)		,						get_height(node.get_left()			)			) + 1
     node.set_height(a			)
     return node




def 						UpperCamelCase	(			a			)				->     Any:






     '''simple docstring'''
     while True:
          __magic_name__						       =    root.get_right()
          if right_child is None:
               break
          __magic_name__						       =    right_child
     return root.get_data()




def 						UpperCamelCase	(			a			)				->     Any:






     '''simple docstring'''
     while True:
          __magic_name__						       =    root.get_left()
          if left_child is None:
               break
          __magic_name__						       =    left_child
     return root.get_data()




def 						UpperCamelCase	(			a		,						a			)				->     MyNode | None:






     '''simple docstring'''
     __magic_name__						       =    root.get_left()
     __magic_name__						       =    root.get_right()
     if root.get_data() == data:
          if left_child is not None and right_child is not None:
               __magic_name__						       =    get_left_most(a			)
               root.set_data(a			)
               root.set_right(del_node(a		,						a			)			)
          elif left_child is not None:
               __magic_name__						       =    left_child
          elif right_child is not None:
               __magic_name__						       =    right_child
          else:
               return None
     elif root.get_data() > data:
          if left_child is None:
               print('''No such data'''			)
               return root
          else:
               root.set_left(del_node(a		,						a			)			)
     else:  # root.get_data() < data
          if right_child is None:
               return root
          else:
               root.set_right(del_node(a		,						a			)			)

     if get_height(a			) - get_height(a			) == 2:
          assert right_child is not None
          if get_height(right_child.get_right()			) > get_height(right_child.get_left()			):
               __magic_name__						       =    left_rotation(a			)
          else:
               __magic_name__						       =    rl_rotation(a			)
     elif get_height(a			) - get_height(a			) == -2:
          assert left_child is not None
          if get_height(left_child.get_left()			) > get_height(left_child.get_right()			):
               __magic_name__						       =    right_rotation(a			)
          else:
               __magic_name__						       =    lr_rotation(a			)
     __magic_name__						       =    my_max(get_height(root.get_right()			)		,						get_height(root.get_left()			)			) + 1
     root.set_height(a			)
     return root






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   List[Any]      ):
      __magic_name__						       =    None



 def 			snake_case__  (   self		:   List[Any]      ):
      return get_height(self.root      )



 def 			snake_case__  (   self		:   Optional[int]			,				a__		:   Any      ):
      print('''insert:''' + str(a__      )      )
      __magic_name__						       =    insert_node(self.root			,				a__      )



 def 			snake_case__  (   self		:   Dict			,				a__		:   Any      ):
      print('''delete:''' + str(a__      )      )
      if self.root is None:
           print('''Tree is empty!'''      )
           return
      __magic_name__						       =    del_node(self.root			,				a__      )



 def __str__(   self		:   Optional[Any]			,				):  # a level traversale, gives a more intuitive look on the tree
      __magic_name__						       =    ''''''
      __magic_name__						       =    MyQueue()
      q.push(self.root      )
      __magic_name__						       =    self.get_height()
      if layer == 0:
           return output
      __magic_name__						       =    0
      while not q.is_empty():
           __magic_name__						       =    q.pop()
           __magic_name__						       =    ''' ''' * int(math.pow(2			,				layer - 1      )      )
           output += space
           if node is None:
                output += "*"
                q.push(a__      )
                q.push(a__      )
           else:
                output += str(node.get_data()      )
                q.push(node.get_left()      )
                q.push(node.get_right()      )
           output += space
           __magic_name__						       =    cnt + 1
           for i in range(100      ):
                if cnt == math.pow(2			,				a__      ) - 1:
                     __magic_name__						       =    layer - 1
                     if layer == 0:
                          output += "\n*************************************"
                          return output
                     output += "\n"
                     break
      output += "\n*************************************"
      return output




def 						UpperCamelCase	(			)				->     None:






     '''simple docstring'''
     import doctest

     doctest.testmod()


if __name__ == "__main__":
      _test()
      _lowerCAmelCase         =						AVLtree()
      _lowerCAmelCase         =						list(range(10))
      random.shuffle(lst)
      for i in lst:
            t.insert(i)
            print(str(t))
      random.shuffle(lst)
      for i in lst:
            t.del_node(i)
            print(str(t))






