






'''simple docstring'''
from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

a_					= TypeVar('T')






class      __SCREAMING_SNAKE_CASE      (			Generic[T]				):




 def __init__(      self	:							str					,      __lowercase	:							list[T]					,      __lowercase	:							Callable[[T, T], T]      )	->						None:
       SCREAMING_SNAKE_CASE__     :				Any | T									=None

       SCREAMING_SNAKE_CASE__     :				int									=len(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				list[T]									=[any_type for _ in range(self.N      )] + arr
       SCREAMING_SNAKE_CASE__     :				List[Any]									=fnc
       self.build()




 def      __magic_name__	(      self	:							List[Any]      )	->						None:
       for p in range(self.N - 1					,      0					,      -1      ):
             SCREAMING_SNAKE_CASE__     :				Any									=self.fn(self.st[p * 2]					,      self.st[p * 2 + 1]      )




 def      __magic_name__	(      self	:							Dict					,      __lowercase	:							int					,      __lowercase	:							T      )	->						None:
       p += self.N
       SCREAMING_SNAKE_CASE__     :				Dict									=v
       while p > 1:
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=p // 2
             SCREAMING_SNAKE_CASE__     :				int									=self.fn(self.st[p * 2]					,      self.st[p * 2 + 1]      )




 def      __magic_name__	(      self	:							Optional[int]					,      __lowercase	:							int					,      __lowercase	:							int      )	->						T | None:  # noqa: E741
       SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Tuple									=l + self.N, r + self.N

       SCREAMING_SNAKE_CASE__     :				T | None									=None
       while l <= r:
             if l % 2 == 1:
                   SCREAMING_SNAKE_CASE__     :				Tuple									=self.st[l] if res is None else self.fn(__lowercase					,      self.st[l]      )
             if r % 2 == 0:
                   SCREAMING_SNAKE_CASE__     :				List[Any]									=self.st[r] if res is None else self.fn(__lowercase					,      self.st[r]      )
             SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				str									=(l + 1) // 2, (r - 1) // 2
       return res


if __name__ == "__main__":
  from functools import reduce

  a_					= [1, 1_0, -2, 9, -3, 8, 4, -7, 5, 6, 1_1, -1_2]

  a_					= {
      0: 7,
      1: 2,
      2: 6,
      3: -1_4,
      4: 5,
      5: 4,
      6: 7,
      7: -1_0,
      8: 9,
      9: 1_0,
      1_0: 1_2,
      1_1: 1,
  }

  a_					= SegmentTree(test_array, min)
  a_					= SegmentTree(test_array, max)
  a_					= SegmentTree(test_array, lambda a, b: a + b)





  def  _a(  ):




        '''simple docstring'''






        for i in range(len(UpperCamelCase__							)							):
              for j in range(UpperCamelCase__,	len(UpperCamelCase__							)							):
                    SCREAMING_SNAKE_CASE__     :				Optional[int]									=reduce(UpperCamelCase__,	test_array[i : j + 1]							)
                    SCREAMING_SNAKE_CASE__     :				Optional[Any]									=reduce(UpperCamelCase__,	test_array[i : j + 1]							)
                    SCREAMING_SNAKE_CASE__     :				Any									=reduce(lambda UpperCamelCase__,	UpperCamelCase__							: a + b,	test_array[i : j + 1]							)
                    assert min_range == min_segment_tree.query(UpperCamelCase__,	UpperCamelCase__							)
                    assert max_range == max_segment_tree.query(UpperCamelCase__,	UpperCamelCase__							)
                    assert sum_range == sum_segment_tree.query(UpperCamelCase__,	UpperCamelCase__							)

  test_all_segments()

  for index, value in test_updates.items():
    a_					= value
    min_segment_tree.update(index, value)
    max_segment_tree.update(index, value)
    sum_segment_tree.update(index, value)
    test_all_segments()







'''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      (			lowerCamelCase	,				unittest.TestCase				):
 snake_case_  							=							ShapEImgaImgPipeline
 snake_case_  							=							["""image"""]
 snake_case_  							=							["""image"""]
 snake_case_  							=							[
     """num_images_per_prompt""",
     """num_inference_steps""",
     """generator""",
     """latents""",
     """guidance_scale""",
     """frame_size""",
     """output_type""",
     """return_dict""",
 ]
 snake_case_  							=							False




 @property
 def      __magic_name__	(      self	:							List[Any]      )	->						List[Any]:
       return 32




 @property
 def      __magic_name__	(      self	:							List[str]      )	->						Optional[int]:
       return 32




 @property
 def      __magic_name__	(      self	:							Optional[int]      )	->						Optional[Any]:
       return self.time_input_dim * 4




 @property
 def      __magic_name__	(      self	:							Dict      )	->						Union[str, Any]:
       return 8




 @property
 def      __magic_name__	(      self	:							Optional[int]      )	->						Union[str, Any]:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__     :				Dict									=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					,      )

       SCREAMING_SNAKE_CASE__     :				str									=CLIPVisionModel(__lowercase      )
       return model




 @property
 def      __magic_name__	(      self	:							Tuple      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				int									=CLIPImageProcessor(
           crop_size=2_24					,      do_center_crop=__lowercase					,      do_normalize=__lowercase					,      do_resize=__lowercase					,      image_mean=[0.48145466, 0.4578275, 0.40821073]					,      image_std=[0.26862954, 0.26130258, 0.27577711]					,      resample=3					,      size=2_24					,      )

       return image_processor




 @property
 def      __magic_name__	(      self	:							List[str]      )	->						Dict:
       torch.manual_seed(0      )

       SCREAMING_SNAKE_CASE__     :				str									={
           '''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,
       }

       SCREAMING_SNAKE_CASE__     :				str									=PriorTransformer(**__lowercase      )
       return model




 @property
 def      __magic_name__	(      self	:							Tuple      )	->						List[str]:
       torch.manual_seed(0      )

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									={
           '''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,
           ),
       }
       SCREAMING_SNAKE_CASE__     :				str									=ShapERenderer(**__lowercase      )
       return model




 def      __magic_name__	(      self	:							List[Any]      )	->						List[Any]:
       SCREAMING_SNAKE_CASE__     :				int									=self.dummy_prior
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.dummy_image_encoder
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.dummy_image_processor
       SCREAMING_SNAKE_CASE__     :				Tuple									=self.dummy_renderer

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

       return components




 def      __magic_name__	(      self	:							Any					,      __lowercase	:							List[str]					,      __lowercase	:							Any=0      )	->						Any:
       SCREAMING_SNAKE_CASE__     :				int									=floats_tensor((1, 3, 64, 64)					,      rng=random.Random(__lowercase      )      ).to(__lowercase      )

       if str(__lowercase      ).startswith('''mps'''      ):
             SCREAMING_SNAKE_CASE__     :				List[str]									=torch.manual_seed(__lowercase      )
       else:
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=torch.Generator(device=__lowercase      ).manual_seed(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									={
           '''image''': input_image,
           '''generator''': generator,
           '''num_inference_steps''': 1,
           '''frame_size''': 32,
           '''output_type''': '''np''',
       }
       return inputs




 def      __magic_name__	(      self	:							List[str]      )	->						str:
       SCREAMING_SNAKE_CASE__     :				int									='''cpu'''

       SCREAMING_SNAKE_CASE__     :				Any									=self.get_dummy_components()

       SCREAMING_SNAKE_CASE__     :				str									=self.pipeline_class(**__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									=pipe.to(__lowercase      )

       pipe.set_progress_bar_config(disable=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Dict									=pipe(**self.get_dummy_inputs(__lowercase      )      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=output.images[0]
       SCREAMING_SNAKE_CASE__     :				List[Any]									=image[0, -3:, -3:, -1]

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

       SCREAMING_SNAKE_CASE__     :				List[Any]									=np.array(
           [
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
           ]      )

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




 def      __magic_name__	(      self	:							List[Any]      )	->						List[str]:
       # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
       self._test_inference_batch_consistent(batch_sizes=[1, 2]      )




 def      __magic_name__	(      self	:							Optional[int]      )	->						str:
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=torch_device == '''cpu'''
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=True
       self._test_inference_batch_single_identical(
           batch_size=2					,      test_max_difference=__lowercase					,      relax_max_difference=__lowercase					,      )




 def      __magic_name__	(      self	:							Dict      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				Any									=self.get_dummy_components()
       SCREAMING_SNAKE_CASE__     :				Dict									=self.pipeline_class(**__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=pipe.to(__lowercase      )
       pipe.set_progress_bar_config(disable=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=1
       SCREAMING_SNAKE_CASE__     :				List[str]									=2

       SCREAMING_SNAKE_CASE__     :				Dict									=self.get_dummy_inputs(__lowercase      )

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

       SCREAMING_SNAKE_CASE__     :				List[Any]									=pipe(**__lowercase					,      num_images_per_prompt=__lowercase      )[0]

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






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




 def      __magic_name__	(      self	:							Optional[Any]      )	->						int:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def      __magic_name__	(      self	:							int      )	->						Dict:
       SCREAMING_SNAKE_CASE__     :				List[str]									=load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png'''      )
       SCREAMING_SNAKE_CASE__     :				Dict									=load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/shap_e/test_shap_e_img2img_out.npy'''      )
       SCREAMING_SNAKE_CASE__     :				List[Any]									=ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img'''      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=pipe.to(__lowercase      )
       pipe.set_progress_bar_config(disable=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Tuple									=torch.Generator(device=__lowercase      ).manual_seed(0      )

       SCREAMING_SNAKE_CASE__     :				Tuple									=pipe(
           __lowercase					,      generator=__lowercase					,      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(__lowercase					,      __lowercase      )