
'''simple docstring'''
from copy import deepcopy






class    UpperCAmelCase_				:





 def __init__(    self ,       lowercase_ = None ,       lowercase_ = None):

     if arr is None and size is not None:
         snake_case_ :    Tuple     			=						size
         snake_case_ :    List[Any]     			=						[0] * size
     elif arr is not None:
         self.init(lowercase_)
     else:
         raise ValueError("Either arr or size must be specified")





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    Tuple     			=						len(lowercase_)
     snake_case_ :    List[str]     			=						deepcopy(lowercase_)
     for i in range(1 ,       self.size):
         snake_case_ :    int     			=						self.next_(lowercase_)
         if j < self.size:
             self.tree[j] += self.tree[i]





 def        snake_case__		(    self):
     snake_case_ :    Optional[Any]     			=						self.tree[:]
     for i in range(self.size - 1 ,       0 ,       -1):
         snake_case_ :    str     			=						self.next_(lowercase_)
         if j < self.size:
             arr[j] -= arr[i]
     return arr





 @staticmethod
 def        snake_case__		(    lowercase_):
     return index + (index & (-index))





 @staticmethod
 def        snake_case__		(    lowercase_):
     return index - (index & (-index))





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_):
     if index == 0:
         self.tree[0] += value
         return
     while index < self.size:
         self.tree[index] += value
         snake_case_ :    Union[str, Any]     			=						self.next_(lowercase_)





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_):
     self.add(lowercase_ ,       value - self.get(lowercase_))





 def        snake_case__		(    self ,       lowercase_):
     if right == 0:
         return 0
     snake_case_ :    List[Any]     			=						self.tree[0]
     right -= 1  # make right inclusive
     while right > 0:
         result += self.tree[right]
         snake_case_ :    List[str]     			=						self.prev(lowercase_)
     return result





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_):
     return self.prefix(lowercase_) - self.prefix(lowercase_)





 def        snake_case__		(    self ,       lowercase_):
     return self.query(lowercase_ ,       index + 1)





 def        snake_case__		(    self ,       lowercase_):
     value -= self.tree[0]
     if value < 0:
         return -1

     snake_case_ :    int     			=						1  # Largest power of 2 <= size
     while j * 2 < self.size:
         j *= 2

     snake_case_ :    int     			=						0

     while j > 0:
         if i + j < self.size and self.tree[i + j] <= value:
             value -= self.tree[i + j]
             i += j
         j //= 2
     return i


if __name__ == "__main__":
   import doctest

   doctest.testmod()






'''simple docstring'''
import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


a_											=						logging.get_logger(__name__)

enable_full_determinism()






class    UpperCAmelCase_				(	snake_case__    ,						snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						UNetaDModel
 UpperCAmelCase_		    =						"""sample"""





 @property
 def        snake_case__		(    self):
     snake_case_ :    Optional[Any]     			=						4
     snake_case_ :    int     			=						3
     snake_case_ :    Dict     			=						(32, 32)

     snake_case_ :    str     			=						floats_tensor((batch_size, num_channels) + sizes).to(lowercase_)
     snake_case_ :    Union[str, Any]     			=						torch.tensor([10]).to(lowercase_)

     return {"sample": noise, "timestep": time_step}





 @property
 def        snake_case__		(    self):
     return (3, 32, 32)





 @property
 def        snake_case__		(    self):
     return (3, 32, 32)





 def        snake_case__		(    self):
     snake_case_ :    str     			=						{
         "block_out_channels": (32, 64),
         "down_block_types": ("DownBlock2D", "AttnDownBlock2D"),
         "up_block_types": ("AttnUpBlock2D", "UpBlock2D"),
         "attention_head_dim": 3,
         "out_channels": 3,
         "in_channels": 3,
         "layers_per_block": 2,
         "sample_size": 32,
     }
     snake_case_ :    List[Any]     			=						self.dummy_input
     return init_dict, inputs_dict






class    UpperCAmelCase_				(	snake_case__    ,						snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						UNetaDModel
 UpperCAmelCase_		    =						"""sample"""





 @property
 def        snake_case__		(    self):
     snake_case_ :    List[Any]     			=						4
     snake_case_ :    Dict     			=						4
     snake_case_ :    Dict     			=						(32, 32)

     snake_case_ :    Dict     			=						floats_tensor((batch_size, num_channels) + sizes).to(lowercase_)
     snake_case_ :    Any     			=						torch.tensor([10]).to(lowercase_)

     return {"sample": noise, "timestep": time_step}





 @property
 def        snake_case__		(    self):
     return (4, 32, 32)





 @property
 def        snake_case__		(    self):
     return (4, 32, 32)





 def        snake_case__		(    self):
     snake_case_ :    Any     			=						{
         "sample_size": 32,
         "in_channels": 4,
         "out_channels": 4,
         "layers_per_block": 2,
         "block_out_channels": (32, 64),
         "attention_head_dim": 32,
         "down_block_types": ("DownBlock2D", "DownBlock2D"),
         "up_block_types": ("UpBlock2D", "UpBlock2D"),
     }
     snake_case_ :    Any     			=						self.dummy_input
     return init_dict, inputs_dict





 def        snake_case__		(    self):
     snake_case_			, snake_case_ :    Tuple     			=						UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" ,       output_loading_info=lowercase_)

     self.assertIsNotNone(lowercase_)
     self.assertEqual(len(loading_info["missing_keys"]) ,       0)

     model.to(lowercase_)
     snake_case_ :    int     			=						model(**self.dummy_input).sample

     assert image is not None, "Make sure output is not None"





 @unittest.skipIf(torch_device != "cuda" ,       "This test is supposed to run on GPU")
 def        snake_case__		(    self):
     snake_case_			, snake_case_ :    Optional[Any]     			=						UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" ,       output_loading_info=lowercase_)
     model.to(lowercase_)
     snake_case_ :    str     			=						model(**self.dummy_input).sample

     assert image is not None, "Make sure output is not None"





 @unittest.skipIf(torch_device != "cuda" ,       "This test is supposed to run on GPU")
 def        snake_case__		(    self):
     # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
     snake_case_			, snake_case_ :    Any     			=						UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" ,       output_loading_info=lowercase_)
     model_accelerate.to(lowercase_)
     model_accelerate.eval()

     snake_case_ :    Optional[int]     			=						torch.randn(
         1 ,       model_accelerate.config.in_channels ,       model_accelerate.config.sample_size ,       model_accelerate.config.sample_size ,       generator=torch.manual_seed(0) ,       )
     snake_case_ :    List[str]     			=						noise.to(lowercase_)
     snake_case_ :    Any     			=						torch.tensor([10] * noise.shape[0]).to(lowercase_)

     snake_case_ :    Optional[int]     			=						model_accelerate(lowercase_ ,       lowercase_)["sample"]

     # two models don't need to stay in the device at the same time
     del model_accelerate
     torch.cuda.empty_cache()
     gc.collect()

     snake_case_			, snake_case_ :    Union[str, Any]     			=						UNetaDModel.from_pretrained(
         "fusing/unet-ldm-dummy-update" ,       output_loading_info=lowercase_ ,       low_cpu_mem_usage=lowercase_)
     model_normal_load.to(lowercase_)
     model_normal_load.eval()
     snake_case_ :    Optional[Any]     			=						model_normal_load(lowercase_ ,       lowercase_)["sample"]

     assert torch_all_close(lowercase_ ,       lowercase_ ,       rtol=1E-3)





 def        snake_case__		(    self):
     snake_case_ :    Any     			=						UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update")
     model.eval()
     model.to(lowercase_)

     snake_case_ :    List[str]     			=						torch.randn(
         1 ,       model.config.in_channels ,       model.config.sample_size ,       model.config.sample_size ,       generator=torch.manual_seed(0) ,       )
     snake_case_ :    List[str]     			=						noise.to(lowercase_)
     snake_case_ :    str     			=						torch.tensor([10] * noise.shape[0]).to(lowercase_)

     with torch.no_grad():
         snake_case_ :    Tuple     			=						model(lowercase_ ,       lowercase_).sample

     snake_case_ :    Optional[int]     			=						output[0, -1, -3:, -3:].flatten().cpu()
     # fmt: off
     snake_case_ :    Tuple     			=						torch.tensor([-13.3_258, -20.1_100, -15.9_873, -17.6_617, -23.0_596, -17.9_419, -13.3_675, -16.1_889, -12.3_800])
     # fmt: on

     self.assertTrue(torch_all_close(lowercase_ ,       lowercase_ ,       rtol=1E-3))






class    UpperCAmelCase_				(	snake_case__    ,						snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						UNetaDModel
 UpperCAmelCase_		    =						"""sample"""





 @property
 def        snake_case__		(    self ,       lowercase_=(32, 32)):
     snake_case_ :    List[Any]     			=						4
     snake_case_ :    str     			=						3

     snake_case_ :    str     			=						floats_tensor((batch_size, num_channels) + sizes).to(lowercase_)
     snake_case_ :    Dict     			=						torch.tensor(batch_size * [10]).to(dtype=torch.intaa ,       device=lowercase_)

     return {"sample": noise, "timestep": time_step}





 @property
 def        snake_case__		(    self):
     return (3, 32, 32)





 @property
 def        snake_case__		(    self):
     return (3, 32, 32)





 def        snake_case__		(    self):
     snake_case_ :    List[str]     			=						{
         "block_out_channels": [32, 64, 64, 64],
         "in_channels": 3,
         "layers_per_block": 1,
         "out_channels": 3,
         "time_embedding_type": "fourier",
         "norm_eps": 1E-6,
         "mid_block_scale_factor": math.sqrt(2.0),
         "norm_num_groups": None,
         "down_block_types": [
             "SkipDownBlock2D",
             "AttnSkipDownBlock2D",
             "SkipDownBlock2D",
             "SkipDownBlock2D",
         ],
         "up_block_types": [
             "SkipUpBlock2D",
             "SkipUpBlock2D",
             "AttnSkipUpBlock2D",
             "SkipUpBlock2D",
         ],
     }
     snake_case_ :    Optional[Any]     			=						self.dummy_input
     return init_dict, inputs_dict





 @slow
 def        snake_case__		(    self):
     snake_case_			, snake_case_ :    Optional[int]     			=						UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256" ,       output_loading_info=lowercase_)
     self.assertIsNotNone(lowercase_)
     self.assertEqual(len(loading_info["missing_keys"]) ,       0)

     model.to(lowercase_)
     snake_case_ :    Dict     			=						self.dummy_input
     snake_case_ :    Tuple     			=						floats_tensor((4, 3) + (2_56, 2_56)).to(lowercase_)
     snake_case_ :    Tuple     			=						noise
     snake_case_ :    Tuple     			=						model(**lowercase_)

     assert image is not None, "Make sure output is not None"





 @slow
 def        snake_case__		(    self):
     snake_case_ :    Dict     			=						UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256")
     model.to(lowercase_)

     snake_case_ :    List[Any]     			=						4
     snake_case_ :    str     			=						3
     snake_case_ :    Dict     			=						(2_56, 2_56)

     snake_case_ :    Tuple     			=						torch.ones((batch_size, num_channels) + sizes).to(lowercase_)
     snake_case_ :    List[Any]     			=						torch.tensor(batch_size * [1E-4]).to(lowercase_)

     with torch.no_grad():
         snake_case_ :    int     			=						model(lowercase_ ,       lowercase_).sample

     snake_case_ :    List[str]     			=						output[0, -3:, -3:, -1].flatten().cpu()
     # fmt: off
     snake_case_ :    Union[str, Any]     			=						torch.tensor([-4_842.8_691, -6_499.6_631, -3_800.1_953, -7_978.2_686, -10_980.7_129, -20_028.8_535, 8_148.2_822, 2_342.2_905, 567.7_608])
     # fmt: on

     self.assertTrue(torch_all_close(lowercase_ ,       lowercase_ ,       rtol=1E-2))





 def        snake_case__		(    self):
     snake_case_ :    List[Any]     			=						UNetaDModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update")
     model.to(lowercase_)

     snake_case_ :    Dict     			=						4
     snake_case_ :    str     			=						3
     snake_case_ :    List[Any]     			=						(32, 32)

     snake_case_ :    int     			=						torch.ones((batch_size, num_channels) + sizes).to(lowercase_)
     snake_case_ :    List[Any]     			=						torch.tensor(batch_size * [1E-4]).to(lowercase_)

     with torch.no_grad():
         snake_case_ :    Optional[Any]     			=						model(lowercase_ ,       lowercase_).sample

     snake_case_ :    List[Any]     			=						output[0, -3:, -3:, -1].flatten().cpu()
     # fmt: off
     snake_case_ :    Union[str, Any]     			=						torch.tensor([-0.0_325, -0.0_900, -0.0_869, -0.0_332, -0.0_725, -0.0_270, -0.0_101, 0.0_227, 0.0_256])
     # fmt: on

     self.assertTrue(torch_all_close(lowercase_ ,       lowercase_ ,       rtol=1E-2))





 def        snake_case__		(    self):
     # not required for this model
     pass




