
"""simple docstring"""




import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()





class  snake_case     (							__lowercase       ,			__lowercase       ,			__lowercase       ,			unittest.TestCase   ):
							UpperCAmelCase__						     =      StableUnCLIPPipeline
							UpperCAmelCase__						     =      TEXT_TO_IMAGE_PARAMS
							UpperCAmelCase__						     =      TEXT_TO_IMAGE_BATCH_PARAMS
							UpperCAmelCase__						     =      TEXT_TO_IMAGE_IMAGE_PARAMS
							UpperCAmelCase__						     =      TEXT_TO_IMAGE_IMAGE_PARAMS

							# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
							UpperCAmelCase__						     =      False

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 32
									SCREAMING_SNAKE_CASE_						 = embedder_hidden_size

									# prior components

									torch.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )

									torch.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = CLIPTextModelWithProjection(
									    CLIPTextConfig(
									        bos_token_id=0       ,       eos_token_id=2       ,       hidden_size=UpperCamelCase_       ,       projection_dim=UpperCamelCase_       ,       intermediate_size=37       ,       layer_norm_eps=1e-0_5       ,       num_attention_heads=4       ,       num_hidden_layers=5       ,       pad_token_id=1       ,       vocab_size=10_00       ,       )    )

									torch.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = PriorTransformer(
									    num_attention_heads=2       ,       attention_head_dim=12       ,       embedding_dim=UpperCamelCase_       ,       num_layers=1       ,       )

									torch.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = DDPMScheduler(
									    variance_type='''fixed_small_log'''       ,       prediction_type='''sample'''       ,       num_train_timesteps=10_00       ,       clip_sample=UpperCamelCase_       ,       clip_sample_range=5.0       ,       beta_schedule='''squaredcos_cap_v2'''       ,       )

									# regular denoising components

									torch.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = StableUnCLIPImageNormalizer(embedding_dim=UpperCamelCase_    )
									SCREAMING_SNAKE_CASE_						 = DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''    )

									torch.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )

									torch.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = CLIPTextModel(
									    CLIPTextConfig(
									        bos_token_id=0       ,       eos_token_id=2       ,       hidden_size=UpperCamelCase_       ,       projection_dim=32       ,       intermediate_size=37       ,       layer_norm_eps=1e-0_5       ,       num_attention_heads=4       ,       num_hidden_layers=5       ,       pad_token_id=1       ,       vocab_size=10_00       ,       )    )

									torch.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = UNetaDConditionModel(
									    sample_size=32       ,       in_channels=4       ,       out_channels=4       ,       down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')       ,       up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')       ,       block_out_channels=(32, 64)       ,       attention_head_dim=(2, 4)       ,       class_embed_type='''projection'''       ,       projection_class_embeddings_input_dim=embedder_projection_dim * 2       ,       cross_attention_dim=UpperCamelCase_       ,       layers_per_block=1       ,       upcast_attention=UpperCamelCase_       ,       use_linear_projection=UpperCamelCase_       ,       )

									torch.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = DDIMScheduler(
									    beta_schedule='''scaled_linear'''       ,       beta_start=0.0_00_85       ,       beta_end=0.0_12       ,       prediction_type='''v_prediction'''       ,       set_alpha_to_one=UpperCamelCase_       ,       steps_offset=1       ,       )

									torch.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = AutoencoderKL()

									SCREAMING_SNAKE_CASE_						 = {
									    # prior components
									    "prior_tokenizer": prior_tokenizer,
									    "prior_text_encoder": prior_text_encoder,
									    "prior": prior,
									    "prior_scheduler": prior_scheduler,
									    # image noising components
									    "image_normalizer": image_normalizer,
									    "image_noising_scheduler": image_noising_scheduler,
									    # regular denoising components
									    "tokenizer": tokenizer,
									    "text_encoder": text_encoder,
									    "unet": unet,
									    "scheduler": scheduler,
									    "vae": vae,
									}

									return components

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=0    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = torch_device == "cpu"

									self._test_attention_slicing_forward_pass(test_max_difference=UpperCamelCase_    )







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = torch_device in ["cpu", "mps"]

									self._test_inference_batch_single_identical(test_max_difference=UpperCamelCase_    )







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

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'''    )

									SCREAMING_SNAKE_CASE_						 = StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''       ,       torch_dtype=torch.floataa    )
									pipe.to(UpperCamelCase_    )
									pipe.set_progress_bar_config(disable=UpperCamelCase_    )
									# stable unclip will oom when integration tests are run on a V100,
									# so turn on memory savings
									pipe.enable_attention_slicing()
									pipe.enable_sequential_cpu_offload()

									SCREAMING_SNAKE_CASE_						 = torch.Generator(device='''cpu'''    ).manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = pipe('''anime turle'''       ,       generator=UpperCamelCase_       ,       output_type='''np'''    )

									SCREAMING_SNAKE_CASE_						 = output.images[0]

									assert image.shape == (7_68, 7_68, 3)

									assert_mean_pixel_difference(UpperCamelCase_       ,       UpperCamelCase_    )







							def _lowercase					(self    ):

									"""simple docstring"""







									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									SCREAMING_SNAKE_CASE_						 = StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''       ,       torch_dtype=torch.floataa    )
									SCREAMING_SNAKE_CASE_						 = pipe.to(UpperCamelCase_    )
									pipe.set_progress_bar_config(disable=UpperCamelCase_    )
									pipe.enable_attention_slicing()
									pipe.enable_sequential_cpu_offload()

									SCREAMING_SNAKE_CASE_						 = pipe(
									    '''anime turtle'''       ,       prior_num_inference_steps=2       ,       num_inference_steps=2       ,       output_type='''np'''       ,       )

									SCREAMING_SNAKE_CASE_						 = torch.cuda.max_memory_allocated()
									# make sure that less than 7 GB is allocated
									assert mem_bytes < 7 * 10**9

"""simple docstring"""




from collections.abc import Sequence
from queue import Queue





class  snake_case     :

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

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = start
									SCREAMING_SNAKE_CASE_						 = end
									SCREAMING_SNAKE_CASE_						 = val
									SCREAMING_SNAKE_CASE_						 = (start + end) // 2
									SCREAMING_SNAKE_CASE_						 = left
									SCREAMING_SNAKE_CASE_						 = right







							def __repr__(self    ):

									"""simple docstring"""







									return f'SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'







class  snake_case     :

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = collection
									SCREAMING_SNAKE_CASE_						 = function
									if self.collection:
											SCREAMING_SNAKE_CASE_						 = self._build_tree(0       ,       len(SCREAMING_SNAKE_CASE_    ) - 1    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									self._update_tree(self.root       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									return self._query_range(self.root       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									if start == end:
											return SegmentTreeNode(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       self.collection[start]    )
									SCREAMING_SNAKE_CASE_						 = (start + end) // 2
									SCREAMING_SNAKE_CASE_						 = self._build_tree(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = self._build_tree(mid + 1       ,       SCREAMING_SNAKE_CASE_    )
									return SegmentTreeNode(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       self.fn(left.val       ,       right.val    )       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									if node.start == i and node.end == i:
											SCREAMING_SNAKE_CASE_						 = val
											return
									if i <= node.mid:
											self._update_tree(node.left       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
									else:
											self._update_tree(node.right       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = self.fn(node.left.val       ,       node.right.val    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									if node.start == i and node.end == j:
											return node.val

									if i <= node.mid:
											if j <= node.mid:
													# range in left child tree
													return self._query_range(node.left       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											else:
													# range in left child tree and right child tree
													return self.fn(
													    self._query_range(node.left       ,       SCREAMING_SNAKE_CASE_       ,       node.mid    )       ,       self._query_range(node.right       ,       node.mid + 1       ,       SCREAMING_SNAKE_CASE_    )       ,       )
									else:
											# range in right child tree
											return self._query_range(node.right       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )







							def _lowercase					(self    ):

									"""simple docstring"""







									if self.root is not None:
											SCREAMING_SNAKE_CASE_						 = Queue()
											queue.put(self.root    )
											while not queue.empty():
													SCREAMING_SNAKE_CASE_						 = queue.get()
													yield node

													if node.left is not None:
															queue.put(node.left    )

													if node.right is not None:
															queue.put(node.right    )


if __name__ == "__main__":
						import operator

						for fn in [operator.add, max, min]:
												print('*' * 50)
												lowerCAmelCase__							=				SegmentTree([2, 1, 5, 3, 4], fn)
												for node in arr.traverse():
																		print(node)
												print()

												arr.update(1, 5)
												for node in arr.traverse():
																		print(node)
												print()

												print(arr.query_range(3, 4))  # 7
												print(arr.query_range(2, 2))  # 5
												print(arr.query_range(1, 3))  # 13
												print()