





import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class     A_		(			__a      ,      __a      ,      unittest.TestCase	):
			_A   :str  				=      StableDiffusionXLImgaImgPipeline
			_A   :Tuple  				=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
			_A   :Optional[Any]  				=      PipelineTesterMixin.required_optional_params - {'''latents'''}
			_A   :Optional[int]  				=      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			_A   :List[Any]  				=      IMAGE_TO_IMAGE_IMAGE_PARAMS
			_A   :List[Any]  				=      IMAGE_TO_IMAGE_IMAGE_PARAMS







			def 						SCREAMING_SNAKE_CASE__	(				self  :							Dict      ):
							torch.manual_seed(0      )
							lowercase							=     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""")   ,      attention_head_dim=(2, 4)   ,      use_linear_projection=snake_case_   ,      addition_embed_type="""text_time"""   ,      addition_time_embed_dim=8   ,      transformer_layers_per_block=(1, 2)   ,      projection_class_embeddings_input_dim=80   ,      cross_attention_dim=64   ,      )
							lowercase							=     EulerDiscreteScheduler(
							    beta_start=0.00_085   ,      beta_end=0.012   ,      steps_offset=1   ,      beta_schedule="""scaled_linear"""   ,      timestep_spacing="""leading"""   ,      )
							torch.manual_seed(0      )
							lowercase							=     AutoencoderKL(
							    block_out_channels=[32, 64]   ,      in_channels=3   ,      out_channels=3   ,      down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]   ,      up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]   ,      latent_channels=4   ,      sample_size=1_28   ,      )
							torch.manual_seed(0      )
							lowercase							=     CLIPTextConfig(
							    bos_token_id=0   ,      eos_token_id=2   ,      hidden_size=32   ,      intermediate_size=37   ,      layer_norm_eps=1E-05   ,      num_attention_heads=4   ,      num_hidden_layers=5   ,      pad_token_id=1   ,      vocab_size=10_00   ,      hidden_act="""gelu"""   ,      projection_dim=32   ,      )
							lowercase							=     CLIPTextModel(snake_case_      )
							lowercase							=     CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   ,      local_files_only=snake_case_      )

							lowercase							=     CLIPTextModelWithProjection(snake_case_      )
							lowercase							=     CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   ,      local_files_only=snake_case_      )

							lowercase							=     {
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """vae""": vae,
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """text_encoder_2""": text_encoder_a,
							    """tokenizer_2""": tokenizer_a,
							    # "safety_checker": None,
							    # "feature_extractor": None,
							}
							return components







			def 						SCREAMING_SNAKE_CASE__	(				self  :							Optional[int]   ,      snake_case__  :							Union[str, Any]   ,      snake_case__  :							Tuple=0      ):
							lowercase							=     floats_tensor((1, 3, 32, 32)   ,      rng=random.Random(snake_case_      )      ).to(snake_case_      )
							lowercase							=     image / 2 + 0.5
							if str(snake_case_      ).startswith("""mps"""      ):
											lowercase							=     torch.manual_seed(snake_case_      )
							else:
											lowercase							=     torch.Generator(device=snake_case_      ).manual_seed(snake_case_      )
							lowercase							=     {
							    """prompt""": """A painting of a squirrel eating a burger""",
							    """image""": image,
							    """generator""": generator,
							    """num_inference_steps""": 2,
							    """guidance_scale""": 5.0,
							    """output_type""": """numpy""",
							    """strength""": 0.75,
							}
							return inputs







			def 						SCREAMING_SNAKE_CASE__	(				self  :							Optional[Any]      ):
							lowercase							=     """cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowercase							=     self.get_dummy_components()
							lowercase							=     StableDiffusionXLImgaImgPipeline(**snake_case_      )
							lowercase							=     sd_pipe.to(snake_case_      )
							sd_pipe.set_progress_bar_config(disable=snake_case_      )

							lowercase							=     self.get_dummy_inputs(snake_case_      )
							lowercase							=     sd_pipe(**snake_case_      ).images
							lowercase							=     image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)

							lowercase							=     np.array([0.4_656, 0.4_840, 0.4_439, 0.6_698, 0.5_574, 0.4_524, 0.5_799, 0.5_943, 0.5_165]      )

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







			def 						SCREAMING_SNAKE_CASE__	(				self  :							Optional[Any]      ):
							super().test_attention_slicing_forward_pass(expected_max_diff=3E-3      )







			def 						SCREAMING_SNAKE_CASE__	(				self  :							Union[str, Any]      ):
							super().test_inference_batch_single_identical(expected_max_diff=3E-3      )







			def 						SCREAMING_SNAKE_CASE__	(				self  :							List[Any]      ):
							pass







			def 						SCREAMING_SNAKE_CASE__	(				self  :							List[Any]      ):
							lowercase							=     self.get_dummy_components()
							lowercase							=     StableDiffusionXLImgaImgPipeline(**snake_case_      )
							lowercase							=     sd_pipe.to(snake_case_      )
							lowercase							=     sd_pipe.to(snake_case_      )
							sd_pipe.set_progress_bar_config(disable=snake_case_      )

							# forward without prompt embeds
							lowercase							=     self.get_dummy_inputs(snake_case_      )
							lowercase							=     3 * ["""this is a negative prompt"""]
							lowercase							=     negative_prompt
							lowercase							=     3 * [inputs["""prompt"""]]

							lowercase							=     sd_pipe(**snake_case_      )
							lowercase							=     output.images[0, -3:, -3:, -1]

							# forward with prompt embeds
							lowercase							=     self.get_dummy_inputs(snake_case_      )
							lowercase							=     3 * ["""this is a negative prompt"""]
							lowercase							=     3 * [inputs.pop("""prompt"""      )]

							(
							    (
							    lowercase						
							)					,    (
							    lowercase						
							)					,    (
							    lowercase						
							)					,    (
							    lowercase						
							)					,    						
							)							=     sd_pipe.encode_prompt(snake_case_   ,      negative_prompt=snake_case_      )

							lowercase							=     sd_pipe(
							    **snake_case_   ,      prompt_embeds=snake_case_   ,      negative_prompt_embeds=snake_case_   ,      pooled_prompt_embeds=snake_case_   ,      negative_pooled_prompt_embeds=snake_case_   ,      )
							lowercase							=     output.images[0, -3:, -3:, -1]

							# make sure that it's equal
							assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()      ).max() < 1E-4

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







			def 						SCREAMING_SNAKE_CASE__	(				self  :							str      ):
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()







			def 						SCREAMING_SNAKE_CASE__	(				self  :							Optional[int]   ,      snake_case__  :							List[Any]   ,      snake_case__  :							Any="cpu"   ,      snake_case__  :							Tuple=torch.floataa   ,      snake_case__  :							Dict=0      ):
							lowercase							=     torch.Generator(device=snake_case_      ).manual_seed(snake_case_      )
							lowercase							=     np.random.RandomState(snake_case_      ).standard_normal((1, 4, 64, 64)      )
							lowercase							=     torch.from_numpy(snake_case_      ).to(device=snake_case_   ,      dtype=snake_case_      )
							lowercase							=     {
							    """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      ):
							lowercase							=     DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base"""      )
							pipe.to(snake_case_      )
							pipe.set_progress_bar_config(disable=snake_case_      )

							lowercase							=     self.get_inputs(snake_case_      )
							lowercase							=     pipe(**snake_case_      ).images
							lowercase							=     image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 5_12, 5_12, 3)
							lowercase							=     np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506]      )
							assert np.abs(image_slice - expected_slice      ).max() < 7E-3







from collections.abc import Sequence
from queue import Queue






class     __a			:



	def __init__(    self				:     str					,						snake_case_				:     List[str]					,						snake_case_				:     Tuple					,						snake_case_				:     Tuple					,						snake_case_				:     Optional[Any]=None					,						snake_case_				:     Dict=None)->				Optional[int]:
						__lowerCAmelCase						 =start
						__lowerCAmelCase						 =end
						__lowerCAmelCase						 =val
						__lowerCAmelCase						 =(start + end) // 2
						__lowerCAmelCase						 =left
						__lowerCAmelCase						 =right



	def __repr__(    self				:     List[Any])->				Dict:
						return F"""SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"""






class     __a			:



	def __init__(    self				:     List[str]					,						snake_case_				:     Sequence					,						snake_case_				:     int)->				str:
						__lowerCAmelCase						 =collection
						__lowerCAmelCase						 =function
						if self.collection:
											__lowerCAmelCase						 =self._build_tree(0					,						len(snake_case_) - 1)



	def 		UpperCamelCase				(    self				:     Dict					,						snake_case_				:     Tuple					,						snake_case_				:     Optional[int])->				Optional[int]:
						self._update_tree(self.root					,						snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     Optional[Any]					,						snake_case_				:     Any					,						snake_case_				:     Union[str, Any])->				Union[str, Any]:
						return self._query_range(self.root					,						snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     List[str]					,						snake_case_				:     List[str]					,						snake_case_				:     List[str])->				List[Any]:
						if start == end:
											return SegmentTreeNode(snake_case_					,						snake_case_					,						self.collection[start])
						__lowerCAmelCase						 =(start + end) // 2
						__lowerCAmelCase						 =self._build_tree(snake_case_					,						snake_case_)
						__lowerCAmelCase						 =self._build_tree(mid + 1					,						snake_case_)
						return SegmentTreeNode(snake_case_					,						snake_case_					,						self.fn(left.val					,						right.val)					,						snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     Dict					,						snake_case_				:     Tuple					,						snake_case_				:     int					,						snake_case_				:     int)->				List[Any]:
						if node.start == i and node.end == i:
											__lowerCAmelCase						 =val
											return
						if i <= node.mid:
											self._update_tree(node.left					,						snake_case_					,						snake_case_)
						else:
											self._update_tree(node.right					,						snake_case_					,						snake_case_)
						__lowerCAmelCase						 =self.fn(node.left.val					,						node.right.val)



	def 		UpperCamelCase				(    self				:     Tuple					,						snake_case_				:     str					,						snake_case_				:     str					,						snake_case_				:     List[Any])->				Optional[int]:
						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					,						snake_case_					,						snake_case_)
											else:
																# range in left child tree and right child tree
																return self.fn(
																    self._query_range(node.left					,						snake_case_					,						node.mid)					,						self._query_range(node.right					,						node.mid + 1					,						snake_case_)					,						)
						else:
											# range in right child tree
											return self._query_range(node.right					,						snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     List[str])->				Tuple:
						if self.root is not None:
											__lowerCAmelCase						 =Queue()
											queue.put(self.root)
											while not queue.empty():
																__lowerCAmelCase						 =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)
								lowercase_													=      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()




