





import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
@skip_mps
class SCREAMING_SNAKE_CASE_						(					snake_case_       ,				snake_case_       ,				unittest.TestCase					):
						__magic_name__:			Optional[int]       =       StableDiffusionPanoramaPipeline
						__magic_name__:			Optional[Any]       =       TEXT_TO_IMAGE_PARAMS
						__magic_name__:			Union[str, Any]       =       TEXT_TO_IMAGE_BATCH_PARAMS
						__magic_name__:			Optional[int]       =       TEXT_TO_IMAGE_IMAGE_PARAMS
						__magic_name__:			Optional[Any]       =       TEXT_TO_IMAGE_IMAGE_PARAMS






						def UpperCAmelCase_				(      self      :      Dict	)							->    Union[str, Any]:







							"""simple docstring"""


							torch.manual_seed(0	)
							snake_case_		:			Optional[Any]											=	UNetaDConditionModel(
							    block_out_channels=(32, 64)		,   layers_per_block=1		,   sample_size=32		,   in_channels=4		,   out_channels=4		,   down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')		,   up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')		,   cross_attention_dim=32		,   )
							snake_case_		:			str											=	DDIMScheduler()
							torch.manual_seed(0	)
							snake_case_		:			Union[str, Any]											=	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		,   )
							torch.manual_seed(0	)
							snake_case_		:			str											=	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=1000		,   )
							snake_case_		:			Any											=	CLIPTextModel(__UpperCamelCase	)
							snake_case_		:			List[Any]											=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

							snake_case_		:			Any											=	{
							    'unet': unet,
							    'scheduler': scheduler,
							    'vae': vae,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'safety_checker': None,
							    'feature_extractor': None,
							}
							return components






						def UpperCAmelCase_				(      self      :      int		,   _A      :      Any		,   _A      :      Optional[int]=0	)							->    Tuple:







							"""simple docstring"""


							snake_case_		:			List[str]											=	torch.manual_seed(__UpperCamelCase	)
							snake_case_		:			List[str]											=	{
							    'prompt': 'a photo of the dolomites',
							    'generator': generator,
							    # Setting height and width to None to prevent OOMs on CPU.
							    'height': None,
							    'width': None,
							    'num_inference_steps': 1,
							    'guidance_scale': 6.0,
							    'output_type': 'numpy',
							}
							return inputs






						def UpperCAmelCase_				(      self      :      int	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	'cpu'  # ensure determinism for the device-dependent torch.Generator
							snake_case_		:			List[str]											=	self.get_dummy_components()
							snake_case_		:			Union[str, Any]											=	StableDiffusionPanoramaPipeline(**__UpperCamelCase	)
							snake_case_		:			Union[str, Any]											=	sd_pipe.to(__UpperCamelCase	)
							sd_pipe.set_progress_bar_config(disable=__UpperCamelCase	)

							snake_case_		:			str											=	self.get_dummy_inputs(__UpperCamelCase	)
							snake_case_		:			List[Any]											=	sd_pipe(**__UpperCamelCase	).images
							snake_case_		:			Dict											=	image[0, -3:, -3:, -1]
							assert image.shape == (1, 64, 64, 3)

							snake_case_		:			List[Any]											=	np.array([0.6_1_8_6, 0.5_3_7_4, 0.4_9_1_5, 0.4_1_3_5, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_7, 0.4_7_5_7]	)

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






						def UpperCAmelCase_				(      self      :      Any	)							->    List[Any]:







							"""simple docstring"""


							super().test_inference_batch_consistent(batch_sizes=[1, 2]	)






						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    Tuple:







							"""simple docstring"""


							super().test_inference_batch_single_identical(batch_size=2		,   expected_max_diff=3.25E-3	)






						def UpperCAmelCase_				(      self      :      int	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	'cpu'  # ensure determinism for the device-dependent torch.Generator
							snake_case_		:			List[str]											=	self.get_dummy_components()
							snake_case_		:			List[Any]											=	StableDiffusionPanoramaPipeline(**__UpperCamelCase	)
							snake_case_		:			List[Any]											=	sd_pipe.to(__UpperCamelCase	)
							sd_pipe.set_progress_bar_config(disable=__UpperCamelCase	)

							snake_case_		:			List[str]											=	self.get_dummy_inputs(__UpperCamelCase	)
							snake_case_		:			Optional[Any]											=	'french fries'
							snake_case_		:			Union[str, Any]											=	sd_pipe(**__UpperCamelCase		,   negative_prompt=__UpperCamelCase	)
							snake_case_		:			List[Any]											=	output.images
							snake_case_		:			Union[str, Any]											=	image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)

							snake_case_		:			List[Any]											=	np.array([0.6_1_8_7, 0.5_3_7_5, 0.4_9_1_5, 0.4_1_3_6, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_6, 0.4_7_5_7]	)

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






						def UpperCAmelCase_				(      self      :      Dict	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			List[Any]											=	'cpu'  # ensure determinism for the device-dependent torch.Generator
							snake_case_		:			List[Any]											=	self.get_dummy_components()
							snake_case_		:			str											=	StableDiffusionPanoramaPipeline(**__UpperCamelCase	)
							snake_case_		:			List[str]											=	sd_pipe.to(__UpperCamelCase	)
							sd_pipe.set_progress_bar_config(disable=__UpperCamelCase	)

							snake_case_		:			Optional[Any]											=	self.get_dummy_inputs(__UpperCamelCase	)
							snake_case_		:			Any											=	sd_pipe(**__UpperCamelCase		,   view_batch_size=2	)
							snake_case_		:			Tuple											=	output.images
							snake_case_		:			List[str]											=	image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)

							snake_case_		:			List[str]											=	np.array([0.6_1_8_7, 0.5_3_7_5, 0.4_9_1_5, 0.4_1_3_6, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_6, 0.4_7_5_7]	)

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






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			Any											=	'cpu'  # ensure determinism for the device-dependent torch.Generator
							snake_case_		:			List[Any]											=	self.get_dummy_components()
							snake_case_		:			Union[str, Any]											=	EulerAncestralDiscreteScheduler(
							    beta_start=0.0_0_0_8_5		,   beta_end=0.0_1_2		,   beta_schedule='scaled_linear'	)
							snake_case_		:			Optional[int]											=	StableDiffusionPanoramaPipeline(**__UpperCamelCase	)
							snake_case_		:			int											=	sd_pipe.to(__UpperCamelCase	)
							sd_pipe.set_progress_bar_config(disable=__UpperCamelCase	)

							snake_case_		:			Optional[int]											=	self.get_dummy_inputs(__UpperCamelCase	)
							snake_case_		:			Tuple											=	sd_pipe(**__UpperCamelCase	).images
							snake_case_		:			Dict											=	image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)

							snake_case_		:			int											=	np.array([0.4_0_2_4, 0.6_5_1_0, 0.4_9_0_1, 0.5_3_7_8, 0.5_8_1_3, 0.5_6_2_2, 0.4_7_9_5, 0.4_4_6_7, 0.4_9_5_2]	)

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






						def UpperCAmelCase_				(      self      :      str	)							->    Tuple:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	'cpu'  # ensure determinism for the device-dependent torch.Generator
							snake_case_		:			int											=	self.get_dummy_components()
							snake_case_		:			int											=	PNDMScheduler(
							    beta_start=0.0_0_0_8_5		,   beta_end=0.0_1_2		,   beta_schedule='scaled_linear'		,   skip_prk_steps=__UpperCamelCase	)
							snake_case_		:			List[str]											=	StableDiffusionPanoramaPipeline(**__UpperCamelCase	)
							snake_case_		:			Optional[int]											=	sd_pipe.to(__UpperCamelCase	)
							sd_pipe.set_progress_bar_config(disable=__UpperCamelCase	)

							snake_case_		:			List[Any]											=	self.get_dummy_inputs(__UpperCamelCase	)
							snake_case_		:			Dict											=	sd_pipe(**__UpperCamelCase	).images
							snake_case_		:			List[str]											=	image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)

							snake_case_		:			List[str]											=	np.array([0.6_3_9_1, 0.6_2_9_1, 0.4_8_6_1, 0.5_1_3_4, 0.5_5_5_2, 0.4_5_7_8, 0.5_0_3_2, 0.5_0_2_3, 0.4_5_3_9]	)

							assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    List[str]:







							"""simple docstring"""


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






						def UpperCAmelCase_				(      self      :      Any		,   _A      :      str=0	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			List[Any]											=	torch.manual_seed(__UpperCamelCase	)
							snake_case_		:			str											=	{
							    'prompt': 'a photo of the dolomites',
							    'generator': generator,
							    'num_inference_steps': 3,
							    'guidance_scale': 7.5,
							    'output_type': 'numpy',
							}
							return inputs






						def UpperCAmelCase_				(      self      :      Tuple	)							->    List[Any]:







							"""simple docstring"""


							snake_case_		:			Any											=	'stabilityai/stable-diffusion-2-base'
							snake_case_		:			Tuple											=	DDIMScheduler.from_pretrained(__UpperCamelCase		,   subfolder='scheduler'	)
							snake_case_		:			int											=	StableDiffusionPanoramaPipeline.from_pretrained(__UpperCamelCase		,   scheduler=__UpperCamelCase		,   safety_checker=__UpperCamelCase	)
							pipe.to(__UpperCamelCase	)
							pipe.set_progress_bar_config(disable=__UpperCamelCase	)
							pipe.enable_attention_slicing()

							snake_case_		:			int											=	self.get_inputs()
							snake_case_		:			List[str]											=	pipe(**__UpperCamelCase	).images
							snake_case_		:			Tuple											=	image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 2048, 3)

							snake_case_		:			str											=	np.array(
							    [
							        0.3_6_9_6_8_3_9_2,
							        0.2_7_0_2_5_3_7_2,
							        0.3_2_4_4_6_7_6_6,
							        0.2_8_3_7_9_3_8_7,
							        0.3_6_3_6_3_2_7_4,
							        0.3_0_7_3_3_3_4_7,
							        0.2_7_1_0_0_0_2_7,
							        0.2_7_0_5_4_1_2_5,
							        0.2_5_5_3_6_0_9_6,
							    ]	)

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






						def UpperCAmelCase_				(      self      :      Tuple	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			List[str]											=	StableDiffusionPanoramaPipeline.from_pretrained(
							    'stabilityai/stable-diffusion-2-base'		,   safety_checker=__UpperCamelCase	)
							snake_case_		:			Optional[int]											=	LMSDiscreteScheduler.from_config(pipe.scheduler.config	)
							pipe.to(__UpperCamelCase	)
							pipe.set_progress_bar_config(disable=__UpperCamelCase	)
							pipe.enable_attention_slicing()

							snake_case_		:			Any											=	self.get_inputs()
							snake_case_		:			Optional[int]											=	pipe(**__UpperCamelCase	).images
							snake_case_		:			Union[str, Any]											=	image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 2048, 3)

							snake_case_		:			int											=	np.array(
							    [
							        [
							            0.0,
							            0.0,
							            0.0,
							            0.0,
							            0.0,
							            0.0,
							            0.0,
							            0.0,
							            0.0,
							        ]
							    ]	)

							assert np.abs(expected_slice - image_slice	).max() < 1E-3






						def UpperCAmelCase_				(      self      :      List[str]	)							->    Optional[Any]:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	0

							def callback_fn(_A      :      int		,   _A      :      int		,   _A      :      torch.FloatTensor	) -> None:
								snake_case_		:			Tuple											=	True
								nonlocal number_of_steps
								number_of_steps += 1
								if step == 1:
									snake_case_		:			Any											=	latents.detach().cpu().numpy()
									assert latents.shape == (1, 4, 64, 256)
									snake_case_		:			List[Any]											=	latents[0, -3:, -3:, -1]

									snake_case_		:			Tuple											=	np.array(
									    [
									        0.1_8_6_8_1_8_6_9,
									        0.3_3_9_0_7_8_1_6,
									        0.5_3_6_1_2_7_6,
									        0.1_4_4_3_2_8_6_5,
									        -0.0_2_8_5_6_6_1_1,
									        -0.7_3_9_4_1_1_2_3,
									        0.2_3_3_9_7_9_8_7,
									        0.4_7_3_2_2_6_8_2,
									        -0.3_7_8_2_3_1_6_4,
									    ]	)
									assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5E-2
								elif step == 2:
									snake_case_		:			Dict											=	latents.detach().cpu().numpy()
									assert latents.shape == (1, 4, 64, 256)
									snake_case_		:			Any											=	latents[0, -3:, -3:, -1]

									snake_case_		:			Any											=	np.array(
									    [
									        0.1_8_5_3_9_6_4_5,
									        0.3_3_9_8_7_2_4_8,
									        0.5_3_7_8_5_5_9,
									        0.1_4_4_3_7_1_4_2,
									        -0.0_2_4_5_5_2_6_1,
									        -0.7_3_3_8_3_1_7,
									        0.2_3_9_9_0_7_5_5,
									        0.4_7_3_5_6_2_7_2,
									        -0.3_7_8_6_5_0_5,
									    ]	)

									assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5E-2

							snake_case_		:			Any											=	False

							snake_case_		:			Dict											=	'stabilityai/stable-diffusion-2-base'
							snake_case_		:			Dict											=	DDIMScheduler.from_pretrained(__UpperCamelCase		,   subfolder='scheduler'	)
							snake_case_		:			List[Any]											=	StableDiffusionPanoramaPipeline.from_pretrained(__UpperCamelCase		,   scheduler=__UpperCamelCase		,   safety_checker=__UpperCamelCase	)
							snake_case_		:			Optional[Any]											=	pipe.to(__UpperCamelCase	)
							pipe.set_progress_bar_config(disable=__UpperCamelCase	)
							pipe.enable_attention_slicing()

							snake_case_		:			str											=	self.get_inputs()
							pipe(**__UpperCamelCase		,   callback=__UpperCamelCase		,   callback_steps=1	)
							assert callback_fn.has_been_called
							assert number_of_steps == 3






						def UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    Dict:







							"""simple docstring"""


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

							snake_case_		:			List[Any]											=	'stabilityai/stable-diffusion-2-base'
							snake_case_		:			int											=	DDIMScheduler.from_pretrained(__UpperCamelCase		,   subfolder='scheduler'	)
							snake_case_		:			List[str]											=	StableDiffusionPanoramaPipeline.from_pretrained(__UpperCamelCase		,   scheduler=__UpperCamelCase		,   safety_checker=__UpperCamelCase	)
							snake_case_		:			Tuple											=	pipe.to(__UpperCamelCase	)
							pipe.set_progress_bar_config(disable=__UpperCamelCase	)
							pipe.enable_attention_slicing(1	)
							pipe.enable_sequential_cpu_offload()

							snake_case_		:			int											=	self.get_inputs()
							snake_case_		:			Tuple											=	pipe(**__UpperCamelCase	)

							snake_case_		:			List[Any]											=	torch.cuda.max_memory_allocated()
							# make sure that less than 5.2 GB is allocated
							assert mem_bytes < 5.5 * 10**9








from __future__ import annotations

from dataclasses import dataclass
@dataclass
class SCREAMING_SNAKE_CASE_						:
						__magic_name__:			float
						__magic_name__:			TreeNode | None       =       None
						__magic_name__:			TreeNode | None       =       None


def 							SCREAMING_SNAKE_CASE__						(    __a   ):

	# Validation
	def is_valid_tree(__a   ) -> bool:
		if node is None:
			return True

		if not isinstance(__a   , __a   ):
			return False

		try:
			float(node.data   )
		except (TypeError, ValueError):
			return False

		return is_valid_tree(node.left   ) and is_valid_tree(node.right   )

	if not is_valid_tree(__a   ):
		raise ValueError(
		    'Each node should be type of TreeNode and data should be float.'   )

	def is_binary_search_tree_recursive_check(
	    __a   , __a   , __a   ) -> bool:

		if node is None:
			return True

		return (
		    left_bound < node.data < right_bound
		    and is_binary_search_tree_recursive_check(node.left   , __a   , node.data   )
		    and is_binary_search_tree_recursive_check(
		        node.right   , node.data   , __a   )
		)

	return is_binary_search_tree_recursive_check(__a   , -float('inf'   )   , float('inf'   )   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()

