




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 							__UpperCamelCase			( __UpperCAmelCase     ,					__UpperCAmelCase     ,					unittest.TestCase  ):





				'''simple docstring'''



				__a							: Union[str, Any]										=StableDiffusionPanoramaPipeline
				__a							: Union[str, Any]										=TEXT_TO_IMAGE_PARAMS
				__a							: Union[str, Any]										=TEXT_TO_IMAGE_BATCH_PARAMS
				__a							: List[Any]										=TEXT_TO_IMAGE_IMAGE_PARAMS
				__a							: List[str]										=TEXT_TO_IMAGE_IMAGE_PARAMS



				def       __snake_case		( self						):
											torch.manual_seed(0						)
											lowerCAmelCase   =		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       , )
											lowerCAmelCase   =		DDIMScheduler()
											torch.manual_seed(0						)
											lowerCAmelCase   =		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						)
											lowerCAmelCase   =		CLIPTextConfig(
											    bos_token_id=0       , eos_token_id=2       , hidden_size=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       , )
											lowerCAmelCase   =		CLIPTextModel(UpperCAmelCase_						)
											lowerCAmelCase   =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

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



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_=0						):
											lowerCAmelCase   =		torch.manual_seed(UpperCAmelCase_						)
											lowerCAmelCase   =		{
											    '''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       __snake_case		( self						):
											lowerCAmelCase   =		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
											lowerCAmelCase   =		self.get_dummy_components()
											lowerCAmelCase   =		StableDiffusionPanoramaPipeline(**UpperCAmelCase_						)
											lowerCAmelCase   =		sd_pipe.to(UpperCAmelCase_						)
											sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

											lowerCAmelCase   =		self.get_dummy_inputs(UpperCAmelCase_						)
											lowerCAmelCase   =		sd_pipe(**UpperCAmelCase_						).images
											lowerCAmelCase   =		image[0, -3:, -3:, -1]
											assert image.shape == (1, 64, 64, 3)

											lowerCAmelCase   =		np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757]						)

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



				def       __snake_case		( self						):
											super().test_inference_batch_consistent(batch_sizes=[1, 2]						)



				def       __snake_case		( self						):
											super().test_inference_batch_single_identical(batch_size=2       , expected_max_diff=3.2_5E-3						)



				def       __snake_case		( self						):
											lowerCAmelCase   =		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
											lowerCAmelCase   =		self.get_dummy_components()
											lowerCAmelCase   =		StableDiffusionPanoramaPipeline(**UpperCAmelCase_						)
											lowerCAmelCase   =		sd_pipe.to(UpperCAmelCase_						)
											sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

											lowerCAmelCase   =		self.get_dummy_inputs(UpperCAmelCase_						)
											lowerCAmelCase   =		'''french fries'''
											lowerCAmelCase   =		sd_pipe(**UpperCAmelCase_       , negative_prompt=UpperCAmelCase_						)
											lowerCAmelCase   =		output.images
											lowerCAmelCase   =		image[0, -3:, -3:, -1]

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

											lowerCAmelCase   =		np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]						)

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



				def       __snake_case		( self						):
											lowerCAmelCase   =		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
											lowerCAmelCase   =		self.get_dummy_components()
											lowerCAmelCase   =		StableDiffusionPanoramaPipeline(**UpperCAmelCase_						)
											lowerCAmelCase   =		sd_pipe.to(UpperCAmelCase_						)
											sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

											lowerCAmelCase   =		self.get_dummy_inputs(UpperCAmelCase_						)
											lowerCAmelCase   =		sd_pipe(**UpperCAmelCase_       , view_batch_size=2						)
											lowerCAmelCase   =		output.images
											lowerCAmelCase   =		image[0, -3:, -3:, -1]

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

											lowerCAmelCase   =		np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]						)

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



				def       __snake_case		( self						):
											lowerCAmelCase   =		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
											lowerCAmelCase   =		self.get_dummy_components()
											lowerCAmelCase   =		EulerAncestralDiscreteScheduler(
											    beta_start=0.00085       , beta_end=0.012       , beta_schedule='''scaled_linear'''						)
											lowerCAmelCase   =		StableDiffusionPanoramaPipeline(**UpperCAmelCase_						)
											lowerCAmelCase   =		sd_pipe.to(UpperCAmelCase_						)
											sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

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

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

											lowerCAmelCase   =		np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952]						)

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






				def       __snake_case		( self						):
											lowerCAmelCase   =		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
											lowerCAmelCase   =		self.get_dummy_components()
											lowerCAmelCase   =		PNDMScheduler(
											    beta_start=0.00085       , beta_end=0.012       , beta_schedule='''scaled_linear'''       , skip_prk_steps=UpperCAmelCase_						)
											lowerCAmelCase   =		StableDiffusionPanoramaPipeline(**UpperCAmelCase_						)
											lowerCAmelCase   =		sd_pipe.to(UpperCAmelCase_						)
											sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

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

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

											lowerCAmelCase   =		np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539]						)

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




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





				'''simple docstring'''



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



				def       __snake_case		( self       , UpperCAmelCase_=0						):
											lowerCAmelCase   =		torch.manual_seed(UpperCAmelCase_						)
											lowerCAmelCase   =		{
											    '''prompt''': '''a photo of the dolomites''',
											    '''generator''': generator,
											    '''num_inference_steps''': 3,
											    '''guidance_scale''': 7.5,
											    '''output_type''': '''numpy''',
											}
											return inputs



				def       __snake_case		( self						):
											lowerCAmelCase   =		'''stabilityai/stable-diffusion-2-base'''
											lowerCAmelCase   =		DDIMScheduler.from_pretrained(UpperCAmelCase_       , subfolder='''scheduler'''						)
											lowerCAmelCase   =		StableDiffusionPanoramaPipeline.from_pretrained(UpperCAmelCase_       , scheduler=UpperCAmelCase_       , safety_checker=UpperCAmelCase_						)
											pipe.to(UpperCAmelCase_						)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)
											pipe.enable_attention_slicing()

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

											assert image.shape == (1, 5_12, 20_48, 3)

											lowerCAmelCase   =		np.array(
											    [
											        0.36968392,
											        0.27025372,
											        0.32446766,
											        0.28379387,
											        0.36363274,
											        0.30733347,
											        0.27100027,
											        0.27054125,
											        0.25536096,
											    ]						)

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



				def       __snake_case		( self						):
											lowerCAmelCase   =		StableDiffusionPanoramaPipeline.from_pretrained(
											    '''stabilityai/stable-diffusion-2-base'''       , safety_checker=UpperCAmelCase_						)
											lowerCAmelCase   =		LMSDiscreteScheduler.from_config(pipe.scheduler.config						)
											pipe.to(UpperCAmelCase_						)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)
											pipe.enable_attention_slicing()

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

											assert image.shape == (1, 5_12, 20_48, 3)

											lowerCAmelCase   =		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       __snake_case		( self						):
											lowerCAmelCase   =		0

											def callback_fn(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						) -> None:
																		lowerCAmelCase   =		True
																		nonlocal number_of_steps
																		number_of_steps += 1
																		if step == 1:
																									lowerCAmelCase   =		latents.detach().cpu().numpy()
																									assert latents.shape == (1, 4, 64, 2_56)
																									lowerCAmelCase   =		latents[0, -3:, -3:, -1]

																									lowerCAmelCase   =		np.array(
																									    [
																									        0.18681869,
																									        0.33907816,
																									        0.5361276,
																									        0.14432865,
																									        -0.02856611,
																									        -0.73941123,
																									        0.23397987,
																									        0.47322682,
																									        -0.37823164,
																									    ]						)
																									assert np.abs(latents_slice.flatten() - expected_slice						).max() < 5E-2
																		elif step == 2:
																									lowerCAmelCase   =		latents.detach().cpu().numpy()
																									assert latents.shape == (1, 4, 64, 2_56)
																									lowerCAmelCase   =		latents[0, -3:, -3:, -1]

																									lowerCAmelCase   =		np.array(
																									    [
																									        0.18539645,
																									        0.33987248,
																									        0.5378559,
																									        0.14437142,
																									        -0.02455261,
																									        -0.7338317,
																									        0.23990755,
																									        0.47356272,
																									        -0.3786505,
																									    ]						)

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

											lowerCAmelCase   =		False

											lowerCAmelCase   =		'''stabilityai/stable-diffusion-2-base'''
											lowerCAmelCase   =		DDIMScheduler.from_pretrained(UpperCAmelCase_       , subfolder='''scheduler'''						)
											lowerCAmelCase   =		StableDiffusionPanoramaPipeline.from_pretrained(UpperCAmelCase_       , scheduler=UpperCAmelCase_       , safety_checker=UpperCAmelCase_						)
											lowerCAmelCase   =		pipe.to(UpperCAmelCase_						)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)
											pipe.enable_attention_slicing()

											lowerCAmelCase   =		self.get_inputs()
											pipe(**UpperCAmelCase_       , callback=UpperCAmelCase_       , callback_steps=1						)
											assert callback_fn.has_been_called
											assert number_of_steps == 3






				def       __snake_case		( self						):
											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()
											torch.cuda.reset_peak_memory_stats()

											lowerCAmelCase   =		'''stabilityai/stable-diffusion-2-base'''
											lowerCAmelCase   =		DDIMScheduler.from_pretrained(UpperCAmelCase_       , subfolder='''scheduler'''						)
											lowerCAmelCase   =		StableDiffusionPanoramaPipeline.from_pretrained(UpperCAmelCase_       , scheduler=UpperCAmelCase_       , safety_checker=UpperCAmelCase_						)
											lowerCAmelCase   =		pipe.to(UpperCAmelCase_						)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)
											pipe.enable_attention_slicing(1						)
											pipe.enable_sequential_cpu_offload()

											lowerCAmelCase   =		self.get_inputs()
											lowerCAmelCase   =		pipe(**UpperCAmelCase_						)

											lowerCAmelCase   =		torch.cuda.max_memory_allocated()
											# make sure that less than 5.2 GB is allocated
											assert mem_bytes < 5.5 * 10**9









from collections.abc import Sequence



def       UpperCAmelCase       (     _snake_case						, _snake_case = False							):
							if not arr:
														return 0

							lowerCAmelCase   =		0 if allow_empty_subarrays else float('''-inf'''							)
							lowerCAmelCase   =		0.0
							for num in arr:
														lowerCAmelCase   =		max(0 if allow_empty_subarrays else num						, curr_sum + num							)
														lowerCAmelCase   =		max(_snake_case						, _snake_case							)

							return max_sum


if __name__ == "__main__":
		from doctest import testmod

		testmod()

		UpperCAmelCase_						=[-2, 1, -3, 4, -1, 2, 1, -5, 4]
		print(F'''{max_subarray_sum(nums) = }''')



