




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) = }''')



