


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 							lowerCamelCase   (  _UpperCamelCase							,						_UpperCamelCase							,						unittest.TestCase ):
	_lowerCAmelCase     :		List[str]										=						StableDiffusionPanoramaPipeline
	_lowerCAmelCase     :		str										=						TEXT_TO_IMAGE_PARAMS
	_lowerCAmelCase     :		int										=						TEXT_TO_IMAGE_BATCH_PARAMS
	_lowerCAmelCase     :		Tuple										=						TEXT_TO_IMAGE_IMAGE_PARAMS
	_lowerCAmelCase     :		str										=						TEXT_TO_IMAGE_IMAGE_PARAMS


	def  A(    self):
							torch.manual_seed(0)
							__UpperCAmelCase			:							Any	    =	UNetaDConditionModel(
							    block_out_channels=(3_2, 6_4)				,     layers_per_block=1				,     sample_size=3_2				,     in_channels=4				,     out_channels=4				,     down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,     up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,     cross_attention_dim=3_2				,     )
							__UpperCAmelCase			:							int	    =	DDIMScheduler()
							torch.manual_seed(0)
							__UpperCAmelCase			:							str	    =	AutoencoderKL(
							    block_out_channels=[3_2, 6_4]				,     in_channels=3				,     out_channels=3				,     down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,     up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,     latent_channels=4				,     )
							torch.manual_seed(0)
							__UpperCAmelCase			:							Optional[int]	    =	CLIPTextConfig(
							    bos_token_id=0				,     eos_token_id=2				,     hidden_size=3_2				,     intermediate_size=3_7				,     layer_norm_eps=1e-05				,     num_attention_heads=4				,     num_hidden_layers=5				,     pad_token_id=1				,     vocab_size=1_0_0_0				,     )
							__UpperCAmelCase			:							Dict	    =	CLIPTextModel(lowercase__)
							__UpperCAmelCase			:							Dict	    =	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

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


	def  A(    self				,     lowercase__				,     lowercase__=0):
							__UpperCAmelCase			:							str	    =	torch.manual_seed(lowercase__)
							__UpperCAmelCase			:							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  A(    self):
							__UpperCAmelCase			:							int	    =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							__UpperCAmelCase			:							Optional[Any]	    =	self.get_dummy_components()
							__UpperCAmelCase			:							Any	    =	StableDiffusionPanoramaPipeline(**lowercase__)
							__UpperCAmelCase			:							Dict	    =	sd_pipe.to(lowercase__)
							sd_pipe.set_progress_bar_config(disable=lowercase__)

							__UpperCAmelCase			:							int	    =	self.get_dummy_inputs(lowercase__)
							__UpperCAmelCase			:							List[Any]	    =	sd_pipe(**lowercase__).images
							__UpperCAmelCase			:							Union[str, Any]	    =	image[0, -3:, -3:, -1]
							assert image.shape == (1, 6_4, 6_4, 3)

							__UpperCAmelCase			:							Tuple	    =	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  A(    self):
							super().test_inference_batch_consistent(batch_sizes=[1, 2])


	def  A(    self):
							super().test_inference_batch_single_identical(batch_size=2				,     expected_max_diff=3.25e-3)


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

							__UpperCAmelCase			:							List[str]	    =	self.get_dummy_inputs(lowercase__)
							__UpperCAmelCase			:							Dict	    =	'''french fries'''
							__UpperCAmelCase			:							Any	    =	sd_pipe(**lowercase__				,     negative_prompt=lowercase__)
							__UpperCAmelCase			:							Any	    =	output.images
							__UpperCAmelCase			:							Any	    =	image[0, -3:, -3:, -1]

							assert image.shape == (1, 6_4, 6_4, 3)

							__UpperCAmelCase			:							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  A(    self):
							__UpperCAmelCase			:							int	    =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							__UpperCAmelCase			:							int	    =	self.get_dummy_components()
							__UpperCAmelCase			:							Optional[int]	    =	StableDiffusionPanoramaPipeline(**lowercase__)
							__UpperCAmelCase			:							int	    =	sd_pipe.to(lowercase__)
							sd_pipe.set_progress_bar_config(disable=lowercase__)

							__UpperCAmelCase			:							List[Any]	    =	self.get_dummy_inputs(lowercase__)
							__UpperCAmelCase			:							Optional[Any]	    =	sd_pipe(**lowercase__				,     view_batch_size=2)
							__UpperCAmelCase			:							List[Any]	    =	output.images
							__UpperCAmelCase			:							Dict	    =	image[0, -3:, -3:, -1]

							assert image.shape == (1, 6_4, 6_4, 3)

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

							__UpperCAmelCase			:							List[str]	    =	self.get_dummy_inputs(lowercase__)
							__UpperCAmelCase			:							int	    =	sd_pipe(**lowercase__).images
							__UpperCAmelCase			:							Any	    =	image[0, -3:, -3:, -1]

							assert image.shape == (1, 6_4, 6_4, 3)

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

							__UpperCAmelCase			:							List[Any]	    =	self.get_dummy_inputs(lowercase__)
							__UpperCAmelCase			:							Dict	    =	sd_pipe(**lowercase__).images
							__UpperCAmelCase			:							Tuple	    =	image[0, -3:, -3:, -1]

							assert image.shape == (1, 6_4, 6_4, 3)

							__UpperCAmelCase			:							Union[str, Any]	    =	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 							lowerCamelCase   (  unittest.TestCase ):


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


	def  A(    self				,     lowercase__=0):
							__UpperCAmelCase			:							int	    =	torch.manual_seed(lowercase__)
							__UpperCAmelCase			:							str	    =	{
							    '''prompt''': '''a photo of the dolomites''',
							    '''generator''': generator,
							    '''num_inference_steps''': 3,
							    '''guidance_scale''': 7.5,
							    '''output_type''': '''numpy''',
							}
							return inputs


	def  A(    self):
							__UpperCAmelCase			:							Any	    =	'''stabilityai/stable-diffusion-2-base'''
							__UpperCAmelCase			:							Optional[int]	    =	DDIMScheduler.from_pretrained(lowercase__				,     subfolder='''scheduler''')
							__UpperCAmelCase			:							Dict	    =	StableDiffusionPanoramaPipeline.from_pretrained(lowercase__				,     scheduler=lowercase__				,     safety_checker=lowercase__)
							pipe.to(lowercase__)
							pipe.set_progress_bar_config(disable=lowercase__)
							pipe.enable_attention_slicing()

							__UpperCAmelCase			:							List[str]	    =	self.get_inputs()
							__UpperCAmelCase			:							Union[str, Any]	    =	pipe(**lowercase__).images
							__UpperCAmelCase			:							Union[str, Any]	    =	image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 5_1_2, 2_0_4_8, 3)

							__UpperCAmelCase			:							List[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  A(    self):
							__UpperCAmelCase			:							Tuple	    =	StableDiffusionPanoramaPipeline.from_pretrained(
							    '''stabilityai/stable-diffusion-2-base'''				,     safety_checker=lowercase__)
							__UpperCAmelCase			:							int	    =	LMSDiscreteScheduler.from_config(pipe.scheduler.config)
							pipe.to(lowercase__)
							pipe.set_progress_bar_config(disable=lowercase__)
							pipe.enable_attention_slicing()

							__UpperCAmelCase			:							Optional[int]	    =	self.get_inputs()
							__UpperCAmelCase			:							Any	    =	pipe(**lowercase__).images
							__UpperCAmelCase			:							int	    =	image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 5_1_2, 2_0_4_8, 3)

							__UpperCAmelCase			:							List[Any]	    =	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  A(    self):
							__UpperCAmelCase			:							Dict	    =	0

							def callback_fn(lowercase__				,     lowercase__				,     lowercase__) -> None:
													__UpperCAmelCase			:							Optional[Any]	    =	True
													nonlocal number_of_steps
													number_of_steps += 1
													if step == 1:
																			__UpperCAmelCase			:							Union[str, Any]	    =	latents.detach().cpu().numpy()
																			assert latents.shape == (1, 4, 6_4, 2_5_6)
																			__UpperCAmelCase			:							str	    =	latents[0, -3:, -3:, -1]

																			__UpperCAmelCase			:							Union[str, Any]	    =	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:
																			__UpperCAmelCase			:							List[str]	    =	latents.detach().cpu().numpy()
																			assert latents.shape == (1, 4, 6_4, 2_5_6)
																			__UpperCAmelCase			:							Optional[int]	    =	latents[0, -3:, -3:, -1]

																			__UpperCAmelCase			:							Tuple	    =	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

							__UpperCAmelCase			:							Tuple	    =	False

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

							__UpperCAmelCase			:							Optional[int]	    =	self.get_inputs()
							pipe(**lowercase__				,     callback=lowercase__				,     callback_steps=1)
							assert callback_fn.has_been_called
							assert number_of_steps == 3


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

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

							__UpperCAmelCase			:							int	    =	self.get_inputs()
							__UpperCAmelCase			:							Optional[Any]	    =	pipe(**lowercase__)

							__UpperCAmelCase			:							List[Any]	    =	torch.cuda.max_memory_allocated()
							# make sure that less than 5.2 GB is allocated
							assert mem_bytes < 5.5 * 1_0**9





import inspect
import os
import sys
import unittest

import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu




class 							lowerCamelCase   (  unittest.TestCase ):


	def  A(    self):
							__UpperCAmelCase			:							List[Any]	    =	inspect.getfile(accelerate.test_utils)
							__UpperCAmelCase			:							Dict	    =	os.path.sep.join(mod_file.split(os.path.sep)[:-1] + ['''scripts''', '''test_script.py'''])
							__UpperCAmelCase			:							Union[str, Any]	    =	os.path.sep.join(inspect.getfile(self.__class__).split(os.path.sep)[:-1])


	@require_tpu
	def  A(    self):
							__UpperCAmelCase			:							Dict	    =	F"\n            {self.test_dir}/xla_spawn.py\n            --num_cores 8\n            {self.test_file_path}\n        ".split()
							__UpperCAmelCase			:							Union[str, Any]	    =	[sys.executable] + distributed_args
							execute_subprocess_async(lowercase__				,     env=os.environ.copy())

