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 					lowercase__						(   UpperCamelCase_	,							UpperCamelCase_	,							unittest.TestCase):
						UpperCamelCase_     =		StableDiffusionPanoramaPipeline
						UpperCamelCase_     =		TEXT_TO_IMAGE_PARAMS
						UpperCamelCase_     =		TEXT_TO_IMAGE_BATCH_PARAMS
						UpperCamelCase_     =		TEXT_TO_IMAGE_IMAGE_PARAMS
						UpperCamelCase_     =		TEXT_TO_IMAGE_IMAGE_PARAMS


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												torch.manual_seed(0						)
												SCREAMING_SNAKE_CASE							:    Union[str, 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    ,		)
												SCREAMING_SNAKE_CASE							:    str							=							DDIMScheduler()
												torch.manual_seed(0						)
												SCREAMING_SNAKE_CASE							:    Optional[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						)
												SCREAMING_SNAKE_CASE							:    int							=							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    ,		)
												SCREAMING_SNAKE_CASE							:    Any							=							CLIPTextModel(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    str							=							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

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


						def        __A       (       self	:				List[Any]    ,		UpperCamelCase__	:				Tuple    ,		UpperCamelCase__	:				int=0						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							torch.manual_seed(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    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	:				List[Any]						):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE							:    str							=							self.get_dummy_inputs(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Dict							=							sd_pipe(**UpperCamelCase__						).images
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							image[0, -3:, -3:, -1]
												assert image.shape == (1, 64, 64, 3)

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							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        __A       (       self	:				Dict						):





												'''simple docstring'''





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


						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





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


						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE							:    Tuple							=							self.get_dummy_inputs(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							'''french fries'''
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							sd_pipe(**UpperCamelCase__    ,		negative_prompt=UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Any							=							output.images
												SCREAMING_SNAKE_CASE							:    Dict							=							image[0, -3:, -3:, -1]

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

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							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        __A       (       self	:				Tuple						):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE							:    Dict							=							self.get_dummy_inputs(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							sd_pipe(**UpperCamelCase__    ,		view_batch_size=2						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							output.images
												SCREAMING_SNAKE_CASE							:    Any							=							image[0, -3:, -3:, -1]

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

												SCREAMING_SNAKE_CASE							:    str							=							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        __A       (       self	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
												SCREAMING_SNAKE_CASE							:    str							=							self.get_dummy_components()
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							EulerAncestralDiscreteScheduler(
												    beta_start=0.0_0085    ,		beta_end=0.012    ,		beta_schedule='''scaled_linear'''						)
												SCREAMING_SNAKE_CASE							:    Dict							=							StableDiffusionPanoramaPipeline(**UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    int							=							sd_pipe.to(UpperCamelCase__						)
												sd_pipe.set_progress_bar_config(disable=UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.get_dummy_inputs(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Any							=							sd_pipe(**UpperCamelCase__						).images
												SCREAMING_SNAKE_CASE							:    List[Any]							=							image[0, -3:, -3:, -1]

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

												SCREAMING_SNAKE_CASE							:    Any							=							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        __A       (       self	:				Any						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.get_dummy_components()
												SCREAMING_SNAKE_CASE							:    Tuple							=							PNDMScheduler(
												    beta_start=0.0_0085    ,		beta_end=0.012    ,		beta_schedule='''scaled_linear'''    ,		skip_prk_steps=UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							StableDiffusionPanoramaPipeline(**UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    int							=							sd_pipe.to(UpperCamelCase__						)
												sd_pipe.set_progress_bar_config(disable=UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    List[str]							=							self.get_dummy_inputs(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    str							=							sd_pipe(**UpperCamelCase__						).images
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							image[0, -3:, -3:, -1]

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

												SCREAMING_SNAKE_CASE							:    List[Any]							=							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 					lowercase__						(   unittest.TestCase):


						def        __A       (       self	:				Dict						):





												'''simple docstring'''





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


						def        __A       (       self	:				Dict    ,		UpperCamelCase__	:				Optional[Any]=0						):





												'''simple docstring'''





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


						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.get_inputs()
												SCREAMING_SNAKE_CASE							:    Tuple							=							pipe(**UpperCamelCase__						).images
												SCREAMING_SNAKE_CASE							:    Dict							=							image[0, -3:, -3:, -1].flatten()

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

												SCREAMING_SNAKE_CASE							:    str							=							np.array(
												    [
												        0.3696_8392,
												        0.2702_5372,
												        0.3244_6766,
												        0.2837_9387,
												        0.3636_3274,
												        0.3073_3347,
												        0.2710_0027,
												        0.2705_4125,
												        0.2553_6096,
												    ]						)

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


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							StableDiffusionPanoramaPipeline.from_pretrained(
												    '''stabilityai/stable-diffusion-2-base'''    ,		safety_checker=UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							LMSDiscreteScheduler.from_config(pipe.scheduler.config						)
												pipe.to(UpperCamelCase__						)
												pipe.set_progress_bar_config(disable=UpperCamelCase__						)
												pipe.enable_attention_slicing()

												SCREAMING_SNAKE_CASE							:    Dict							=							self.get_inputs()
												SCREAMING_SNAKE_CASE							:    Any							=							pipe(**UpperCamelCase__						).images
												SCREAMING_SNAKE_CASE							:    Any							=							image[0, -3:, -3:, -1].flatten()

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

												SCREAMING_SNAKE_CASE							:    List[str]							=							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	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							0

												def callback_fn(UpperCamelCase__	:				int    ,		UpperCamelCase__	:				int    ,		UpperCamelCase__	:				torch.FloatTensor						) -> None:
																		SCREAMING_SNAKE_CASE							:    Dict							=							True
																		nonlocal number_of_steps
																		number_of_steps += 1
																		if step == 1:
																								SCREAMING_SNAKE_CASE							:    Optional[int]							=							latents.detach().cpu().numpy()
																								assert latents.shape == (1, 4, 64, 256)
																								SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							latents[0, -3:, -3:, -1]

																								SCREAMING_SNAKE_CASE							:    Dict							=							np.array(
																								    [
																								        0.1868_1869,
																								        0.3390_7816,
																								        0.536_1276,
																								        0.1443_2865,
																								        -0.0285_6611,
																								        -0.7394_1123,
																								        0.2339_7987,
																								        0.4732_2682,
																								        -0.3782_3164,
																								    ]						)
																								assert np.abs(latents_slice.flatten() - expected_slice						).max() < 5E-2
																		elif step == 2:
																								SCREAMING_SNAKE_CASE							:    Dict							=							latents.detach().cpu().numpy()
																								assert latents.shape == (1, 4, 64, 256)
																								SCREAMING_SNAKE_CASE							:    Optional[Any]							=							latents[0, -3:, -3:, -1]

																								SCREAMING_SNAKE_CASE							:    int							=							np.array(
																								    [
																								        0.1853_9645,
																								        0.3398_7248,
																								        0.537_8559,
																								        0.1443_7142,
																								        -0.0245_5261,
																								        -0.733_8317,
																								        0.2399_0755,
																								        0.4735_6272,
																								        -0.378_6505,
																								    ]						)

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

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							False

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

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.get_inputs()
												pipe(**UpperCamelCase__    ,		callback=UpperCamelCase__    ,		callback_steps=1						)
												assert callback_fn.has_been_called
												assert number_of_steps == 3



						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





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

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

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							self.get_inputs()
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							pipe(**UpperCamelCase__						)

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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCamelCase		:       List[Any]									=     {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       Optional[Any]									=     ['GLPNFeatureExtractor']
					__UpperCamelCase		:       List[str]									=     ['GLPNImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       str									=     [
					    'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GLPNForDepthEstimation',
					    'GLPNLayer',
					    'GLPNModel',
					    'GLPNPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_glpn import GLPNFeatureExtractor
										from .image_processing_glpn import GLPNImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_glpn import (
										    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GLPNForDepthEstimation,
										    GLPNLayer,
										    GLPNModel,
										    GLPNPreTrainedModel,
										)


else:
					import sys

					__UpperCamelCase		:       Optional[Any]									=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





