'''simple docstring'''




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     a__      (   UpperCAmelCase__							,   UpperCAmelCase__							,   unittest.TestCase ):
     lowerCamelCase  :     int							=StableDiffusionPanoramaPipeline
     lowerCamelCase  :     Dict							=TEXT_TO_IMAGE_PARAMS
     lowerCamelCase  :     Union[str, Any]							=TEXT_TO_IMAGE_BATCH_PARAMS
     lowerCamelCase  :     Dict							=TEXT_TO_IMAGE_IMAGE_PARAMS
     lowerCamelCase  :     int							=TEXT_TO_IMAGE_IMAGE_PARAMS






     def    SCREAMING_SNAKE_CASE__   ( self		:		Dict    ):





         """simple docstring"""



         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(a    )
         __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    SCREAMING_SNAKE_CASE__   ( self		:		List[str]  ,				a		:		Dict  ,				a		:		Union[str, Any]=0    ):





         """simple docstring"""



         __lowerCamelCase		=  torch.manual_seed(a    )
         __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    SCREAMING_SNAKE_CASE__   ( self		:		Union[str, Any]    ):





         """simple docstring"""



         __lowerCamelCase		=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase		=  self.get_dummy_components()
         __lowerCamelCase		=  StableDiffusionPanoramaPipeline(**a    )
         __lowerCamelCase		=  sd_pipe.to(a    )
         sd_pipe.set_progress_bar_config(disable=a    )

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

         __lowerCamelCase		=  np.array([0.61_86, 0.53_74, 0.49_15, 0.41_35, 0.41_14, 0.45_63, 0.51_28, 0.49_77, 0.47_57]    )

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






     def    SCREAMING_SNAKE_CASE__   ( self		:		Tuple    ):





         """simple docstring"""



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






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[Any]    ):





         """simple docstring"""



         super().test_inference_batch_single_identical(batch_size=2  ,				expected_max_diff=3.2_5e-3    )






     def    SCREAMING_SNAKE_CASE__   ( self		:		List[Any]    ):





         """simple docstring"""



         __lowerCamelCase		=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase		=  self.get_dummy_components()
         __lowerCamelCase		=  StableDiffusionPanoramaPipeline(**a    )
         __lowerCamelCase		=  sd_pipe.to(a    )
         sd_pipe.set_progress_bar_config(disable=a    )

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

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

         __lowerCamelCase		=  np.array([0.61_87, 0.53_75, 0.49_15, 0.41_36, 0.41_14, 0.45_63, 0.51_28, 0.49_76, 0.47_57]    )

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






     def    SCREAMING_SNAKE_CASE__   ( self		:		Dict    ):





         """simple docstring"""



         __lowerCamelCase		=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase		=  self.get_dummy_components()
         __lowerCamelCase		=  StableDiffusionPanoramaPipeline(**a    )
         __lowerCamelCase		=  sd_pipe.to(a    )
         sd_pipe.set_progress_bar_config(disable=a    )

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

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

         __lowerCamelCase		=  np.array([0.61_87, 0.53_75, 0.49_15, 0.41_36, 0.41_14, 0.45_63, 0.51_28, 0.49_76, 0.47_57]    )

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






     def    SCREAMING_SNAKE_CASE__   ( self		:		Any    ):





         """simple docstring"""



         __lowerCamelCase		=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase		=  self.get_dummy_components()
         __lowerCamelCase		=  EulerAncestralDiscreteScheduler(
             beta_start=0.0_00_85  ,				beta_end=0.0_12  ,				beta_schedule='''scaled_linear'''    )
         __lowerCamelCase		=  StableDiffusionPanoramaPipeline(**a    )
         __lowerCamelCase		=  sd_pipe.to(a    )
         sd_pipe.set_progress_bar_config(disable=a    )

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

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

         __lowerCamelCase		=  np.array([0.40_24, 0.65_10, 0.49_01, 0.53_78, 0.58_13, 0.56_22, 0.47_95, 0.44_67, 0.49_52]    )

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






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





         """simple docstring"""



         __lowerCamelCase		=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase		=  self.get_dummy_components()
         __lowerCamelCase		=  PNDMScheduler(
             beta_start=0.0_00_85  ,				beta_end=0.0_12  ,				beta_schedule='''scaled_linear'''  ,				skip_prk_steps=a    )
         __lowerCamelCase		=  StableDiffusionPanoramaPipeline(**a    )
         __lowerCamelCase		=  sd_pipe.to(a    )
         sd_pipe.set_progress_bar_config(disable=a    )

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

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

         __lowerCamelCase		=  np.array([0.63_91, 0.62_91, 0.48_61, 0.51_34, 0.55_52, 0.45_78, 0.50_32, 0.50_23, 0.45_39]    )

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




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






     def    SCREAMING_SNAKE_CASE__   ( self		:		Any    ):





         """simple docstring"""



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






     def    SCREAMING_SNAKE_CASE__   ( self		:		str  ,				a		:		str=0    ):





         """simple docstring"""



         __lowerCamelCase		=  torch.manual_seed(a    )
         __lowerCamelCase		=  {
             '''prompt''': '''a photo of the dolomites''',
             '''generator''': generator,
             '''num_inference_steps''': 3,
             '''guidance_scale''': 7.5,
             '''output_type''': '''numpy''',
         }
         return inputs






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





         """simple docstring"""



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

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

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

         __lowerCamelCase		=  np.array(
             [
                 0.36_96_83_92,
                 0.27_02_53_72,
                 0.32_44_67_66,
                 0.28_37_93_87,
                 0.36_36_32_74,
                 0.30_73_33_47,
                 0.27_10_00_27,
                 0.27_05_41_25,
                 0.25_53_60_96,
             ]    )

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






     def    SCREAMING_SNAKE_CASE__   ( self		:		Any    ):





         """simple docstring"""



         __lowerCamelCase		=  StableDiffusionPanoramaPipeline.from_pretrained(
             '''stabilityai/stable-diffusion-2-base'''  ,				safety_checker=a    )
         __lowerCamelCase		=  LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
         pipe.to(a    )
         pipe.set_progress_bar_config(disable=a    )
         pipe.enable_attention_slicing()

         __lowerCamelCase		=  self.get_inputs()
         __lowerCamelCase		=  pipe(**a    ).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    SCREAMING_SNAKE_CASE__   ( self		:		Dict    ):





         """simple docstring"""



         __lowerCamelCase		=  0

         def callback_fn(a		:		int  ,				a		:		int  ,				a		:		torch.FloatTensor    ) -> 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(
                     [
                         0.18_68_18_69,
                         0.33_90_78_16,
                         0.5_36_12_76,
                         0.14_43_28_65,
                         -0.02_85_66_11,
                         -0.73_94_11_23,
                         0.23_39_79_87,
                         0.47_32_26_82,
                         -0.37_82_31_64,
                     ]    )
                 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(
                     [
                         0.18_53_96_45,
                         0.33_98_72_48,
                         0.5_37_85_59,
                         0.14_43_71_42,
                         -0.02_45_52_61,
                         -0.7_33_83_17,
                         0.23_99_07_55,
                         0.47_35_62_72,
                         -0.3_78_65_05,
                     ]    )

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

         __lowerCamelCase		=  False

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

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






     def    SCREAMING_SNAKE_CASE__   ( self		:		Tuple    ):





         """simple docstring"""



         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(a  ,				subfolder='''scheduler'''    )
         __lowerCamelCase		=  StableDiffusionPanoramaPipeline.from_pretrained(a  ,				scheduler=a  ,				safety_checker=a    )
         __lowerCamelCase		=  pipe.to(a    )
         pipe.set_progress_bar_config(disable=a    )
         pipe.enable_attention_slicing(1    )
         pipe.enable_sequential_cpu_offload()

         __lowerCamelCase		=  self.get_inputs()
         __lowerCamelCase		=  pipe(**a    )

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




import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class   _lowerCamelCase (					UpperCamelCase					,							unittest.TestCase			):




       """simple docstring"""
       snake_case      =    DDIMPipeline
       snake_case      =    UNCONDITIONAL_IMAGE_GENERATION_PARAMS
       snake_case      =    PipelineTesterMixin.required_optional_params - {
           "num_images_per_prompt",
           "latents",
           "callback",
           "callback_steps",
       }
       snake_case      =    UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
       snake_case      =    False



       def        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





         torch.manual_seed(0		)
         A_      :				List[str]		     =					UNetaDModel(
             block_out_channels=(32, 64)				,       layers_per_block=2				,       sample_size=32				,       in_channels=3				,       out_channels=3				,       down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')				,       up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')				,       )
         A_      :				Optional[Any]		     =					DDIMScheduler()
         A_      :				str		     =					{'''unet''': unet, '''scheduler''': scheduler}
         return components



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=0		)->Optional[Any]:




         '''simple docstring'''





         if str(_SCREAMING_SNAKE_CASE		).startswith('''mps'''		):
           A_      :				Any		     =					torch.manual_seed(_SCREAMING_SNAKE_CASE		)
         else:
           A_      :				Optional[int]		     =					torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
         A_      :				Any		     =					{
             '''batch_size''': 1,
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }
         return inputs



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




         '''simple docstring'''





         A_      :				Optional[int]		     =					'''cpu'''

         A_      :				Dict		     =					self.get_dummy_components()
         A_      :				str		     =					self.pipeline_class(**_SCREAMING_SNAKE_CASE		)
         pipe.to(_SCREAMING_SNAKE_CASE		)
         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				str		     =					self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
         A_      :				Any		     =					pipe(**_SCREAMING_SNAKE_CASE		).images
         A_      :				int		     =					image[0, -3:, -3:, -1]

         self.assertEqual(image.shape				,       (1, 32, 32, 3)		)
         A_      :				List[Any]		     =					np.array(
             [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]		)
         A_      :				str		     =					np.abs(image_slice.flatten() - expected_slice		).max()
         self.assertLessEqual(_SCREAMING_SNAKE_CASE				,       1e-3		)



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




         '''simple docstring'''





         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3		)



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




         '''simple docstring'''





         super().test_save_load_local(expected_max_difference=3e-3		)



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




         '''simple docstring'''





         super().test_save_load_optional_components(expected_max_difference=3e-3		)







       def        _snake_case       (      self		)->Any:




         '''simple docstring'''





         super().test_inference_batch_single_identical(expected_max_diff=3e-3		)






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




       """simple docstring"""



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




         '''simple docstring'''





         A_      :				int		     =					'''google/ddpm-cifar10-32'''

         A_      :				Tuple		     =					UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
         A_      :				str		     =					DDIMScheduler()

         A_      :				str		     =					DDIMPipeline(unet=_SCREAMING_SNAKE_CASE				,       scheduler=_SCREAMING_SNAKE_CASE		)
         ddim.to(_SCREAMING_SNAKE_CASE		)
         ddim.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[int]		     =					torch.manual_seed(0		)
         A_      :				Any		     =					ddim(generator=_SCREAMING_SNAKE_CASE				,       eta=0.0				,       output_type='''numpy'''		).images

         A_      :				Any		     =					image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A_      :				Any		     =					np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3]		)

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







       def        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





         A_      :				Tuple		     =					'''google/ddpm-ema-bedroom-256'''

         A_      :				int		     =					UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
         A_      :				Any		     =					DDIMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[Any]		     =					DDIMPipeline(unet=_SCREAMING_SNAKE_CASE				,       scheduler=_SCREAMING_SNAKE_CASE		)
         ddpm.to(_SCREAMING_SNAKE_CASE		)
         ddpm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				Dict		     =					torch.manual_seed(0		)
         A_      :				List[str]		     =					ddpm(generator=_SCREAMING_SNAKE_CASE				,       output_type='''numpy'''		).images

         A_      :				Any		     =					image[0, -3:, -3:, -1]

         assert image.shape == (1, 256, 256, 3)
         A_      :				Tuple		     =					np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9]		)

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




