





"""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 							lowercase__			(   __SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE					,     unittest.TestCase	):

			"""simple docstring"""
			__lowerCAmelCase     :				Tuple										=  StableDiffusionPanoramaPipeline
			__lowerCAmelCase     :				int										=  TEXT_TO_IMAGE_PARAMS
			__lowerCAmelCase     :				Union[str, Any]										=  TEXT_TO_IMAGE_BATCH_PARAMS
			__lowerCAmelCase     :				List[str]										=  TEXT_TO_IMAGE_IMAGE_PARAMS
			__lowerCAmelCase     :				Optional[Any]										=  TEXT_TO_IMAGE_IMAGE_PARAMS







			def   _a      (      self    ):






									'''simple docstring'''







									torch.manual_seed(0    )
									UpperCamelCase				:		int				   =			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				:		str				   =			DDIMScheduler()
									torch.manual_seed(0    )
									UpperCamelCase				:		Dict				   =			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				:		Tuple				   =			CLIPTextConfig(
									    bos_token_id=0						,   eos_token_id=2						,   hidden_size=3_2						,   intermediate_size=3_7						,   layer_norm_eps=1e-0_5						,   num_attention_heads=4						,   num_hidden_layers=5						,   pad_token_id=1						,   vocab_size=1_0_0_0						,   )
									UpperCamelCase				:		Union[str, Any]				   =			CLIPTextModel(_A    )
									UpperCamelCase				:		str				   =			CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

									UpperCamelCase				:		Optional[Any]				   =			{
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """vae""": vae,
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """safety_checker""": None,
									    """feature_extractor""": None,
									}
									return components







			def   _a      (      self						,   _A						,   _A=0    ):






									'''simple docstring'''







									UpperCamelCase				:		Tuple				   =			torch.manual_seed(_A    )
									UpperCamelCase				:		Optional[Any]				   =			{
									    """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    ):






									'''simple docstring'''







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

									UpperCamelCase				:		str				   =			self.get_dummy_inputs(_A    )
									UpperCamelCase				:		Any				   =			sd_pipe(**_A    ).images
									UpperCamelCase				:		Optional[int]				   =			image[0, -3:, -3:, -1]
									assert image.shape == (1, 6_4, 6_4, 3)

									UpperCamelCase				:		Optional[Any]				   =			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   _a      (      self    ):






									'''simple docstring'''







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







			def   _a      (      self    ):






									'''simple docstring'''







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







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			"""cpu"""  # ensure determinism for the device-dependent torch.Generator
									UpperCamelCase				:		int				   =			self.get_dummy_components()
									UpperCamelCase				:		List[Any]				   =			StableDiffusionPanoramaPipeline(**_A    )
									UpperCamelCase				:		List[str]				   =			sd_pipe.to(_A    )
									sd_pipe.set_progress_bar_config(disable=_A    )

									UpperCamelCase				:		List[Any]				   =			self.get_dummy_inputs(_A    )
									UpperCamelCase				:		List[Any]				   =			"""french fries"""
									UpperCamelCase				:		int				   =			sd_pipe(**_A						,   negative_prompt=_A    )
									UpperCamelCase				:		Optional[int]				   =			output.images
									UpperCamelCase				:		Union[str, Any]				   =			image[0, -3:, -3:, -1]

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

									UpperCamelCase				:		Optional[int]				   =			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   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			"""cpu"""  # ensure determinism for the device-dependent torch.Generator
									UpperCamelCase				:		Dict				   =			self.get_dummy_components()
									UpperCamelCase				:		Any				   =			StableDiffusionPanoramaPipeline(**_A    )
									UpperCamelCase				:		List[str]				   =			sd_pipe.to(_A    )
									sd_pipe.set_progress_bar_config(disable=_A    )

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

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

									UpperCamelCase				:		Union[str, Any]				   =			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   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		str				   =			"""cpu"""  # ensure determinism for the device-dependent torch.Generator
									UpperCamelCase				:		List[str]				   =			self.get_dummy_components()
									UpperCamelCase				:		Optional[Any]				   =			EulerAncestralDiscreteScheduler(
									    beta_start=0.0_00_85						,   beta_end=0.0_12						,   beta_schedule="""scaled_linear"""    )
									UpperCamelCase				:		Optional[int]				   =			StableDiffusionPanoramaPipeline(**_A    )
									UpperCamelCase				:		str				   =			sd_pipe.to(_A    )
									sd_pipe.set_progress_bar_config(disable=_A    )

									UpperCamelCase				:		Any				   =			self.get_dummy_inputs(_A    )
									UpperCamelCase				:		Optional[Any]				   =			sd_pipe(**_A    ).images
									UpperCamelCase				:		int				   =			image[0, -3:, -3:, -1]

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

									UpperCamelCase				:		Any				   =			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   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		List[Any]				   =			"""cpu"""  # ensure determinism for the device-dependent torch.Generator
									UpperCamelCase				:		Optional[int]				   =			self.get_dummy_components()
									UpperCamelCase				:		Any				   =			PNDMScheduler(
									    beta_start=0.0_00_85						,   beta_end=0.0_12						,   beta_schedule="""scaled_linear"""						,   skip_prk_steps=_A    )
									UpperCamelCase				:		Any				   =			StableDiffusionPanoramaPipeline(**_A    )
									UpperCamelCase				:		Dict				   =			sd_pipe.to(_A    )
									sd_pipe.set_progress_bar_config(disable=_A    )

									UpperCamelCase				:		List[Any]				   =			self.get_dummy_inputs(_A    )
									UpperCamelCase				:		int				   =			sd_pipe(**_A    ).images
									UpperCamelCase				:		Dict				   =			image[0, -3:, -3:, -1]

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

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

			"""simple docstring"""







			def   _a      (      self    ):






									'''simple docstring'''







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







			def   _a      (      self						,   _A=0    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[Any]				   =			torch.manual_seed(_A    )
									UpperCamelCase				:		Optional[Any]				   =			{
									    """prompt""": """a photo of the dolomites""",
									    """generator""": generator,
									    """num_inference_steps""": 3,
									    """guidance_scale""": 7.5,
									    """output_type""": """numpy""",
									}
									return inputs







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[Any]				   =			"""stabilityai/stable-diffusion-2-base"""
									UpperCamelCase				:		Union[str, Any]				   =			DDIMScheduler.from_pretrained(_A						,   subfolder="""scheduler"""    )
									UpperCamelCase				:		Union[str, Any]				   =			StableDiffusionPanoramaPipeline.from_pretrained(_A						,   scheduler=_A						,   safety_checker=_A    )
									pipe.to(_A    )
									pipe.set_progress_bar_config(disable=_A    )
									pipe.enable_attention_slicing()

									UpperCamelCase				:		List[Any]				   =			self.get_inputs()
									UpperCamelCase				:		Tuple				   =			pipe(**_A    ).images
									UpperCamelCase				:		List[str]				   =			image[0, -3:, -3:, -1].flatten()

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

									UpperCamelCase				:		Optional[Any]				   =			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   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			StableDiffusionPanoramaPipeline.from_pretrained(
									    """stabilityai/stable-diffusion-2-base"""						,   safety_checker=_A    )
									UpperCamelCase				:		Tuple				   =			LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
									pipe.to(_A    )
									pipe.set_progress_bar_config(disable=_A    )
									pipe.enable_attention_slicing()

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

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

									UpperCamelCase				:		Union[str, 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    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[Any]				   =			0

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

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

																					UpperCamelCase				:		Dict				   =			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

									UpperCamelCase				:		List[Any]				   =			False

									UpperCamelCase				:		Union[str, Any]				   =			"""stabilityai/stable-diffusion-2-base"""
									UpperCamelCase				:		Dict				   =			DDIMScheduler.from_pretrained(_A						,   subfolder="""scheduler"""    )
									UpperCamelCase				:		Optional[Any]				   =			StableDiffusionPanoramaPipeline.from_pretrained(_A						,   scheduler=_A						,   safety_checker=_A    )
									UpperCamelCase				:		int				   =			pipe.to(_A    )
									pipe.set_progress_bar_config(disable=_A    )
									pipe.enable_attention_slicing()

									UpperCamelCase				:		Tuple				   =			self.get_inputs()
									pipe(**_A						,   callback=_A						,   callback_steps=1    )
									assert callback_fn.has_been_called
									assert number_of_steps == 3







			def   _a      (      self    ):






									'''simple docstring'''







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

									UpperCamelCase				:		Optional[Any]				   =			"""stabilityai/stable-diffusion-2-base"""
									UpperCamelCase				:		Any				   =			DDIMScheduler.from_pretrained(_A						,   subfolder="""scheduler"""    )
									UpperCamelCase				:		List[Any]				   =			StableDiffusionPanoramaPipeline.from_pretrained(_A						,   scheduler=_A						,   safety_checker=_A    )
									UpperCamelCase				:		Dict				   =			pipe.to(_A    )
									pipe.set_progress_bar_config(disable=_A    )
									pipe.enable_attention_slicing(1    )
									pipe.enable_sequential_cpu_offload()

									UpperCamelCase				:		int				   =			self.get_inputs()
									UpperCamelCase				:		List[str]				   =			pipe(**_A    )

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










"""simple docstring"""






import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin



class 					__UpperCAmelCase							(						unittest.TestCase						,  _UpperCamelCase  ):
				def       UpperCAmelCase      (		self    :			Dict    )	->	List[Any]:



							'''simple docstring'''



							a__  :	Any		 =			load_tool("text-classification"    )
							self.tool.setup()
							a__  :	List[Any]		 =			load_tool("text-classification"	, remote=a_    )
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	str		 =			self.tool("That's quite cool"	, ["positive", "negative"]    )
							self.assertEqual(a_	, "positive"    )
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Optional[int]:



							'''simple docstring'''



							a__  :	List[str]		 =			self.remote_tool("That's quite cool"	, ["positive", "negative"]    )
							self.assertEqual(a_	, "positive"    )
				def       UpperCAmelCase      (		self    :			Dict    )	->	Any:



							'''simple docstring'''



							a__  :	List[str]		 =			self.tool(text="That's quite cool"	, labels=["positive", "negative"]    )
							self.assertEqual(a_	, "positive"    )






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



							'''simple docstring'''



							a__  :	Tuple		 =			self.remote_tool(text="That's quite cool"	, labels=["positive", "negative"]    )
							self.assertEqual(a_	, "positive"    )