



'''simple docstring'''


import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
						    MODEL_MAPPING,
						    LevitForImageClassification,
						    LevitForImageClassificationWithTeacher,
						    LevitModel,
						)
						from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import LevitImageProcessor




class      __UpperCamelCase       (_UpperCAmelCase			):







	def   _a   (    self						)    ->    Any:
							'''simple docstring'''






							lowercase        =							self.config_class(**self.inputs_dict						)
							self.parent.assertTrue(hasattr(_lowerCAmelCase ,				"""hidden_sizes"""						)						)
							self.parent.assertTrue(hasattr(_lowerCAmelCase ,				"""num_attention_heads"""						)						)




class      __UpperCamelCase       :







	def __init__(    self ,				_lowerCAmelCase ,				_lowerCAmelCase=13 ,				_lowerCAmelCase=64 ,				_lowerCAmelCase=3 ,				_lowerCAmelCase=3 ,				_lowerCAmelCase=2 ,				_lowerCAmelCase=1 ,				_lowerCAmelCase=16 ,				_lowerCAmelCase=[128, 256, 384] ,				_lowerCAmelCase=[4, 6, 8] ,				_lowerCAmelCase=[2, 3, 4] ,				_lowerCAmelCase=[16, 16, 16] ,				_lowerCAmelCase=0 ,				_lowerCAmelCase=[2, 2, 2] ,				_lowerCAmelCase=[2, 2, 2] ,				_lowerCAmelCase=0.02 ,				_lowerCAmelCase=True ,				_lowerCAmelCase=True ,				_lowerCAmelCase=2 ,				)    ->    int:
							'''simple docstring'''






							lowercase        =							parent
							lowercase        =							batch_size
							lowercase        =							image_size
							lowercase        =							num_channels
							lowercase        =							kernel_size
							lowercase        =							stride
							lowercase        =							padding
							lowercase        =							hidden_sizes
							lowercase        =							num_attention_heads
							lowercase        =							depths
							lowercase        =							key_dim
							lowercase        =							drop_path_rate
							lowercase        =							patch_size
							lowercase        =							attention_ratio
							lowercase        =							mlp_ratio
							lowercase        =							initializer_range
							lowercase        =							[
							    ["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
							    ["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
							]
							lowercase        =							is_training
							lowercase        =							use_labels
							lowercase        =							num_labels
							lowercase        =							initializer_range







	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

							lowercase        =							None
							if self.use_labels:
													lowercase        =							ids_tensor([self.batch_size] ,				self.num_labels						)

							lowercase        =							self.get_config()
							return config, pixel_values, labels







	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






							return LevitConfig(
							    image_size=self.image_size ,				num_channels=self.num_channels ,				kernel_size=self.kernel_size ,				stride=self.stride ,				padding=self.padding ,				patch_size=self.patch_size ,				hidden_sizes=self.hidden_sizes ,				num_attention_heads=self.num_attention_heads ,				depths=self.depths ,				key_dim=self.key_dim ,				drop_path_rate=self.drop_path_rate ,				mlp_ratio=self.mlp_ratio ,				attention_ratio=self.attention_ratio ,				initializer_range=self.initializer_range ,				down_ops=self.down_ops ,				)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)    ->    Any:
							'''simple docstring'''






							lowercase        =							LevitModel(config=_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.eval()
							lowercase        =							model(_lowerCAmelCase						)
							lowercase        =							(self.image_size, self.image_size)
							lowercase     ,   lowercase        =							image_size[0], image_size[1]
							for _ in range(4						):
													lowercase        =							floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1						)
													lowercase        =							floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1						)
							self.parent.assertEqual(
							    result.last_hidden_state.shape ,				(self.batch_size, ceil(height / 4						) * ceil(width / 4						), self.hidden_sizes[-1]) ,				)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							self.num_labels
							lowercase        =							LevitForImageClassification(_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.eval()
							lowercase        =							model(_lowerCAmelCase ,				labels=_lowerCAmelCase						)
							self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)						)







	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							self.prepare_config_and_inputs()
							lowercase     ,   lowercase     ,   lowercase        =							config_and_inputs
							lowercase        =							{"""pixel_values""": pixel_values}
							return config, inputs_dict




@require_torch
class      __UpperCamelCase       (_UpperCAmelCase			,   _UpperCAmelCase			,   unittest.TestCase			):
	__A			  =					(
	    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
	    if is_torch_available()
	    else ()
	)
	__A			  =					(
	    {
	        """feature-extraction""": LevitModel,
	        """image-classification""": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
	    }
	    if is_torch_available()
	    else {}
	)

	__A			  =					False
	__A			  =					False
	__A			  =					False
	__A			  =					False
	__A			  =					False







	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






							lowercase        =							LevitModelTester(self						)
							lowercase        =							ConfigTester(self ,				config_class=_lowerCAmelCase ,				has_text_modality=_lowerCAmelCase ,				hidden_size=37						)







	def   _a   (    self						)    ->    Optional[int]:
							'''simple docstring'''






							self.create_and_test_config_common_properties()
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							return







	@unittest.skip(reason="""Levit does not use inputs_embeds"""						)
	def   _a   (    self						)    ->    Any:
							'''simple docstring'''






							pass







	@unittest.skip(reason="""Levit does not support input and output embeddings"""						)
	def   _a   (    self						)    ->    Optional[int]:
							'''simple docstring'''






							pass







	@unittest.skip(reason="""Levit does not output attentions"""						)
	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							pass







	def   _a   (    self						)    ->    Any:
							'''simple docstring'''






							lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowercase        =							model_class(_lowerCAmelCase						)
													lowercase        =							inspect.signature(model.forward						)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													lowercase        =							[*signature.parameters.keys()]

													lowercase        =							["""pixel_values"""]
													self.assertListEqual(arg_names[:1] ,				_lowerCAmelCase						)







	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






							def check_hidden_states_output(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						):
													lowercase        =							model_class(_lowerCAmelCase						)
													model.to(_lowerCAmelCase						)
													model.eval()

													with torch.no_grad():
																			lowercase        =							model(**self._prepare_for_class(_lowerCAmelCase ,				_lowerCAmelCase						)						)

													lowercase        =							outputs.hidden_states

													lowercase        =							len(self.model_tester.depths						) + 1
													self.assertEqual(len(_lowerCAmelCase						) ,				_lowerCAmelCase						)

													lowercase        =							(self.model_tester.image_size, self.model_tester.image_size)
													lowercase     ,   lowercase        =							image_size[0], image_size[1]
													for _ in range(4						):
																			lowercase        =							floor(
																			    (
																			        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																			        / self.model_tester.stride
																			    )
																			    + 1						)
																			lowercase        =							floor(
																			    (
																			        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																			        / self.model_tester.stride
																			    )
																			    + 1						)
													# verify the first hidden states (first block)
													self.assertListEqual(
													    list(hidden_states[0].shape[-2:]						) ,				[
													        height * width,
													        self.model_tester.hidden_sizes[0],
													    ] ,				)

							lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowercase        =							True
													check_hidden_states_output(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													lowercase        =							True

													check_hidden_states_output(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)







	@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""						)
	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							pass







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase=False						)    ->    Dict:
							'''simple docstring'''






							lowercase        =							super()._prepare_for_class(_lowerCAmelCase ,				_lowerCAmelCase ,				return_labels=_lowerCAmelCase						)

							if return_labels:
													if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																			del inputs_dict["labels"]

							return inputs_dict







	def   _a   (    self						)    ->    Union[str, Any]:
							'''simple docstring'''






							lowercase        =							self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_lowerCAmelCase						)







	def   _a   (    self						)    ->    Optional[int]:
							'''simple docstring'''






							lowercase        =							self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase						)







	def   _a   (    self						)    ->    Any:
							'''simple docstring'''






							if not self.model_tester.is_training:
													return

							lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs_for_common()
							lowercase        =							True

							for model_class in self.all_model_classes:
													# LevitForImageClassificationWithTeacher supports inference-only
													if (
													    model_class in get_values(_lowerCAmelCase						)
													    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
													):
																			continue
													lowercase        =							model_class(_lowerCAmelCase						)
													model.to(_lowerCAmelCase						)
													model.train()
													lowercase        =							self._prepare_for_class(_lowerCAmelCase ,				_lowerCAmelCase ,				return_labels=_lowerCAmelCase						)
													lowercase        =							model(**_lowerCAmelCase						).loss
													loss.backward()







	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs_for_common()
							if not self.model_tester.is_training:
													return

							lowercase        =							False
							lowercase        =							True

							for model_class in self.all_model_classes:
													if model_class in get_values(_lowerCAmelCase						) or not model_class.supports_gradient_checkpointing:
																			continue
													# LevitForImageClassificationWithTeacher supports inference-only
													if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																			continue
													lowercase        =							model_class(_lowerCAmelCase						)
													model.gradient_checkpointing_enable()
													model.to(_lowerCAmelCase						)
													model.train()
													lowercase        =							self._prepare_for_class(_lowerCAmelCase ,				_lowerCAmelCase ,				return_labels=_lowerCAmelCase						)
													lowercase        =							model(**_lowerCAmelCase						).loss
													loss.backward()







	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs_for_common()

							lowercase        =							[
							    {"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
							    {"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
							    {"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
							]

							for model_class in self.all_model_classes:
													if (
													    model_class
													    not in [
													        *get_values(_lowerCAmelCase						),
													    ]
													    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
													):
																			continue

													for problem_type in problem_types:
																			with self.subTest(msg=F"""Testing {model_class} with {problem_type['title']}"""						):
																									lowercase        =							problem_type["""title"""]
																									lowercase        =							problem_type["""num_labels"""]

																									lowercase        =							model_class(_lowerCAmelCase						)
																									model.to(_lowerCAmelCase						)
																									model.train()

																									lowercase        =							self._prepare_for_class(_lowerCAmelCase ,				_lowerCAmelCase ,				return_labels=_lowerCAmelCase						)

																									if problem_type["num_labels"] > 1:
																															lowercase        =							inputs["""labels"""].unsqueeze(1						).repeat(1 ,				problem_type["""num_labels"""]						)

																									lowercase        =							inputs["""labels"""].to(problem_type["""dtype"""]						)

																									# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																									# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																									# they have the same size." which is a symptom something in wrong for the regression problem.
																									# See https://github.com/huggingface/transformers/issues/11780
																									with warnings.catch_warnings(record=_lowerCAmelCase						) as warning_list:
																															lowercase        =							model(**_lowerCAmelCase						).loss
																									for w in warning_list:
																															if "Using a target size that is different to the input size" in str(w.message						):
																																					raise ValueError(
																																					    F"""Something is going wrong in the regression problem: intercepted {w.message}"""						)

																									loss.backward()







	@slow
	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowercase        =							LevitModel.from_pretrained(_lowerCAmelCase						)
													self.assertIsNotNone(_lowerCAmelCase						)







def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
						return image




@require_torch
@require_vision
class      __UpperCamelCase       (unittest.TestCase			):







	@cached_property
	def   _a   (    self						)    ->    Any:
							'''simple docstring'''






							return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)







	@slow
	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(
							    _lowerCAmelCase						)

							lowercase        =							self.default_image_processor
							lowercase        =							prepare_img()
							lowercase        =							image_processor(images=_lowerCAmelCase ,				return_tensors="""pt"""						).to(_lowerCAmelCase						)

							# forward pass
							with torch.no_grad():
													lowercase        =							model(**_lowerCAmelCase						)

							# verify the logits
							lowercase        =							torch.Size((1, 1000)						)
							self.assertEqual(outputs.logits.shape ,				_lowerCAmelCase						)

							lowercase        =							torch.tensor([1.0448, -0.3745, -1.8317]						).to(_lowerCAmelCase						)

							self.assertTrue(torch.allclose(outputs.logits[0, :3] ,				_lowerCAmelCase ,				atol=1E-4						)						)







'''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      __UpperCamelCase       (_UpperCAmelCase			,   _UpperCAmelCase			,   unittest.TestCase			):
	__A			  =					StableDiffusionPanoramaPipeline
	__A			  =					TEXT_TO_IMAGE_PARAMS
	__A			  =					TEXT_TO_IMAGE_BATCH_PARAMS
	__A			  =					TEXT_TO_IMAGE_IMAGE_PARAMS
	__A			  =					TEXT_TO_IMAGE_IMAGE_PARAMS







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							torch.manual_seed(0						)
							lowercase        =							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 ,				)
							lowercase        =							DDIMScheduler()
							torch.manual_seed(0						)
							lowercase        =							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						)
							lowercase        =							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 ,				)
							lowercase        =							CLIPTextModel(_lowerCAmelCase						)
							lowercase        =							CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)

							lowercase        =							{
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """vae""": vae,
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """safety_checker""": None,
							    """feature_extractor""": None,
							}
							return components







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase=0						)    ->    Optional[int]:
							'''simple docstring'''






							lowercase        =							torch.manual_seed(_lowerCAmelCase						)
							lowercase        =							{
							    """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						)    ->    int:
							'''simple docstring'''






							lowercase        =							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowercase        =							self.get_dummy_components()
							lowercase        =							StableDiffusionPanoramaPipeline(**_lowerCAmelCase						)
							lowercase        =							sd_pipe.to(_lowerCAmelCase						)
							sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

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

							lowercase        =							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						)    ->    Union[str, Any]:
							'''simple docstring'''






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







	def   _a   (    self						)    ->    str:
							'''simple docstring'''






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







	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							lowercase        =							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowercase        =							self.get_dummy_components()
							lowercase        =							StableDiffusionPanoramaPipeline(**_lowerCAmelCase						)
							lowercase        =							sd_pipe.to(_lowerCAmelCase						)
							sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

							lowercase        =							self.get_dummy_inputs(_lowerCAmelCase						)
							lowercase        =							"""french fries"""
							lowercase        =							sd_pipe(**_lowerCAmelCase ,				negative_prompt=_lowerCAmelCase						)
							lowercase        =							output.images
							lowercase        =							image[0, -3:, -3:, -1]

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

							lowercase        =							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'''






							lowercase        =							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowercase        =							self.get_dummy_components()
							lowercase        =							StableDiffusionPanoramaPipeline(**_lowerCAmelCase						)
							lowercase        =							sd_pipe.to(_lowerCAmelCase						)
							sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

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

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

							lowercase        =							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						)    ->    Any:
							'''simple docstring'''






							lowercase        =							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowercase        =							self.get_dummy_components()
							lowercase        =							EulerAncestralDiscreteScheduler(
							    beta_start=0.0_0085 ,				beta_end=0.012 ,				beta_schedule="""scaled_linear"""						)
							lowercase        =							StableDiffusionPanoramaPipeline(**_lowerCAmelCase						)
							lowercase        =							sd_pipe.to(_lowerCAmelCase						)
							sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

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

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

							lowercase        =							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						)    ->    Dict:
							'''simple docstring'''






							lowercase        =							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowercase        =							self.get_dummy_components()
							lowercase        =							PNDMScheduler(
							    beta_start=0.0_0085 ,				beta_end=0.012 ,				beta_schedule="""scaled_linear""" ,				skip_prk_steps=_lowerCAmelCase						)
							lowercase        =							StableDiffusionPanoramaPipeline(**_lowerCAmelCase						)
							lowercase        =							sd_pipe.to(_lowerCAmelCase						)
							sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

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

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

							lowercase        =							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			):







	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






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







	def   _a   (    self ,				_lowerCAmelCase=0						)    ->    Optional[int]:
							'''simple docstring'''






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







	def   _a   (    self						)    ->    Union[str, Any]:
							'''simple docstring'''






							lowercase        =							"""stabilityai/stable-diffusion-2-base"""
							lowercase        =							DDIMScheduler.from_pretrained(_lowerCAmelCase ,				subfolder="""scheduler"""						)
							lowercase        =							StableDiffusionPanoramaPipeline.from_pretrained(_lowerCAmelCase ,				scheduler=_lowerCAmelCase ,				safety_checker=_lowerCAmelCase						)
							pipe.to(_lowerCAmelCase						)
							pipe.set_progress_bar_config(disable=_lowerCAmelCase						)
							pipe.enable_attention_slicing()

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

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

							lowercase        =							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						)    ->    str:
							'''simple docstring'''






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

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

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

							lowercase        =							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						)    ->    Any:
							'''simple docstring'''






							lowercase        =							0

							def callback_fn(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						) -> None:
													lowercase        =							True
													nonlocal number_of_steps
													number_of_steps += 1
													if step == 1:
																			lowercase        =							latents.detach().cpu().numpy()
																			assert latents.shape == (1, 4, 64, 256)
																			lowercase        =							latents[0, -3:, -3:, -1]

																			lowercase        =							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:
																			lowercase        =							latents.detach().cpu().numpy()
																			assert latents.shape == (1, 4, 64, 256)
																			lowercase        =							latents[0, -3:, -3:, -1]

																			lowercase        =							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

							lowercase        =							False

							lowercase        =							"""stabilityai/stable-diffusion-2-base"""
							lowercase        =							DDIMScheduler.from_pretrained(_lowerCAmelCase ,				subfolder="""scheduler"""						)
							lowercase        =							StableDiffusionPanoramaPipeline.from_pretrained(_lowerCAmelCase ,				scheduler=_lowerCAmelCase ,				safety_checker=_lowerCAmelCase						)
							lowercase        =							pipe.to(_lowerCAmelCase						)
							pipe.set_progress_bar_config(disable=_lowerCAmelCase						)
							pipe.enable_attention_slicing()

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







	def   _a   (    self						)    ->    int:
							'''simple docstring'''






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

							lowercase        =							"""stabilityai/stable-diffusion-2-base"""
							lowercase        =							DDIMScheduler.from_pretrained(_lowerCAmelCase ,				subfolder="""scheduler"""						)
							lowercase        =							StableDiffusionPanoramaPipeline.from_pretrained(_lowerCAmelCase ,				scheduler=_lowerCAmelCase ,				safety_checker=_lowerCAmelCase						)
							lowercase        =							pipe.to(_lowerCAmelCase						)
							pipe.set_progress_bar_config(disable=_lowerCAmelCase						)
							pipe.enable_attention_slicing(1						)
							pipe.enable_sequential_cpu_offload()

							lowercase        =							self.get_inputs()
							lowercase        =							pipe(**_lowerCAmelCase						)

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


