

import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm
       import torch_xla.debug.metrics as met






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  def __init__(							self		:	str			,     *__lowerCamelCase		:	Union[str, Any]			,     __lowerCamelCase		:	List[Any]=None			,     __lowerCamelCase		:	List[Any]=None			,     **__lowerCamelCase		:	int      )	->							Dict:
       super().__init__(*__lowerCamelCase			,     **__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					eval_examples
       SCREAMING_SNAKE_CASE__					=					post_process_function






  def    lowercase_	(							self		:	List[str]			,     __lowerCamelCase		:	Optional[Dataset] = None			,     __lowerCamelCase		:	str=None			,     __lowerCamelCase		:	Optional[List[str]] = None			,     __lowerCamelCase		:	str = "eval"			,     **__lowerCamelCase		:	Optional[int]			,     )	->							Dict[str, float]:
       SCREAMING_SNAKE_CASE__					=					gen_kwargs.copy()
       SCREAMING_SNAKE_CASE__					=					(
           gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length'''      ) is not None else self.args.generation_max_length
       )
       SCREAMING_SNAKE_CASE__					=					(
           gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''      ) is not None else self.args.generation_num_beams
       )
       SCREAMING_SNAKE_CASE__					=					gen_kwargs

       SCREAMING_SNAKE_CASE__					=					self.eval_dataset if eval_dataset is None else eval_dataset
       SCREAMING_SNAKE_CASE__					=					self.get_eval_dataloader(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					self.eval_examples if eval_examples is None else eval_examples

       # Temporarily disable metric computation, we will do it in the loop here.
       SCREAMING_SNAKE_CASE__					=					self.compute_metrics
       SCREAMING_SNAKE_CASE__					=					None
       SCREAMING_SNAKE_CASE__					=					time.time()
       SCREAMING_SNAKE_CASE__					=					self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       try:
            SCREAMING_SNAKE_CASE__					=					eval_loop(
                __lowerCamelCase			,     description='''Evaluation'''			,     prediction_loss_only=True if compute_metrics is None else None			,     ignore_keys=__lowerCamelCase			,     metric_key_prefix=__lowerCamelCase			,     )
       finally:
            SCREAMING_SNAKE_CASE__					=					compute_metrics
       SCREAMING_SNAKE_CASE__					=					self.args.eval_batch_size * self.args.world_size
       if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
            start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
       output.metrics.update(
           speed_metrics(
               __lowerCamelCase			,     __lowerCamelCase			,     num_samples=output.num_samples			,     num_steps=math.ceil(output.num_samples / total_batch_size      )			,     )      )

       if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
            # Only the main node write the results by default
            SCREAMING_SNAKE_CASE__					=					self.post_process_function(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase      )
            SCREAMING_SNAKE_CASE__					=					self.compute_metrics(__lowerCamelCase      )

            # Prefix all keys with metric_key_prefix + '_'
            for key in list(metrics.keys()      ):
                 if not key.startswith(f'''{metric_key_prefix}_'''      ):
                      SCREAMING_SNAKE_CASE__					=					metrics.pop(__lowerCamelCase      )

            metrics.update(output.metrics      )
       else:
            SCREAMING_SNAKE_CASE__					=					output.metrics

       if self.args.should_log:
            # Only the main node log the results by default
            self.log(__lowerCamelCase      )

       if self.args.tpu_metrics_debug or self.args.debug:
            # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
            xm.master_print(met.metrics_report()      )

       SCREAMING_SNAKE_CASE__					=					self.callback_handler.on_evaluate(self.args			,     self.state			,     self.control			,     __lowerCamelCase      )
       return metrics







  def    lowercase_	(							self		:	int			,     __lowerCamelCase		:	Any			,     __lowerCamelCase		:	Dict			,     __lowerCamelCase		:	Optional[int]=None			,     __lowerCamelCase		:	str = "test"			,     **__lowerCamelCase		:	List[Any]      )	->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__					=					gen_kwargs.copy()

       SCREAMING_SNAKE_CASE__					=					self.get_test_dataloader(__lowerCamelCase      )

       # Temporarily disable metric computation, we will do it in the loop here.
       SCREAMING_SNAKE_CASE__					=					self.compute_metrics
       SCREAMING_SNAKE_CASE__					=					None
       SCREAMING_SNAKE_CASE__					=					time.time()
       SCREAMING_SNAKE_CASE__					=					self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       try:
            SCREAMING_SNAKE_CASE__					=					eval_loop(
                __lowerCamelCase			,     description='''Prediction'''			,     prediction_loss_only=True if compute_metrics is None else None			,     ignore_keys=__lowerCamelCase			,     metric_key_prefix=__lowerCamelCase			,     )
       finally:
            SCREAMING_SNAKE_CASE__					=					compute_metrics

       SCREAMING_SNAKE_CASE__					=					self.args.eval_batch_size * self.args.world_size
       if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
            start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
       output.metrics.update(
           speed_metrics(
               __lowerCamelCase			,     __lowerCamelCase			,     num_samples=output.num_samples			,     num_steps=math.ceil(output.num_samples / total_batch_size      )			,     )      )
       if self.post_process_function is None or self.compute_metrics is None:
            return output

       SCREAMING_SNAKE_CASE__					=					self.post_process_function(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     '''predict'''      )
       SCREAMING_SNAKE_CASE__					=					self.compute_metrics(__lowerCamelCase      )

       # Prefix all keys with metric_key_prefix + '_'
       for key in list(metrics.keys()      ):
            if not key.startswith(f'''{metric_key_prefix}_'''      ):
                 SCREAMING_SNAKE_CASE__					=					metrics.pop(__lowerCamelCase      )
       metrics.update(output.metrics      )
       return PredictionOutput(predictions=predictions.predictions			,     label_ids=predictions.label_ids			,     metrics=__lowerCamelCase      )





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__	( A__		,  A__		,  unittest.TestCase ):






  """simple docstring"""


  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    lowercase_	(							self		:	Union[str, Any]      )	->							List[str]:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					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__					=					DDIMScheduler()
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					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__					=					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__					=					CLIPTextModel(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

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






  def    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	Union[str, Any]			,     __lowerCamelCase		:	List[str]=0      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					torch.manual_seed(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					{
           '''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    lowercase_	(							self		:	Optional[int]      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe.to(__lowerCamelCase      )
       sd_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

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

       SCREAMING_SNAKE_CASE__					=					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    lowercase_	(							self		:	Any      )	->							Optional[Any]:
       super().test_inference_batch_consistent(batch_sizes=[1, 2]      )






  def    lowercase_	(							self		:	List[Any]      )	->							List[Any]:
       super().test_inference_batch_single_identical(batch_size=2			,     expected_max_diff=3.25e-3      )






  def    lowercase_	(							self		:	Union[str, Any]      )	->							str:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe.to(__lowerCamelCase      )
       sd_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

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

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

       SCREAMING_SNAKE_CASE__					=					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    lowercase_	(							self		:	Tuple      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe.to(__lowerCamelCase      )
       sd_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

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

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

       SCREAMING_SNAKE_CASE__					=					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    lowercase_	(							self		:	Optional[Any]      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					=					EulerAncestralDiscreteScheduler(
           beta_start=0.00085			,     beta_end=0.012			,     beta_schedule='''scaled_linear'''      )
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe.to(__lowerCamelCase      )
       sd_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

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

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

       SCREAMING_SNAKE_CASE__					=					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    lowercase_	(							self		:	Optional[int]      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					=					PNDMScheduler(
           beta_start=0.00085			,     beta_end=0.012			,     beta_schedule='''scaled_linear'''			,     skip_prk_steps=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe.to(__lowerCamelCase      )
       sd_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

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

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

       SCREAMING_SNAKE_CASE__					=					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 ):






  """simple docstring"""


  def    lowercase_	(							self		:	Tuple      )	->							str:
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






  def    lowercase_	(							self		:	Any			,     __lowerCamelCase		:	Dict=0      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					torch.manual_seed(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					{
           '''prompt''': '''a photo of the dolomites''',
           '''generator''': generator,
           '''num_inference_steps''': 3,
           '''guidance_scale''': 7.5,
           '''output_type''': '''numpy''',
       }
       return inputs






  def    lowercase_	(							self		:	Union[str, Any]      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					'''stabilityai/stable-diffusion-2-base'''
       SCREAMING_SNAKE_CASE__					=					DDIMScheduler.from_pretrained(__lowerCamelCase			,     subfolder='''scheduler'''      )
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline.from_pretrained(__lowerCamelCase			,     scheduler=__lowerCamelCase			,     safety_checker=__lowerCamelCase      )
       pipe.to(__lowerCamelCase      )
       pipe.set_progress_bar_config(disable=__lowerCamelCase      )
       pipe.enable_attention_slicing()

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

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

       SCREAMING_SNAKE_CASE__					=					np.array(
           [
               0.36968392,
               0.27025372,
               0.32446766,
               0.28379387,
               0.36363274,
               0.30733347,
               0.27100027,
               0.27054125,
               0.25536096,
           ]      )

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






  def    lowercase_	(							self		:	Optional[Any]      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline.from_pretrained(
           '''stabilityai/stable-diffusion-2-base'''			,     safety_checker=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
       pipe.to(__lowerCamelCase      )
       pipe.set_progress_bar_config(disable=__lowerCamelCase      )
       pipe.enable_attention_slicing()

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

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

       SCREAMING_SNAKE_CASE__					=					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    lowercase_	(							self		:	List[str]      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					0

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

                 SCREAMING_SNAKE_CASE__					=					np.array(
                     [
                         0.18681869,
                         0.33907816,
                         0.5361276,
                         0.14432865,
                         -0.02856611,
                         -0.73941123,
                         0.23397987,
                         0.47322682,
                         -0.37823164,
                     ]      )
                 assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5e-2
            elif step == 2:
                 SCREAMING_SNAKE_CASE__					=					latents.detach().cpu().numpy()
                 assert latents.shape == (1, 4, 64, 256)
                 SCREAMING_SNAKE_CASE__					=					latents[0, -3:, -3:, -1]

                 SCREAMING_SNAKE_CASE__					=					np.array(
                     [
                         0.18539645,
                         0.33987248,
                         0.5378559,
                         0.14437142,
                         -0.02455261,
                         -0.7338317,
                         0.23990755,
                         0.47356272,
                         -0.3786505,
                     ]      )

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

       SCREAMING_SNAKE_CASE__					=					False

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

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







  def    lowercase_	(							self		:	Tuple      )	->							Tuple:
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

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

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

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


