

import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


_SCREAMING_SNAKE_CASE  :						str         =    logging.getLogger(__name__)
torch.set_grad_enabled(False)
_SCREAMING_SNAKE_CASE  :						List[Any]         =    '''cuda''' if torch.cuda.is_available() else '''cpu'''
def        UpperCAmelCase_     (		_A   ,	_A=1_00   ,	_A=" "       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					text.split(_A       )
     return [character.join(text[i : i + n]       ).strip() for i in range(0   ,	len(_A       )   ,	_A       )]
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					[], []
     for title, text in zip(documents['''title''']   ,	documents['''text''']       ):
          if text is not None:
               for passage in split_text(_A       ):
                    titles.append(title if title is not None else ''''''       )
                    texts.append(_A       )
     return {"title": titles, "text": texts}
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					ctx_tokenizer(
         documents['''title''']   ,	documents['''text''']   ,	truncation=_A   ,	padding='''longest'''   ,	return_tensors='''pt'''       )['''input_ids''']
     SCREAMING_SNAKE_CASE__					=					ctx_encoder(input_ids.to(device=_A       )   ,	return_dict=_A       ).pooler_output
     return {"embeddings": embeddings.detach().cpu().numpy()}
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A   ,	):





     '''simple docstring'''




     logger.info('''Step 1 - Create the dataset'''       )
     ######################################

     # The dataset needed for RAG must have three columns:
     # - title (string): title of the document
     # - text (string): text of a passage of the document
     # - embeddings (array of dimension d): DPR representation of the passage
     # Let's say you have documents in tab-separated csv files with columns "title" and "text"
     assert os.path.isfile(rag_example_args.csv_path       ), "Please provide a valid path to a csv file"

     # You can load a Dataset object this way
     SCREAMING_SNAKE_CASE__					=					load_dataset(
         '''csv'''   ,	data_files=[rag_example_args.csv_path]   ,	split='''train'''   ,	delimiter='''\t'''   ,	column_names=['''title''', '''text''']       )

     # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

     # Then split the documents into passages of 100 words
     SCREAMING_SNAKE_CASE__					=					dataset.map(_A   ,	batched=_A   ,	num_proc=processing_args.num_proc       )

     # And compute the embeddings
     SCREAMING_SNAKE_CASE__					=					DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name       ).to(device=_A       )
     SCREAMING_SNAKE_CASE__					=					DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name       )
     SCREAMING_SNAKE_CASE__					=					Features(
         {'''text''': Value('''string'''       ), '''title''': Value('''string'''       ), '''embeddings''': Sequence(Value('''float32'''       )       )}       )  # optional, save as float32 instead of float64 to save space
     SCREAMING_SNAKE_CASE__					=					dataset.map(
         partial(_A   ,	ctx_encoder=_A   ,	ctx_tokenizer=_A       )   ,	batched=_A   ,	batch_size=processing_args.batch_size   ,	features=_A   ,	)

     # And finally save your dataset
     SCREAMING_SNAKE_CASE__					=					os.path.join(rag_example_args.output_dir   ,	'''my_knowledge_dataset'''       )
     dataset.save_to_disk(_A       )
     # from datasets import load_from_disk
     # dataset = load_from_disk(passages_path)  # to reload the dataset

     ######################################
     logger.info('''Step 2 - Index the dataset'''       )
     ######################################

     # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
     SCREAMING_SNAKE_CASE__					=					faiss.IndexHNSWFlat(index_hnsw_args.d   ,	index_hnsw_args.m   ,	faiss.METRIC_INNER_PRODUCT       )
     dataset.add_faiss_index('''embeddings'''   ,	custom_index=_A       )

     # And save the index
     SCREAMING_SNAKE_CASE__					=					os.path.join(rag_example_args.output_dir   ,	'''my_knowledge_dataset_hnsw_index.faiss'''       )
     dataset.get_index('''embeddings'''       ).save(_A       )
     # dataset.load_faiss_index("embeddings", index_path)  # to reload the index






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default=str(Path(A__ ).parent / "test_run" / "dummy-kb" / "my_knowledge_dataset.csv" )		,  metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"}		,  )
  a     =      field(
      default=A__		,  metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."}		,  )
  a     =      field(
      default="facebook/rag-sequence-nq"		,  metadata={"help": "The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"}		,  )
  a     =      field(
      default="facebook/dpr-ctx_encoder-multiset-base"		,  metadata={
          "help": (
              "The DPR context encoder model to use. Either 'facebook/dpr-ctx_encoder-single-nq-base' or"
              " 'facebook/dpr-ctx_encoder-multiset-base'"
          )
      }		,  )
  a     =      field(
      default=str(Path(A__ ).parent / "test_run" / "dummy-kb" )		,  metadata={"help": "Path to a directory where the dataset passages and the index will be saved"}		,  )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default=A__		,  metadata={
          "help": "The number of processes to use to split the documents into passages. Default is single process."
      }		,  )
  a     =      field(
      default=16		,  metadata={
          "help": "The batch size to use when computing the passages embeddings using the DPR context encoder."
      }		,  )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default=7_68		,  metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}		,  )
  a     =      field(
      default=1_28		,  metadata={
          "help": (
              "The number of bi-directional links created for every new element during the HNSW index construction."
          )
      }		,  )


if __name__ == "__main__":
       logging.basicConfig(level=logging.WARNING)
       logger.setLevel(logging.INFO)

       _SCREAMING_SNAKE_CASE  :						Union[str, Any]         =    HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
       _SCREAMING_SNAKE_CASE  :						Union[str, Any]         =    parser.parse_args_into_dataclasses()
       with TemporaryDirectory() as tmp_dir:
              _SCREAMING_SNAKE_CASE  :						List[Any]         =    rag_example_args.output_dir or tmp_dir
              main(rag_example_args, processing_args, index_hnsw_args)






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


