

import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple

import torch
from audiocraft.models import MusicGen

from transformers import (
    AutoFeatureExtractor,
    AutoTokenizer,
    EncodecModel,
    MusicgenDecoderConfig,
    MusicgenForConditionalGeneration,
    MusicgenProcessor,
    TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging


logging.set_verbosity_info()
a_     :    Any     		=   logging.get_logger(__name__)


a_     :    Dict     		=   ["model.decoder.embed_positions.weights"]


def 							__lowerCAmelCase     (							_UpperCamelCase		:	List[str]						)		->				List[str]:
 '''simple docstring'''


 if "emb" in name:
  SCREAMING_SNAKE_CASE		    =      name.replace('emb'		,				'model.decoder.embed_tokens'						)
 if "transformer" in name:
  SCREAMING_SNAKE_CASE		    =      name.replace('transformer'		,				'model.decoder'						)
 if "cross_attention" in name:
  SCREAMING_SNAKE_CASE		    =      name.replace('cross_attention'		,				'encoder_attn'						)
 if "linear1" in name:
  SCREAMING_SNAKE_CASE		    =      name.replace('linear1'		,				'fc1'						)
 if "linear2" in name:
  SCREAMING_SNAKE_CASE		    =      name.replace('linear2'		,				'fc2'						)
 if "norm1" in name:
  SCREAMING_SNAKE_CASE		    =      name.replace('norm1'		,				'self_attn_layer_norm'						)
 if "norm_cross" in name:
  SCREAMING_SNAKE_CASE		    =      name.replace('norm_cross'		,				'encoder_attn_layer_norm'						)
 if "norm2" in name:
  SCREAMING_SNAKE_CASE		    =      name.replace('norm2'		,				'final_layer_norm'						)
 if "out_norm" in name:
  SCREAMING_SNAKE_CASE		    =      name.replace('out_norm'		,				'model.decoder.layer_norm'						)
 if "linears" in name:
  SCREAMING_SNAKE_CASE		    =      name.replace('linears'		,				'lm_heads'						)
 if "condition_provider.conditioners.description.output_proj" in name:
  SCREAMING_SNAKE_CASE		    =      name.replace('condition_provider.conditioners.description.output_proj'		,				'enc_to_dec_proj'						)
 return name


def 							__lowerCAmelCase     (							_UpperCamelCase		:	OrderedDict		,				_UpperCamelCase		:	int						)		->				Tuple[Dict, Dict]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      list(state_dict.keys()						)
 SCREAMING_SNAKE_CASE		    =      {}
 for key in keys:
  SCREAMING_SNAKE_CASE		    =      state_dict.pop(_UpperCamelCase						)
  SCREAMING_SNAKE_CASE		    =      rename_keys(_UpperCamelCase						)
  if "in_proj_weight" in key:
   # split fused qkv proj
   SCREAMING_SNAKE_CASE		    =      val[:hidden_size, :]
   SCREAMING_SNAKE_CASE		    =      val[hidden_size : 2 * hidden_size, :]
   SCREAMING_SNAKE_CASE		    =      val[-hidden_size:, :]
  elif "enc_to_dec_proj" in key:
   SCREAMING_SNAKE_CASE		    =      val
  else:
   SCREAMING_SNAKE_CASE		    =      val
 return state_dict, enc_dec_proj_state_dict


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str						)		->				MusicgenDecoderConfig:
 '''simple docstring'''


 if checkpoint == "small":
  # default config values
  SCREAMING_SNAKE_CASE		    =      10_24
  SCREAMING_SNAKE_CASE		    =      24
  SCREAMING_SNAKE_CASE		    =      16
 elif checkpoint == "medium":
  SCREAMING_SNAKE_CASE		    =      15_36
  SCREAMING_SNAKE_CASE		    =      48
  SCREAMING_SNAKE_CASE		    =      24
 elif checkpoint == "large":
  SCREAMING_SNAKE_CASE		    =      20_48
  SCREAMING_SNAKE_CASE		    =      48
  SCREAMING_SNAKE_CASE		    =      32
 else:
  raise ValueError(f"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."""						)
 SCREAMING_SNAKE_CASE		    =      MusicgenDecoderConfig(
     hidden_size=_UpperCamelCase		,				ffn_dim=hidden_size * 4		,				num_hidden_layers=_UpperCamelCase		,				num_attention_heads=_UpperCamelCase		,				)
 return config


@torch.no_grad()
def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	Union[str, Any]=None		,				_UpperCamelCase		:	Union[str, Any]=None		,				_UpperCamelCase		:	List[Any]="cpu"						)		->				Any:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      MusicGen.get_pretrained(_UpperCamelCase		,				device=_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      decoder_config_from_checkpoint(_UpperCamelCase						)

 SCREAMING_SNAKE_CASE		    =      fairseq_model.lm.state_dict()
 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      rename_state_dict(
     _UpperCamelCase		,				hidden_size=decoder_config.hidden_size						)

 SCREAMING_SNAKE_CASE		    =      TaEncoderModel.from_pretrained('t5-base'						)
 SCREAMING_SNAKE_CASE		    =      EncodecModel.from_pretrained('facebook/encodec_32khz'						)
 SCREAMING_SNAKE_CASE		    =      MusicgenForCausalLM(_UpperCamelCase						).eval()

 # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      decoder.load_state_dict(_UpperCamelCase		,				strict=_UpperCamelCase						)

 for key in missing_keys.copy():
  if key.startswith(('text_encoder', 'audio_encoder')						) or key in EXPECTED_MISSING_KEYS:
   missing_keys.remove(_UpperCamelCase						)

 if len(_UpperCamelCase						) > 0:
  raise ValueError(f"""Missing key(s) in state_dict: {missing_keys}"""						)

 if len(_UpperCamelCase						) > 0:
  raise ValueError(f"""Unexpected key(s) in state_dict: {unexpected_keys}"""						)

 # init the composite model
 SCREAMING_SNAKE_CASE		    =      MusicgenForConditionalGeneration(text_encoder=_UpperCamelCase		,				audio_encoder=_UpperCamelCase		,				decoder=_UpperCamelCase						)

 # load the pre-trained enc-dec projection (from the decoder state dict)
 model.enc_to_dec_proj.load_state_dict(_UpperCamelCase						)

 # check we can do a forward pass
 SCREAMING_SNAKE_CASE		    =      torch.arange(0		,				8		,				dtype=torch.long						).reshape(2		,				-1						)
 SCREAMING_SNAKE_CASE		    =      input_ids.reshape(2 * 4		,				-1						)

 with torch.no_grad():
  SCREAMING_SNAKE_CASE		    =      model(input_ids=_UpperCamelCase		,				decoder_input_ids=_UpperCamelCase						).logits

 if logits.shape != (8, 1, 20_48):
  raise ValueError('Incorrect shape for logits'						)

 # now construct the processor
 SCREAMING_SNAKE_CASE		    =      AutoTokenizer.from_pretrained('t5-base'						)
 SCREAMING_SNAKE_CASE		    =      AutoFeatureExtractor.from_pretrained('facebook/encodec_32khz'		,				padding_side='left'						)

 SCREAMING_SNAKE_CASE		    =      MusicgenProcessor(feature_extractor=_UpperCamelCase		,				tokenizer=_UpperCamelCase						)

 # set the appropriate bos/pad token ids
 SCREAMING_SNAKE_CASE		    =      20_48
 SCREAMING_SNAKE_CASE		    =      20_48

 # set other default generation config params
 SCREAMING_SNAKE_CASE		    =      int(30 * audio_encoder.config.frame_rate						)
 SCREAMING_SNAKE_CASE		    =      True
 SCREAMING_SNAKE_CASE		    =      3.0

 if pytorch_dump_folder is not None:
  Path(_UpperCamelCase						).mkdir(exist_ok=_UpperCamelCase						)
  logger.info(f"""Saving model {checkpoint} to {pytorch_dump_folder}"""						)
  model.save_pretrained(_UpperCamelCase						)
  processor.save_pretrained(_UpperCamelCase						)

 if repo_id:
  logger.info(f"""Pushing model {checkpoint} to {repo_id}"""						)
  model.push_to_hub(_UpperCamelCase						)
  processor.push_to_hub(_UpperCamelCase						)


if __name__ == "__main__":
      a_     :    List[Any]     		=   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--checkpoint",
          default="small",
          type=str,
          help="Checkpoint size of the MusicGen model you'd like to convert. Can be one of: `['small', 'medium', 'large']`.",
      )
      parser.add_argument(
          "--pytorch_dump_folder",
          required=True,
          default=None,
          type=str,
          help="Path to the output PyTorch model directory.",
      )
      parser.add_argument(
          "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
      )
      parser.add_argument(
          "--device", default="cpu", type=str, help="Torch device to run the conversion, either cpu or cuda."
      )

      a_     :    int     		=   parser.parse_args()
      convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()




class   UpperCamelCase			(  unittest.TestCase ):

    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






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

    @property
    def UpperCamelCase      (      self			:   Optional[int]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      1
     SCREAMING_SNAKE_CASE		    =      3
     SCREAMING_SNAKE_CASE		    =      (3_2, 3_2)

     SCREAMING_SNAKE_CASE		    =      floats_tensor((batch_size, num_channels) + sizes				,     rng=random.Random(0     )     ).to(snake_case__     )
     return image

    @property
    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      UNetaDConditionModel(
         block_out_channels=(3_2, 3_2, 6_4)				,     layers_per_block=2				,     sample_size=3_2				,     in_channels=7				,     out_channels=4				,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D')				,     up_block_types=('CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'UpBlock2D')				,     cross_attention_dim=3_2				,     attention_head_dim=8				,     use_linear_projection=snake_case__				,     only_cross_attention=(True, True, False)				,     num_class_embeds=1_0_0				,     )
     return model

    @property
    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      AutoencoderKL(
         block_out_channels=[3_2, 3_2, 6_4]				,     in_channels=3				,     out_channels=3				,     down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D']				,     up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']				,     latent_channels=4				,     )
     return model

    @property
    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      CLIPTextConfig(
         bos_token_id=0				,     eos_token_id=2				,     hidden_size=3_2				,     intermediate_size=3_7				,     layer_norm_eps=1E-05				,     num_attention_heads=4				,     num_hidden_layers=5				,     pad_token_id=1				,     vocab_size=1_0_0_0				,     hidden_act='gelu'				,     projection_dim=5_1_2				,     )
     return CLIPTextModel(snake_case__     )

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      'cpu'  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE		    =      self.dummy_cond_unet_upscale
     SCREAMING_SNAKE_CASE		    =      DDPMScheduler()
     SCREAMING_SNAKE_CASE		    =      DDIMScheduler(prediction_type='v_prediction'     )
     SCREAMING_SNAKE_CASE		    =      self.dummy_vae
     SCREAMING_SNAKE_CASE		    =      self.dummy_text_encoder
     SCREAMING_SNAKE_CASE		    =      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

     SCREAMING_SNAKE_CASE		    =      self.dummy_image.cpu().permute(0				,     2				,     3				,     1     )[0]
     SCREAMING_SNAKE_CASE		    =      Image.fromarray(np.uinta(snake_case__     )     ).convert('RGB'     ).resize((6_4, 6_4)     )

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE		    =      StableDiffusionUpscalePipeline(
         unet=snake_case__				,     low_res_scheduler=snake_case__				,     scheduler=snake_case__				,     vae=snake_case__				,     text_encoder=snake_case__				,     tokenizer=snake_case__				,     max_noise_level=3_5_0				,     )
     SCREAMING_SNAKE_CASE		    =      sd_pipe.to(snake_case__     )
     sd_pipe.set_progress_bar_config(disable=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      'A painting of a squirrel eating a burger'
     SCREAMING_SNAKE_CASE		    =      torch.Generator(device=snake_case__     ).manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      sd_pipe(
         [prompt]				,     image=snake_case__				,     generator=snake_case__				,     guidance_scale=6.0				,     noise_level=2_0				,     num_inference_steps=2				,     output_type='np'				,     )

     SCREAMING_SNAKE_CASE		    =      output.images

     SCREAMING_SNAKE_CASE		    =      torch.Generator(device=snake_case__     ).manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      sd_pipe(
         [prompt]				,     image=snake_case__				,     generator=snake_case__				,     guidance_scale=6.0				,     noise_level=2_0				,     num_inference_steps=2				,     output_type='np'				,     return_dict=snake_case__				,     )[0]

     SCREAMING_SNAKE_CASE		    =      image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE		    =      image_from_tuple[0, -3:, -3:, -1]

     SCREAMING_SNAKE_CASE		    =      low_res_image.size[0] * 4
     assert image.shape == (1, expected_height_width, expected_height_width, 3)
     SCREAMING_SNAKE_CASE		    =      np.array([0.3_113, 0.3_910, 0.4_272, 0.4_859, 0.5_061, 0.4_652, 0.5_362, 0.5_715, 0.5_661]     )

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

    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      'cpu'  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE		    =      self.dummy_cond_unet_upscale
     SCREAMING_SNAKE_CASE		    =      DDPMScheduler()
     SCREAMING_SNAKE_CASE		    =      DDIMScheduler(prediction_type='v_prediction'     )
     SCREAMING_SNAKE_CASE		    =      self.dummy_vae
     SCREAMING_SNAKE_CASE		    =      self.dummy_text_encoder
     SCREAMING_SNAKE_CASE		    =      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

     SCREAMING_SNAKE_CASE		    =      self.dummy_image.cpu().permute(0				,     2				,     3				,     1     )[0]
     SCREAMING_SNAKE_CASE		    =      Image.fromarray(np.uinta(snake_case__     )     ).convert('RGB'     ).resize((6_4, 6_4)     )

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE		    =      StableDiffusionUpscalePipeline(
         unet=snake_case__				,     low_res_scheduler=snake_case__				,     scheduler=snake_case__				,     vae=snake_case__				,     text_encoder=snake_case__				,     tokenizer=snake_case__				,     max_noise_level=3_5_0				,     )
     SCREAMING_SNAKE_CASE		    =      sd_pipe.to(snake_case__     )
     sd_pipe.set_progress_bar_config(disable=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      'A painting of a squirrel eating a burger'
     SCREAMING_SNAKE_CASE		    =      sd_pipe(
         2 * [prompt]				,     image=2 * [low_res_image]				,     guidance_scale=6.0				,     noise_level=2_0				,     num_inference_steps=2				,     output_type='np'				,     )
     SCREAMING_SNAKE_CASE		    =      output.images
     assert image.shape[0] == 2

     SCREAMING_SNAKE_CASE		    =      torch.Generator(device=snake_case__     ).manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      sd_pipe(
         [prompt]				,     image=snake_case__				,     generator=snake_case__				,     num_images_per_prompt=2				,     guidance_scale=6.0				,     noise_level=2_0				,     num_inference_steps=2				,     output_type='np'				,     )
     SCREAMING_SNAKE_CASE		    =      output.images
     assert image.shape[0] == 2




    @unittest.skipIf(torch_device != 'cuda'				,     'This test requires a GPU'     )
    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.dummy_cond_unet_upscale
     SCREAMING_SNAKE_CASE		    =      DDPMScheduler()
     SCREAMING_SNAKE_CASE		    =      DDIMScheduler(prediction_type='v_prediction'     )
     SCREAMING_SNAKE_CASE		    =      self.dummy_vae
     SCREAMING_SNAKE_CASE		    =      self.dummy_text_encoder
     SCREAMING_SNAKE_CASE		    =      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

     SCREAMING_SNAKE_CASE		    =      self.dummy_image.cpu().permute(0				,     2				,     3				,     1     )[0]
     SCREAMING_SNAKE_CASE		    =      Image.fromarray(np.uinta(snake_case__     )     ).convert('RGB'     ).resize((6_4, 6_4)     )

     # put models in fp16, except vae as it overflows in fp16
     SCREAMING_SNAKE_CASE		    =      unet.half()
     SCREAMING_SNAKE_CASE		    =      text_encoder.half()

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE		    =      StableDiffusionUpscalePipeline(
         unet=snake_case__				,     low_res_scheduler=snake_case__				,     scheduler=snake_case__				,     vae=snake_case__				,     text_encoder=snake_case__				,     tokenizer=snake_case__				,     max_noise_level=3_5_0				,     )
     SCREAMING_SNAKE_CASE		    =      sd_pipe.to(snake_case__     )
     sd_pipe.set_progress_bar_config(disable=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      'A painting of a squirrel eating a burger'
     SCREAMING_SNAKE_CASE		    =      torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      sd_pipe(
         [prompt]				,     image=snake_case__				,     generator=snake_case__				,     num_inference_steps=2				,     output_type='np'				,     ).images

     SCREAMING_SNAKE_CASE		    =      low_res_image.size[0] * 4
     assert image.shape == (1, expected_height_width, expected_height_width, 3)




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

    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






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

    def UpperCamelCase      (      self			:   Optional[int]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/sd2-upscale/low_res_cat.png'     )
     SCREAMING_SNAKE_CASE		    =      load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
         '/upsampled_cat.npy'     )

     SCREAMING_SNAKE_CASE		    =      'stabilityai/stable-diffusion-x4-upscaler'
     SCREAMING_SNAKE_CASE		    =      StableDiffusionUpscalePipeline.from_pretrained(snake_case__     )
     pipe.to(snake_case__     )
     pipe.set_progress_bar_config(disable=snake_case__     )
     pipe.enable_attention_slicing()

     SCREAMING_SNAKE_CASE		    =      'a cat sitting on a park bench'

     SCREAMING_SNAKE_CASE		    =      torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      pipe(
         prompt=snake_case__				,     image=snake_case__				,     generator=snake_case__				,     output_type='np'				,     )
     SCREAMING_SNAKE_CASE		    =      output.images[0]

     assert image.shape == (5_1_2, 5_1_2, 3)
     assert np.abs(expected_image - image     ).max() < 1E-3

    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/sd2-upscale/low_res_cat.png'     )
     SCREAMING_SNAKE_CASE		    =      load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
         '/upsampled_cat_fp16.npy'     )

     SCREAMING_SNAKE_CASE		    =      'stabilityai/stable-diffusion-x4-upscaler'
     SCREAMING_SNAKE_CASE		    =      StableDiffusionUpscalePipeline.from_pretrained(
         snake_case__				,     torch_dtype=torch.floataa				,     )
     pipe.to(snake_case__     )
     pipe.set_progress_bar_config(disable=snake_case__     )
     pipe.enable_attention_slicing()

     SCREAMING_SNAKE_CASE		    =      'a cat sitting on a park bench'

     SCREAMING_SNAKE_CASE		    =      torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      pipe(
         prompt=snake_case__				,     image=snake_case__				,     generator=snake_case__				,     output_type='np'				,     )
     SCREAMING_SNAKE_CASE		    =      output.images[0]

     assert image.shape == (5_1_2, 5_1_2, 3)
     assert np.abs(expected_image - image     ).max() < 5E-1




    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






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

     SCREAMING_SNAKE_CASE		    =      load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/sd2-upscale/low_res_cat.png'     )

     SCREAMING_SNAKE_CASE		    =      'stabilityai/stable-diffusion-x4-upscaler'
     SCREAMING_SNAKE_CASE		    =      StableDiffusionUpscalePipeline.from_pretrained(
         snake_case__				,     torch_dtype=torch.floataa				,     )
     pipe.to(snake_case__     )
     pipe.set_progress_bar_config(disable=snake_case__     )
     pipe.enable_attention_slicing(1     )
     pipe.enable_sequential_cpu_offload()

     SCREAMING_SNAKE_CASE		    =      'a cat sitting on a park bench'

     SCREAMING_SNAKE_CASE		    =      torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      pipe(
         prompt=snake_case__				,     image=snake_case__				,     generator=snake_case__				,     num_inference_steps=5				,     output_type='np'				,     )

     SCREAMING_SNAKE_CASE		    =      torch.cuda.max_memory_allocated()
     # make sure that less than 2.9 GB is allocated
     assert mem_bytes < 2.9 * 1_0**9

