


import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case            =   logging.get_logger(__name__)

set_seed(770)


__snake_case            =   {
    "c_attn": "att_proj",
    "c_proj": "out_proj",
    "c_fc": "in_proj",
    "transformer.": "",
    "h.": "layers.",
    "ln_1": "layernorm_1",
    "ln_2": "layernorm_2",
    "ln_f": "layernorm_final",
    "wpe": "position_embeds_layer",
    "wte": "input_embeds_layer",
}


__snake_case            =   {
    "text_small": {
        "repo_id": "suno/bark",
        "file_name": "text.pt",
    },
    "coarse_small": {
        "repo_id": "suno/bark",
        "file_name": "coarse.pt",
    },
    "fine_small": {
        "repo_id": "suno/bark",
        "file_name": "fine.pt",
    },
    "text": {
        "repo_id": "suno/bark",
        "file_name": "text_2.pt",
    },
    "coarse": {
        "repo_id": "suno/bark",
        "file_name": "coarse_2.pt",
    },
    "fine": {
        "repo_id": "suno/bark",
        "file_name": "fine_2.pt",
    },
}

__snake_case            =   os.path.dirname(os.path.abspath(__file__))
__snake_case            =   os.path.join(os.path.expanduser("~"), ".cache")
__snake_case            =   os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Dict       ,  SCREAMING_SNAKE_CASE_   :		str=False	):





			"""simple docstring"""




			UpperCamelCase         =					model_type
			if use_small:
						key += "_small"
			return os.path.join(SCREAMING_SNAKE_CASE_       ,  REMOTE_MODEL_PATHS[key]["""file_name"""]	)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Any       ,  SCREAMING_SNAKE_CASE_   :		Tuple	):





			"""simple docstring"""




			os.makedirs(SCREAMING_SNAKE_CASE_       ,  exist_ok=SCREAMING_SNAKE_CASE_	)
			hf_hub_download(repo_id=SCREAMING_SNAKE_CASE_       ,  filename=SCREAMING_SNAKE_CASE_       ,  local_dir=SCREAMING_SNAKE_CASE_	)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_SNAKE_CASE_   :		Tuple=False       ,  SCREAMING_SNAKE_CASE_   :		Tuple="text"	):





			"""simple docstring"""




			if model_type == "text":
						UpperCamelCase         =					BarkSemanticModel
						UpperCamelCase         =					BarkSemanticConfig
						UpperCamelCase         =					BarkSemanticGenerationConfig
			elif model_type == "coarse":
						UpperCamelCase         =					BarkCoarseModel
						UpperCamelCase         =					BarkCoarseConfig
						UpperCamelCase         =					BarkCoarseGenerationConfig
			elif model_type == "fine":
						UpperCamelCase         =					BarkFineModel
						UpperCamelCase         =					BarkFineConfig
						UpperCamelCase         =					BarkFineGenerationConfig
			else:
						raise NotImplementedError()
			UpperCamelCase         =					f'{model_type}_small' if use_small else model_type
			UpperCamelCase         =					REMOTE_MODEL_PATHS[model_key]
			if not os.path.exists(SCREAMING_SNAKE_CASE_	):
						logger.info(f'{model_type} model not found, downloading into `{CACHE_DIR}`.'	)
						_download(model_info["""repo_id"""]       ,  model_info["""file_name"""]	)
			UpperCamelCase         =					torch.load(SCREAMING_SNAKE_CASE_       ,  map_location=SCREAMING_SNAKE_CASE_	)
			# this is a hack
			UpperCamelCase         =					checkpoint["""model_args"""]
			if "input_vocab_size" not in model_args:
						UpperCamelCase         =					model_args["""vocab_size"""]
						UpperCamelCase         =					model_args["""vocab_size"""]
						del model_args["vocab_size"]

			# convert Bark model arguments to HF Bark model arguments
			UpperCamelCase         =					model_args.pop("""n_head"""	)
			UpperCamelCase         =					model_args.pop("""n_embd"""	)
			UpperCamelCase         =					model_args.pop("""n_layer"""	)

			UpperCamelCase         =					ConfigClass(**checkpoint["""model_args"""]	)
			UpperCamelCase         =					ModelClass(config=SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					GenerationConfigClass()

			UpperCamelCase         =					model_generation_config
			UpperCamelCase         =					checkpoint["""model"""]
			# fixup checkpoint
			UpperCamelCase         =					"""_orig_mod."""
			for k, v in list(state_dict.items()	):
						if k.startswith(SCREAMING_SNAKE_CASE_	):
									# replace part of the key with corresponding layer name in HF implementation
									UpperCamelCase         =					k[len(SCREAMING_SNAKE_CASE_	) :]
									for old_layer_name in new_layer_name_dict:
												UpperCamelCase         =					new_k.replace(SCREAMING_SNAKE_CASE_       ,  new_layer_name_dict[old_layer_name]	)

									UpperCamelCase         =					state_dict.pop(SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					set(state_dict.keys()	) - set(model.state_dict().keys()	)
			UpperCamelCase         =					{k for k in extra_keys if not k.endswith(""".attn.bias"""	)}
			UpperCamelCase         =					set(model.state_dict().keys()	) - set(state_dict.keys()	)
			UpperCamelCase         =					{k for k in missing_keys if not k.endswith(""".attn.bias"""	)}
			if len(SCREAMING_SNAKE_CASE_	) != 0:
						raise ValueError(f'extra keys found: {extra_keys}'	)
			if len(SCREAMING_SNAKE_CASE_	) != 0:
						raise ValueError(f'missing keys: {missing_keys}'	)
			model.load_state_dict(SCREAMING_SNAKE_CASE_       ,  strict=SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					model.num_parameters(exclude_embeddings=SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					checkpoint["""best_val_loss"""].item()
			logger.info(f'model loaded: {round(n_params/1e6       ,  1	)}M params, {round(SCREAMING_SNAKE_CASE_       ,  3	)} loss'	)
			model.eval()
			model.to(SCREAMING_SNAKE_CASE_	)
			del checkpoint, state_dict

			return model






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_SNAKE_CASE_   :		Dict=False       ,  SCREAMING_SNAKE_CASE_   :		str="text"	):





			"""simple docstring"""




			if model_type not in ("text", "coarse", "fine"):
						raise NotImplementedError()

			UpperCamelCase         =					"""cpu"""  # do conversion on cpu

			UpperCamelCase         =					_get_ckpt_path(SCREAMING_SNAKE_CASE_       ,  use_small=SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					_load_model(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  model_type=SCREAMING_SNAKE_CASE_       ,  use_small=SCREAMING_SNAKE_CASE_	)

			# load bark initial model
			UpperCamelCase         =					_bark_load_model(SCREAMING_SNAKE_CASE_       ,  """cpu"""       ,  model_type=SCREAMING_SNAKE_CASE_       ,  use_small=SCREAMING_SNAKE_CASE_	)

			if model_type == "text":
						UpperCamelCase         =					bark_model["""model"""]

			if model.num_parameters(exclude_embeddings=SCREAMING_SNAKE_CASE_	) != bark_model.get_num_params():
						raise ValueError("""initial and new models don't have the same number of parameters"""	)

			# check if same output as the bark model
			UpperCamelCase         =					5
			UpperCamelCase         =					10

			if model_type in ["text", "coarse"]:
						UpperCamelCase         =					torch.randint(256       ,  (batch_size, sequence_length)       ,  dtype=torch.int	)
						UpperCamelCase         =					bark_model(SCREAMING_SNAKE_CASE_	)[0]

						UpperCamelCase         =					model(SCREAMING_SNAKE_CASE_	)

						# take last logits
						UpperCamelCase         =					output_new_model_total.logits[:, [-1], :]

			else:
						UpperCamelCase         =					3
						UpperCamelCase         =					8
						UpperCamelCase         =					torch.randint(256       ,  (batch_size, sequence_length, n_codes_total)       ,  dtype=torch.int	)

						UpperCamelCase         =					model(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)
						UpperCamelCase         =					bark_model(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)

						UpperCamelCase         =					output_new_model_total.logits

			# output difference should come from the difference of self-attention implementation design
			if output_new_model.shape != output_old_model.shape:
						raise ValueError("""initial and new outputs don't have the same shape"""	)
			if (output_new_model - output_old_model).abs().max().item() > 1e-3:
						raise ValueError("""initial and new outputs are not equal"""	)

			Path(SCREAMING_SNAKE_CASE_	).mkdir(exist_ok=SCREAMING_SNAKE_CASE_	)
			model.save_pretrained(SCREAMING_SNAKE_CASE_	)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Dict       ,  SCREAMING_SNAKE_CASE_   :		List[str]       ,  SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_SNAKE_CASE_   :		Dict       ,  SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_SNAKE_CASE_   :		Dict       ,  ):





			"""simple docstring"""




			UpperCamelCase         =					os.path.join(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					BarkSemanticConfig.from_pretrained(os.path.join(SCREAMING_SNAKE_CASE_       ,  """config.json"""	)	)
			UpperCamelCase         =					BarkCoarseConfig.from_pretrained(os.path.join(SCREAMING_SNAKE_CASE_       ,  """config.json"""	)	)
			UpperCamelCase         =					BarkFineConfig.from_pretrained(os.path.join(SCREAMING_SNAKE_CASE_       ,  """config.json"""	)	)
			UpperCamelCase         =					EncodecConfig.from_pretrained("""facebook/encodec_24khz"""	)

			UpperCamelCase         =					BarkSemanticModel.from_pretrained(SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					BarkCoarseModel.from_pretrained(SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					BarkFineModel.from_pretrained(SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					EncodecModel.from_pretrained("""facebook/encodec_24khz"""	)

			UpperCamelCase         =					BarkConfig.from_sub_model_configs(
			    SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					BarkGenerationConfig.from_sub_model_configs(
			    semantic.generation_config       ,  coarseAcoustic.generation_config       ,  fineAcoustic.generation_config	)

			UpperCamelCase         =					BarkModel(SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					semantic
			UpperCamelCase         =					coarseAcoustic
			UpperCamelCase         =					fineAcoustic
			UpperCamelCase         =					codec

			UpperCamelCase         =					bark_generation_config

			Path(SCREAMING_SNAKE_CASE_	).mkdir(exist_ok=SCREAMING_SNAKE_CASE_	)
			bark.save_pretrained(SCREAMING_SNAKE_CASE_       ,  repo_id=SCREAMING_SNAKE_CASE_       ,  push_to_hub=SCREAMING_SNAKE_CASE_	)


if __name__ == "__main__":
					__snake_case            =   argparse.ArgumentParser()
					# Required parameters

					parser.add_argument("model_type", type=str, help="text, coarse or fine.")
					parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
					parser.add_argument("--is_small", action="store_true", help="convert the small version instead of the large.")

					__snake_case            =   parser.parse_args()

					load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)








import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class    UpperCAmelCase					(       __snake_case      , unittest.TestCase    ):
			lowercase						= KandinskyInpaintPipeline
			lowercase						= ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
			lowercase						= [
			    """prompt""",
			    """negative_prompt""",
			    """image_embeds""",
			    """negative_image_embeds""",
			    """image""",
			    """mask_image""",
			]
			lowercase						= [
			    """generator""",
			    """height""",
			    """width""",
			    """latents""",
			    """guidance_scale""",
			    """negative_prompt""",
			    """num_inference_steps""",
			    """return_dict""",
			    """guidance_scale""",
			    """num_images_per_prompt""",
			    """output_type""",
			    """return_dict""",
			]
			lowercase						= False




			@property
			def   lowerCamelCase_					(						self							:		Union[str, Any]						):







						"""simple docstring"""







						return 3_2




			@property
			def   lowerCamelCase_					(						self							:		Optional[Any]						):







						"""simple docstring"""







						return 3_2




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







						"""simple docstring"""







						return self.time_input_dim




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







						"""simple docstring"""







						return self.time_input_dim * 4




			@property
			def   lowerCamelCase_					(						self							:		str						):







						"""simple docstring"""







						return 1_0_0




			@property
			def   lowerCamelCase_					(						self							:		Optional[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""						)
						return tokenizer




			@property
			def   lowerCamelCase_					(						self							:		Any						):







						"""simple docstring"""







						torch.manual_seed(0						)
						UpperCamelCase         =					MCLIPConfig(
						    numDims=self.cross_attention_dim       ,       transformerDimensions=self.text_embedder_hidden_size       ,       hidden_size=self.text_embedder_hidden_size       ,       intermediate_size=3_7       ,       num_attention_heads=4       ,       num_hidden_layers=5       ,       vocab_size=1_0_0_5       ,       )

						UpperCamelCase         =					MultilingualCLIP(__magic_name__						)
						UpperCamelCase         =					text_encoder.eval()

						return text_encoder




			@property
			def   lowerCamelCase_					(						self							:		List[str]						):







						"""simple docstring"""







						torch.manual_seed(0						)

						UpperCamelCase         =					{
						    """in_channels""": 9,
						    # Out channels is double in channels because predicts mean and variance
						    """out_channels""": 8,
						    """addition_embed_type""": """text_image""",
						    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
						    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
						    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
						    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
						    """layers_per_block""": 1,
						    """encoder_hid_dim""": self.text_embedder_hidden_size,
						    """encoder_hid_dim_type""": """text_image_proj""",
						    """cross_attention_dim""": self.cross_attention_dim,
						    """attention_head_dim""": 4,
						    """resnet_time_scale_shift""": """scale_shift""",
						    """class_embed_type""": None,
						}

						UpperCamelCase         =					UNetaDConditionModel(**__magic_name__						)
						return model




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







						"""simple docstring"""







						return {
						    "block_out_channels": [3_2, 6_4],
						    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
						    "in_channels": 3,
						    "latent_channels": 4,
						    "layers_per_block": 1,
						    "norm_num_groups": 8,
						    "norm_type": "spatial",
						    "num_vq_embeddings": 1_2,
						    "out_channels": 3,
						    "up_block_types": [
						        "AttnUpDecoderBlock2D",
						        "UpDecoderBlock2D",
						    ],
						    "vq_embed_dim": 4,
						}




			@property
			def   lowerCamelCase_					(						self							:		Union[str, Any]						):







						"""simple docstring"""







						torch.manual_seed(0						)
						UpperCamelCase         =					VQModel(**self.dummy_movq_kwargs						)
						return model




			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						UpperCamelCase         =					self.dummy_text_encoder
						UpperCamelCase         =					self.dummy_tokenizer
						UpperCamelCase         =					self.dummy_unet
						UpperCamelCase         =					self.dummy_movq

						UpperCamelCase         =					DDIMScheduler(
						    num_train_timesteps=1_0_0_0       ,       beta_schedule="""linear"""       ,       beta_start=0.00_085       ,       beta_end=0.012       ,       clip_sample=__magic_name__       ,       set_alpha_to_one=__magic_name__       ,       steps_offset=1       ,       prediction_type="""epsilon"""       ,       thresholding=__magic_name__       ,       )

						UpperCamelCase         =					{
						    """text_encoder""": text_encoder,
						    """tokenizer""": tokenizer,
						    """unet""": unet,
						    """scheduler""": scheduler,
						    """movq""": movq,
						}

						return components




			def   lowerCamelCase_					(						self							:		Tuple       ,       __magic_name__							:		Tuple       ,       __magic_name__							:		Optional[int]=0						):







						"""simple docstring"""







						UpperCamelCase         =					floats_tensor((1, self.cross_attention_dim)       ,       rng=random.Random(__magic_name__						)						).to(__magic_name__						)
						UpperCamelCase         =					floats_tensor((1, self.cross_attention_dim)       ,       rng=random.Random(seed + 1						)						).to(__magic_name__						)
						# create init_image
						UpperCamelCase         =					floats_tensor((1, 3, 6_4, 6_4)       ,       rng=random.Random(__magic_name__						)						).to(__magic_name__						)
						UpperCamelCase         =					image.cpu().permute(0       ,       2       ,       3       ,       1						)[0]
						UpperCamelCase         =					Image.fromarray(np.uinta(__magic_name__						)						).convert("""RGB"""						).resize((2_5_6, 2_5_6)						)
						# create mask
						UpperCamelCase         =					np.ones((6_4, 6_4)       ,       dtype=np.floataa						)
						UpperCamelCase         =					0

						if str(__magic_name__						).startswith("""mps"""						):
									UpperCamelCase         =					torch.manual_seed(__magic_name__						)
						else:
									UpperCamelCase         =					torch.Generator(device=__magic_name__						).manual_seed(__magic_name__						)
						UpperCamelCase         =					{
						    """prompt""": """horse""",
						    """image""": init_image,
						    """mask_image""": mask,
						    """image_embeds""": image_embeds,
						    """negative_image_embeds""": negative_image_embeds,
						    """generator""": generator,
						    """height""": 6_4,
						    """width""": 6_4,
						    """num_inference_steps""": 2,
						    """guidance_scale""": 4.0,
						    """output_type""": """np""",
						}
						return inputs




			def   lowerCamelCase_					(						self							:		Union[str, Any]						):







						"""simple docstring"""







						UpperCamelCase         =					"""cpu"""

						UpperCamelCase         =					self.get_dummy_components()

						UpperCamelCase         =					self.pipeline_class(**__magic_name__						)
						UpperCamelCase         =					pipe.to(__magic_name__						)

						pipe.set_progress_bar_config(disable=__magic_name__						)

						UpperCamelCase         =					pipe(**self.get_dummy_inputs(__magic_name__						)						)
						UpperCamelCase         =					output.images

						UpperCamelCase         =					pipe(
						    **self.get_dummy_inputs(__magic_name__						)       ,       return_dict=__magic_name__       ,       )[0]

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

						print(F'image.shape {image.shape}'						)

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

						UpperCamelCase         =					np.array(
						    [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]						)

						assert (
						    np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
						), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
						assert (
						    np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2
						), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'





			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						super().test_inference_batch_single_identical(expected_max_diff=3e-3						)




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




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







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





			def   lowerCamelCase_					(						self							:		Union[str, Any]						):







						"""simple docstring"""







						UpperCamelCase         =					load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""						)

						UpperCamelCase         =					load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""						)
						UpperCamelCase         =					np.ones((7_6_8, 7_6_8)       ,       dtype=np.floataa						)
						UpperCamelCase         =					0

						UpperCamelCase         =					"""a hat"""

						UpperCamelCase         =					KandinskyPriorPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-1-prior"""       ,       torch_dtype=torch.floataa						)
						pipe_prior.to(__magic_name__						)

						UpperCamelCase         =					KandinskyInpaintPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-1-inpaint"""       ,       torch_dtype=torch.floataa						)
						UpperCamelCase         =					pipeline.to(__magic_name__						)
						pipeline.set_progress_bar_config(disable=__magic_name__						)

						UpperCamelCase         =					torch.Generator(device="""cpu"""						).manual_seed(0						)
						UpperCamelCase  ,  UpperCamelCase         =					pipe_prior(
						    __magic_name__       ,       generator=__magic_name__       ,       num_inference_steps=5       ,       negative_prompt=""""""       ,       ).to_tuple()

						UpperCamelCase         =					pipeline(
						    __magic_name__       ,       image=__magic_name__       ,       mask_image=__magic_name__       ,       image_embeds=__magic_name__       ,       negative_image_embeds=__magic_name__       ,       generator=__magic_name__       ,       num_inference_steps=1_0_0       ,       height=7_6_8       ,       width=7_6_8       ,       output_type="""np"""       ,       )

						UpperCamelCase         =					output.images[0]

						assert image.shape == (7_6_8, 7_6_8, 3)

						assert_mean_pixel_difference(__magic_name__       ,       __magic_name__						)




