
'''simple docstring'''







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				:   str     =				logging.get_logger(__name__)

set_seed(770)


_snake_case				:   Tuple     =				{
    """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				:   Optional[Any]     =				{
    """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				:   Union[str, Any]     =				os.path.dirname(os.path.abspath(__file__))
_snake_case				:   List[Any]     =				os.path.join(os.path.expanduser("""~"""), """.cache""")
_snake_case				:   str     =				os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[str]	,			_SCREAMING_SNAKE_CASE		:   Tuple=False  ):
						_SCREAMING_SNAKE_CASE				= model_type
						if use_small:
												key += "_small"
						return os.path.join(_SCREAMING_SNAKE_CASE	,			REMOTE_MODEL_PATHS[key]["file_name"]  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Any	,			_SCREAMING_SNAKE_CASE		:   Tuple  ):
						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 			_a		(	_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   List[str]	,			_SCREAMING_SNAKE_CASE		:   Any=False	,			_SCREAMING_SNAKE_CASE		:   Tuple="text"  ):
						if model_type == "text":
												_SCREAMING_SNAKE_CASE				= BarkSemanticModel
												_SCREAMING_SNAKE_CASE				= BarkSemanticConfig
												_SCREAMING_SNAKE_CASE				= BarkSemanticGenerationConfig
						elif model_type == "coarse":
												_SCREAMING_SNAKE_CASE				= BarkCoarseModel
												_SCREAMING_SNAKE_CASE				= BarkCoarseConfig
												_SCREAMING_SNAKE_CASE				= BarkCoarseGenerationConfig
						elif model_type == "fine":
												_SCREAMING_SNAKE_CASE				= BarkFineModel
												_SCREAMING_SNAKE_CASE				= BarkFineConfig
												_SCREAMING_SNAKE_CASE				= BarkFineGenerationConfig
						else:
												raise NotImplementedError()
						_SCREAMING_SNAKE_CASE				= F'{model_type}_small' if use_small else model_type
						_SCREAMING_SNAKE_CASE				= 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"]  )
						_SCREAMING_SNAKE_CASE				= torch.load(_SCREAMING_SNAKE_CASE	,			map_location=_SCREAMING_SNAKE_CASE  )
						# this is a hack
						_SCREAMING_SNAKE_CASE				= checkpoint["model_args"]
						if "input_vocab_size" not in model_args:
												_SCREAMING_SNAKE_CASE				= model_args["vocab_size"]
												_SCREAMING_SNAKE_CASE				= model_args["vocab_size"]
												del model_args["vocab_size"]

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

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

						_SCREAMING_SNAKE_CASE				= model_generation_config
						_SCREAMING_SNAKE_CASE				= checkpoint["model"]
						# fixup checkpoint
						_SCREAMING_SNAKE_CASE				= "_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
																		_SCREAMING_SNAKE_CASE				= k[len(_SCREAMING_SNAKE_CASE  ) :]
																		for old_layer_name in new_layer_name_dict:
																								_SCREAMING_SNAKE_CASE				= new_k.replace(_SCREAMING_SNAKE_CASE	,			new_layer_name_dict[old_layer_name]  )

																		_SCREAMING_SNAKE_CASE				= state_dict.pop(_SCREAMING_SNAKE_CASE  )

						_SCREAMING_SNAKE_CASE				= set(state_dict.keys()  ) - set(model.state_dict().keys()  )
						_SCREAMING_SNAKE_CASE				= {k for k in extra_keys if not k.endswith(".attn.bias"  )}
						_SCREAMING_SNAKE_CASE				= set(model.state_dict().keys()  ) - set(state_dict.keys()  )
						_SCREAMING_SNAKE_CASE				= {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  )
						_SCREAMING_SNAKE_CASE				= model.num_parameters(exclude_embeddings=_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= 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 			_a		(	_SCREAMING_SNAKE_CASE		:   Dict	,			_SCREAMING_SNAKE_CASE		:   Union[str, Any]=False	,			_SCREAMING_SNAKE_CASE		:   List[Any]="text"  ):
						if model_type not in ("text", "coarse", "fine"):
												raise NotImplementedError()

						_SCREAMING_SNAKE_CASE				= "cpu"  # do conversion on cpu

						_SCREAMING_SNAKE_CASE				= _get_ckpt_path(_SCREAMING_SNAKE_CASE	,			use_small=_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= _load_model(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			model_type=_SCREAMING_SNAKE_CASE	,			use_small=_SCREAMING_SNAKE_CASE  )

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

						if model_type == "text":
												_SCREAMING_SNAKE_CASE				= 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
						_SCREAMING_SNAKE_CASE				= 5
						_SCREAMING_SNAKE_CASE				= 10

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

												_SCREAMING_SNAKE_CASE				= model(_SCREAMING_SNAKE_CASE  )

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

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

												_SCREAMING_SNAKE_CASE				= model(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )
												_SCREAMING_SNAKE_CASE				= bark_model(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )

												_SCREAMING_SNAKE_CASE				= 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 			_a		(	_SCREAMING_SNAKE_CASE		:   Any	,			_SCREAMING_SNAKE_CASE		:   List[Any]	,			_SCREAMING_SNAKE_CASE		:   Tuple	,			_SCREAMING_SNAKE_CASE		:   Optional[int]	,			_SCREAMING_SNAKE_CASE		:   List[Any]	,			_SCREAMING_SNAKE_CASE		:   Optional[int]	,			):
						_SCREAMING_SNAKE_CASE				= os.path.join(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )

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

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

						_SCREAMING_SNAKE_CASE				= BarkConfig.from_sub_model_configs(
						    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )

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

						_SCREAMING_SNAKE_CASE				= BarkModel(_SCREAMING_SNAKE_CASE  )

						_SCREAMING_SNAKE_CASE				= semantic
						_SCREAMING_SNAKE_CASE				= coarseAcoustic
						_SCREAMING_SNAKE_CASE				= fineAcoustic
						_SCREAMING_SNAKE_CASE				= codec

						_SCREAMING_SNAKE_CASE				= 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				:   List[str]     =				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				:   Tuple     =				parser.parse_args()

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

'''simple docstring'''







import gc
import random
import unittest

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

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, 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 			lowerCAmelCase (				__UpperCAmelCase		,     unittest.TestCase			):
		a							:					Optional[int]       =					KandinskyImgaImgPipeline
		a							:					Union[str, Any]       =					["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
		a							:					Union[str, Any]       =					[
		    """prompt""",
		    """negative_prompt""",
		    """image_embeds""",
		    """negative_image_embeds""",
		    """image""",
		]
		a							:					int       =					[
		    """generator""",
		    """height""",
		    """width""",
		    """strength""",
		    """guidance_scale""",
		    """negative_prompt""",
		    """num_inference_steps""",
		    """return_dict""",
		    """guidance_scale""",
		    """num_images_per_prompt""",
		    """output_type""",
		    """return_dict""",
		]
		a							:					Tuple       =					False




		@property
		def     lowercase       (				self      ):
								return 32




		@property
		def     lowercase       (				self      ):
								return 32




		@property
		def     lowercase       (				self      ):
								return self.time_input_dim




		@property
		def     lowercase       (				self      ):
								return self.time_input_dim * 4




		@property
		def     lowercase       (				self      ):
								return 100




		@property
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"      )
								return tokenizer




		@property
		def     lowercase       (				self      ):
								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= MCLIPConfig(
								    numDims=self.cross_attention_dim					,						transformerDimensions=self.text_embedder_hidden_size					,						hidden_size=self.text_embedder_hidden_size					,						intermediate_size=37					,						num_attention_heads=4					,						num_hidden_layers=5					,						vocab_size=1_005					,						)

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

								return text_encoder




		@property
		def     lowercase       (				self      ):
								torch.manual_seed(0      )

								_SCREAMING_SNAKE_CASE				= {
								    "in_channels": 4,
								    # 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,
								}

								_SCREAMING_SNAKE_CASE				= UNetaDConditionModel(**UpperCamelCase      )
								return model




		@property
		def     lowercase       (				self      ):
								return {
								    "block_out_channels": [32, 64],
								    "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": 12,
								    "out_channels": 3,
								    "up_block_types": [
								        "AttnUpDecoderBlock2D",
								        "UpDecoderBlock2D",
								    ],
								    "vq_embed_dim": 4,
								}




		@property
		def     lowercase       (				self      ):
								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= VQModel(**self.dummy_movq_kwargs      )
								return model




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.dummy_text_encoder
								_SCREAMING_SNAKE_CASE				= self.dummy_tokenizer
								_SCREAMING_SNAKE_CASE				= self.dummy_unet
								_SCREAMING_SNAKE_CASE				= self.dummy_movq

								_SCREAMING_SNAKE_CASE				= {
								    "num_train_timesteps": 1_000,
								    "beta_schedule": "linear",
								    "beta_start": 0.0_00_85,
								    "beta_end": 0.0_12,
								    "clip_sample": False,
								    "set_alpha_to_one": False,
								    "steps_offset": 0,
								    "prediction_type": "epsilon",
								    "thresholding": False,
								}

								_SCREAMING_SNAKE_CASE				= DDIMScheduler(**UpperCamelCase      )

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

								return components




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase=0      ):
								_SCREAMING_SNAKE_CASE				= floats_tensor((1, self.cross_attention_dim)					,						rng=random.Random(UpperCamelCase      )      ).to(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= floats_tensor((1, self.cross_attention_dim)					,						rng=random.Random(seed + 1      )      ).to(UpperCamelCase      )
								# create init_image
								_SCREAMING_SNAKE_CASE				= floats_tensor((1, 3, 64, 64)					,						rng=random.Random(UpperCamelCase      )      ).to(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= image.cpu().permute(0					,						2					,						3					,						1      )[0]
								_SCREAMING_SNAKE_CASE				= Image.fromarray(np.uinta(UpperCamelCase      )      ).convert("RGB"      ).resize((256, 256)      )

								if str(UpperCamelCase      ).startswith("mps"      ):
														_SCREAMING_SNAKE_CASE				= torch.manual_seed(UpperCamelCase      )
								else:
														_SCREAMING_SNAKE_CASE				= torch.Generator(device=UpperCamelCase      ).manual_seed(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= {
								    "prompt": "horse",
								    "image": init_image,
								    "image_embeds": image_embeds,
								    "negative_image_embeds": negative_image_embeds,
								    "generator": generator,
								    "height": 64,
								    "width": 64,
								    "num_inference_steps": 10,
								    "guidance_scale": 7.0,
								    "strength": 0.2,
								    "output_type": "np",
								}
								return inputs




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= "cpu"

								_SCREAMING_SNAKE_CASE				= self.get_dummy_components()

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

								pipe.set_progress_bar_config(disable=UpperCamelCase      )

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

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

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

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

								_SCREAMING_SNAKE_CASE				= np.array(
								    [0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33]      )
								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()}'


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




		def     lowercase       (				self      ):
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/kandinsky/kandinsky_img2img_frog.npy"      )

								_SCREAMING_SNAKE_CASE				= load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"      )
								_SCREAMING_SNAKE_CASE				= "A red cartoon frog, 4k"

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

								_SCREAMING_SNAKE_CASE				= KandinskyImgaImgPipeline.from_pretrained(
								    "kandinsky-community/kandinsky-2-1"					,						torch_dtype=torch.floataa      )
								_SCREAMING_SNAKE_CASE				= pipeline.to(UpperCamelCase      )

								pipeline.set_progress_bar_config(disable=UpperCamelCase      )

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

								_SCREAMING_SNAKE_CASE				= pipeline(
								    UpperCamelCase					,						image=UpperCamelCase					,						image_embeds=UpperCamelCase					,						negative_image_embeds=UpperCamelCase					,						generator=UpperCamelCase					,						num_inference_steps=100					,						height=768					,						width=768					,						strength=0.2					,						output_type="np"					,						)

								_SCREAMING_SNAKE_CASE				= output.images[0]

								assert image.shape == (768, 768, 3)

								assert_mean_pixel_difference(UpperCamelCase					,						UpperCamelCase      )