


import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
A   :					List[Any]	=      logging.get_logger("transformers.models.encodec")

A   :					Optional[int]	=      {
    "quantizer.vq.layers.*._codebook.inited": "quantizer.layers.*.codebook.inited",
    "quantizer.vq.layers.*._codebook.cluster_size": "quantizer.layers.*.codebook.cluster_size",
    "quantizer.vq.layers.*._codebook.embed": "quantizer.layers.*.codebook.embed",
    "quantizer.vq.layers.*._codebook.embed_avg": "quantizer.layers.*.codebook.embed_avg",
}
A   :					Dict	=      {
    "encoder.model.0.conv.conv": "encoder.layers.0.conv",
    "encoder.model.1.block.1.conv.conv": "encoder.layers.1.block.1.conv",
    "encoder.model.1.block.3.conv.conv": "encoder.layers.1.block.3.conv",
    "encoder.model.1.shortcut.conv.conv": "encoder.layers.1.shortcut.conv",
    "encoder.model.3.conv.conv": "encoder.layers.3.conv",
    "encoder.model.4.block.1.conv.conv": "encoder.layers.4.block.1.conv",
    "encoder.model.4.block.3.conv.conv": "encoder.layers.4.block.3.conv",
    "encoder.model.4.shortcut.conv.conv": "encoder.layers.4.shortcut.conv",
    "encoder.model.6.conv.conv": "encoder.layers.6.conv",
    "encoder.model.7.block.1.conv.conv": "encoder.layers.7.block.1.conv",
    "encoder.model.7.block.3.conv.conv": "encoder.layers.7.block.3.conv",
    "encoder.model.7.shortcut.conv.conv": "encoder.layers.7.shortcut.conv",
    "encoder.model.9.conv.conv": "encoder.layers.9.conv",
    "encoder.model.10.block.1.conv.conv": "encoder.layers.10.block.1.conv",
    "encoder.model.10.block.3.conv.conv": "encoder.layers.10.block.3.conv",
    "encoder.model.10.shortcut.conv.conv": "encoder.layers.10.shortcut.conv",
    "encoder.model.12.conv.conv": "encoder.layers.12.conv",
    "encoder.model.13.lstm": "encoder.layers.13.lstm",
    "encoder.model.15.conv.conv": "encoder.layers.15.conv",
}
A   :					str	=      {
    "encoder.model.0.conv.norm": "encoder.layers.0.norm",
    "encoder.model.1.block.1.conv.norm": "encoder.layers.1.block.1.norm",
    "encoder.model.1.block.3.conv.norm": "encoder.layers.1.block.3.norm",
    "encoder.model.1.shortcut.conv.norm": "encoder.layers.1.shortcut.norm",
    "encoder.model.3.conv.norm": "encoder.layers.3.norm",
    "encoder.model.4.block.1.conv.norm": "encoder.layers.4.block.1.norm",
    "encoder.model.4.block.3.conv.norm": "encoder.layers.4.block.3.norm",
    "encoder.model.4.shortcut.conv.norm": "encoder.layers.4.shortcut.norm",
    "encoder.model.6.conv.norm": "encoder.layers.6.norm",
    "encoder.model.7.block.1.conv.norm": "encoder.layers.7.block.1.norm",
    "encoder.model.7.block.3.conv.norm": "encoder.layers.7.block.3.norm",
    "encoder.model.7.shortcut.conv.norm": "encoder.layers.7.shortcut.norm",
    "encoder.model.9.conv.norm": "encoder.layers.9.norm",
    "encoder.model.10.block.1.conv.norm": "encoder.layers.10.block.1.norm",
    "encoder.model.10.block.3.conv.norm": "encoder.layers.10.block.3.norm",
    "encoder.model.10.shortcut.conv.norm": "encoder.layers.10.shortcut.norm",
    "encoder.model.12.conv.norm": "encoder.layers.12.norm",
    "encoder.model.15.conv.norm": "encoder.layers.15.norm",
}
A   :					List[str]	=      {
    "decoder.model.0.conv.conv": "decoder.layers.0.conv",
    "decoder.model.1.lstm": "decoder.layers.1.lstm",
    "decoder.model.3.convtr.convtr": "decoder.layers.3.conv",
    "decoder.model.4.block.1.conv.conv": "decoder.layers.4.block.1.conv",
    "decoder.model.4.block.3.conv.conv": "decoder.layers.4.block.3.conv",
    "decoder.model.4.shortcut.conv.conv": "decoder.layers.4.shortcut.conv",
    "decoder.model.6.convtr.convtr": "decoder.layers.6.conv",
    "decoder.model.7.block.1.conv.conv": "decoder.layers.7.block.1.conv",
    "decoder.model.7.block.3.conv.conv": "decoder.layers.7.block.3.conv",
    "decoder.model.7.shortcut.conv.conv": "decoder.layers.7.shortcut.conv",
    "decoder.model.9.convtr.convtr": "decoder.layers.9.conv",
    "decoder.model.10.block.1.conv.conv": "decoder.layers.10.block.1.conv",
    "decoder.model.10.block.3.conv.conv": "decoder.layers.10.block.3.conv",
    "decoder.model.10.shortcut.conv.conv": "decoder.layers.10.shortcut.conv",
    "decoder.model.12.convtr.convtr": "decoder.layers.12.conv",
    "decoder.model.13.block.1.conv.conv": "decoder.layers.13.block.1.conv",
    "decoder.model.13.block.3.conv.conv": "decoder.layers.13.block.3.conv",
    "decoder.model.13.shortcut.conv.conv": "decoder.layers.13.shortcut.conv",
    "decoder.model.15.conv.conv": "decoder.layers.15.conv",
}
A   :					int	=      {
    "decoder.model.0.conv.norm": "decoder.layers.0.norm",
    "decoder.model.3.convtr.norm": "decoder.layers.3.norm",
    "decoder.model.4.block.1.conv.norm": "decoder.layers.4.block.1.norm",
    "decoder.model.4.block.3.conv.norm": "decoder.layers.4.block.3.norm",
    "decoder.model.4.shortcut.conv.norm": "decoder.layers.4.shortcut.norm",
    "decoder.model.6.convtr.norm": "decoder.layers.6.norm",
    "decoder.model.7.block.1.conv.norm": "decoder.layers.7.block.1.norm",
    "decoder.model.7.block.3.conv.norm": "decoder.layers.7.block.3.norm",
    "decoder.model.7.shortcut.conv.norm": "decoder.layers.7.shortcut.norm",
    "decoder.model.9.convtr.norm": "decoder.layers.9.norm",
    "decoder.model.10.block.1.conv.norm": "decoder.layers.10.block.1.norm",
    "decoder.model.10.block.3.conv.norm": "decoder.layers.10.block.3.norm",
    "decoder.model.10.shortcut.conv.norm": "decoder.layers.10.shortcut.norm",
    "decoder.model.12.convtr.norm": "decoder.layers.12.norm",
    "decoder.model.13.block.1.conv.norm": "decoder.layers.13.block.1.norm",
    "decoder.model.13.block.3.conv.norm": "decoder.layers.13.block.3.norm",
    "decoder.model.13.shortcut.conv.norm": "decoder.layers.13.shortcut.norm",
    "decoder.model.15.conv.norm": "decoder.layers.15.norm",
}
A   :					Union[str, Any]	=      {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
A   :					Dict	=      {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
A   :					str	=      []
A   :					List[str]	=      []

def    lowercase_	(	_A    :					Union[str, Any]					,      _A    :					Union[str, Any]					,      _A    :					Optional[Any]					,      _A    :					Optional[int]					,      _A    :					int		):

      """simple docstring"""




      for attribute in key.split("."		):
            lowerCamelCase__	:       Optional[Any]					=						getattr(_A					,      _A		)

      if weight_type is not None:
            lowerCamelCase__	:       List[str]					=						getattr(_A					,      _A		).shape
      else:
            lowerCamelCase__	:       List[str]					=						hf_pointer.shape

      if hf_shape != value.shape:
            raise ValueError(
                F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
                F" {value.shape} for {full_name}"		)

      if weight_type == "weight":
            lowerCamelCase__	:       Dict					=						value
      elif weight_type == "weight_g":
            lowerCamelCase__	:       str					=						value
      elif weight_type == "weight_v":
            lowerCamelCase__	:       Optional[int]					=						value
      elif weight_type == "bias":
            lowerCamelCase__	:       Tuple					=						value
      elif weight_type == "running_mean":
            lowerCamelCase__	:       List[Any]					=						value
      elif weight_type == "running_var":
            lowerCamelCase__	:       str					=						value
      elif weight_type == "num_batches_tracked":
            lowerCamelCase__	:       List[Any]					=						value
      elif weight_type == "weight_ih_l0":
            lowerCamelCase__	:       Optional[Any]					=						value
      elif weight_type == "weight_hh_l0":
            lowerCamelCase__	:       Tuple					=						value
      elif weight_type == "bias_ih_l0":
            lowerCamelCase__	:       str					=						value
      elif weight_type == "bias_hh_l0":
            lowerCamelCase__	:       Optional[int]					=						value
      elif weight_type == "weight_ih_l1":
            lowerCamelCase__	:       Dict					=						value
      elif weight_type == "weight_hh_l1":
            lowerCamelCase__	:       str					=						value
      elif weight_type == "bias_ih_l1":
            lowerCamelCase__	:       Optional[Any]					=						value
      elif weight_type == "bias_hh_l1":
            lowerCamelCase__	:       int					=						value
      else:
            lowerCamelCase__	:       Optional[Any]					=						value

      logger.info(F"{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}."		)

def    lowercase_	(	_A    :					Tuple					,      _A    :					Any		):

      """simple docstring"""




      for key in ignore_keys:
            if key.endswith(".*"		):
                  if name.startswith(key[:-1]		):
                        return True
            elif ".*." in key:
                  lowerCamelCase__					,     lowerCamelCase__	:       Any					=						key.split(".*."		)
                  if prefix in name and suffix in name:
                        return True
            elif key in name:
                  return True
      return False

def    lowercase_	(	_A    :					List[Any]					,      _A    :					Optional[int]					,      _A    :					Optional[Any]		):

      """simple docstring"""




      lowerCamelCase__	:       Dict					=						[]

      if model_name == "encodec_24khz" or "encodec_32khz":
            lowerCamelCase__	:       str					=						MAPPING_24K
      elif model_name == "encodec_48khz":
            lowerCamelCase__	:       Any					=						MAPPING_48K
      else:
            raise ValueError(F"Unsupported model: {model_name}"		)

      for name, value in orig_dict.items():
            if should_ignore(_A					,      _A		):
                  logger.info(F"{name} was ignored"		)
                  continue

            lowerCamelCase__	:       int					=						False
            for key, mapped_key in MAPPING.items():
                  if "*" in key:
                        lowerCamelCase__					,     lowerCamelCase__	:       Any					=						key.split(".*."		)
                        if prefix in name and suffix in name:
                              lowerCamelCase__	:       Optional[Any]					=						suffix

                  if key in name:
                        # HACK otherwise .embed gets initialized with .embed_avg too
                        if key.endswith("embed"		) and name.endswith("embed_avg"		):
                              continue

                        lowerCamelCase__	:       Optional[Any]					=						True
                        if "*" in mapped_key:
                              lowerCamelCase__	:       Optional[Any]					=						name.split(_A		)[0].split("."		)[-2]
                              lowerCamelCase__	:       str					=						mapped_key.replace("*"					,      _A		)
                        if "weight_g" in name:
                              lowerCamelCase__	:       Any					=						"weight_g"
                        elif "weight_v" in name:
                              lowerCamelCase__	:       List[Any]					=						"weight_v"
                        elif "weight_ih_l0" in name:
                              lowerCamelCase__	:       Union[str, Any]					=						"weight_ih_l0"
                        elif "weight_hh_l0" in name:
                              lowerCamelCase__	:       Dict					=						"weight_hh_l0"
                        elif "bias_ih_l0" in name:
                              lowerCamelCase__	:       Optional[Any]					=						"bias_ih_l0"
                        elif "bias_hh_l0" in name:
                              lowerCamelCase__	:       Optional[int]					=						"bias_hh_l0"
                        elif "weight_ih_l1" in name:
                              lowerCamelCase__	:       Dict					=						"weight_ih_l1"
                        elif "weight_hh_l1" in name:
                              lowerCamelCase__	:       Optional[Any]					=						"weight_hh_l1"
                        elif "bias_ih_l1" in name:
                              lowerCamelCase__	:       List[str]					=						"bias_ih_l1"
                        elif "bias_hh_l1" in name:
                              lowerCamelCase__	:       List[Any]					=						"bias_hh_l1"
                        elif "bias" in name:
                              lowerCamelCase__	:       str					=						"bias"
                        elif "weight" in name:
                              lowerCamelCase__	:       List[Any]					=						"weight"
                        elif "running_mean" in name:
                              lowerCamelCase__	:       Any					=						"running_mean"
                        elif "running_var" in name:
                              lowerCamelCase__	:       List[Any]					=						"running_var"
                        elif "num_batches_tracked" in name:
                              lowerCamelCase__	:       str					=						"num_batches_tracked"
                        else:
                              lowerCamelCase__	:       Optional[int]					=						None
                        set_recursively(_A					,      _A					,      _A					,      _A					,      _A		)
                  continue
            if not is_used:
                  unused_weights.append(_A		)

      logger.warning(F"Unused weights: {unused_weights}"		)

@torch.no_grad()
def    lowercase_	(	_A    :					Tuple					,      _A    :					Union[str, Any]					,      _A    :					List[str]					,      _A    :					List[str]=None					,      _A    :					Optional[int]=None					,      ):

      """simple docstring"""




      if config_path is not None:
            lowerCamelCase__	:       str					=						EncodecConfig.from_pretrained(_A		)
      else:
            lowerCamelCase__	:       Optional[int]					=						EncodecConfig()

      if model_name == "encodec_24khz":
            pass  # config is already correct
      elif model_name == "encodec_32khz":
            lowerCamelCase__	:       Union[str, Any]					=						[8, 5, 4, 4]
            lowerCamelCase__	:       Tuple					=						[2.2]
            lowerCamelCase__	:       int					=						64
            lowerCamelCase__	:       List[Any]					=						32000
            lowerCamelCase__	:       Optional[int]					=						2048
            lowerCamelCase__	:       Optional[Any]					=						False
            lowerCamelCase__	:       int					=						False
            lowerCamelCase__	:       int					=						False
      elif model_name == "encodec_48khz":
            lowerCamelCase__	:       Any					=						[8, 5, 4, 2]
            lowerCamelCase__	:       str					=						[3.0, 6.0, 12.0, 24.0]
            lowerCamelCase__	:       Dict					=						48000
            lowerCamelCase__	:       Optional[int]					=						2
            lowerCamelCase__	:       Union[str, Any]					=						False
            lowerCamelCase__	:       str					=						"time_group_norm"
            lowerCamelCase__	:       int					=						True
            lowerCamelCase__	:       Optional[Any]					=						1.0
            lowerCamelCase__	:       Union[str, Any]					=						0.01
      else:
            raise ValueError(F"Unknown model name: {model_name}"		)

      lowerCamelCase__	:       Dict					=						EncodecModel(_A		)

      lowerCamelCase__	:       Dict					=						EncodecFeatureExtractor(
          feature_size=config.audio_channels					,      sampling_rate=config.sampling_rate					,      chunk_length_s=config.chunk_length_s					,      overlap=config.overlap					,      )
      feature_extractor.save_pretrained(_A		)

      lowerCamelCase__	:       Dict					=						torch.load(_A		)
      if "best_state" in original_checkpoint:
            # we might have a training state saved, in which case discard the yaml results and just retain the weights
            lowerCamelCase__	:       List[str]					=						original_checkpoint["best_state"]
      recursively_load_weights(_A					,      _A					,      _A		)
      model.save_pretrained(_A		)

      if repo_id:
            print("Pushing to the hub..."		)
            feature_extractor.push_to_hub(_A		)
            model.push_to_hub(_A		)


if __name__ == "__main__":
    A   :					Dict	=      argparse.ArgumentParser()
    parser.add_argument(
        "--model",
        default="encodec_24khz",
        type=str,
        help="The model to convert. Should be one of 'encodec_24khz', 'encodec_32khz', 'encodec_48khz'.",
    )
    parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
    parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
    parser.add_argument(
        "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
    )
    parser.add_argument(
        "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
    )

    A   :					List[Any]	=      parser.parse_args()
    convert_checkpoint(
        args.model,
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.push_to_hub,
    )






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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     _lowercase				(							lowercase__							,						unittest.TestCase):


       """simple docstring"""




       A__      		=    KandinskyVaaImgaImgPipeline
       A__      		=    ["image_embeds", "negative_image_embeds", "image"]
       A__      		=    [
           "image_embeds",
           "negative_image_embeds",
           "image",
       ]
       A__      		=    [
           "generator",
           "height",
           "width",
           "strength",
           "guidance_scale",
           "num_inference_steps",
           "return_dict",
           "guidance_scale",
           "num_images_per_prompt",
           "output_type",
           "return_dict",
       ]
       A__      		=    False





       @property
       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             return 32





       @property
       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             return 32





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


             '''simple docstring'''



             return self.time_input_dim





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


             '''simple docstring'''



             return self.time_input_dim * 4





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


             '''simple docstring'''



             return 100





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


             '''simple docstring'''



             torch.manual_seed(0 )

             lowerCamelCase__	:       Optional[Any]					=						{
                 "in_channels": 4,
                 # Out channels is double in channels because predicts mean and variance
                 "out_channels": 8,
                 "addition_embed_type": "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": "image_proj",
                 "cross_attention_dim": self.cross_attention_dim,
                 "attention_head_dim": 4,
                 "resnet_time_scale_shift": "scale_shift",
                 "class_embed_type": None,
             }

             lowerCamelCase__	:       Tuple					=						UNetaDConditionModel(**__lowerCamelCase )
             return model





       @property
       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             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 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             torch.manual_seed(0 )
             lowerCamelCase__	:       int					=						VQModel(**self.dummy_movq_kwargs )
             return model





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						self.dummy_unet
             lowerCamelCase__	:       Optional[Any]					=						self.dummy_movq

             lowerCamelCase__	:       Optional[int]					=						{
                 "num_train_timesteps": 1000,
                 "beta_schedule": "linear",
                 "beta_start": 0.0_0_0_8_5,
                 "beta_end": 0.0_1_2,
                 "clip_sample": False,
                 "set_alpha_to_one": False,
                 "steps_offset": 0,
                 "prediction_type": "epsilon",
                 "thresholding": False,
             }

             lowerCamelCase__	:       List[Any]					=						DDIMScheduler(**__lowerCamelCase )

             lowerCamelCase__	:       Tuple					=						{
                 "unet": unet,
                 "scheduler": scheduler,
                 "movq": movq,
             }

             return components





       def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    int=0 ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						floats_tensor((1, self.text_embedder_hidden_size)      ,   rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
             lowerCamelCase__	:       Union[str, Any]					=						floats_tensor((1, self.text_embedder_hidden_size)      ,   rng=random.Random(seed + 1 ) ).to(
                 __lowerCamelCase )
             # create init_image
             lowerCamelCase__	:       Union[str, Any]					=						floats_tensor((1, 3, 64, 64)      ,   rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
             lowerCamelCase__	:       List[Any]					=						image.cpu().permute(0      ,   2      ,   3      ,   1 )[0]
             lowerCamelCase__	:       Optional[int]					=						Image.fromarray(np.uinta(__lowerCamelCase ) ).convert("RGB" ).resize((256, 256) )

             if str(__lowerCamelCase ).startswith("mps" ):
                   lowerCamelCase__	:       Optional[int]					=						torch.manual_seed(__lowerCamelCase )
             else:
                   lowerCamelCase__	:       List[str]					=						torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
             lowerCamelCase__	:       Tuple					=						{
                 "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 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						"cpu"

             lowerCamelCase__	:       str					=						self.get_dummy_components()

             lowerCamelCase__	:       Optional[int]					=						self.pipeline_class(**__lowerCamelCase )
             lowerCamelCase__	:       List[str]					=						pipe.to(__lowerCamelCase )

             pipe.set_progress_bar_config(disable=__lowerCamelCase )

             lowerCamelCase__	:       Optional[Any]					=						pipe(**self.get_dummy_inputs(__lowerCamelCase ) )
             lowerCamelCase__	:       List[str]					=						output.images

             lowerCamelCase__	:       Optional[Any]					=						pipe(
                 **self.get_dummy_inputs(__lowerCamelCase )      ,   return_dict=__lowerCamelCase      ,   )[0]

             lowerCamelCase__	:       int					=						image[0, -3:, -3:, -1]
             lowerCamelCase__	:       int					=						image_from_tuple[0, -3:, -3:, -1]

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

             lowerCamelCase__	:       str					=						np.array(
                 [0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_3] )
             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     _lowercase				(							unittest.TestCase):


       """simple docstring"""




       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



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





       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						load_numpy(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/kandinskyv22/kandinskyv22_img2img_frog.npy" )

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

             lowerCamelCase__	:       str					=						KandinskyVaaPriorPipeline.from_pretrained(
                 "kandinsky-community/kandinsky-2-2-prior"      ,   torch_dtype=torch.floataa )
             pipe_prior.to(__lowerCamelCase )

             lowerCamelCase__	:       Tuple					=						KandinskyVaaImgaImgPipeline.from_pretrained(
                 "kandinsky-community/kandinsky-2-2-decoder"      ,   torch_dtype=torch.floataa )
             lowerCamelCase__	:       str					=						pipeline.to(__lowerCamelCase )

             pipeline.set_progress_bar_config(disable=__lowerCamelCase )

             lowerCamelCase__	:       Tuple					=						torch.Generator(device="cpu" ).manual_seed(0 )
             lowerCamelCase__					,     lowerCamelCase__	:       List[str]					=						pipe_prior(
                 __lowerCamelCase      ,   generator=__lowerCamelCase      ,   num_inference_steps=5      ,   negative_prompt=""      ,   ).to_tuple()

             lowerCamelCase__	:       Optional[Any]					=						pipeline(
                 image=__lowerCamelCase      ,   image_embeds=__lowerCamelCase      ,   negative_image_embeds=__lowerCamelCase      ,   generator=__lowerCamelCase      ,   num_inference_steps=100      ,   height=768      ,   width=768      ,   strength=0.2      ,   output_type="np"      ,   )

             lowerCamelCase__	:       Union[str, Any]					=						output.images[0]

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

             assert_mean_pixel_difference(__lowerCamelCase      ,   __lowerCamelCase )


