

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()
__UpperCAmelCase    				=     logging.get_logger('transformers.models.encodec')

__UpperCAmelCase    				=     {
    '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',
}
__UpperCAmelCase    				=     {
    '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',
}
__UpperCAmelCase    				=     {
    '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',
}
__UpperCAmelCase    				=     {
    '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',
}
__UpperCAmelCase    				=     {
    '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',
}
__UpperCAmelCase    				=     {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
__UpperCAmelCase    				=     {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
__UpperCAmelCase    				=     []
__UpperCAmelCase    				=     []







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      str:

     """simple docstring"""







     for attribute in key.split("."				):
          _lowerCAmelCase   :					int        =    getattr(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)

     if weight_type is not None:
          _lowerCAmelCase   :					Tuple        =    getattr(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				).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   :					str        =    value
     elif weight_type == "weight_g":
          _lowerCAmelCase   :					List[Any]        =    value
     elif weight_type == "weight_v":
          _lowerCAmelCase   :					Dict        =    value
     elif weight_type == "bias":
          _lowerCAmelCase   :					str        =    value
     elif weight_type == "running_mean":
          _lowerCAmelCase   :					List[str]        =    value
     elif weight_type == "running_var":
          _lowerCAmelCase   :					Optional[Any]        =    value
     elif weight_type == "num_batches_tracked":
          _lowerCAmelCase   :					List[str]        =    value
     elif weight_type == "weight_ih_l0":
          _lowerCAmelCase   :					Any        =    value
     elif weight_type == "weight_hh_l0":
          _lowerCAmelCase   :					Optional[Any]        =    value
     elif weight_type == "bias_ih_l0":
          _lowerCAmelCase   :					List[str]        =    value
     elif weight_type == "bias_hh_l0":
          _lowerCAmelCase   :					Dict        =    value
     elif weight_type == "weight_ih_l1":
          _lowerCAmelCase   :					List[str]        =    value
     elif weight_type == "weight_hh_l1":
          _lowerCAmelCase   :					Tuple        =    value
     elif weight_type == "bias_ih_l1":
          _lowerCAmelCase   :					Union[str, Any]        =    value
     elif weight_type == "bias_hh_l1":
          _lowerCAmelCase   :					Tuple        =    value
     else:
          _lowerCAmelCase   :					Dict        =    value

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







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      str:

     """simple docstring"""







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







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Dict:

     """simple docstring"""







     _lowerCAmelCase   :					Optional[Any]        =    []

     if model_name == "encodec_24khz" or "encodec_32khz":
          _lowerCAmelCase   :					str        =    MAPPING_24K
     elif model_name == "encodec_48khz":
          _lowerCAmelCase   :					List[Any]        =    MAPPING_48K
     else:
          raise ValueError(f'''Unsupported model: {model_name}'''				)

     for name, value in orig_dict.items():
          if should_ignore(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				):
               logger.info(f'''{name} was ignored'''				)
               continue

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

     logger.warning(f'''Unused weights: {unused_weights}'''				)







@torch.no_grad()
def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=None		,							SCREAMING_SNAKE_CASE=None		,							)       ->      Union[str, Any]:

     """simple docstring"""







     if config_path is not None:
          _lowerCAmelCase   :					Optional[int]        =    EncodecConfig.from_pretrained(SCREAMING_SNAKE_CASE				)
     else:
          _lowerCAmelCase   :					Any        =    EncodecConfig()

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

     _lowerCAmelCase   :					Tuple        =    EncodecModel(SCREAMING_SNAKE_CASE				)

     _lowerCAmelCase   :					str        =    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(SCREAMING_SNAKE_CASE				)

     _lowerCAmelCase   :					List[str]        =    torch.load(SCREAMING_SNAKE_CASE				)
     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   :					str        =    original_checkpoint["best_state"]
     recursively_load_weights(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)
     model.save_pretrained(SCREAMING_SNAKE_CASE				)

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


if __name__ == "__main__":
  __UpperCAmelCase    				=     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.'
  )

  __UpperCAmelCase    				=     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,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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 A__    ( A  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					List[str]      =						KandinskyVaaControlnetImgaImgPipeline
 _lowercase     :					Tuple      =						['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
 _lowercase     :					str      =						['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
 _lowercase     :					Optional[Any]      =						[
     '''generator''',
     '''height''',
     '''width''',
     '''strength''',
     '''guidance_scale''',
     '''num_inference_steps''',
     '''return_dict''',
     '''guidance_scale''',
     '''num_images_per_prompt''',
     '''output_type''',
     '''return_dict''',
 ]
 _lowercase     :					Tuple      =						False

 @property
 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      return 3_2

 @property
 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      return 3_2

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






      '''simple docstring'''




      return self.time_input_dim

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






      '''simple docstring'''




      return self.time_input_dim * 4

 @property
 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      return 1_0_0

 @property
 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      torch.manual_seed(0       )

      _lowerCAmelCase   :					Tuple        =    {
          "in_channels": 8,
          # Out channels is double in channels because predicts mean and variance
          "out_channels": 8,
          "addition_embed_type": "image_hint",
          "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   :					Optional[int]        =    UNetaDConditionModel(**A_       )
      return model

 @property
 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      return {
          "block_out_channels": [3_2, 3_2, 6_4, 6_4],
          "down_block_types": [
              "DownEncoderBlock2D",
              "DownEncoderBlock2D",
              "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
          "vq_embed_dim": 4,
      }

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






      '''simple docstring'''




      torch.manual_seed(0       )
      _lowerCAmelCase   :					List[str]        =    VQModel(**self.dummy_movq_kwargs       )
      return model

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    self.dummy_unet
      _lowerCAmelCase   :					List[Any]        =    self.dummy_movq

      _lowerCAmelCase   :					Tuple        =    {
          "num_train_timesteps": 1_0_0_0,
          "beta_schedule": "linear",
          "beta_start": 0.00085,
          "beta_end": 0.012,
          "clip_sample": False,
          "set_alpha_to_one": False,
          "steps_offset": 0,
          "prediction_type": "epsilon",
          "thresholding": False,
      }

      _lowerCAmelCase   :					int        =    DDIMScheduler(**A_       )

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

      return components

 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	Union[str, Any]     ,						A_ :	Optional[int]=0       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    floats_tensor((1, self.text_embedder_hidden_size)     ,						rng=random.Random(A_       )       ).to(A_       )
      _lowerCAmelCase   :					str        =    floats_tensor((1, self.text_embedder_hidden_size)     ,						rng=random.Random(seed + 1       )       ).to(
          A_       )
      # create init_image
      _lowerCAmelCase   :					Dict        =    floats_tensor((1, 3, 6_4, 6_4)     ,						rng=random.Random(A_       )       ).to(A_       )
      _lowerCAmelCase   :					Dict        =    image.cpu().permute(0     ,						2     ,						3     ,						1       )[0]
      _lowerCAmelCase   :					Tuple        =    Image.fromarray(np.uinta(A_       )       ).convert("RGB"       ).resize((2_5_6, 2_5_6)       )
      # create hint
      _lowerCAmelCase   :					Union[str, Any]        =    floats_tensor((1, 3, 6_4, 6_4)     ,						rng=random.Random(A_       )       ).to(A_       )

      if str(A_       ).startswith("mps"       ):
           _lowerCAmelCase   :					Tuple        =    torch.manual_seed(A_       )
      else:
           _lowerCAmelCase   :					Any        =    torch.Generator(device=A_       ).manual_seed(A_       )
      _lowerCAmelCase   :					Union[str, Any]        =    {
          "image": init_image,
          "image_embeds": image_embeds,
          "negative_image_embeds": negative_image_embeds,
          "hint": hint,
          "generator": generator,
          "height": 6_4,
          "width": 6_4,
          "num_inference_steps": 1_0,
          "guidance_scale": 7.0,
          "strength": 0.2,
          "output_type": "np",
      }
      return inputs


 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    "cpu"

      _lowerCAmelCase   :					Tuple        =    self.get_dummy_components()

      _lowerCAmelCase   :					Optional[int]        =    self.pipeline_class(**A_       )
      _lowerCAmelCase   :					Optional[Any]        =    pipe.to(A_       )

      pipe.set_progress_bar_config(disable=A_       )

      _lowerCAmelCase   :					List[str]        =    pipe(**self.get_dummy_inputs(A_       )       )
      _lowerCAmelCase   :					List[Any]        =    output.images

      _lowerCAmelCase   :					str        =    pipe(
          **self.get_dummy_inputs(A_       )     ,						return_dict=A_     ,						)[0]

      _lowerCAmelCase   :					int        =    image[0, -3:, -3:, -1]
      _lowerCAmelCase   :					List[str]        =    image_from_tuple[0, -3:, -3:, -1]

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

      _lowerCAmelCase   :					List[Any]        =    np.array(
          [0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736]       )
      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 A__    ( unittest.TestCase ):



 """simple docstring"""







 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




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


 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"       )

      _lowerCAmelCase   :					str        =    load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"       )
      _lowerCAmelCase   :					Union[str, Any]        =    init_image.resize((5_1_2, 5_1_2)       )

      _lowerCAmelCase   :					Dict        =    load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/kandinskyv22/hint_image_cat.png"       )
      _lowerCAmelCase   :					Tuple        =    torch.from_numpy(np.array(A_       )       ).float() / 255.0
      _lowerCAmelCase   :					Optional[int]        =    hint.permute(2     ,						0     ,						1       ).unsqueeze(0       )

      _lowerCAmelCase   :					List[str]        =    "A robot, 4k photo"

      _lowerCAmelCase   :					Dict        =    KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-2-prior"     ,						torch_dtype=torch.floataa       )
      pipe_prior.to(A_       )

      _lowerCAmelCase   :					str        =    KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-2-controlnet-depth"     ,						torch_dtype=torch.floataa       )
      _lowerCAmelCase   :					Optional[int]        =    pipeline.to(A_       )

      pipeline.set_progress_bar_config(disable=A_       )

      _lowerCAmelCase   :					Dict        =    torch.Generator(device="cpu"       ).manual_seed(0       )

      _lowerCAmelCase      ,			_lowerCAmelCase   :					Tuple        =    pipe_prior(
          A_     ,						image=A_     ,						strength=0.85     ,						generator=A_     ,						negative_prompt=""     ,						).to_tuple()

      _lowerCAmelCase   :					List[Any]        =    pipeline(
          image=A_     ,						image_embeds=A_     ,						negative_image_embeds=A_     ,						hint=A_     ,						generator=A_     ,						num_inference_steps=1_0_0     ,						height=5_1_2     ,						width=5_1_2     ,						strength=0.5     ,						output_type="np"     ,						)

      _lowerCAmelCase   :					str        =    output.images[0]

      assert image.shape == (5_1_2, 5_1_2, 3)

      assert_mean_pixel_difference(A_     ,						A_       )






