import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__UpperCamelCase		:       List[str]									=     logging.get_logger(__name__)

__UpperCamelCase		:       List[Any]									=     {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.linear_k''': '''encoder.layers.*.self_attn.linear_k''',
    '''self_attn.linear_v''': '''encoder.layers.*.self_attn.linear_v''',
    '''self_attn.linear_q''': '''encoder.layers.*.self_attn.linear_q''',
    '''self_attn.pos_bias_u''': '''encoder.layers.*.self_attn.pos_bias_u''',
    '''self_attn.pos_bias_v''': '''encoder.layers.*.self_attn.pos_bias_v''',
    '''self_attn.linear_out''': '''encoder.layers.*.self_attn.linear_out''',
    '''self_attn.linear_pos''': '''encoder.layers.*.self_attn.linear_pos''',
    '''self_attn.rotary_emb''': '''encoder.embed_positions''',
    '''self_attn_layer_norm''': '''encoder.layers.*.self_attn_layer_norm''',
    '''conv_module.pointwise_conv1''': '''encoder.layers.*.conv_module.pointwise_conv1''',
    '''conv_module.pointwise_conv2''': '''encoder.layers.*.conv_module.pointwise_conv2''',
    '''conv_module.depthwise_conv''': '''encoder.layers.*.conv_module.depthwise_conv''',
    '''conv_module.batch_norm''': '''encoder.layers.*.conv_module.batch_norm''',
    '''conv_module.layer_norm''': '''encoder.layers.*.conv_module.layer_norm''',
    '''ffn1.w_1''': '''encoder.layers.*.ffn1.intermediate_dense''',
    '''ffn1.w_2''': '''encoder.layers.*.ffn1.output_dense''',
    '''ffn1.layer_norm''': '''encoder.layers.*.ffn1_layer_norm''',
    '''ffn2.w_1''': '''encoder.layers.*.ffn2.intermediate_dense''',
    '''ffn2.w_2''': '''encoder.layers.*.ffn2.output_dense''',
    '''ffn2.layer_norm''': '''encoder.layers.*.ffn2_layer_norm''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
__UpperCamelCase		:       Tuple									=     [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]
def       A	(    _lowercase		,  _lowercase		,  _lowercase		,  _lowercase		,  _lowercase						):





						for attribute in key.split('''.'''						):
												SCREAMING_SNAKE_CASE							:    List[Any]							=							getattr(__UpperCamelCase		,  __UpperCamelCase						)

						if weight_type is not None:
												SCREAMING_SNAKE_CASE							:    Any							=							getattr(__UpperCamelCase		,  __UpperCamelCase						).shape
						else:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							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":
												SCREAMING_SNAKE_CASE							:    Tuple							=							value
						elif weight_type == "weight_g":
												SCREAMING_SNAKE_CASE							:    List[str]							=							value
						elif weight_type == "weight_v":
												SCREAMING_SNAKE_CASE							:    Tuple							=							value
						elif weight_type == "bias":
												SCREAMING_SNAKE_CASE							:    Tuple							=							value
						elif weight_type == "running_mean":
												SCREAMING_SNAKE_CASE							:    List[str]							=							value
						elif weight_type == "running_var":
												SCREAMING_SNAKE_CASE							:    Tuple							=							value
						elif weight_type == "num_batches_tracked":
												SCREAMING_SNAKE_CASE							:    Dict							=							value
						elif weight_type == "inv_freq":
												SCREAMING_SNAKE_CASE							:    int							=							value
						else:
												SCREAMING_SNAKE_CASE							:    Dict							=							value

						logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    int							=							[]
						SCREAMING_SNAKE_CASE							:    List[str]							=							fairseq_model.state_dict()

						SCREAMING_SNAKE_CASE							:    Dict							=							hf_model.wavaveca_conformer.feature_extractor

						for name, value in fairseq_dict.items():
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							False
												if "conv_layers" in name:
																		load_conv_layer(
																		    __UpperCamelCase		,  __UpperCamelCase		,  __UpperCamelCase		,  __UpperCamelCase		,  hf_model.config.feat_extract_norm == '''group'''		,  )
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							True
												else:
																		for key, mapped_key in MAPPING.items():
																								SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							'''wav2vec2_conformer.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																								if key in name or key.split('''w2v_model.'''						)[-1] == name.split('''.'''						)[0]:
																														SCREAMING_SNAKE_CASE							:    Tuple							=							True
																														if "*" in mapped_key:
																																				SCREAMING_SNAKE_CASE							:    Dict							=							name.split(__UpperCamelCase						)[0].split('''.'''						)[-2]
																																				SCREAMING_SNAKE_CASE							:    Optional[Any]							=							mapped_key.replace('''*'''		,  __UpperCamelCase						)
																														if "pos_bias_u" in name:
																																				SCREAMING_SNAKE_CASE							:    List[Any]							=							None
																														elif "pos_bias_v" in name:
																																				SCREAMING_SNAKE_CASE							:    Optional[Any]							=							None
																														elif "weight_g" in name:
																																				SCREAMING_SNAKE_CASE							:    List[Any]							=							'''weight_g'''
																														elif "weight_v" in name:
																																				SCREAMING_SNAKE_CASE							:    List[str]							=							'''weight_v'''
																														elif "bias" in name:
																																				SCREAMING_SNAKE_CASE							:    List[Any]							=							'''bias'''
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				SCREAMING_SNAKE_CASE							:    List[str]							=							'''weight'''
																														elif "running_mean" in name:
																																				SCREAMING_SNAKE_CASE							:    Any							=							'''running_mean'''
																														elif "inv_freq" in name:
																																				SCREAMING_SNAKE_CASE							:    Optional[Any]							=							'''inv_freq'''
																														elif "running_var" in name:
																																				SCREAMING_SNAKE_CASE							:    Any							=							'''running_var'''
																														elif "num_batches_tracked" in name:
																																				SCREAMING_SNAKE_CASE							:    Any							=							'''num_batches_tracked'''
																														else:
																																				SCREAMING_SNAKE_CASE							:    Optional[Any]							=							None
																														set_recursively(__UpperCamelCase		,  __UpperCamelCase		,  __UpperCamelCase		,  __UpperCamelCase		,  __UpperCamelCase						)
																								continue
												if not is_used:
																		unused_weights.append(__UpperCamelCase						)

						logger.warning(f"""Unused weights: {unused_weights}"""						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase		,  _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Tuple							=							full_name.split('''conv_layers.'''						)[-1]
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							name.split('''.'''						)
						SCREAMING_SNAKE_CASE							:    List[Any]							=							int(items[0]						)
						SCREAMING_SNAKE_CASE							:    Tuple							=							int(items[1]						)

						if type_id == 0:
												if "bias" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																								raise ValueError(
																								    f"""{full_name} has size {value.shape}, but"""
																								    f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""						)
																		SCREAMING_SNAKE_CASE							:    List[str]							=							value
																		logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""						)
												elif "weight" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																								raise ValueError(
																								    f"""{full_name} has size {value.shape}, but"""
																								    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""						)
																		SCREAMING_SNAKE_CASE							:    int							=							value
																		logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""						)
						elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
												if "bias" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																								raise ValueError(
																								    f"""{full_name} has size {value.shape}, but"""
																								    f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""						)
																		SCREAMING_SNAKE_CASE							:    List[str]							=							value
																		logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
												elif "weight" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																								raise ValueError(
																								    f"""{full_name} has size {value.shape}, but"""
																								    f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""						)
																		SCREAMING_SNAKE_CASE							:    int							=							value
																		logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
						else:
												unused_weights.append(__UpperCamelCase						)
@torch.no_grad()
def       A	(    _lowercase		,  _lowercase		,  _lowercase=None		,  _lowercase=None		,  _lowercase=True						):





						if config_path is not None:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							WavaVecaConformerConfig.from_pretrained(__UpperCamelCase		,  hidden_act='''swish'''						)
						else:
												SCREAMING_SNAKE_CASE							:    int							=							WavaVecaConformerConfig()

						if "rope" in checkpoint_path:
												SCREAMING_SNAKE_CASE							:    List[str]							=							'''rotary'''

						if is_finetuned:
												if dict_path:
																		SCREAMING_SNAKE_CASE							:    str							=							Dictionary.load(__UpperCamelCase						)

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		SCREAMING_SNAKE_CASE							:    Tuple							=							target_dict.pad_index
																		SCREAMING_SNAKE_CASE							:    List[str]							=							target_dict.bos_index
																		SCREAMING_SNAKE_CASE							:    Dict							=							target_dict.eos_index
																		SCREAMING_SNAKE_CASE							:    str							=							len(target_dict.symbols						)
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							os.path.join(__UpperCamelCase		,  '''vocab.json'''						)
																		if not os.path.isdir(__UpperCamelCase						):
																								logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__UpperCamelCase						)						)
																								return
																		os.makedirs(__UpperCamelCase		,  exist_ok=__UpperCamelCase						)
																		SCREAMING_SNAKE_CASE							:    str							=							target_dict.indices

																		# fairseq has the <pad> and <s> switched
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							0
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							1
																		with open(__UpperCamelCase		,  '''w'''		,  encoding='''utf-8'''						) as vocab_handle:
																								json.dump(__UpperCamelCase		,  __UpperCamelCase						)
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							WavaVecaCTCTokenizer(
																		    __UpperCamelCase		,  unk_token=target_dict.unk_word		,  pad_token=target_dict.pad_word		,  bos_token=target_dict.bos_word		,  eos_token=target_dict.eos_word		,  word_delimiter_token='''|'''		,  do_lower_case=__UpperCamelCase		,  )
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							True if config.feat_extract_norm == '''layer''' else False
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							WavaVecaFeatureExtractor(
																		    feature_size=1		,  sampling_rate=16_000		,  padding_value=0		,  do_normalize=__UpperCamelCase		,  return_attention_mask=__UpperCamelCase		,  )
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							WavaVecaProcessor(feature_extractor=__UpperCamelCase		,  tokenizer=__UpperCamelCase						)
																		processor.save_pretrained(__UpperCamelCase						)

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							WavaVecaConformerForCTC(__UpperCamelCase						)
						else:
												SCREAMING_SNAKE_CASE							:    int							=							WavaVecaConformerForPreTraining(__UpperCamelCase						)

						if is_finetuned:
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    List[str]							=							fairseq.checkpoint_utils.load_model_ensemble_and_task(
												    [checkpoint_path]		,  arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''						)[:-1]						)}						)
						else:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							argparse.Namespace(task='''audio_pretraining'''						)
												SCREAMING_SNAKE_CASE							:    Dict							=							fairseq.tasks.setup_task(__UpperCamelCase						)

												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Optional[int]							=							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		,  task=__UpperCamelCase						)

						SCREAMING_SNAKE_CASE							:    str							=							model[0].eval()

						recursively_load_weights(__UpperCamelCase		,  __UpperCamelCase		,  not is_finetuned						)

						hf_wavavec.save_pretrained(__UpperCamelCase						)


if __name__ == "__main__":
					__UpperCamelCase		:       str									=     argparse.ArgumentParser()
					parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
					parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
					parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
					parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
					parser.add_argument(
					    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
					)
					__UpperCamelCase		:       Any									=     parser.parse_args()
					convert_wavaveca_conformer_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)






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 __snake_case				(						lowerCamelCase_   ,		unittest.TestCase		):
   lowerCAmelCase_														=							KandinskyVaaImgaImgPipeline
   lowerCAmelCase_														=							["image_embeds", "negative_image_embeds", "image"]
   lowerCAmelCase_														=							[
       "image_embeds",
       "negative_image_embeds",
       "image",
   ]
   lowerCAmelCase_														=							[
       "generator",
       "height",
       "width",
       "strength",
       "guidance_scale",
       "num_inference_steps",
       "return_dict",
       "guidance_scale",
       "num_images_per_prompt",
       "output_type",
       "return_dict",
   ]
   lowerCAmelCase_														=							False




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



     """simple docstring"""






     return 32




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



     """simple docstring"""






     return 32




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



     """simple docstring"""






     return self.time_input_dim




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



     """simple docstring"""






     return self.time_input_dim * 4




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



     """simple docstring"""






     return 1_00




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



     """simple docstring"""






     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""": """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,
     }

     SCREAMING_SNAKE_CASE__										=      UNetaDConditionModel(**_lowercase      )
     return model




   @property
   def 	__a						(    self			:				str      ):



     """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 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






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




   def 	__a						(    self			:				Tuple      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.dummy_unet
     SCREAMING_SNAKE_CASE__										=      self.dummy_movq

     SCREAMING_SNAKE_CASE__										=      {
         """num_train_timesteps""": 10_00,
         """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(**_lowercase      )

     SCREAMING_SNAKE_CASE__										=      {
         """unet""": unet,
         """scheduler""": scheduler,
         """movq""": movq,
     }

     return components




   def 	__a						(    self			:				Optional[Any]       ,	_lowercase			:				Any       ,	_lowercase			:				Tuple=0      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      floats_tensor((1, self.text_embedder_hidden_size)       ,	rng=random.Random(_lowercase      )      ).to(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      floats_tensor((1, self.text_embedder_hidden_size)       ,	rng=random.Random(seed + 1      )      ).to(
         _lowercase      )
     # create init_image
     SCREAMING_SNAKE_CASE__										=      floats_tensor((1, 3, 64, 64)       ,	rng=random.Random(_lowercase      )      ).to(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      image.cpu().permute(0       ,	2       ,	3       ,	1      )[0]
     SCREAMING_SNAKE_CASE__										=      Image.fromarray(np.uinta(_lowercase      )      ).convert("""RGB"""      ).resize((2_56, 2_56)      )

     if str(_lowercase      ).startswith("""mps"""      ):
       SCREAMING_SNAKE_CASE__										=      torch.manual_seed(_lowercase      )
     else:
       SCREAMING_SNAKE_CASE__										=      torch.Generator(device=_lowercase      ).manual_seed(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      {
         """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 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      """cpu"""

     SCREAMING_SNAKE_CASE__										=      self.get_dummy_components()

     SCREAMING_SNAKE_CASE__										=      self.pipeline_class(**_lowercase      )
     SCREAMING_SNAKE_CASE__										=      pipe.to(_lowercase      )

     pipe.set_progress_bar_config(disable=_lowercase      )

     SCREAMING_SNAKE_CASE__										=      pipe(**self.get_dummy_inputs(_lowercase      )      )
     SCREAMING_SNAKE_CASE__										=      output.images

     SCREAMING_SNAKE_CASE__										=      pipe(
         **self.get_dummy_inputs(_lowercase      )       ,	return_dict=_lowercase       ,	)[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(
         [0.6_19_97_78, 0.63_98_44_06, 0.46_14_57_85, 0.62_94_49_84, 0.5_62_22_15, 0.47_30_61_32, 0.47_44_14_56, 0.4_60_76_06, 0.48_71_92_63]      )
     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 __snake_case				(						unittest.TestCase		):




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






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




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      load_numpy(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/kandinskyv22/kandinskyv22_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__										=      KandinskyVaaPriorPipeline.from_pretrained(
         """kandinsky-community/kandinsky-2-2-prior"""       ,	torch_dtype=torch.floataa      )
     pipe_prior.to(_lowercase      )

     SCREAMING_SNAKE_CASE__										=      KandinskyVaaImgaImgPipeline.from_pretrained(
         """kandinsky-community/kandinsky-2-2-decoder"""       ,	torch_dtype=torch.floataa      )
     SCREAMING_SNAKE_CASE__										=      pipeline.to(_lowercase      )

     pipeline.set_progress_bar_config(disable=_lowercase      )

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

     SCREAMING_SNAKE_CASE__										=      pipeline(
         image=_lowercase       ,	image_embeds=_lowercase       ,	negative_image_embeds=_lowercase       ,	generator=_lowercase       ,	num_inference_steps=1_00       ,	height=7_68       ,	width=7_68       ,	strength=0.2       ,	output_type="""np"""       ,	)

     SCREAMING_SNAKE_CASE__										=      output.images[0]

     assert image.shape == (7_68, 7_68, 3)

     assert_mean_pixel_difference(_lowercase       ,	_lowercase      )
