



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 							A     (					_UpperCAmelCase    :       List[str]							,       _UpperCAmelCase    :       List[str]							,       _UpperCAmelCase    :       Union[str, Any]							,       _UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       List[str]   )    ->			List[str]:







			'''simple docstring'''
			for attribute in key.split('.'   ):
						_UpperCAmelCase			=       getattr(_UpperCAmelCase							,       _UpperCAmelCase   )

			if weight_type is not None:
						_UpperCAmelCase			=       getattr(_UpperCAmelCase							,       _UpperCAmelCase   ).shape
			else:
						_UpperCAmelCase			=       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":
						_UpperCAmelCase			=       value
			elif weight_type == "weight_g":
						_UpperCAmelCase			=       value
			elif weight_type == "weight_v":
						_UpperCAmelCase			=       value
			elif weight_type == "bias":
						_UpperCAmelCase			=       value
			elif weight_type == "running_mean":
						_UpperCAmelCase			=       value
			elif weight_type == "running_var":
						_UpperCAmelCase			=       value
			elif weight_type == "num_batches_tracked":
						_UpperCAmelCase			=       value
			elif weight_type == "weight_ih_l0":
						_UpperCAmelCase			=       value
			elif weight_type == "weight_hh_l0":
						_UpperCAmelCase			=       value
			elif weight_type == "bias_ih_l0":
						_UpperCAmelCase			=       value
			elif weight_type == "bias_hh_l0":
						_UpperCAmelCase			=       value
			elif weight_type == "weight_ih_l1":
						_UpperCAmelCase			=       value
			elif weight_type == "weight_hh_l1":
						_UpperCAmelCase			=       value
			elif weight_type == "bias_ih_l1":
						_UpperCAmelCase			=       value
			elif weight_type == "bias_hh_l1":
						_UpperCAmelCase			=       value
			else:
						_UpperCAmelCase			=       value

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





def 							A     (					_UpperCAmelCase    :       Optional[Any]							,       _UpperCAmelCase    :       int   )    ->			Optional[Any]:







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





def 							A     (					_UpperCAmelCase    :       Any							,       _UpperCAmelCase    :       str							,       _UpperCAmelCase    :       Any   )    ->			Optional[int]:







			'''simple docstring'''
			_UpperCAmelCase			=       []

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

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

						_UpperCAmelCase			=       False
						for key, mapped_key in MAPPING.items():
									if "*" in key:
												_UpperCAmelCase	,					_UpperCAmelCase			=       key.split('.*.'   )
												if prefix in name and suffix in name:
															_UpperCAmelCase			=       suffix

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

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

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





@torch.no_grad()
def 							A     (					_UpperCAmelCase    :       Any							,       _UpperCAmelCase    :       List[Any]							,       _UpperCAmelCase    :       Any							,       _UpperCAmelCase    :       List[str]=None							,       _UpperCAmelCase    :       Union[str, Any]=None							,       )    ->			Any:







			'''simple docstring'''
			if config_path is not None:
						_UpperCAmelCase			=       EncodecConfig.from_pretrained(_UpperCAmelCase   )
			else:
						_UpperCAmelCase			=       EncodecConfig()

			if model_name == "encodec_24khz":
						pass  # config is already correct
			elif model_name == "encodec_32khz":
						_UpperCAmelCase			=       [8, 5, 4, 4]
						_UpperCAmelCase			=       [2.2]
						_UpperCAmelCase			=       64
						_UpperCAmelCase			=       32_000
						_UpperCAmelCase			=       2_048
						_UpperCAmelCase			=       False
						_UpperCAmelCase			=       False
						_UpperCAmelCase			=       False
			elif model_name == "encodec_48khz":
						_UpperCAmelCase			=       [8, 5, 4, 2]
						_UpperCAmelCase			=       [3.0, 6.0, 12.0, 24.0]
						_UpperCAmelCase			=       48_000
						_UpperCAmelCase			=       2
						_UpperCAmelCase			=       False
						_UpperCAmelCase			=       'time_group_norm'
						_UpperCAmelCase			=       True
						_UpperCAmelCase			=       1.0
						_UpperCAmelCase			=       0.01
			else:
						raise ValueError(F"Unknown model name: {model_name}"   )

			_UpperCAmelCase			=       EncodecModel(_UpperCAmelCase   )

			_UpperCAmelCase			=       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(_UpperCAmelCase   )

			_UpperCAmelCase			=       torch.load(_UpperCAmelCase   )
			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
						_UpperCAmelCase			=       original_checkpoint['best_state']
			recursively_load_weights(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase   )
			model.save_pretrained(_UpperCAmelCase   )

			if repo_id:
						print('Pushing to the hub...'   )
						feature_extractor.push_to_hub(_UpperCAmelCase   )
						model.push_to_hub(_UpperCAmelCase   )


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 transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference







class       __lowerCAmelCase   (      A    ,						unittest.TestCase						):
			UpperCamelCase        =					ShapEImgaImgPipeline
			UpperCamelCase        =					['''image''']
			UpperCamelCase        =					['''image''']
			UpperCamelCase        =					[
			    '''num_images_per_prompt''',
			    '''num_inference_steps''',
			    '''generator''',
			    '''latents''',
			    '''guidance_scale''',
			    '''frame_size''',
			    '''output_type''',
			    '''return_dict''',
			]
			UpperCamelCase        =					False



			@property
			def    _lowerCamelCase	(						self       :  List[Any])       ->   List[str]:



						"""simple docstring"""






						return 32



			@property
			def    _lowerCamelCase	(						self       :  List[Any])       ->   int:



						"""simple docstring"""






						return 32



			@property
			def    _lowerCamelCase	(						self       :  str)       ->   List[str]:



						"""simple docstring"""






						return self.time_input_dim * 4



			@property
			def    _lowerCamelCase	(						self       :  List[str])       ->   List[Any]:



						"""simple docstring"""






						return 8



			@property
			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   Dict:



						"""simple docstring"""






						torch.manual_seed(0)
						_UpperCAmelCase			=       CLIPVisionConfig(
						    hidden_size=self.text_embedder_hidden_size			,		image_size=64			,		projection_dim=self.text_embedder_hidden_size			,		intermediate_size=37			,		num_attention_heads=4			,		num_channels=3			,		num_hidden_layers=5			,		patch_size=1			,		)

						_UpperCAmelCase			=       CLIPVisionModel(A)
						return model



			@property
			def    _lowerCamelCase	(						self       :  Optional[int])       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       CLIPImageProcessor(
						    crop_size=2_24			,		do_center_crop=A			,		do_normalize=A			,		do_resize=A			,		image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]			,		image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]			,		resample=3			,		size=2_24			,		)

						return image_processor



			@property
			def    _lowerCamelCase	(						self       :  int)       ->   Tuple:



						"""simple docstring"""






						torch.manual_seed(0)

						_UpperCAmelCase			=       {
						    'num_attention_heads': 2,
						    'attention_head_dim': 16,
						    'embedding_dim': self.time_input_dim,
						    'num_embeddings': 32,
						    'embedding_proj_dim': self.text_embedder_hidden_size,
						    'time_embed_dim': self.time_embed_dim,
						    'num_layers': 1,
						    'clip_embed_dim': self.time_input_dim * 2,
						    'additional_embeddings': 0,
						    'time_embed_act_fn': 'gelu',
						    'norm_in_type': 'layer',
						    'embedding_proj_norm_type': 'layer',
						    'encoder_hid_proj_type': None,
						    'added_emb_type': None,
						}

						_UpperCAmelCase			=       PriorTransformer(**A)
						return model



			@property
			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   List[str]:



						"""simple docstring"""






						torch.manual_seed(0)

						_UpperCAmelCase			=       {
						    'param_shapes': (
						        (self.renderer_dim, 93),
						        (self.renderer_dim, 8),
						        (self.renderer_dim, 8),
						        (self.renderer_dim, 8),
						    ),
						    'd_latent': self.time_input_dim,
						    'd_hidden': self.renderer_dim,
						    'n_output': 12,
						    'background': (
						        0.1,
						        0.1,
						        0.1,
						    ),
						}
						_UpperCAmelCase			=       ShapERenderer(**A)
						return model



			def    _lowerCamelCase	(						self       :  Any)       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase			=       self.dummy_prior
						_UpperCAmelCase			=       self.dummy_image_encoder
						_UpperCAmelCase			=       self.dummy_image_processor
						_UpperCAmelCase			=       self.dummy_renderer

						_UpperCAmelCase			=       HeunDiscreteScheduler(
						    beta_schedule='exp'			,		num_train_timesteps=10_24			,		prediction_type='sample'			,		use_karras_sigmas=A			,		clip_sample=A			,		clip_sample_range=1.0			,		)
						_UpperCAmelCase			=       {
						    'prior': prior,
						    'image_encoder': image_encoder,
						    'image_processor': image_processor,
						    'renderer': renderer,
						    'scheduler': scheduler,
						}

						return components



			def    _lowerCamelCase	(						self       :  List[str]			,		A       :  Optional[Any]			,		A       :  Tuple=0)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       floats_tensor((1, 3, 64, 64)			,		rng=random.Random(A)).to(A)

						if str(A).startswith('mps'):
									_UpperCAmelCase			=       torch.manual_seed(A)
						else:
									_UpperCAmelCase			=       torch.Generator(device=A).manual_seed(A)
						_UpperCAmelCase			=       {
						    'image': input_image,
						    'generator': generator,
						    'num_inference_steps': 1,
						    'frame_size': 32,
						    'output_type': 'np',
						}
						return inputs



			def    _lowerCamelCase	(						self       :  List[Any])       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       'cpu'

						_UpperCAmelCase			=       self.get_dummy_components()

						_UpperCAmelCase			=       self.pipeline_class(**A)
						_UpperCAmelCase			=       pipe.to(A)

						pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase			=       pipe(**self.get_dummy_inputs(A))
						_UpperCAmelCase			=       output.images[0]
						_UpperCAmelCase			=       image[0, -3:, -3:, -1]

						assert image.shape == (20, 32, 32, 3)

						_UpperCAmelCase			=       np.array(
						    [
						        0.0_0_0_3_9_2_1_6,
						        0.0_0_0_3_9_2_1_6,
						        0.0_0_0_3_9_2_1_6,
						        0.0_0_0_3_9_2_1_6,
						        0.0_0_0_3_9_2_1_6,
						        0.0_0_0_3_9_2_1_6,
						        0.0_0_0_3_9_2_1_6,
						        0.0_0_0_3_9_2_1_6,
						        0.0_0_0_3_9_2_1_6,
						    ])

						assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2



			def    _lowerCamelCase	(						self       :  int)       ->   Tuple:



						"""simple docstring"""






						self._test_inference_batch_consistent(batch_sizes=[1, 2])



			def    _lowerCamelCase	(						self       :  Tuple)       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       torch_device == 'cpu'
						_UpperCAmelCase			=       True
						self._test_inference_batch_single_identical(
						    batch_size=2			,		test_max_difference=A			,		relax_max_difference=A			,		)



			def    _lowerCamelCase	(						self       :  Tuple)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       self.get_dummy_components()
						_UpperCAmelCase			=       self.pipeline_class(**A)
						_UpperCAmelCase			=       pipe.to(A)
						pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase			=       1
						_UpperCAmelCase			=       2

						_UpperCAmelCase			=       self.get_dummy_inputs(A)

						for key in inputs.keys():
									if key in self.batch_params:
												_UpperCAmelCase			=       batch_size * [inputs[key]]

						_UpperCAmelCase			=       pipe(**A			,		num_images_per_prompt=A)[0]

						assert images.shape[0] == batch_size * num_images_per_prompt







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



			def    _lowerCamelCase	(						self       :  Optional[int])       ->   str:



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  Dict)       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png')
						_UpperCAmelCase			=       load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/shap_e/test_shap_e_img2img_out.npy')
						_UpperCAmelCase			=       ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img')
						_UpperCAmelCase			=       pipe.to(A)
						pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase			=       torch.Generator(device=A).manual_seed(0)

						_UpperCAmelCase			=       pipe(
						    A			,		generator=A			,		guidance_scale=3.0			,		num_inference_steps=64			,		frame_size=64			,		output_type='np'			,		).images[0]

						assert images.shape == (20, 64, 64, 3)

						assert_mean_pixel_difference(A			,		A)
