






import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						__magic_name__      :       Optional[int]       						= os.path.join(args.tf_model_dir,   """parameters.json"""			)
						__magic_name__      :       Optional[int]       						= json.loads(open(lowerCamelCase__			).read()			)
						if not params:
												raise ValueError(
												    f'It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.'			)
						if not args.output.endswith(""".pt"""			):
												__magic_name__      :       List[Any]       						= args.output + """.pt"""
						__magic_name__      :       Optional[Any]       						= OrderedDict()
						with tf.device("""/CPU:0"""			):
												__magic_name__      :       Optional[int]       						= tf.train.load_checkpoint(args.tf_model_dir			)
												__magic_name__      :       List[Any]       						= reader.get_variable_to_shape_map()
												for key_name in shapes.keys():
																		__magic_name__      :       Dict       						= reader.get_tensor(lowerCamelCase__			).astype(np.floataa			)
																		if key_name.endswith("""/adam_m"""			) or key_name.endswith("""/adam_v"""			):
																								continue
																		if key_name.startswith("""pasts/"""			):
																								if key_name.startswith("""pasts/mlp"""			):
																														__magic_name__      :       str       						= int(key_name[9]			)
																								elif key_name.startswith("""pasts/out"""			):
																														__magic_name__      :       Any       						= 8
																								__magic_name__      :       List[Any]       						= """model.sqout.%d.weight""" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																								__magic_name__      :       Any       						= vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
																								__magic_name__      :       Dict       						= torch.tensor(lowerCamelCase__			)
																		elif key_name.startswith("""model/moe"""			):
																								__magic_name__      :       List[str]       						= int(key_name[9:].split("""/"""			)[0]			)
																								if key_name.endswith("""/switch_gating/kernel"""			):
																														__magic_name__      :       str       						= """model.blocks.%d.feed_forward.mlp.router.classifier.weight""" % player
																														__magic_name__      :       Optional[int]       						= vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
																														__magic_name__      :       Tuple       						= torch.tensor(lowerCamelCase__			)
																								elif key_name.endswith("""/softmlp/kernel"""			):
																														__magic_name__      :       Any       						= """model.blocks.%d.feed_forward.soft_bypass_mlp.weight""" % player
																														__magic_name__      :       Union[str, Any]       						= vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
																														__magic_name__      :       Optional[Any]       						= torch.tensor(lowerCamelCase__			)
																								elif key_name.endswith("""/wo/kernel"""			) or key_name.endswith("""/wi/kernel"""			):
																														__magic_name__      :       Any       						= key_name[-9:-7]
																														for i in range(16			):
																																				__magic_name__      :       str       						= """model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight""" % (player, i, nlayer)
																																				__magic_name__      :       str       						= (
																																				    vnp[i].transpose([1, 0]			).copy()
																																				)  # In Mesh-Tensorflow, it is one array, so it is divided
																																				__magic_name__      :       str       						= torch.tensor(lowerCamelCase__			)
																		elif key_name.startswith("""model/mlp"""			):
																								__magic_name__      :       Any       						= int(key_name[9:].split("""/"""			)[0]			)
																								if key_name.endswith("""/p1/kernel"""			):
																														__magic_name__      :       str       						= """model.blocks.%d.feed_forward.mlp.wi.weight""" % player
																														__magic_name__      :       Optional[int]       						= vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
																														__magic_name__      :       Optional[int]       						= torch.tensor(lowerCamelCase__			)
																								elif key_name.endswith("""/p1/bias"""			):
																														__magic_name__      :       Dict       						= """model.blocks.%d.feed_forward.mlp.wi.bias""" % player
																														__magic_name__      :       Any       						= vnp.copy()  # same because it is one dimensional
																														__magic_name__      :       List[Any]       						= torch.tensor(lowerCamelCase__			)
																								elif key_name.endswith("""/p2/kernel"""			):
																														__magic_name__      :       Union[str, Any]       						= """model.blocks.%d.feed_forward.mlp.wo.weight""" % player
																														__magic_name__      :       Dict       						= vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
																														__magic_name__      :       Optional[Any]       						= torch.tensor(lowerCamelCase__			)
																								elif key_name.endswith("""/p2/bias"""			):
																														__magic_name__      :       List[Any]       						= """model.blocks.%d.feed_forward.mlp.wo.bias""" % player
																														__magic_name__      :       str       						= vnp.copy()  # same because it is one dimensional
																														__magic_name__      :       Tuple       						= torch.tensor(lowerCamelCase__			)
																		elif key_name.startswith("""model/ln"""			):
																								__magic_name__      :       List[str]       						= int(key_name[8:].split("""/"""			)[0]			)
																								if key_name.endswith("""/b"""			):
																														__magic_name__      :       str       						= """model.blocks.%d.feed_forward.norm.bias""" % player
																														__magic_name__      :       Optional[Any]       						= vnp.copy()  # same because it is one dimensional
																														__magic_name__      :       List[Any]       						= torch.tensor(lowerCamelCase__			)
																								elif key_name.endswith("""/g"""			):
																														__magic_name__      :       str       						= """model.blocks.%d.feed_forward.norm.weight""" % player
																														__magic_name__      :       Optional[Any]       						= vnp.copy()  # same because it is one dimensional
																														__magic_name__      :       int       						= torch.tensor(lowerCamelCase__			)
																		elif key_name.startswith("""model/att"""			):
																								__magic_name__      :       Optional[int]       						= int(key_name[9:].split("""/"""			)[0]			)
																								if key_name.endswith("""/qkv/kernel"""			):
																														__magic_name__      :       int       						= vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																														__magic_name__      :       List[str]       						= state[:, 0, :, :]
																														__magic_name__      :       Tuple       						= state[:, 1, :, :]
																														__magic_name__      :       str       						= state[:, 2, :, :]
																														__magic_name__      :       List[Any]       						= (
																														    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]			)
																														    .transpose([1, 0]			)
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														__magic_name__      :       Union[str, Any]       						= (
																														    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]			)
																														    .transpose([1, 0]			)
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														__magic_name__      :       Optional[Any]       						= (
																														    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]			)
																														    .transpose([1, 0]			)
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														__magic_name__      :       Any       						= """model.blocks.%d.self_attn.self_attn.q_proj.weight""" % player
																														__magic_name__      :       List[Any]       						= torch.tensor(lowerCamelCase__			)
																														__magic_name__      :       List[str]       						= """model.blocks.%d.self_attn.self_attn.k_proj.weight""" % player
																														__magic_name__      :       Optional[Any]       						= torch.tensor(lowerCamelCase__			)
																														__magic_name__      :       List[Any]       						= """model.blocks.%d.self_attn.self_attn.v_proj.weight""" % player
																														__magic_name__      :       List[Any]       						= torch.tensor(lowerCamelCase__			)
																								elif key_name.endswith("""/o/kernel"""			):
																														__magic_name__      :       Union[str, Any]       						= """model.blocks.%d.self_attn.self_attn.out_proj.weight""" % player
																														__magic_name__      :       Optional[int]       						= (
																														    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]			).transpose([1, 0]			).copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														__magic_name__      :       Any       						= torch.tensor(lowerCamelCase__			)
																		elif key_name.startswith("""model/an"""			):
																								__magic_name__      :       Optional[int]       						= int(key_name[8:].split("""/"""			)[0]			)
																								if key_name.endswith("""/b"""			):
																														__magic_name__      :       Tuple       						= """model.blocks.%d.self_attn.norm.bias""" % player
																														__magic_name__      :       List[str]       						= vnp.copy()  # same because it is one dimensional
																														__magic_name__      :       Tuple       						= torch.tensor(lowerCamelCase__			)
																								elif key_name.endswith("""/g"""			):
																														__magic_name__      :       Optional[int]       						= """model.blocks.%d.self_attn.norm.weight""" % player
																														__magic_name__      :       Any       						= vnp.copy()  # same because it is one dimensional
																														__magic_name__      :       List[str]       						= torch.tensor(lowerCamelCase__			)
																		elif (
																		    key_name.startswith("""model/wte"""			)
																		    or key_name.startswith("""model/wpe"""			)
																		    or key_name.startswith("""model/ete"""			)
																		):
																								__magic_name__      :       int       						= {"""wte""": """embed_tokens""", """wpe""": """position_embeddings""", """ete""": """extra_position_embeddings"""}[
																								    key_name[-3:]
																								]
																								__magic_name__      :       Any       						= """model.%s.weight""" % nlayer
																								__magic_name__      :       Union[str, Any]       						= vnp.copy()  # same in embedded
																								__magic_name__      :       int       						= torch.tensor(lowerCamelCase__			)
																								if key_name.startswith("""model/wte"""			):
																														__magic_name__      :       List[str]       						= """lm_head.weight"""
																														__magic_name__      :       Any       						= vnp.copy()  # same in embedded
																														__magic_name__      :       Optional[int]       						= torch.tensor(lowerCamelCase__			)
																		elif key_name.startswith("""model/wob"""			):
																								__magic_name__      :       List[Any]       						= """final_logits_bias"""
																								__magic_name__      :       str       						= vnp.copy()  # same in embedded
																								__magic_name__      :       Optional[int]       						= state.reshape((1, -1)			)
																								__magic_name__      :       List[Any]       						= torch.tensor(lowerCamelCase__			)
																		elif key_name == "model/dense/kernel":
																								__magic_name__      :       int       						= """model.last_project.weight"""
																								__magic_name__      :       str       						= vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
																								__magic_name__      :       int       						= torch.tensor(lowerCamelCase__			)
																		elif key_name == "model/dense_1/bias":
																								__magic_name__      :       List[Any]       						= """model.last_project.bias"""
																								__magic_name__      :       str       						= vnp.copy()  # same because it is one dimensional
																								__magic_name__      :       Tuple       						= torch.tensor(lowerCamelCase__			)
						torch.save(lowerCamelCase__,   args.output			)


if __name__ == "__main__":
		__magic_name__:					Tuple							=     argparse.ArgumentParser(
		    description="model converter.", formatter_class=argparse.ArgumentDefaultsHelpFormatter
		)
		parser.add_argument("--tf_model_dir", metavar="PATH", type=str, required=True, help="import model")
		parser.add_argument("--output", metavar="PATH", type=str, required=True, help="output model")
		__magic_name__:					Optional[Any]							=     parser.parse_args()
		convert_tf_gptsan_to_pt(args)








import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()





class 	snake_case__ (							_lowerCAmelCase						,  _lowerCAmelCase						,  _lowerCAmelCase						,  unittest.TestCase				):
	lowercase__						:					str											=   StableUnCLIPImgaImgPipeline
	lowercase__						:					Any											=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS
	lowercase__						:					Tuple											=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	lowercase__						:					Union[str, Any]											=   frozenset(
	    []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	lowercase__						:					Union[str, Any]											=   frozenset([]				)
	def       __magic_name__    (    self			)							->				Tuple:
							__magic_name__      :       Any       						= 32
							__magic_name__      :       Union[str, Any]       						= embedder_hidden_size

							# image encoding components

							__magic_name__      :       Optional[int]       						= CLIPImageProcessor(crop_size=32      ,       size=32			)

							torch.manual_seed(0			)
							__magic_name__      :       Optional[Any]       						= CLIPVisionModelWithProjection(
							    CLIPVisionConfig(
							        hidden_size=lowerCAmelCase__      ,       projection_dim=lowerCAmelCase__      ,       num_hidden_layers=5      ,       num_attention_heads=4      ,       image_size=32      ,       intermediate_size=37      ,       patch_size=1      ,       )			)

							# regular denoising components

							torch.manual_seed(0			)
							__magic_name__      :       Any       						= StableUnCLIPImageNormalizer(embedding_dim=lowerCAmelCase__			)
							__magic_name__      :       int       						= DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""			)

							torch.manual_seed(0			)
							__magic_name__      :       Tuple       						= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

							torch.manual_seed(0			)
							__magic_name__      :       Optional[Any]       						= CLIPTextModel(
							    CLIPTextConfig(
							        bos_token_id=0      ,       eos_token_id=2      ,       hidden_size=lowerCAmelCase__      ,       projection_dim=32      ,       intermediate_size=37      ,       layer_norm_eps=1e-0_5      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=10_00      ,       )			)

							torch.manual_seed(0			)
							__magic_name__      :       str       						= UNetaDConditionModel(
							    sample_size=32      ,       in_channels=4      ,       out_channels=4      ,       down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""")      ,       up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""")      ,       block_out_channels=(32, 64)      ,       attention_head_dim=(2, 4)      ,       class_embed_type="""projection"""      ,       projection_class_embeddings_input_dim=embedder_projection_dim * 2      ,       cross_attention_dim=lowerCAmelCase__      ,       layers_per_block=1      ,       upcast_attention=lowerCAmelCase__      ,       use_linear_projection=lowerCAmelCase__      ,       )

							torch.manual_seed(0			)
							__magic_name__      :       Optional[Any]       						= DDIMScheduler(
							    beta_schedule="""scaled_linear"""      ,       beta_start=0.0_0_0_8_5      ,       beta_end=0.0_1_2      ,       prediction_type="""v_prediction"""      ,       set_alpha_to_one=lowerCAmelCase__      ,       steps_offset=1      ,       )

							torch.manual_seed(0			)
							__magic_name__      :       List[str]       						= AutoencoderKL()

							__magic_name__      :       List[str]       						= {
							    # image encoding components
							    """feature_extractor""": feature_extractor,
							    """image_encoder""": image_encoder.eval(),
							    # image noising components
							    """image_normalizer""": image_normalizer.eval(),
							    """image_noising_scheduler""": image_noising_scheduler,
							    # regular denoising components
							    """tokenizer""": tokenizer,
							    """text_encoder""": text_encoder.eval(),
							    """unet""": unet.eval(),
							    """scheduler""": scheduler,
							    """vae""": vae.eval(),
							}

							return components
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__=0      ,       lowerCAmelCase__=True			)							->				List[Any]:
							if str(lowerCAmelCase__			).startswith("""mps"""			):
													__magic_name__      :       Optional[int]       						= torch.manual_seed(lowerCAmelCase__			)
							else:
													__magic_name__      :       Optional[int]       						= torch.Generator(device=lowerCAmelCase__			).manual_seed(lowerCAmelCase__			)

							__magic_name__      :       Any       						= floats_tensor((1, 3, 32, 32)      ,       rng=random.Random(lowerCAmelCase__			)			).to(lowerCAmelCase__			)

							if pil_image:
													__magic_name__      :       Optional[Any]       						= input_image * 0.5 + 0.5
													__magic_name__      :       int       						= input_image.clamp(0      ,       1			)
													__magic_name__      :       Union[str, Any]       						= input_image.cpu().permute(0      ,       2      ,       3      ,       1			).float().numpy()
													__magic_name__      :       Optional[int]       						= DiffusionPipeline.numpy_to_pil(lowerCAmelCase__			)[0]

							return {
							    "prompt": "An anime racoon running a marathon",
							    "image": input_image,
							    "generator": generator,
							    "num_inference_steps": 2,
							    "output_type": "np",
							}
	@skip_mps
	def       __magic_name__    (    self			)							->				List[Any]:
							__magic_name__      :       Optional[Any]       						= """cpu"""  # ensure determinism for the device-dependent torch.Generator
							__magic_name__      :       List[str]       						= self.get_dummy_components()
							__magic_name__      :       int       						= StableUnCLIPImgaImgPipeline(**lowerCAmelCase__			)
							__magic_name__      :       List[str]       						= sd_pipe.to(lowerCAmelCase__			)
							sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__			)

							__magic_name__      :       Any       						= self.get_dummy_inputs(lowerCAmelCase__			)
							inputs.update({"""image_embeds""": None}			)
							__magic_name__      :       List[str]       						= sd_pipe(**lowerCAmelCase__			).images
							__magic_name__      :       Dict       						= image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							__magic_name__      :       int       						= np.array([0.3_8_7_2, 0.7_2_2_4, 0.5_6_0_1, 0.4_7_4_1, 0.6_8_7_2, 0.5_8_1_4, 0.4_6_3_6, 0.3_8_6_7, 0.5_0_7_8]			)

							assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3
	def       __magic_name__    (    self			)							->				Dict:
							__magic_name__      :       int       						= torch_device in ["""cpu""", """mps"""]

							self._test_attention_slicing_forward_pass(test_max_difference=lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							__magic_name__      :       Tuple       						= torch_device in ["""cpu""", """mps"""]

							self._test_inference_batch_single_identical(test_max_difference=lowerCAmelCase__			)







	@unittest.skipIf(
	    torch_device != """cuda""" or not is_xformers_available()      ,       reason="""XFormers attention is only available with CUDA and `xformers` installed"""      ,       )
	def       __magic_name__    (    self			)							->				str:
							self._test_xformers_attention_forwardGenerator_pass(test_max_difference=lowerCAmelCase__			)





@slow
@require_torch_gpu
class 	snake_case__ (							unittest.TestCase				):
	def       __magic_name__    (    self			)							->				int:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()
	def       __magic_name__    (    self			)							->				List[Any]:
							__magic_name__      :       Optional[Any]       						= load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"""			)

							__magic_name__      :       int       						= load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy"""			)

							__magic_name__      :       List[Any]       						= StableUnCLIPImgaImgPipeline.from_pretrained(
							    """fusing/stable-unclip-2-1-l-img2img"""      ,       torch_dtype=torch.floataa			)
							pipe.to(lowerCAmelCase__			)
							pipe.set_progress_bar_config(disable=lowerCAmelCase__			)
							# stable unclip will oom when integration tests are run on a V100,
							# so turn on memory savings
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							__magic_name__      :       Union[str, Any]       						= torch.Generator(device="""cpu"""			).manual_seed(0			)
							__magic_name__      :       Union[str, Any]       						= pipe(lowerCAmelCase__      ,       """anime turle"""      ,       generator=lowerCAmelCase__      ,       output_type="""np"""			)

							__magic_name__      :       Optional[Any]       						= output.images[0]

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

							assert_mean_pixel_difference(lowerCAmelCase__      ,       lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				List[Any]:
							__magic_name__      :       Union[str, Any]       						= load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"""			)

							__magic_name__      :       Dict       						= load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy"""			)

							__magic_name__      :       Union[str, Any]       						= StableUnCLIPImgaImgPipeline.from_pretrained(
							    """fusing/stable-unclip-2-1-h-img2img"""      ,       torch_dtype=torch.floataa			)
							pipe.to(lowerCAmelCase__			)
							pipe.set_progress_bar_config(disable=lowerCAmelCase__			)
							# stable unclip will oom when integration tests are run on a V100,
							# so turn on memory savings
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							__magic_name__      :       List[str]       						= torch.Generator(device="""cpu"""			).manual_seed(0			)
							__magic_name__      :       Any       						= pipe(lowerCAmelCase__      ,       """anime turle"""      ,       generator=lowerCAmelCase__      ,       output_type="""np"""			)

							__magic_name__      :       Any       						= output.images[0]

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

							assert_mean_pixel_difference(lowerCAmelCase__      ,       lowerCAmelCase__			)







	def       __magic_name__    (    self			)							->				str:
							__magic_name__      :       Any       						= load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"""			)

							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							__magic_name__      :       Union[str, Any]       						= StableUnCLIPImgaImgPipeline.from_pretrained(
							    """fusing/stable-unclip-2-1-h-img2img"""      ,       torch_dtype=torch.floataa			)
							__magic_name__      :       int       						= pipe.to(lowerCAmelCase__			)
							pipe.set_progress_bar_config(disable=lowerCAmelCase__			)
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							__magic_name__      :       List[Any]       						= pipe(
							    lowerCAmelCase__      ,       """anime turtle"""      ,       num_inference_steps=2      ,       output_type="""np"""      ,       )

							__magic_name__      :       List[str]       						= torch.cuda.max_memory_allocated()
							# make sure that less than 7 GB is allocated
							assert mem_bytes < 7 * 10**9
