




import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.12.2"):
					raise Exception("requires fairseq >= 0.12.2")
if version.parse(fairseq.__version__) > version.parse("2"):
					raise Exception("requires fairseq < v2")

logging.set_verbosity_info()
__A        =    logging.get_logger(__name__)

__A        =    "Hello, World!"
__A        =    "en_XX"







def 			lowerCamelCase_		(       UpperCamelCase__				:      str ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      bool      )     ->       Optional[Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    Path('data_bin'      )
				__lowerCamelCase		       =    FairseqXmodModel.from_pretrained(
				    model_name_or_path=str(Path(UpperCamelCase__      ).parent      ) ,     checkpoint_file=Path(UpperCamelCase__      ).name ,     _name='xmod_base' ,     arch='xmod_base' ,     task='multilingual_masked_lm' ,     data_name_or_path=str(UpperCamelCase__      ) ,     bpe='sentencepiece' ,     sentencepiece_model=str(Path(UpperCamelCase__      ).parent / 'sentencepiece.bpe.model'      ) ,     src_dict=str(data_dir / 'dict.txt'      ) ,     )
				xmod.eval()  # disable dropout
				print(UpperCamelCase__      )

				__lowerCamelCase		       =    xmod.model.encoder.sentence_encoder
				__lowerCamelCase		       =    XmodConfig(
				    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings ,     hidden_size=xmod.cfg.model.encoder_embed_dim ,     num_hidden_layers=xmod.cfg.model.encoder_layers ,     num_attention_heads=xmod.cfg.model.encoder_attention_heads ,     intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim ,     max_position_embeddings=514 ,     type_vocab_size=1 ,     layer_norm_eps=1E-5 ,     pre_norm=xmod.cfg.model.encoder_normalize_before ,     adapter_reduction_factor=getattr(xmod.cfg.model ,     'bottleneck' ,     2      ) ,     adapter_layer_norm=xmod.cfg.model.adapter_layer_norm ,     adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm ,     ln_before_adapter=xmod.cfg.model.ln_before_adapter ,     languages=xmod.cfg.model.languages ,     )
				if classification_head:
								__lowerCamelCase		       =    xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

				print('Our X-MOD config:' ,     UpperCamelCase__      )

				__lowerCamelCase		       =    XmodForSequenceClassification(UpperCamelCase__      ) if classification_head else XmodForMaskedLM(UpperCamelCase__      )
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				__lowerCamelCase		       =    xmod_sent_encoder.embed_tokens.weight
				__lowerCamelCase		       =    xmod_sent_encoder.embed_positions.weight
				__lowerCamelCase		       =    torch.zeros_like(
				    model.roberta.embeddings.token_type_embeddings.weight      )  # just zero them out b/c xmod doesn't use them.

				__lowerCamelCase		       =    xmod_sent_encoder.layernorm_embedding.weight
				__lowerCamelCase		       =    xmod_sent_encoder.layernorm_embedding.bias

				for i in range(config.num_hidden_layers      ):
								# Encoder: start of layer
								__lowerCamelCase		       =    model.roberta.encoder.layer[i]
								__lowerCamelCase		       =    xmod_sent_encoder.layers[i]

								# self attention
								__lowerCamelCase		       =    layer.attention.self
								if not (
								    xmod_layer.self_attn.k_proj.weight.data.shape
								    == xmod_layer.self_attn.q_proj.weight.data.shape
								    == xmod_layer.self_attn.v_proj.weight.data.shape
								    == torch.Size((config.hidden_size, config.hidden_size)      )
								):
												raise AssertionError('Dimensions of self-attention weights do not match.'      )

								__lowerCamelCase		       =    xmod_layer.self_attn.q_proj.weight
								__lowerCamelCase		       =    xmod_layer.self_attn.q_proj.bias
								__lowerCamelCase		       =    xmod_layer.self_attn.k_proj.weight
								__lowerCamelCase		       =    xmod_layer.self_attn.k_proj.bias
								__lowerCamelCase		       =    xmod_layer.self_attn.v_proj.weight
								__lowerCamelCase		       =    xmod_layer.self_attn.v_proj.bias

								# self-attention output
								__lowerCamelCase		       =    layer.attention.output
								if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
												raise AssertionError('Dimensions of self-attention output weights do not match.'      )
								__lowerCamelCase		       =    xmod_layer.self_attn.out_proj.weight
								__lowerCamelCase		       =    xmod_layer.self_attn.out_proj.bias
								__lowerCamelCase		       =    xmod_layer.self_attn_layer_norm.weight
								__lowerCamelCase		       =    xmod_layer.self_attn_layer_norm.bias

								# intermediate
								__lowerCamelCase		       =    layer.intermediate
								if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
												raise AssertionError('Dimensions of intermediate weights do not match.'      )
								__lowerCamelCase		       =    xmod_layer.fca.weight
								__lowerCamelCase		       =    xmod_layer.fca.bias

								# output
								__lowerCamelCase		       =    layer.output
								if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
												raise AssertionError('Dimensions of feed-forward weights do not match.'      )
								__lowerCamelCase		       =    xmod_layer.fca.weight
								__lowerCamelCase		       =    xmod_layer.fca.bias
								__lowerCamelCase		       =    xmod_layer.final_layer_norm.weight
								__lowerCamelCase		       =    xmod_layer.final_layer_norm.bias
								if bert_output.adapter_layer_norm is not None:
												__lowerCamelCase		       =    xmod_layer.adapter_layer_norm.weight
												__lowerCamelCase		       =    xmod_layer.adapter_layer_norm.bias

								if sorted(bert_output.adapter_modules.keys()      ) != sorted(xmod_layer.adapter_modules.keys()      ):
												raise AssertionError('Lists of language adapters do not match.'      )
								for lang_code, adapter in xmod_layer.adapter_modules.items():
												__lowerCamelCase		       =    bert_output.adapter_modules[lang_code]
												__lowerCamelCase		       =    xmod_layer.adapter_modules[lang_code]
												__lowerCamelCase		       =    from_adapter.fca.weight
												__lowerCamelCase		       =    from_adapter.fca.bias
												__lowerCamelCase		       =    from_adapter.fca.weight
												__lowerCamelCase		       =    from_adapter.fca.bias

								# end of layer

				if xmod_sent_encoder.layer_norm is not None:
								__lowerCamelCase		       =    xmod_sent_encoder.layer_norm.weight
								__lowerCamelCase		       =    xmod_sent_encoder.layer_norm.bias

				if classification_head:
								__lowerCamelCase		       =    xmod.model.classification_heads['mnli'].dense.weight
								__lowerCamelCase		       =    xmod.model.classification_heads['mnli'].dense.bias
								__lowerCamelCase		       =    xmod.model.classification_heads['mnli'].out_proj.weight
								__lowerCamelCase		       =    xmod.model.classification_heads['mnli'].out_proj.bias
				else:
								# LM Head
								__lowerCamelCase		       =    xmod.model.encoder.lm_head.dense.weight
								__lowerCamelCase		       =    xmod.model.encoder.lm_head.dense.bias
								__lowerCamelCase		       =    xmod.model.encoder.lm_head.layer_norm.weight
								__lowerCamelCase		       =    xmod.model.encoder.lm_head.layer_norm.bias
								__lowerCamelCase		       =    xmod.model.encoder.lm_head.weight
								__lowerCamelCase		       =    xmod.model.encoder.lm_head.bias

				# Let's check that we get the same results.
				__lowerCamelCase		       =    xmod.encode(UpperCamelCase__      ).unsqueeze(0      )  # batch of size 1
				model.roberta.set_default_language(UpperCamelCase__      )

				__lowerCamelCase		       =    model(UpperCamelCase__      )[0]
				if classification_head:
								__lowerCamelCase		       =    xmod.model.classification_heads['mnli'](xmod.extract_features(UpperCamelCase__      )      )
				else:
								__lowerCamelCase		       =    xmod.model(UpperCamelCase__ ,     lang_id=[SAMPLE_LANGUAGE]      )[0]
				print(our_output.shape ,     their_output.shape      )
				__lowerCamelCase		       =    torch.max(torch.abs(our_output - their_output      )      ).item()
				print(F"""max_absolute_diff = {max_absolute_diff}"""      )  # ~ 1e-7
				__lowerCamelCase		       =    torch.allclose(UpperCamelCase__ ,     UpperCamelCase__ ,     atol=1E-3      )
				print('Do both models output the same tensors?' ,     '🔥' if success else '💩'      )
				if not success:
								raise Exception('Something went wRoNg'      )

				Path(UpperCamelCase__      ).mkdir(parents=UpperCamelCase__ ,     exist_ok=UpperCamelCase__      )
				print(F"""Saving model to {pytorch_dump_folder_path}"""      )
				model.save_pretrained(UpperCamelCase__      )


if __name__ == "__main__":
					__A        =    argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--xmod_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
					)
					parser.add_argument(
					    "--classification_head", action="store_true", help="Whether to convert a final classification head."
					)
					__A        =    parser.parse_args()
					convert_xmod_checkpoint_to_pytorch(
					    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)









"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
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


enable_full_determinism()






class 		__snake_case					(		unittest.TestCase     ):





 """simple docstring"""


 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




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

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Union[str, Any]						 =     1
     __A    :						Any						 =     3
     __A    :						List[str]						 =     (32, 32)

     __A    :						List[str]						 =     floats_tensor((batch_size, num_channels) + sizes					,						rng=random.Random(0  )  ).to(__lowerCamelCase  )
     return image

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     torch.manual_seed(0  )
     __A    :						List[Any]						 =     UNetaDConditionModel(
         block_out_channels=(32, 32, 64)					,						layers_per_block=2					,						sample_size=32					,						in_channels=7					,						out_channels=4					,						down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''', '''CrossAttnDownBlock2D''')					,						up_block_types=('''CrossAttnUpBlock2D''', '''CrossAttnUpBlock2D''', '''UpBlock2D''')					,						cross_attention_dim=32					,						attention_head_dim=8					,						use_linear_projection=__lowerCamelCase					,						only_cross_attention=(True, True, False)					,						num_class_embeds=100					,						)
     return model

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     torch.manual_seed(0  )
     __A    :						Any						 =     AutoencoderKL(
         block_out_channels=[32, 32, 64]					,						in_channels=3					,						out_channels=3					,						down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''', '''DownEncoderBlock2D''']					,						up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']					,						latent_channels=4					,						)
     return model

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     torch.manual_seed(0  )
     __A    :						Tuple						 =     CLIPTextConfig(
         bos_token_id=0					,						eos_token_id=2					,						hidden_size=32					,						intermediate_size=37					,						layer_norm_eps=1e-0_5					,						num_attention_heads=4					,						num_hidden_layers=5					,						pad_token_id=1					,						vocab_size=1000					,						hidden_act='''gelu'''					,						projection_dim=512					,						)
     return CLIPTextModel(__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[Any]						 =     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
     __A    :						int						 =     self.dummy_cond_unet_upscale
     __A    :						Union[str, Any]						 =     DDPMScheduler()
     __A    :						Dict						 =     DDIMScheduler(prediction_type='''v_prediction'''  )
     __A    :						int						 =     self.dummy_vae
     __A    :						int						 =     self.dummy_text_encoder
     __A    :						Union[str, Any]						 =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

     __A    :						Tuple						 =     self.dummy_image.cpu().permute(0					,						2					,						3					,						1  )[0]
     __A    :						Any						 =     Image.fromarray(np.uinta(__lowerCamelCase  )  ).convert('''RGB'''  ).resize((64, 64)  )

     # make sure here that pndm scheduler skips prk
     __A    :						Dict						 =     StableDiffusionUpscalePipeline(
         unet=__lowerCamelCase					,						low_res_scheduler=__lowerCamelCase					,						scheduler=__lowerCamelCase					,						vae=__lowerCamelCase					,						text_encoder=__lowerCamelCase					,						tokenizer=__lowerCamelCase					,						max_noise_level=350					,						)
     __A    :						str						 =     sd_pipe.to(__lowerCamelCase  )
     sd_pipe.set_progress_bar_config(disable=__lowerCamelCase  )

     __A    :						List[str]						 =     '''A painting of a squirrel eating a burger'''
     __A    :						Any						 =     torch.Generator(device=__lowerCamelCase  ).manual_seed(0  )
     __A    :						List[str]						 =     sd_pipe(
         [prompt]					,						image=__lowerCamelCase					,						generator=__lowerCamelCase					,						guidance_scale=6.0					,						noise_level=20					,						num_inference_steps=2					,						output_type='''np'''					,						)

     __A    :						Union[str, Any]						 =     output.images

     __A    :						List[str]						 =     torch.Generator(device=__lowerCamelCase  ).manual_seed(0  )
     __A    :						str						 =     sd_pipe(
         [prompt]					,						image=__lowerCamelCase					,						generator=__lowerCamelCase					,						guidance_scale=6.0					,						noise_level=20					,						num_inference_steps=2					,						output_type='''np'''					,						return_dict=__lowerCamelCase					,						)[0]

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

     __A    :						Dict						 =     low_res_image.size[0] * 4
     assert image.shape == (1, expected_height_width, expected_height_width, 3)
     __A    :						str						 =     np.array([0.3_1_1_3, 0.3_9_1_0, 0.4_2_7_2, 0.4_8_5_9, 0.5_0_6_1, 0.4_6_5_2, 0.5_3_6_2, 0.5_7_1_5, 0.5_6_6_1]  )

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

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Tuple						 =     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
     __A    :						Dict						 =     self.dummy_cond_unet_upscale
     __A    :						List[str]						 =     DDPMScheduler()
     __A    :						str						 =     DDIMScheduler(prediction_type='''v_prediction'''  )
     __A    :						Optional[int]						 =     self.dummy_vae
     __A    :						Optional[Any]						 =     self.dummy_text_encoder
     __A    :						Optional[Any]						 =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

     __A    :						List[Any]						 =     self.dummy_image.cpu().permute(0					,						2					,						3					,						1  )[0]
     __A    :						int						 =     Image.fromarray(np.uinta(__lowerCamelCase  )  ).convert('''RGB'''  ).resize((64, 64)  )

     # make sure here that pndm scheduler skips prk
     __A    :						Any						 =     StableDiffusionUpscalePipeline(
         unet=__lowerCamelCase					,						low_res_scheduler=__lowerCamelCase					,						scheduler=__lowerCamelCase					,						vae=__lowerCamelCase					,						text_encoder=__lowerCamelCase					,						tokenizer=__lowerCamelCase					,						max_noise_level=350					,						)
     __A    :						Any						 =     sd_pipe.to(__lowerCamelCase  )
     sd_pipe.set_progress_bar_config(disable=__lowerCamelCase  )

     __A    :						Any						 =     '''A painting of a squirrel eating a burger'''
     __A    :						Any						 =     sd_pipe(
         2 * [prompt]					,						image=2 * [low_res_image]					,						guidance_scale=6.0					,						noise_level=20					,						num_inference_steps=2					,						output_type='''np'''					,						)
     __A    :						Union[str, Any]						 =     output.images
     assert image.shape[0] == 2

     __A    :						Optional[Any]						 =     torch.Generator(device=__lowerCamelCase  ).manual_seed(0  )
     __A    :						Any						 =     sd_pipe(
         [prompt]					,						image=__lowerCamelCase					,						generator=__lowerCamelCase					,						num_images_per_prompt=2					,						guidance_scale=6.0					,						noise_level=20					,						num_inference_steps=2					,						output_type='''np'''					,						)
     __A    :						Union[str, Any]						 =     output.images
     assert image.shape[0] == 2







 @unittest.skipIf(torch_device != '''cuda'''					,						'''This test requires a GPU'''  )
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[Any]						 =     self.dummy_cond_unet_upscale
     __A    :						int						 =     DDPMScheduler()
     __A    :						List[Any]						 =     DDIMScheduler(prediction_type='''v_prediction'''  )
     __A    :						Optional[Any]						 =     self.dummy_vae
     __A    :						List[str]						 =     self.dummy_text_encoder
     __A    :						Dict						 =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

     __A    :						Union[str, Any]						 =     self.dummy_image.cpu().permute(0					,						2					,						3					,						1  )[0]
     __A    :						int						 =     Image.fromarray(np.uinta(__lowerCamelCase  )  ).convert('''RGB'''  ).resize((64, 64)  )

     # put models in fp16, except vae as it overflows in fp16
     __A    :						Union[str, Any]						 =     unet.half()
     __A    :						Optional[int]						 =     text_encoder.half()

     # make sure here that pndm scheduler skips prk
     __A    :						Optional[int]						 =     StableDiffusionUpscalePipeline(
         unet=__lowerCamelCase					,						low_res_scheduler=__lowerCamelCase					,						scheduler=__lowerCamelCase					,						vae=__lowerCamelCase					,						text_encoder=__lowerCamelCase					,						tokenizer=__lowerCamelCase					,						max_noise_level=350					,						)
     __A    :						Union[str, Any]						 =     sd_pipe.to(__lowerCamelCase  )
     sd_pipe.set_progress_bar_config(disable=__lowerCamelCase  )

     __A    :						Union[str, Any]						 =     '''A painting of a squirrel eating a burger'''
     __A    :						Optional[Any]						 =     torch.manual_seed(0  )
     __A    :						Tuple						 =     sd_pipe(
         [prompt]					,						image=__lowerCamelCase					,						generator=__lowerCamelCase					,						num_inference_steps=2					,						output_type='''np'''					,						).images

     __A    :						str						 =     low_res_image.size[0] * 4
     assert image.shape == (1, expected_height_width, expected_height_width, 3)






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





 """simple docstring"""


 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




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

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[Any]						 =     load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/sd2-upscale/low_res_cat.png'''  )
     __A    :						Dict						 =     load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
         '''/upsampled_cat.npy'''  )

     __A    :						str						 =     '''stabilityai/stable-diffusion-x4-upscaler'''
     __A    :						Optional[Any]						 =     StableDiffusionUpscalePipeline.from_pretrained(__lowerCamelCase  )
     pipe.to(__lowerCamelCase  )
     pipe.set_progress_bar_config(disable=__lowerCamelCase  )
     pipe.enable_attention_slicing()

     __A    :						Union[str, Any]						 =     '''a cat sitting on a park bench'''

     __A    :						Union[str, Any]						 =     torch.manual_seed(0  )
     __A    :						Optional[Any]						 =     pipe(
         prompt=__lowerCamelCase					,						image=__lowerCamelCase					,						generator=__lowerCamelCase					,						output_type='''np'''					,						)
     __A    :						List[str]						 =     output.images[0]

     assert image.shape == (512, 512, 3)
     assert np.abs(expected_image - image  ).max() < 1e-3

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Dict						 =     load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/sd2-upscale/low_res_cat.png'''  )
     __A    :						List[Any]						 =     load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
         '''/upsampled_cat_fp16.npy'''  )

     __A    :						Optional[int]						 =     '''stabilityai/stable-diffusion-x4-upscaler'''
     __A    :						Optional[int]						 =     StableDiffusionUpscalePipeline.from_pretrained(
         __lowerCamelCase					,						torch_dtype=torch.floataa					,						)
     pipe.to(__lowerCamelCase  )
     pipe.set_progress_bar_config(disable=__lowerCamelCase  )
     pipe.enable_attention_slicing()

     __A    :						Dict						 =     '''a cat sitting on a park bench'''

     __A    :						Any						 =     torch.manual_seed(0  )
     __A    :						Optional[int]						 =     pipe(
         prompt=__lowerCamelCase					,						image=__lowerCamelCase					,						generator=__lowerCamelCase					,						output_type='''np'''					,						)
     __A    :						Any						 =     output.images[0]

     assert image.shape == (512, 512, 3)
     assert np.abs(expected_image - image  ).max() < 5e-1







 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




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

     __A    :						Union[str, Any]						 =     load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/sd2-upscale/low_res_cat.png'''  )

     __A    :						List[str]						 =     '''stabilityai/stable-diffusion-x4-upscaler'''
     __A    :						Dict						 =     StableDiffusionUpscalePipeline.from_pretrained(
         __lowerCamelCase					,						torch_dtype=torch.floataa					,						)
     pipe.to(__lowerCamelCase  )
     pipe.set_progress_bar_config(disable=__lowerCamelCase  )
     pipe.enable_attention_slicing(1  )
     pipe.enable_sequential_cpu_offload()

     __A    :						Tuple						 =     '''a cat sitting on a park bench'''

     __A    :						Tuple						 =     torch.manual_seed(0  )
     __A    :						List[str]						 =     pipe(
         prompt=__lowerCamelCase					,						image=__lowerCamelCase					,						generator=__lowerCamelCase					,						num_inference_steps=5					,						output_type='''np'''					,						)

     __A    :						Any						 =     torch.cuda.max_memory_allocated()
     # make sure that less than 2.9 GB is allocated
     assert mem_bytes < 2.9 * 10**9




