





"""simple docstring"""





import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


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


_UpperCamelCase							=      'https://openaipublic.azureedge.net/jukebox/models/'
_UpperCamelCase							=      {
    'jukebox-1b-lyrics': [
        '5b/vqvae.pth.tar',
        '5b/prior_level_0.pth.tar',
        '5b/prior_level_1.pth.tar',
        '1b_lyrics/prior_level_2.pth.tar',
    ],
    'jukebox-5b-lyrics': [
        '5b/vqvae.pth.tar',
        '5b/prior_level_0.pth.tar',
        '5b/prior_level_1.pth.tar',
        '5b_lyrics/prior_level_2.pth.tar',
    ],
}



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Tuple   ):


							'''simple docstring'''







							if key.endswith('''.model.1.bias'''   ) and len(key.split('''.'''   )   ) > 10:
														__lowerCamelCase      :      int         =key.replace('''.model.1.bias'''    ,						'''.conv1d_1.bias'''   )
							elif key.endswith('''.model.1.weight'''   ) and len(key.split('''.'''   )   ) > 10:
														__lowerCamelCase      :      Optional[Any]         =key.replace('''.model.1.weight'''    ,						'''.conv1d_1.weight'''   )
							elif key.endswith('''.model.3.bias'''   ) and len(key.split('''.'''   )   ) > 10:
														__lowerCamelCase      :      Dict         =key.replace('''.model.3.bias'''    ,						'''.conv1d_2.bias'''   )
							elif key.endswith('''.model.3.weight'''   ) and len(key.split('''.'''   )   ) > 10:
														__lowerCamelCase      :      int         =key.replace('''.model.3.weight'''    ,						'''.conv1d_2.weight'''   )

							if "conditioner_blocks.0." in key:
														__lowerCamelCase      :      List[Any]         =key.replace('''conditioner_blocks.0'''    ,						'''conditioner_blocks'''   )

							if "prime_prior" in key:
														__lowerCamelCase      :      Tuple         =key.replace('''prime_prior'''    ,						'''encoder'''   )

							if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
														__lowerCamelCase      :      List[str]         =key.replace('''.emb.'''    ,						'''.'''   )

							if key.endswith('''k'''   ):  # replace vqvae.X.k with vqvae.X.codebook
														return key.replace('''.k'''    ,						'''.codebook'''   )
							if "y_emb." in key:
														return key.replace('''y_emb.'''    ,						'''metadata_embedding.'''   )

							if "x_emb.emb." in key:
														__lowerCamelCase      :      List[str]         =key.replace('''0.x_emb.emb'''    ,						'''embed_tokens'''   )

							if "prime_state_ln" in key:
														return key.replace('''prime_state_ln'''    ,						'''encoder.final_layer_norm'''   )
							if ".ln" in key:
														return key.replace('''.ln'''    ,						'''.layer_norm'''   )
							if "_ln" in key:
														return key.replace('''_ln'''    ,						'''_layer_norm'''   )

							if "prime_state_proj" in key:
														return key.replace('''prime_state_proj'''    ,						'''encoder.proj_in'''   )
							if "prime_x_out" in key:
														return key.replace('''prime_x_out'''    ,						'''encoder.lm_head'''   )
							if "prior.x_out" in key:
														return key.replace('''x_out'''    ,						'''fc_proj_out'''   )
							if "x_emb" in key:
														return key.replace('''x_emb'''    ,						'''embed_tokens'''   )

							return key



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[Any]    ,						SCREAMING_SNAKE_CASE       :							Any    ,						SCREAMING_SNAKE_CASE       :							Tuple    ,						SCREAMING_SNAKE_CASE       :							List[str]   ):


							'''simple docstring'''







							__lowerCamelCase      :      int         ={}
							import re

							__lowerCamelCase      :      List[str]         =re.compile(r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''   )
							__lowerCamelCase      :      Union[str, Any]         =re.compile(
							    r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''   )
							__lowerCamelCase      :      int         =re.compile(r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''   )

							__lowerCamelCase      :      List[str]         =re.compile(r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''   )
							__lowerCamelCase      :      int         =re.compile(
							    r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''   )
							__lowerCamelCase      :      str         =re.compile(r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''   )

							__lowerCamelCase      :      int         =re.compile(r'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'''   )
							__lowerCamelCase      :      List[str]         =re.compile(
							    r'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''   )
							__lowerCamelCase      :      Tuple         =re.compile(r'''conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)'''   )

							for original_key, value in state_dict.items():
														# rename vqvae.encoder keys
														if re_encoder_block_conv_in.fullmatch(SCREAMING_SNAKE_CASE   ):
																					__lowerCamelCase      :      Optional[int]         =re_encoder_block_conv_in.match(SCREAMING_SNAKE_CASE   )
																					__lowerCamelCase      :      int         =regex_match.groups()
																					__lowerCamelCase      :      int         =int(groups[2]   ) * 2 + int(groups[3]   )
																					__lowerCamelCase      :      Tuple         =F'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'
																					__lowerCamelCase      :      int         =re_encoder_block_conv_in.sub(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

														elif re_encoder_block_resnet.fullmatch(SCREAMING_SNAKE_CASE   ):
																					__lowerCamelCase      :      Union[str, Any]         =re_encoder_block_resnet.match(SCREAMING_SNAKE_CASE   )
																					__lowerCamelCase      :      List[str]         =regex_match.groups()
																					__lowerCamelCase      :      Any         =int(groups[2]   ) * 2 + int(groups[3]   )
																					__lowerCamelCase      :      Optional[int]         ={'''1''': 1, '''3''': 2}[groups[-2]]
																					__lowerCamelCase      :      Union[str, Any]         =F'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'
																					__lowerCamelCase      :      str         =F'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
																					__lowerCamelCase      :      List[Any]         =prefix + resnet_block
																					__lowerCamelCase      :      Tuple         =re_encoder_block_resnet.sub(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

														elif re_encoder_block_proj_out.fullmatch(SCREAMING_SNAKE_CASE   ):
																					__lowerCamelCase      :      Optional[int]         =re_encoder_block_proj_out.match(SCREAMING_SNAKE_CASE   )
																					__lowerCamelCase      :      List[Any]         =regex_match.groups()
																					__lowerCamelCase      :      Optional[int]         =F'encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'
																					__lowerCamelCase      :      Tuple         =re_encoder_block_proj_out.sub(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

														# rename vqvae.decoder keys
														elif re_decoder_block_conv_out.fullmatch(SCREAMING_SNAKE_CASE   ):
																					__lowerCamelCase      :      Optional[int]         =re_decoder_block_conv_out.match(SCREAMING_SNAKE_CASE   )
																					__lowerCamelCase      :      List[str]         =regex_match.groups()
																					__lowerCamelCase      :      Any         =int(groups[2]   ) * 2 + int(groups[3]   ) - 2
																					__lowerCamelCase      :      Any         =F'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'
																					__lowerCamelCase      :      str         =re_decoder_block_conv_out.sub(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

														elif re_decoder_block_resnet.fullmatch(SCREAMING_SNAKE_CASE   ):
																					__lowerCamelCase      :      Union[str, Any]         =re_decoder_block_resnet.match(SCREAMING_SNAKE_CASE   )
																					__lowerCamelCase      :      List[str]         =regex_match.groups()
																					__lowerCamelCase      :      Union[str, Any]         =int(groups[2]   ) * 2 + int(groups[3]   ) - 2
																					__lowerCamelCase      :      List[str]         ={'''1''': 1, '''3''': 2}[groups[-2]]
																					__lowerCamelCase      :      List[Any]         =F'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'
																					__lowerCamelCase      :      str         =F'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
																					__lowerCamelCase      :      Tuple         =prefix + resnet_block
																					__lowerCamelCase      :      Union[str, Any]         =re_decoder_block_resnet.sub(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

														elif re_decoder_block_proj_in.fullmatch(SCREAMING_SNAKE_CASE   ):
																					__lowerCamelCase      :      int         =re_decoder_block_proj_in.match(SCREAMING_SNAKE_CASE   )
																					__lowerCamelCase      :      List[Any]         =regex_match.groups()
																					__lowerCamelCase      :      Tuple         =F'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
																					__lowerCamelCase      :      Union[str, Any]         =re_decoder_block_proj_in.sub(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

														# rename prior cond.model to upsampler.upsample_block and resnet
														elif re_prior_cond_conv_out.fullmatch(SCREAMING_SNAKE_CASE   ):
																					__lowerCamelCase      :      Any         =re_prior_cond_conv_out.match(SCREAMING_SNAKE_CASE   )
																					__lowerCamelCase      :      int         =regex_match.groups()
																					__lowerCamelCase      :      Optional[Any]         =int(groups[1]   ) * 2 + int(groups[2]   ) - 2
																					__lowerCamelCase      :      str         =F'conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'
																					__lowerCamelCase      :      List[Any]         =re_prior_cond_conv_out.sub(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

														elif re_prior_cond_resnet.fullmatch(SCREAMING_SNAKE_CASE   ):
																					__lowerCamelCase      :      Optional[int]         =re_prior_cond_resnet.match(SCREAMING_SNAKE_CASE   )
																					__lowerCamelCase      :      Tuple         =regex_match.groups()
																					__lowerCamelCase      :      Optional[Any]         =int(groups[1]   ) * 2 + int(groups[2]   ) - 2
																					__lowerCamelCase      :      int         ={'''1''': 1, '''3''': 2}[groups[-2]]
																					__lowerCamelCase      :      List[Any]         =F'conditioner_blocks.upsampler.upsample_block.{block_index}.'
																					__lowerCamelCase      :      Union[str, Any]         =F'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
																					__lowerCamelCase      :      Tuple         =prefix + resnet_block
																					__lowerCamelCase      :      List[Any]         =re_prior_cond_resnet.sub(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

														elif re_prior_cond_proj_in.fullmatch(SCREAMING_SNAKE_CASE   ):
																					__lowerCamelCase      :      List[Any]         =re_prior_cond_proj_in.match(SCREAMING_SNAKE_CASE   )
																					__lowerCamelCase      :      Union[str, Any]         =regex_match.groups()
																					__lowerCamelCase      :      str         =F'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
																					__lowerCamelCase      :      int         =re_prior_cond_proj_in.sub(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

														# keep original key
														else:
																					__lowerCamelCase      :      str         =original_key

														__lowerCamelCase      :      Optional[int]         =replace_key(SCREAMING_SNAKE_CASE   )

														if F'{key_prefix}.{key}' not in model_state_dict or key is None:
																					print(F'failed converting {original_key} to {key}, does not match'   )

														# handle missmatched shape
														elif value.shape != model_state_dict[F'{key_prefix}.{key}'].shape:
																					__lowerCamelCase      :      Union[str, Any]         =model_state_dict[F'{key_prefix}.{key}']
																					print(F'{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'   )
																					__lowerCamelCase      :      int         =original_key

														__lowerCamelCase      :      Union[str, Any]         =original_key
														__lowerCamelCase      :      Union[str, Any]         =value

							return new_dict



@torch.no_grad()
def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[str]=None    ,						SCREAMING_SNAKE_CASE       :							Tuple=None   ):


							'''simple docstring'''







							for file in MODEL_MAPPING[model_name]:
														if not os.path.isfile(F'{pytorch_dump_folder_path}/{file.split("/"   )[-1]}'   ):
																					__lowerCamelCase      :      int         =requests.get(F'{PREFIX}{file}'    ,						allow_redirects=SCREAMING_SNAKE_CASE   )
																					os.makedirs(F'{pytorch_dump_folder_path}/'    ,						exist_ok=SCREAMING_SNAKE_CASE   )
																					open(F'{pytorch_dump_folder_path}/{file.split("/"   )[-1]}'    ,						'''wb'''   ).write(r.content   )

							__lowerCamelCase      :      Any         =MODEL_MAPPING[model_name.split('''/'''   )[-1]]

							__lowerCamelCase      :      List[str]         =JukeboxConfig.from_pretrained(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Union[str, Any]         =JukeboxModel(SCREAMING_SNAKE_CASE   )

							__lowerCamelCase      :      Union[str, Any]         =[]
							__lowerCamelCase      :      Dict         ={}
							for i, dict_name in enumerate(SCREAMING_SNAKE_CASE   ):
														__lowerCamelCase      :      List[Any]         =torch.load(F'{pytorch_dump_folder_path}/{dict_name.split("/"   )[-1]}'   )['''model''']

														__lowerCamelCase      :      List[Any]         ={}
														for k in old_dic.keys():
																					if k.endswith('''.b'''   ):
																												__lowerCamelCase      :      Tuple         =old_dic[k]
																					elif k.endswith('''.w'''   ):
																												__lowerCamelCase      :      str         =old_dic[k]
																					elif "level_2" not in dict_name and "cond.model." in k:
																												__lowerCamelCase      :      Dict         =old_dic[k]
																					else:
																												__lowerCamelCase      :      Dict         =old_dic[k]

														__lowerCamelCase      :      List[Any]         ='''vqvae''' if i == 0 else F'priors.{3 - i}'
														__lowerCamelCase      :      Any         =fix_jukebox_keys(SCREAMING_SNAKE_CASE    ,						model.state_dict()    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
														weight_dict.append(SCREAMING_SNAKE_CASE   )

							__lowerCamelCase      :      Optional[Any]         =weight_dict.pop(0   )
							model.vqvae.load_state_dict(SCREAMING_SNAKE_CASE   )
							for i in range(len(SCREAMING_SNAKE_CASE   )   ):
														model.priors[i].load_state_dict(weight_dict[2 - i]   )

							Path(SCREAMING_SNAKE_CASE   ).mkdir(exist_ok=SCREAMING_SNAKE_CASE   )
							with open(F'{pytorch_dump_folder_path}/mapping.json'    ,						'''w'''   ) as txtfile:
														json.dump(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

							print(F'Saving model {model_name} to {pytorch_dump_folder_path}'   )
							model.save_pretrained(SCREAMING_SNAKE_CASE   )

							return weight_dict


if __name__ == "__main__":
							_UpperCamelCase							=      argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--model_name',
							    default='jukebox-5b-lyrics',
							    type=str,
							    help='Name of the model you\'d like to convert.',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path',
							    default='jukebox-5b-lyrics-converted',
							    type=str,
							    help='Path to the output PyTorch model directory.',
							)
							_UpperCamelCase							=      parser.parse_args()
							convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)











"""simple docstring"""





import gc
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class 		SCREAMING_SNAKE_CASE_							(    snake_case__       ,  unittest.TestCase   ):
				"""simple docstring"""
				__snake_case   :       Union[str, Any]	 =		LDMTextToImagePipeline
				__snake_case   :       Optional[Any]	 =		TEXT_TO_IMAGE_PARAMS - {
				    """negative_prompt""",
				    """negative_prompt_embeds""",
				    """cross_attention_kwargs""",
				    """prompt_embeds""",
				}
				__snake_case   :       str	 =		PipelineTesterMixin.required_optional_params - {
				    """num_images_per_prompt""",
				    """callback""",
				    """callback_steps""",
				}
				__snake_case   :       Union[str, Any]	 =		TEXT_TO_IMAGE_BATCH_PARAMS
				__snake_case   :       Optional[Any]	 =		False




				def 					__lowercase  (     self  :List[str]			):
											torch.manual_seed(0			)
											__lowerCamelCase      :      str         =UNetaDConditionModel(
											    block_out_channels=(32, 64)						,    layers_per_block=2						,    sample_size=32						,    in_channels=4						,    out_channels=4						,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')						,    up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')						,    cross_attention_dim=32						,    )
											__lowerCamelCase      :      str         =DDIMScheduler(
											    beta_start=0.00085						,    beta_end=0.012						,    beta_schedule='''scaled_linear'''						,    clip_sample=__lowercase						,    set_alpha_to_one=__lowercase						,    )
											torch.manual_seed(0			)
											__lowerCamelCase      :      Optional[int]         =AutoencoderKL(
											    block_out_channels=(32, 64)						,    in_channels=3						,    out_channels=3						,    down_block_types=('''DownEncoderBlock2D''', '''DownEncoderBlock2D''')						,    up_block_types=('''UpDecoderBlock2D''', '''UpDecoderBlock2D''')						,    latent_channels=4						,    )
											torch.manual_seed(0			)
											__lowerCamelCase      :      Any         =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						,    )
											__lowerCamelCase      :      Optional[int]         =CLIPTextModel(__lowercase			)
											__lowerCamelCase      :      Dict         =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

											__lowerCamelCase      :      Optional[int]         ={
											    '''unet''': unet,
											    '''scheduler''': scheduler,
											    '''vqvae''': vae,
											    '''bert''': text_encoder,
											    '''tokenizer''': tokenizer,
											}
											return components




				def 					__lowercase  (     self  :int						,    __lowercase  :Optional[int]						,    __lowercase  :Optional[Any]=0			):
											if str(__lowercase			).startswith('''mps'''			):
																		__lowerCamelCase      :      Any         =torch.manual_seed(__lowercase			)
											else:
																		__lowerCamelCase      :      str         =torch.Generator(device=__lowercase			).manual_seed(__lowercase			)
											__lowerCamelCase      :      Any         ={
											    '''prompt''': '''A painting of a squirrel eating a burger''',
											    '''generator''': generator,
											    '''num_inference_steps''': 2,
											    '''guidance_scale''': 6.0,
											    '''output_type''': '''numpy''',
											}
											return inputs




				def 					__lowercase  (     self  :List[str]			):
											__lowerCamelCase      :      List[str]         ='''cpu'''  # ensure determinism for the device-dependent torch.Generator

											__lowerCamelCase      :      str         =self.get_dummy_components()
											__lowerCamelCase      :      Optional[int]         =LDMTextToImagePipeline(**__lowercase			)
											pipe.to(__lowercase			)
											pipe.set_progress_bar_config(disable=__lowercase			)

											__lowerCamelCase      :      str         =self.get_dummy_inputs(__lowercase			)
											__lowerCamelCase      :      List[Any]         =pipe(**__lowercase			).images
											__lowerCamelCase      :      Optional[int]         =image[0, -3:, -3:, -1]

											assert image.shape == (1, 16, 16, 3)
											__lowerCamelCase      :      Optional[Any]         =np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014]			)

											assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3

@slow
@require_torch_gpu
class 		SCREAMING_SNAKE_CASE_							(    unittest.TestCase   ):
				"""simple docstring"""




				def 					__lowercase  (     self  :Any			):
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()




				def 					__lowercase  (     self  :int						,    __lowercase  :Any						,    __lowercase  :Optional[int]=torch.floataa						,    __lowercase  :Dict=0			):
											__lowerCamelCase      :      List[str]         =torch.manual_seed(__lowercase			)
											__lowerCamelCase      :      List[str]         =np.random.RandomState(__lowercase			).standard_normal((1, 4, 32, 32)			)
											__lowerCamelCase      :      List[str]         =torch.from_numpy(__lowercase			).to(device=__lowercase						,    dtype=__lowercase			)
											__lowerCamelCase      :      Any         ={
											    '''prompt''': '''A painting of a squirrel eating a burger''',
											    '''latents''': latents,
											    '''generator''': generator,
											    '''num_inference_steps''': 3,
											    '''guidance_scale''': 6.0,
											    '''output_type''': '''numpy''',
											}
											return inputs




				def 					__lowercase  (     self  :Tuple			):
											__lowerCamelCase      :      int         =LDMTextToImagePipeline.from_pretrained('''CompVis/ldm-text2im-large-256'''			).to(__lowercase			)
											pipe.set_progress_bar_config(disable=__lowercase			)

											__lowerCamelCase      :      Tuple         =self.get_inputs(__lowercase			)
											__lowerCamelCase      :      Optional[Any]         =pipe(**__lowercase			).images
											__lowerCamelCase      :      Union[str, Any]         =image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 256, 256, 3)
											__lowerCamelCase      :      Union[str, Any]         =np.array([0.51825, 0.52850, 0.52543, 0.54258, 0.52304, 0.52569, 0.54363, 0.55276, 0.56878]			)
											__lowerCamelCase      :      Dict         =np.abs(expected_slice - image_slice			).max()
											assert max_diff < 1e-3

@nightly
@require_torch_gpu
class 		SCREAMING_SNAKE_CASE_							(    unittest.TestCase   ):
				"""simple docstring"""




				def 					__lowercase  (     self  :Any			):
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()




				def 					__lowercase  (     self  :Dict						,    __lowercase  :Optional[Any]						,    __lowercase  :int=torch.floataa						,    __lowercase  :Dict=0			):
											__lowerCamelCase      :      Any         =torch.manual_seed(__lowercase			)
											__lowerCamelCase      :      Dict         =np.random.RandomState(__lowercase			).standard_normal((1, 4, 32, 32)			)
											__lowerCamelCase      :      str         =torch.from_numpy(__lowercase			).to(device=__lowercase						,    dtype=__lowercase			)
											__lowerCamelCase      :      Dict         ={
											    '''prompt''': '''A painting of a squirrel eating a burger''',
											    '''latents''': latents,
											    '''generator''': generator,
											    '''num_inference_steps''': 50,
											    '''guidance_scale''': 6.0,
											    '''output_type''': '''numpy''',
											}
											return inputs




				def 					__lowercase  (     self  :Tuple			):
											__lowerCamelCase      :      Optional[int]         =LDMTextToImagePipeline.from_pretrained('''CompVis/ldm-text2im-large-256'''			).to(__lowercase			)
											pipe.set_progress_bar_config(disable=__lowercase			)

											__lowerCamelCase      :      List[Any]         =self.get_inputs(__lowercase			)
											__lowerCamelCase      :      Optional[int]         =pipe(**__lowercase			).images[0]

											__lowerCamelCase      :      Optional[int]         =load_numpy(
											    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy'''			)
											__lowerCamelCase      :      Dict         =np.abs(expected_image - image			).max()
											assert max_diff < 1e-3




