



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()
__snake_case						:Dict						=					logging.get_logger(__name__)


__snake_case						:Dict						=					'''https://openaipublic.azureedge.net/jukebox/models/'''
__snake_case						:Union[str, Any]						=					{
    '''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 					__snake_case     (      _UpperCAmelCase    ):
    if key.endswith('''.model.1.bias'''    ) and len(key.split('''.'''    )    ) > 10:
        __a							=    key.replace('''.model.1.bias'''       ,			'''.conv1d_1.bias'''    )
    elif key.endswith('''.model.1.weight'''    ) and len(key.split('''.'''    )    ) > 10:
        __a							=    key.replace('''.model.1.weight'''       ,			'''.conv1d_1.weight'''    )
    elif key.endswith('''.model.3.bias'''    ) and len(key.split('''.'''    )    ) > 10:
        __a							=    key.replace('''.model.3.bias'''       ,			'''.conv1d_2.bias'''    )
    elif key.endswith('''.model.3.weight'''    ) and len(key.split('''.'''    )    ) > 10:
        __a							=    key.replace('''.model.3.weight'''       ,			'''.conv1d_2.weight'''    )

    if "conditioner_blocks.0." in key:
        __a							=    key.replace('''conditioner_blocks.0'''       ,			'''conditioner_blocks'''    )

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

    if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
        __a							=    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:
        __a							=    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 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    {}
    import re

    __a							=    re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''    )
    __a							=    re.compile(
        R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''    )
    __a							=    re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''    )

    __a							=    re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''    )
    __a							=    re.compile(
        R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''    )
    __a							=    re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''    )

    __a							=    re.compile(R'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'''    )
    __a							=    re.compile(
        R'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''    )
    __a							=    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(_UpperCAmelCase    ):
            __a							=    re_encoder_block_conv_in.match(_UpperCAmelCase    )
            __a							=    regex_match.groups()
            __a							=    int(groups[2]    ) * 2 + int(groups[3]    )
            __a							=    f'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'
            __a							=    re_encoder_block_conv_in.sub(_UpperCAmelCase       ,			_UpperCAmelCase    )

        elif re_encoder_block_resnet.fullmatch(_UpperCAmelCase    ):
            __a							=    re_encoder_block_resnet.match(_UpperCAmelCase    )
            __a							=    regex_match.groups()
            __a							=    int(groups[2]    ) * 2 + int(groups[3]    )
            __a							=    {'''1''': 1, '''3''': 2}[groups[-2]]
            __a							=    f'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'
            __a							=    f'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
            __a							=    prefix + resnet_block
            __a							=    re_encoder_block_resnet.sub(_UpperCAmelCase       ,			_UpperCAmelCase    )

        elif re_encoder_block_proj_out.fullmatch(_UpperCAmelCase    ):
            __a							=    re_encoder_block_proj_out.match(_UpperCAmelCase    )
            __a							=    regex_match.groups()
            __a							=    f'encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'
            __a							=    re_encoder_block_proj_out.sub(_UpperCAmelCase       ,			_UpperCAmelCase    )

        # rename vqvae.decoder keys
        elif re_decoder_block_conv_out.fullmatch(_UpperCAmelCase    ):
            __a							=    re_decoder_block_conv_out.match(_UpperCAmelCase    )
            __a							=    regex_match.groups()
            __a							=    int(groups[2]    ) * 2 + int(groups[3]    ) - 2
            __a							=    f'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'
            __a							=    re_decoder_block_conv_out.sub(_UpperCAmelCase       ,			_UpperCAmelCase    )

        elif re_decoder_block_resnet.fullmatch(_UpperCAmelCase    ):
            __a							=    re_decoder_block_resnet.match(_UpperCAmelCase    )
            __a							=    regex_match.groups()
            __a							=    int(groups[2]    ) * 2 + int(groups[3]    ) - 2
            __a							=    {'''1''': 1, '''3''': 2}[groups[-2]]
            __a							=    f'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'
            __a							=    f'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
            __a							=    prefix + resnet_block
            __a							=    re_decoder_block_resnet.sub(_UpperCAmelCase       ,			_UpperCAmelCase    )

        elif re_decoder_block_proj_in.fullmatch(_UpperCAmelCase    ):
            __a							=    re_decoder_block_proj_in.match(_UpperCAmelCase    )
            __a							=    regex_match.groups()
            __a							=    f'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
            __a							=    re_decoder_block_proj_in.sub(_UpperCAmelCase       ,			_UpperCAmelCase    )

        # rename prior cond.model to upsampler.upsample_block and resnet
        elif re_prior_cond_conv_out.fullmatch(_UpperCAmelCase    ):
            __a							=    re_prior_cond_conv_out.match(_UpperCAmelCase    )
            __a							=    regex_match.groups()
            __a							=    int(groups[1]    ) * 2 + int(groups[2]    ) - 2
            __a							=    f'conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'
            __a							=    re_prior_cond_conv_out.sub(_UpperCAmelCase       ,			_UpperCAmelCase    )

        elif re_prior_cond_resnet.fullmatch(_UpperCAmelCase    ):
            __a							=    re_prior_cond_resnet.match(_UpperCAmelCase    )
            __a							=    regex_match.groups()
            __a							=    int(groups[1]    ) * 2 + int(groups[2]    ) - 2
            __a							=    {'''1''': 1, '''3''': 2}[groups[-2]]
            __a							=    f'conditioner_blocks.upsampler.upsample_block.{block_index}.'
            __a							=    f'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
            __a							=    prefix + resnet_block
            __a							=    re_prior_cond_resnet.sub(_UpperCAmelCase       ,			_UpperCAmelCase    )

        elif re_prior_cond_proj_in.fullmatch(_UpperCAmelCase    ):
            __a							=    re_prior_cond_proj_in.match(_UpperCAmelCase    )
            __a							=    regex_match.groups()
            __a							=    f'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
            __a							=    re_prior_cond_proj_in.sub(_UpperCAmelCase       ,			_UpperCAmelCase    )

        # keep original key
        else:
            __a							=    original_key

        __a							=    replace_key(_UpperCAmelCase    )

        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:
            __a							=    model_state_dict[f'{key_prefix}.{key}']
            print(f'{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'    )
            __a							=    original_key

        __a							=    original_key
        __a							=    value

    return new_dict





@torch.no_grad()
def 					__snake_case     (      _UpperCAmelCase=None       ,			_UpperCAmelCase=None    ):
    for file in MODEL_MAPPING[model_name]:
        if not os.path.isfile(f'{pytorch_dump_folder_path}/{file.split("/"    )[-1]}'    ):
            __a							=    requests.get(f'{PREFIX}{file}'       ,			allow_redirects=_UpperCAmelCase    )
            os.makedirs(f'{pytorch_dump_folder_path}/'       ,			exist_ok=_UpperCAmelCase    )
            open(f'{pytorch_dump_folder_path}/{file.split("/"    )[-1]}'       ,			'''wb'''    ).write(r.content    )

    __a							=    MODEL_MAPPING[model_name.split('''/'''    )[-1]]

    __a							=    JukeboxConfig.from_pretrained(_UpperCAmelCase    )
    __a							=    JukeboxModel(_UpperCAmelCase    )

    __a							=    []
    __a							=    {}
    for i, dict_name in enumerate(_UpperCAmelCase    ):
        __a							=    torch.load(f'{pytorch_dump_folder_path}/{dict_name.split("/"    )[-1]}'    )['''model''']

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

        __a							=    '''vqvae''' if i == 0 else f'priors.{3 - i}'
        __a							=    fix_jukebox_keys(_UpperCAmelCase       ,			model.state_dict()       ,			_UpperCAmelCase       ,			_UpperCAmelCase    )
        weight_dict.append(_UpperCAmelCase    )

    __a							=    weight_dict.pop(0    )
    model.vqvae.load_state_dict(_UpperCAmelCase    )
    for i in range(len(_UpperCAmelCase    )    ):
        model.priors[i].load_state_dict(weight_dict[2 - i]    )

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

    print(f'Saving model {model_name} to {pytorch_dump_folder_path}'    )
    model.save_pretrained(_UpperCAmelCase    )

    return weight_dict


if __name__ == "__main__":
 __snake_case						:Dict						=					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.''',
 )
 __snake_case						:Optional[Any]						=					parser.parse_args()
 convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)










import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu

class    _A	(     unittest.TestCase   ):



       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

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



       @property
       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a							=    1
           __a							=    3
           __a							=    (32, 32)

           __a							=    floats_tensor((batch_size, num_channels) + sizes					,		rng=random.Random(0)).to(__SCREAMING_SNAKE_CASE)
           return image



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


           '''simple docstring'''

           torch.manual_seed(0)
           __a							=    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					,		)
           return model



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


           '''simple docstring'''

           torch.manual_seed(0)
           __a							=    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					,		)
           return model



       @property
       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           torch.manual_seed(0)
           __a							=    CLIPTextConfig(
               bos_token_id=0					,		eos_token_id=2					,		hidden_size=32					,		intermediate_size=37					,		layer_norm_eps=1E-05					,		num_attention_heads=4					,		num_hidden_layers=5					,		pad_token_id=1					,		vocab_size=1_000					,		)
           return CLIPTextModel(__SCREAMING_SNAKE_CASE)



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


           '''simple docstring'''

           def extract(*__SCREAMING_SNAKE_CASE							:					int					,		**__SCREAMING_SNAKE_CASE							:					Dict):

               class    _A	:



                      def __init__(		self							:					int):


                          '''simple docstring'''

                          __a							=    torch.ones([0])





                      def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


                          '''simple docstring'''

                          self.pixel_values.to(__SCREAMING_SNAKE_CASE)
                          return self


               return Out()

           return extract



       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           __a							=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           __a							=    self.dummy_cond_unet
           __a							=    DDIMScheduler(
               beta_start=0.0_00_85					,		beta_end=0.0_12					,		beta_schedule='''scaled_linear'''					,		clip_sample=__SCREAMING_SNAKE_CASE					,		set_alpha_to_one=__SCREAMING_SNAKE_CASE					,		)

           __a							=    self.dummy_vae
           __a							=    self.dummy_text_encoder
           __a							=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

           # make sure here that pndm scheduler skips prk
           __a							=    StableDiffusionPipeline(
               unet=__SCREAMING_SNAKE_CASE					,		scheduler=__SCREAMING_SNAKE_CASE					,		vae=__SCREAMING_SNAKE_CASE					,		text_encoder=__SCREAMING_SNAKE_CASE					,		tokenizer=__SCREAMING_SNAKE_CASE					,		safety_checker=__SCREAMING_SNAKE_CASE					,		feature_extractor=self.dummy_extractor					,		)
           __a							=    sd_pipe.to(__SCREAMING_SNAKE_CASE)
           sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

           __a							=    '''A painting of a squirrel eating a burger'''

           __a							=    torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(0)
           __a							=    sd_pipe([prompt]					,		generator=__SCREAMING_SNAKE_CASE					,		guidance_scale=6.0					,		num_inference_steps=2					,		output_type='''np''')
           __a							=    output.images

           __a							=    torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(0)
           __a							=    sd_pipe(
               [prompt]					,		generator=__SCREAMING_SNAKE_CASE					,		guidance_scale=6.0					,		num_inference_steps=2					,		output_type='''np'''					,		return_dict=__SCREAMING_SNAKE_CASE					,		)[0]

           __a							=    image[0, -3:, -3:, -1]
           __a							=    image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)
           __a							=    np.array([0.57_56, 0.61_18, 0.50_05, 0.50_41, 0.54_71, 0.47_26, 0.49_76, 0.48_65, 0.48_64])

           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 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           __a							=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           __a							=    self.dummy_cond_unet
           __a							=    PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE)
           __a							=    self.dummy_vae
           __a							=    self.dummy_text_encoder
           __a							=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

           # make sure here that pndm scheduler skips prk
           __a							=    StableDiffusionPipeline(
               unet=__SCREAMING_SNAKE_CASE					,		scheduler=__SCREAMING_SNAKE_CASE					,		vae=__SCREAMING_SNAKE_CASE					,		text_encoder=__SCREAMING_SNAKE_CASE					,		tokenizer=__SCREAMING_SNAKE_CASE					,		safety_checker=__SCREAMING_SNAKE_CASE					,		feature_extractor=self.dummy_extractor					,		)
           __a							=    sd_pipe.to(__SCREAMING_SNAKE_CASE)
           sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

           __a							=    '''A painting of a squirrel eating a burger'''
           __a							=    torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(0)
           __a							=    sd_pipe([prompt]					,		generator=__SCREAMING_SNAKE_CASE					,		guidance_scale=6.0					,		num_inference_steps=2					,		output_type='''np''')

           __a							=    output.images

           __a							=    torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(0)
           __a							=    sd_pipe(
               [prompt]					,		generator=__SCREAMING_SNAKE_CASE					,		guidance_scale=6.0					,		num_inference_steps=2					,		output_type='''np'''					,		return_dict=__SCREAMING_SNAKE_CASE					,		)[0]

           __a							=    image[0, -3:, -3:, -1]
           __a							=    image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)
           __a							=    np.array([0.51_25, 0.57_16, 0.48_28, 0.50_60, 0.56_50, 0.47_68, 0.51_85, 0.48_95, 0.49_93])

           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 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a							=    StableDiffusionPipeline.from_pretrained(
               '''hf-internal-testing/tiny-stable-diffusion-lms-pipe'''					,		safety_checker=__SCREAMING_SNAKE_CASE)
           assert isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           assert isinstance(pipe.scheduler					,		__SCREAMING_SNAKE_CASE)
           assert pipe.safety_checker is None

           __a							=    pipe('''example prompt'''					,		num_inference_steps=2).images[0]
           assert image is not None

           # check that there's no error when saving a pipeline with one of the models being None
           with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(__SCREAMING_SNAKE_CASE)
               __a							=    StableDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE)

           # sanity check that the pipeline still works
           assert pipe.safety_checker is None
           __a							=    pipe('''example prompt'''					,		num_inference_steps=2).images[0]
           assert image is not None





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


           '''simple docstring'''

           __a							=    self.dummy_cond_unet
           __a							=    PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE)
           __a							=    self.dummy_vae
           __a							=    self.dummy_text_encoder
           __a							=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

           # put models in fp16
           __a							=    unet.half()
           __a							=    vae.half()
           __a							=    bert.half()

           # make sure here that pndm scheduler skips prk
           __a							=    StableDiffusionPipeline(
               unet=__SCREAMING_SNAKE_CASE					,		scheduler=__SCREAMING_SNAKE_CASE					,		vae=__SCREAMING_SNAKE_CASE					,		text_encoder=__SCREAMING_SNAKE_CASE					,		tokenizer=__SCREAMING_SNAKE_CASE					,		safety_checker=__SCREAMING_SNAKE_CASE					,		feature_extractor=self.dummy_extractor					,		)
           __a							=    sd_pipe.to(__SCREAMING_SNAKE_CASE)
           sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

           __a							=    '''A painting of a squirrel eating a burger'''
           __a							=    sd_pipe([prompt]					,		num_inference_steps=2					,		output_type='''np''').images

           assert image.shape == (1, 64, 64, 3)


@nightly
@require_torch_gpu
class    _A	(     unittest.TestCase   ):



       def 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           __a							=    StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''					,		safety_checker=__SCREAMING_SNAKE_CASE)
           __a							=    LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
           __a							=    sd_pipe.to(__SCREAMING_SNAKE_CASE)
           sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

           __a							=    (
               '''portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'''
               ''' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'''
               ''' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'''
               ''' children from bahnhof zoo, detailed '''
           )
           __a							=    4_003_660_346
           __a							=    7

           # without safety guidance (sld_guidance_scale = 0)
           __a							=    torch.manual_seed(__SCREAMING_SNAKE_CASE)
           __a							=    sd_pipe(
               [prompt]					,		generator=__SCREAMING_SNAKE_CASE					,		guidance_scale=__SCREAMING_SNAKE_CASE					,		num_inference_steps=50					,		output_type='''np'''					,		width=512					,		height=512					,		sld_guidance_scale=0					,		)

           __a							=    output.images
           __a							=    image[0, -3:, -3:, -1]
           __a							=    [0.22_78, 0.22_31, 0.22_49, 0.23_33, 0.23_03, 0.18_85, 0.22_73, 0.21_44, 0.21_76]

           assert image.shape == (1, 512, 512, 3)

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

           # without safety guidance (strong configuration)
           __a							=    torch.manual_seed(__SCREAMING_SNAKE_CASE)
           __a							=    sd_pipe(
               [prompt]					,		generator=__SCREAMING_SNAKE_CASE					,		guidance_scale=__SCREAMING_SNAKE_CASE					,		num_inference_steps=50					,		output_type='''np'''					,		width=512					,		height=512					,		sld_guidance_scale=2_000					,		sld_warmup_steps=7					,		sld_threshold=0.0_25					,		sld_momentum_scale=0.5					,		sld_mom_beta=0.7					,		)

           __a							=    output.images
           __a							=    image[0, -3:, -3:, -1]
           __a							=    [0.23_83, 0.22_76, 0.2_36, 0.21_92, 0.21_86, 0.20_53, 0.19_71, 0.19_01, 0.17_19]

           assert image.shape == (1, 512, 512, 3)

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



       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           __a							=    StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''					,		safety_checker=__SCREAMING_SNAKE_CASE)
           __a							=    LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
           __a							=    sd_pipe.to(__SCREAMING_SNAKE_CASE)
           sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

           __a							=    '''padme amidala taking a bath artwork, safe for work, no nudity'''
           __a							=    2_734_971_755
           __a							=    7

           __a							=    torch.manual_seed(__SCREAMING_SNAKE_CASE)
           __a							=    sd_pipe(
               [prompt]					,		generator=__SCREAMING_SNAKE_CASE					,		guidance_scale=__SCREAMING_SNAKE_CASE					,		num_inference_steps=50					,		output_type='''np'''					,		width=512					,		height=512					,		sld_guidance_scale=0					,		)

           __a							=    output.images
           __a							=    image[0, -3:, -3:, -1]
           __a							=    [0.35_02, 0.36_22, 0.33_96, 0.36_42, 0.34_78, 0.33_18, 0.35, 0.33_48, 0.32_97]

           assert image.shape == (1, 512, 512, 3)

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

           __a							=    torch.manual_seed(__SCREAMING_SNAKE_CASE)
           __a							=    sd_pipe(
               [prompt]					,		generator=__SCREAMING_SNAKE_CASE					,		guidance_scale=__SCREAMING_SNAKE_CASE					,		num_inference_steps=50					,		output_type='''np'''					,		width=512					,		height=512					,		sld_guidance_scale=2_000					,		sld_warmup_steps=7					,		sld_threshold=0.0_25					,		sld_momentum_scale=0.5					,		sld_mom_beta=0.7					,		)

           __a							=    output.images
           __a							=    image[0, -3:, -3:, -1]
           __a							=    [0.55_31, 0.52_06, 0.48_95, 0.51_56, 0.51_82, 0.47_51, 0.48_02, 0.48_03, 0.44_43]

           assert image.shape == (1, 512, 512, 3)

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





       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           __a							=    StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5''')
           __a							=    sd_pipe.to(__SCREAMING_SNAKE_CASE)
           sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

           __a							=    (
               '''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
               ''' leyendecker'''
           )
           __a							=    1_044_355_234
           __a							=    12

           __a							=    torch.manual_seed(__SCREAMING_SNAKE_CASE)
           __a							=    sd_pipe(
               [prompt]					,		generator=__SCREAMING_SNAKE_CASE					,		guidance_scale=__SCREAMING_SNAKE_CASE					,		num_inference_steps=50					,		output_type='''np'''					,		width=512					,		height=512					,		sld_guidance_scale=0					,		)

           __a							=    output.images
           __a							=    image[0, -3:, -3:, -1]
           __a							=    np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])

           assert image.shape == (1, 512, 512, 3)

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-7

           __a							=    torch.manual_seed(__SCREAMING_SNAKE_CASE)
           __a							=    sd_pipe(
               [prompt]					,		generator=__SCREAMING_SNAKE_CASE					,		guidance_scale=__SCREAMING_SNAKE_CASE					,		num_inference_steps=50					,		output_type='''np'''					,		width=512					,		height=512					,		sld_guidance_scale=2_000					,		sld_warmup_steps=7					,		sld_threshold=0.0_25					,		sld_momentum_scale=0.5					,		sld_mom_beta=0.7					,		)

           __a							=    output.images
           __a							=    image[0, -3:, -3:, -1]
           __a							=    np.array([0.58_18, 0.62_85, 0.68_35, 0.60_19, 0.6_25, 0.67_54, 0.60_96, 0.63_34, 0.65_61])
           assert image.shape == (1, 512, 512, 3)

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





