



import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


__snake_case						:Optional[Any]						=					'''<<<<<<< This should probably be modified because it mentions: '''

__snake_case						:List[Any]						=					'''=======\n>>>>>>>\n'''

__snake_case						:List[str]						=					[
    '''TextEncoderConfig''',
    '''ByteTextEncoder''',
    '''SubwordTextEncoder''',
    '''encoder_config''',
    '''maybe_build_from_corpus''',
    '''manual_dir''',
]

__snake_case						:List[Any]						=					[
    # (pattern, replacement)
    # Order is important here for some replacements
    (r'''tfds\.core''', r'''datasets'''),
    (r'''tf\.io\.gfile\.GFile''', r'''open'''),
    (r'''tf\.([\w\d]+)''', r'''datasets.Value(\'\1\')'''),
    (r'''tfds\.features\.Text\(\)''', r'''datasets.Value(\'string\')'''),
    (r'''tfds\.features\.Text\(''', r'''datasets.Value(\'string\'),'''),
    (r'''features\s*=\s*tfds.features.FeaturesDict\(''', r'''features=datasets.Features('''),
    (r'''tfds\.features\.FeaturesDict\(''', r'''dict('''),
    (r'''The TensorFlow Datasets Authors''', r'''The TensorFlow Datasets Authors and the HuggingFace Datasets Authors'''),
    (r'''tfds\.''', r'''datasets.'''),
    (r'''dl_manager\.manual_dir''', r'''self.config.data_dir'''),
    (r'''self\.builder_config''', r'''self.config'''),
]

def 					__snake_case     (      _UpperCAmelCase    ):
    return ConvertCommand(args.tfds_path       ,			args.datasets_directory    )

class    _A	(     snake_case_   ):



       @staticmethod
       def 		_lowerCamelCase      (		__SCREAMING_SNAKE_CASE							:					ArgumentParser):


           '''simple docstring'''

           __a							=    parser.add_parser(
               '''convert'''					,		help='''Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.'''					,		)
           train_parser.add_argument(
               '''--tfds_path'''					,		type=__SCREAMING_SNAKE_CASE					,		required=__SCREAMING_SNAKE_CASE					,		help='''Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.'''					,		)
           train_parser.add_argument(
               '''--datasets_directory'''					,		type=__SCREAMING_SNAKE_CASE					,		required=__SCREAMING_SNAKE_CASE					,		help='''Path to the HuggingFace Datasets folder.''')
           train_parser.set_defaults(func=__SCREAMING_SNAKE_CASE)



       def __init__(		self							:					int					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					str					,		*__SCREAMING_SNAKE_CASE							:					Tuple):


           '''simple docstring'''

           __a							=    get_logger('''datasets-cli/converting''')

           __a							=    tfds_path
           __a							=    datasets_directory





       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           if os.path.isdir(self._tfds_path):
               __a							=    os.path.abspath(self._tfds_path)
           elif os.path.isfile(self._tfds_path):
               __a							=    os.path.dirname(self._tfds_path)
           else:
               raise ValueError('''--tfds_path is neither a directory nor a file. Please check path.''')

           __a							=    os.path.abspath(self._datasets_directory)

           self._logger.info(F'Converting datasets from {abs_tfds_path} to {abs_datasets_path}')

           __a							=    []
           __a							=    []
           __a							=    {}

           if os.path.isdir(self._tfds_path):
               __a							=    os.listdir(__SCREAMING_SNAKE_CASE)
           else:
               __a							=    [os.path.basename(self._tfds_path)]

           for f_name in file_names:
               self._logger.info(F'Looking at file {f_name}')
               __a							=    os.path.join(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
               __a							=    os.path.join(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)

               if not os.path.isfile(__SCREAMING_SNAKE_CASE) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
                   self._logger.info('''Skipping file''')
                   continue

               with open(__SCREAMING_SNAKE_CASE					,		encoding='''utf-8''') as f:
                   __a							=    f.readlines()

               __a							=    []
               __a							=    False
               __a							=    False
               __a							=    []
               for line in lines:
                   __a							=    line

                   # Convert imports
                   if "import tensorflow.compat.v2 as tf" in out_line:
                       continue
                   elif "@tfds.core" in out_line:
                       continue
                   elif "builder=self" in out_line:
                       continue
                   elif "import tensorflow_datasets.public_api as tfds" in out_line:
                       __a							=    'import datasets\n'
                   elif "import tensorflow" in out_line:
                       # order is important here
                       __a							=    ''
                       continue
                   elif "from absl import logging" in out_line:
                       __a							=    'from datasets import logging\n'
                   elif "getLogger" in out_line:
                       __a							=    out_line.replace('''getLogger'''					,		'''get_logger''')
                   elif any(expression in out_line for expression in TO_HIGHLIGHT):
                       __a							=    True
                       __a							=    list(filter(lambda __SCREAMING_SNAKE_CASE: e in out_line					,		__SCREAMING_SNAKE_CASE))
                       out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(__SCREAMING_SNAKE_CASE) + '''\n''')
                       out_lines.append(__SCREAMING_SNAKE_CASE)
                       out_lines.append(__SCREAMING_SNAKE_CASE)
                       continue
                   else:
                       for pattern, replacement in TO_CONVERT:
                           __a							=    re.sub(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)

                                     # Take care of saving utilities (to later move them together with main script)
                   if "tensorflow_datasets" in out_line:
                       __a							=    re.match(r'''from\stensorflow_datasets.*import\s([^\.\r\n]+)'''					,		__SCREAMING_SNAKE_CASE)
                       tfds_imports.extend(imp.strip() for imp in match.group(1).split(''','''))
                       __a							=    'from . import ' + match.group(1)

                   # Check we have not forget anything
                   if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
                       raise ValueError(F'Error converting {out_line.strip()}')

                   if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
                       __a							=    True
                   out_lines.append(__SCREAMING_SNAKE_CASE)

               if is_builder or "wmt" in f_name:
                   # We create a new directory for each dataset
                   __a							=    f_name.replace('''.py'''					,		'''''')
                   __a							=    os.path.join(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
                   __a							=    os.path.join(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
                   os.makedirs(__SCREAMING_SNAKE_CASE					,		exist_ok=__SCREAMING_SNAKE_CASE)
                   self._logger.info(F'Adding directory {output_dir}')
                   imports_to_builder_map.update({imp: output_dir for imp in tfds_imports})
               else:
                   # Utilities will be moved at the end
                   utils_files.append(__SCREAMING_SNAKE_CASE)

               if needs_manual_update:
                   with_manual_update.append(__SCREAMING_SNAKE_CASE)

               with open(__SCREAMING_SNAKE_CASE					,		'''w'''					,		encoding='''utf-8''') as f:
                   f.writelines(__SCREAMING_SNAKE_CASE)
               self._logger.info(F'Converted in {output_file}')

           for utils_file in utils_files:
               try:
                   __a							=    os.path.basename(__SCREAMING_SNAKE_CASE)
                   __a							=    imports_to_builder_map[f_name.replace('''.py'''					,		'''''')]
                   self._logger.info(F'Moving {dest_folder} to {utils_file}')
                   shutil.copy(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
               except KeyError:
                   self._logger.error(F'Cannot find destination folder for {utils_file}. Please copy manually.')

           if with_manual_update:
               for file_path in with_manual_update:
                   self._logger.warning(
                       F'You need to manually update file {file_path} to remove configurations using \'TextEncoderConfig\'.')










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





