





"""simple docstring"""




from copy import deepcopy
from unittest.case import TestCase

import pytest

from datasets.arrow_dataset import Dataset
from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
from datasets.info import DatasetInfo
from datasets.tasks import (
    AudioClassification,
    AutomaticSpeechRecognition,
    ImageClassification,
    LanguageModeling,
    QuestionAnsweringExtractive,
    Summarization,
    TextClassification,
    task_template_from_dict,
)
from datasets.utils.py_utils import asdict


UpperCamelCase_           ={
    """id""": """5733be284776f41900661182""",
    """title""": """University_of_Notre_Dame""",
    """context""": """Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.""",
    """question""": """To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?""",
    """answers""": {"""text""": ["""Saint Bernadette Soubirous"""], """answer_start""": [515]},
}





@pytest.mark.parametrize(
    '''task_cls'''       ,						[
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ]       ,						)
def        a_				(							_lowercase   ):
						_UpperCamelCase			:					int     =    task_cls()
						_UpperCamelCase			:					Optional[int]     =    asdict(_lowercase   )
						_UpperCamelCase			:					Tuple     =    task_template_from_dict(_lowercase   )
						assert task == reloaded




class  _a						:






				def 				snake_case	( self		:    Dict							)	->      Tuple:




										'''simple docstring'''



										_UpperCamelCase			:					Any     =    LanguageModeling(text_column='''input_text'''							)
										assert {"input_text": "text"} == task.column_mapping






				def 				snake_case	( self		:    str							)	->      List[str]:




										'''simple docstring'''



										_UpperCamelCase			:					Optional[Any]     =    Features({'''text''': Value('''string'''							)}							)
										_UpperCamelCase			:					Dict     =    {'''text_column''': '''input_text'''}
										_UpperCamelCase			:					Dict     =    LanguageModeling.from_dict(lowerCAmelCase__							)
										assert "language-modeling" == task.task
										assert input_schema == task.input_schema




class  _a						(      _lowerCAmelCase				):






				def 				snake_case	( self		:    Union[str, Any]							)	->      List[str]:




										'''simple docstring'''



										_UpperCamelCase			:					List[Any]     =    sorted(['''pos''', '''neg''']							)






				def 				snake_case	( self		:    int							)	->      List[Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Union[str, Any]     =    TextClassification(text_column='''input_text''', label_column='''input_label'''							)
										self.assertDictEqual({'''input_text''': '''text''', '''input_label''': '''labels'''}, task.column_mapping							)






				def 				snake_case	( self		:    Dict							)	->      int:




										'''simple docstring'''



										_UpperCamelCase			:					Union[str, Any]     =    Features({'''text''': Value('''string'''							)}							)
										# Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
										_UpperCamelCase			:					Optional[int]     =    Features({'''labels''': ClassLabel}							)
										_UpperCamelCase			:					Optional[Any]     =    {'''text_column''': '''input_text''', '''label_column''': '''input_labels'''}
										_UpperCamelCase			:					int     =    TextClassification.from_dict(lowerCAmelCase__							)
										self.assertEqual('''text-classification''', task.task							)
										self.assertEqual(lowerCAmelCase__, task.input_schema							)
										self.assertEqual(lowerCAmelCase__, task.label_schema							)






				def 				snake_case	( self		:    int							)	->      Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Dict     =    TextClassification(text_column='''input_text''', label_column='''input_label'''							)
										self.assertEqual(task.label_schema['''labels'''], lowerCAmelCase__							)
										_UpperCamelCase			:					Tuple     =    task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels							)}							)							)
										self.assertEqual(task.label_schema['''labels'''], ClassLabel(names=self.labels							)							)




class  _a						(      _lowerCAmelCase				):






				def 				snake_case	( self		:    List[Any]							)	->      List[Any]:




										'''simple docstring'''



										_UpperCamelCase			:					str     =    QuestionAnsweringExtractive(
										    context_column='''input_context''', question_column='''input_question''', answers_column='''input_answers'''							)
										self.assertDictEqual(
										    {'''input_context''': '''context''', '''input_question''': '''question''', '''input_answers''': '''answers'''}, task.column_mapping							)






				def 				snake_case	( self		:    List[Any]							)	->      Tuple:




										'''simple docstring'''



										_UpperCamelCase			:					Union[str, Any]     =    Features({'''question''': Value('''string'''							), '''context''': Value('''string'''							)}							)
										_UpperCamelCase			:					int     =    Features(
										    {
										        '''answers''': Sequence(
										            {
										                '''text''': Value('''string'''							),
										                '''answer_start''': Value('''int32'''							),
										            }							)
										    }							)
										_UpperCamelCase			:					List[Any]     =    {
										    '''context_column''': '''input_input_context''',
										    '''question_column''': '''input_question''',
										    '''answers_column''': '''input_answers''',
										}
										_UpperCamelCase			:					Any     =    QuestionAnsweringExtractive.from_dict(lowerCAmelCase__							)
										self.assertEqual('''question-answering-extractive''', task.task							)
										self.assertEqual(lowerCAmelCase__, task.input_schema							)
										self.assertEqual(lowerCAmelCase__, task.label_schema							)




class  _a						(      _lowerCAmelCase				):






				def 				snake_case	( self		:    Optional[Any]							)	->      Tuple:




										'''simple docstring'''



										_UpperCamelCase			:					Union[str, Any]     =    Summarization(text_column='''input_text''', summary_column='''input_summary'''							)
										self.assertDictEqual({'''input_text''': '''text''', '''input_summary''': '''summary'''}, task.column_mapping							)






				def 				snake_case	( self		:    Dict							)	->      Tuple:




										'''simple docstring'''



										_UpperCamelCase			:					str     =    Features({'''text''': Value('''string'''							)}							)
										_UpperCamelCase			:					List[str]     =    Features({'''summary''': Value('''string'''							)}							)
										_UpperCamelCase			:					str     =    {'''text_column''': '''input_text''', '''summary_column''': '''input_summary'''}
										_UpperCamelCase			:					int     =    Summarization.from_dict(lowerCAmelCase__							)
										self.assertEqual('''summarization''', task.task							)
										self.assertEqual(lowerCAmelCase__, task.input_schema							)
										self.assertEqual(lowerCAmelCase__, task.label_schema							)




class  _a						(      _lowerCAmelCase				):






				def 				snake_case	( self		:    Union[str, Any]							)	->      Optional[int]:




										'''simple docstring'''



										_UpperCamelCase			:					int     =    AutomaticSpeechRecognition(audio_column='''input_audio''', transcription_column='''input_transcription'''							)
										self.assertDictEqual({'''input_audio''': '''audio''', '''input_transcription''': '''transcription'''}, task.column_mapping							)






				def 				snake_case	( self		:    Optional[int]							)	->      str:




										'''simple docstring'''



										_UpperCamelCase			:					List[str]     =    Features({'''audio''': Audio()}							)
										_UpperCamelCase			:					Dict     =    Features({'''transcription''': Value('''string'''							)}							)
										_UpperCamelCase			:					Dict     =    {
										    '''audio_column''': '''input_audio''',
										    '''transcription_column''': '''input_transcription''',
										}
										_UpperCamelCase			:					Optional[Any]     =    AutomaticSpeechRecognition.from_dict(lowerCAmelCase__							)
										self.assertEqual('''automatic-speech-recognition''', task.task							)
										self.assertEqual(lowerCAmelCase__, task.input_schema							)
										self.assertEqual(lowerCAmelCase__, task.label_schema							)




class  _a						(      _lowerCAmelCase				):






				def 				snake_case	( self		:    Dict							)	->      Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Tuple     =    sorted(['''pos''', '''neg''']							)






				def 				snake_case	( self		:    Tuple							)	->      Tuple:




										'''simple docstring'''



										_UpperCamelCase			:					str     =    AudioClassification(audio_column='''input_audio''', label_column='''input_label'''							)
										self.assertDictEqual({'''input_audio''': '''audio''', '''input_label''': '''labels'''}, task.column_mapping							)






				def 				snake_case	( self		:    Union[str, Any]							)	->      Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Dict     =    Features({'''audio''': Audio()}							)
										_UpperCamelCase			:					Optional[int]     =    Features({'''labels''': ClassLabel}							)
										_UpperCamelCase			:					Optional[Any]     =    {
										    '''audio_column''': '''input_image''',
										    '''label_column''': '''input_label''',
										}
										_UpperCamelCase			:					Optional[int]     =    AudioClassification.from_dict(lowerCAmelCase__							)
										self.assertEqual('''audio-classification''', task.task							)
										self.assertEqual(lowerCAmelCase__, task.input_schema							)
										self.assertEqual(lowerCAmelCase__, task.label_schema							)






				def 				snake_case	( self		:    int							)	->      str:




										'''simple docstring'''



										_UpperCamelCase			:					str     =    AudioClassification(audio_column='''input_audio''', label_column='''input_label'''							)
										self.assertEqual(task.label_schema['''labels'''], lowerCAmelCase__							)
										_UpperCamelCase			:					str     =    task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels							)}							)							)
										self.assertEqual(task.label_schema['''labels'''], ClassLabel(names=self.labels							)							)




class  _a						(      _lowerCAmelCase				):






				def 				snake_case	( self		:    Optional[int]							)	->      List[Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Union[str, Any]     =    sorted(['''pos''', '''neg''']							)






				def 				snake_case	( self		:    Any							)	->      str:




										'''simple docstring'''



										_UpperCamelCase			:					str     =    ImageClassification(image_column='''input_image''', label_column='''input_label'''							)
										self.assertDictEqual({'''input_image''': '''image''', '''input_label''': '''labels'''}, task.column_mapping							)






				def 				snake_case	( self		:    Dict							)	->      Any:




										'''simple docstring'''



										_UpperCamelCase			:					str     =    Features({'''image''': Image()}							)
										_UpperCamelCase			:					Tuple     =    Features({'''labels''': ClassLabel}							)
										_UpperCamelCase			:					Optional[int]     =    {
										    '''image_column''': '''input_image''',
										    '''label_column''': '''input_label''',
										}
										_UpperCamelCase			:					List[str]     =    ImageClassification.from_dict(lowerCAmelCase__							)
										self.assertEqual('''image-classification''', task.task							)
										self.assertEqual(lowerCAmelCase__, task.input_schema							)
										self.assertEqual(lowerCAmelCase__, task.label_schema							)






				def 				snake_case	( self		:    str							)	->      List[Any]:




										'''simple docstring'''



										_UpperCamelCase			:					int     =    ImageClassification(image_column='''input_image''', label_column='''input_label'''							)
										self.assertEqual(task.label_schema['''labels'''], lowerCAmelCase__							)
										_UpperCamelCase			:					Dict     =    task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels							)}							)							)
										self.assertEqual(task.label_schema['''labels'''], ClassLabel(names=self.labels							)							)




class  _a						(      _lowerCAmelCase				):






				def 				snake_case	( self		:    int							)	->      Any:




										'''simple docstring'''



										_UpperCamelCase			:					List[str]     =    DatasetInfo(task_templates=QuestionAnsweringExtractive()							)
										_UpperCamelCase			:					List[str]     =    Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}, info=lowerCAmelCase__							)
										assert isinstance(dataset.info.task_templates, lowerCAmelCase__							)
										assert len(dataset.info.task_templates							) == 1

										def keep_task(lowerCAmelCase__		:    Optional[int]							):
																return x

										def dont_keep_task(lowerCAmelCase__		:    List[str]							):
																_UpperCamelCase			:					Any     =    deepcopy(lowerCAmelCase__							)
																_UpperCamelCase			:					List[Any]     =    0
																return out

										_UpperCamelCase			:					Union[str, Any]     =    dataset.map(lowerCAmelCase__							)
										assert mapped_dataset.info.task_templates == dataset.info.task_templates
										# reload from cache
										_UpperCamelCase			:					Optional[Any]     =    dataset.map(lowerCAmelCase__							)
										assert mapped_dataset.info.task_templates == dataset.info.task_templates

										_UpperCamelCase			:					Tuple     =    dataset.map(lowerCAmelCase__							)
										assert mapped_dataset.info.task_templates == []
										# reload from cache
										_UpperCamelCase			:					Union[str, Any]     =    dataset.map(lowerCAmelCase__							)
										assert mapped_dataset.info.task_templates == []






				def 				snake_case	( self		:    List[Any]							)	->      Any:




										'''simple docstring'''



										_UpperCamelCase			:					Tuple     =    Features({'''text''': Value('''string'''							), '''label''': ClassLabel(names=('''pos''', '''neg''')							)}							)
										_UpperCamelCase			:					Tuple     =    TextClassification(text_column='''text''', label_column='''label'''							)
										_UpperCamelCase			:					str     =    DatasetInfo(features=lowerCAmelCase__, task_templates=lowerCAmelCase__							)
										_UpperCamelCase			:					int     =    Dataset.from_dict({'''text''': ['''A sentence.'''], '''label''': ['''pos''']}, info=lowerCAmelCase__							)

										def process(lowerCAmelCase__		:    Any							):
																return example

										_UpperCamelCase			:					Optional[Any]     =    dataset.remove_columns('''label'''							)
										_UpperCamelCase			:					str     =    modified_dataset.map(lowerCAmelCase__							)
										assert mapped_dataset.info.task_templates == []










"""simple docstring"""




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.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()




class  _a						(      unittest.TestCase				):






				def 				snake_case	( self		:    Any							)	->      Any:




										'''simple docstring'''



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






				@property
				def 				snake_case	( self		:    Optional[int]							)	->      Tuple:




										'''simple docstring'''



										_UpperCamelCase			:					List[str]     =    1
										_UpperCamelCase			:					Union[str, Any]     =    3
										_UpperCamelCase			:					Tuple     =    (3_2, 3_2)

										_UpperCamelCase			:					Any     =    floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0							)							).to(lowerCAmelCase__							)
										return image






				@property
				def 				snake_case	( self		:    Tuple							)	->      Union[str, Any]:




										'''simple docstring'''



										torch.manual_seed(0							)
										_UpperCamelCase			:					Dict     =    UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4), layers_per_block=2, sample_size=3_2, in_channels=4, out_channels=4, down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''), up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''), cross_attention_dim=3_2, )
										return model






				@property
				def 				snake_case	( self		:    Dict							)	->      List[Any]:




										'''simple docstring'''



										torch.manual_seed(0							)
										_UpperCamelCase			:					List[str]     =    AutoencoderKL(
										    block_out_channels=[3_2, 6_4], in_channels=3, out_channels=3, down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''], up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''], latent_channels=4, )
										return model






				@property
				def 				snake_case	( self		:    List[str]							)	->      Tuple:




										'''simple docstring'''



										torch.manual_seed(0							)
										_UpperCamelCase			:					Any     =    CLIPTextConfig(
										    bos_token_id=0, eos_token_id=2, hidden_size=3_2, intermediate_size=3_7, layer_norm_eps=1e-0_5, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=1_0_0_0, )
										return CLIPTextModel(lowerCAmelCase__							)






				@property
				def 				snake_case	( self		:    Dict							)	->      str:




										'''simple docstring'''



										def extract(*lowerCAmelCase__		:    int, **lowerCAmelCase__		:    int							):




																class  _a						:






																				def __init__( self		:    str							)	->      Optional[Any]:




																										'''simple docstring'''



																										_UpperCamelCase			:					Optional[int]     =    torch.ones([0]							)






																				def 				snake_case	( self		:    List[Any], lowerCAmelCase__		:    List[str]							)	->      Tuple:




																										'''simple docstring'''



																										self.pixel_values.to(lowerCAmelCase__							)
																										return self

																return Out()

										return extract






				def 				snake_case	( self		:    List[str]							)	->      int:




										'''simple docstring'''



										_UpperCamelCase			:					Any     =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										_UpperCamelCase			:					Optional[Any]     =    self.dummy_cond_unet
										_UpperCamelCase			:					int     =    DDIMScheduler(
										    beta_start=0.00_085, beta_end=0.012, beta_schedule='''scaled_linear''', clip_sample=lowerCAmelCase__, set_alpha_to_one=lowerCAmelCase__, )

										_UpperCamelCase			:					Union[str, Any]     =    self.dummy_vae
										_UpperCamelCase			:					Any     =    self.dummy_text_encoder
										_UpperCamelCase			:					Any     =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

										# make sure here that pndm scheduler skips prk
										_UpperCamelCase			:					Tuple     =    StableDiffusionPipeline(
										    unet=lowerCAmelCase__, scheduler=lowerCAmelCase__, vae=lowerCAmelCase__, text_encoder=lowerCAmelCase__, tokenizer=lowerCAmelCase__, safety_checker=lowerCAmelCase__, feature_extractor=self.dummy_extractor, )
										_UpperCamelCase			:					str     =    sd_pipe.to(lowerCAmelCase__							)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase			:					Optional[Any]     =    '''A painting of a squirrel eating a burger'''

										_UpperCamelCase			:					Any     =    torch.Generator(device=lowerCAmelCase__							).manual_seed(0							)
										_UpperCamelCase			:					List[Any]     =    sd_pipe([prompt], generator=lowerCAmelCase__, guidance_scale=6.0, num_inference_steps=2, output_type='''np'''							)
										_UpperCamelCase			:					Optional[Any]     =    output.images

										_UpperCamelCase			:					str     =    torch.Generator(device=lowerCAmelCase__							).manual_seed(0							)
										_UpperCamelCase			:					List[Any]     =    sd_pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=6.0, num_inference_steps=2, output_type='''np''', return_dict=lowerCAmelCase__, )[0]

										_UpperCamelCase			:					List[Any]     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					Tuple     =    image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 6_4, 6_4, 3)
										_UpperCamelCase			:					Tuple     =    np.array([0.5_753, 0.6_114, 0.5_001, 0.5_034, 0.5_470, 0.4_729, 0.4_971, 0.4_867, 0.4_867]							)

										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 				snake_case	( self		:    str							)	->      str:




										'''simple docstring'''



										_UpperCamelCase			:					Optional[Any]     =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										_UpperCamelCase			:					Optional[int]     =    self.dummy_cond_unet
										_UpperCamelCase			:					List[str]     =    PNDMScheduler(skip_prk_steps=lowerCAmelCase__							)
										_UpperCamelCase			:					int     =    self.dummy_vae
										_UpperCamelCase			:					Optional[Any]     =    self.dummy_text_encoder
										_UpperCamelCase			:					Union[str, Any]     =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

										# make sure here that pndm scheduler skips prk
										_UpperCamelCase			:					int     =    StableDiffusionPipeline(
										    unet=lowerCAmelCase__, scheduler=lowerCAmelCase__, vae=lowerCAmelCase__, text_encoder=lowerCAmelCase__, tokenizer=lowerCAmelCase__, safety_checker=lowerCAmelCase__, feature_extractor=self.dummy_extractor, )
										_UpperCamelCase			:					List[str]     =    sd_pipe.to(lowerCAmelCase__							)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase			:					Tuple     =    '''A painting of a squirrel eating a burger'''
										_UpperCamelCase			:					Union[str, Any]     =    torch.Generator(device=lowerCAmelCase__							).manual_seed(0							)
										_UpperCamelCase			:					str     =    sd_pipe([prompt], generator=lowerCAmelCase__, guidance_scale=6.0, num_inference_steps=2, output_type='''np'''							)

										_UpperCamelCase			:					Dict     =    output.images

										_UpperCamelCase			:					List[Any]     =    torch.Generator(device=lowerCAmelCase__							).manual_seed(0							)
										_UpperCamelCase			:					int     =    sd_pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=6.0, num_inference_steps=2, output_type='''np''', return_dict=lowerCAmelCase__, )[0]

										_UpperCamelCase			:					Union[str, Any]     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					Union[str, Any]     =    image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 6_4, 6_4, 3)
										_UpperCamelCase			:					Optional[Any]     =    np.array([0.5_122, 0.5_712, 0.4_825, 0.5_053, 0.5_646, 0.4_769, 0.5_179, 0.4_894, 0.4_994]							)

										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 				snake_case	( self		:    List[str]							)	->      int:




										'''simple docstring'''



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

										_UpperCamelCase			:					Optional[Any]     =    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(lowerCAmelCase__							)
																_UpperCamelCase			:					List[str]     =    StableDiffusionPipeline.from_pretrained(lowerCAmelCase__							)

										# sanity check that the pipeline still works
										assert pipe.safety_checker is None
										_UpperCamelCase			:					int     =    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 				snake_case	( self		:    Any							)	->      List[Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Any     =    self.dummy_cond_unet
										_UpperCamelCase			:					Union[str, Any]     =    PNDMScheduler(skip_prk_steps=lowerCAmelCase__							)
										_UpperCamelCase			:					int     =    self.dummy_vae
										_UpperCamelCase			:					List[Any]     =    self.dummy_text_encoder
										_UpperCamelCase			:					Union[str, Any]     =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

										# put models in fp16
										_UpperCamelCase			:					int     =    unet.half()
										_UpperCamelCase			:					Any     =    vae.half()
										_UpperCamelCase			:					List[Any]     =    bert.half()

										# make sure here that pndm scheduler skips prk
										_UpperCamelCase			:					Optional[int]     =    StableDiffusionPipeline(
										    unet=lowerCAmelCase__, scheduler=lowerCAmelCase__, vae=lowerCAmelCase__, text_encoder=lowerCAmelCase__, tokenizer=lowerCAmelCase__, safety_checker=lowerCAmelCase__, feature_extractor=self.dummy_extractor, )
										_UpperCamelCase			:					Optional[int]     =    sd_pipe.to(lowerCAmelCase__							)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase			:					Optional[Any]     =    '''A painting of a squirrel eating a burger'''
										_UpperCamelCase			:					List[str]     =    sd_pipe([prompt], num_inference_steps=2, output_type='''np'''							).images

										assert image.shape == (1, 6_4, 6_4, 3)




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






				def 				snake_case	( self		:    str							)	->      List[Any]:




										'''simple docstring'''



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






				def 				snake_case	( self		:    int							)	->      Any:




										'''simple docstring'''



										_UpperCamelCase			:					int     =    '''cuda'''
										_UpperCamelCase			:					List[Any]     =    StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''							)
										_UpperCamelCase			:					int     =    pipe.to(lowerCAmelCase__							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase			:					str     =    '''a photo of a cat'''
										_UpperCamelCase			:					Tuple     =    {
										    '''editing_prompt''': ['''sunglasses'''],
										    '''reverse_editing_direction''': [False],
										    '''edit_warmup_steps''': 1_0,
										    '''edit_guidance_scale''': 6,
										    '''edit_threshold''': 0.95,
										    '''edit_momentum_scale''': 0.5,
										    '''edit_mom_beta''': 0.6,
										}

										_UpperCamelCase			:					Optional[int]     =    3
										_UpperCamelCase			:					int     =    7

										# no sega enabled
										_UpperCamelCase			:					Tuple     =    torch.Generator(lowerCAmelCase__							)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[Any]     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, )

										_UpperCamelCase			:					Optional[Any]     =    output.images
										_UpperCamelCase			:					Tuple     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					List[str]     =    [
										    0.34_673_113,
										    0.38_492_733,
										    0.37_597_352,
										    0.34_086_335,
										    0.35_650_748,
										    0.35_579_205,
										    0.3_384_763,
										    0.34_340_236,
										    0.3_573_271,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

										# with sega enabled
										# generator = torch.manual_seed(seed)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[int]     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, **lowerCAmelCase__, )

										_UpperCamelCase			:					Dict     =    output.images
										_UpperCamelCase			:					int     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					Optional[int]     =    [
										    0.41_887_826,
										    0.37_728_766,
										    0.30_138_272,
										    0.41_416_335,
										    0.41_664_985,
										    0.36_283_392,
										    0.36_191_246,
										    0.43_364_465,
										    0.43_001_732,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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






				def 				snake_case	( self		:    Tuple							)	->      Tuple:




										'''simple docstring'''



										_UpperCamelCase			:					str     =    '''cuda'''
										_UpperCamelCase			:					str     =    StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''							)
										_UpperCamelCase			:					List[Any]     =    pipe.to(lowerCAmelCase__							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase			:					int     =    '''an image of a crowded boulevard, realistic, 4k'''
										_UpperCamelCase			:					Tuple     =    {
										    '''editing_prompt''': '''crowd, crowded, people''',
										    '''reverse_editing_direction''': True,
										    '''edit_warmup_steps''': 1_0,
										    '''edit_guidance_scale''': 8.3,
										    '''edit_threshold''': 0.9,
										    '''edit_momentum_scale''': 0.5,
										    '''edit_mom_beta''': 0.6,
										}

										_UpperCamelCase			:					Dict     =    9
										_UpperCamelCase			:					Optional[Any]     =    7

										# no sega enabled
										_UpperCamelCase			:					Optional[int]     =    torch.Generator(lowerCAmelCase__							)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					int     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, )

										_UpperCamelCase			:					List[str]     =    output.images
										_UpperCamelCase			:					Union[str, Any]     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					Dict     =    [
										    0.43_497_998,
										    0.91_814_065,
										    0.7_540_739,
										    0.55_580_205,
										    0.8_467_265,
										    0.5_389_691,
										    0.62_574_506,
										    0.58_897_763,
										    0.50_926_757,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

										# with sega enabled
										# generator = torch.manual_seed(seed)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					int     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, **lowerCAmelCase__, )

										_UpperCamelCase			:					int     =    output.images
										_UpperCamelCase			:					Optional[int]     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					List[Any]     =    [
										    0.3_089_719,
										    0.30_500_144,
										    0.29_016_042,
										    0.30_630_964,
										    0.325_687,
										    0.29_419_225,
										    0.2_908_091,
										    0.28_723_598,
										    0.27_696_294,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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






				def 				snake_case	( self		:    str							)	->      str:




										'''simple docstring'''



										_UpperCamelCase			:					int     =    '''cuda'''
										_UpperCamelCase			:					str     =    StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''							)
										_UpperCamelCase			:					Union[str, Any]     =    pipe.to(lowerCAmelCase__							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase			:					Optional[int]     =    '''a castle next to a river'''
										_UpperCamelCase			:					Optional[int]     =    {
										    '''editing_prompt''': ['''boat on a river, boat''', '''monet, impression, sunrise'''],
										    '''reverse_editing_direction''': False,
										    '''edit_warmup_steps''': [1_5, 1_8],
										    '''edit_guidance_scale''': 6,
										    '''edit_threshold''': [0.9, 0.8],
										    '''edit_momentum_scale''': 0.5,
										    '''edit_mom_beta''': 0.6,
										}

										_UpperCamelCase			:					Tuple     =    4_8
										_UpperCamelCase			:					List[str]     =    7

										# no sega enabled
										_UpperCamelCase			:					Union[str, Any]     =    torch.Generator(lowerCAmelCase__							)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					int     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, )

										_UpperCamelCase			:					Optional[Any]     =    output.images
										_UpperCamelCase			:					Optional[Any]     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					str     =    [
										    0.75_163_555,
										    0.76_037_145,
										    0.61_785,
										    0.9_189_673,
										    0.8_627_701,
										    0.85_189_694,
										    0.8_512_813,
										    0.87_012_076,
										    0.8_312_857,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

										# with sega enabled
										# generator = torch.manual_seed(seed)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[int]     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, **lowerCAmelCase__, )

										_UpperCamelCase			:					int     =    output.images
										_UpperCamelCase			:					List[str]     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					Dict     =    [
										    0.73_553_365,
										    0.7_537_271,
										    0.74_341_905,
										    0.66_480_356,
										    0.6_472_925,
										    0.63_039_416,
										    0.64_812_905,
										    0.6_749_717,
										    0.6_517_102,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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






				def 				snake_case	( self		:    Union[str, Any]							)	->      Optional[int]:




										'''simple docstring'''



										_UpperCamelCase			:					Dict     =    '''cuda'''
										_UpperCamelCase			:					List[str]     =    StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5''', torch_dtype=torch.floataa							)
										_UpperCamelCase			:					int     =    pipe.to(lowerCAmelCase__							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase			:					List[Any]     =    '''a photo of a cat'''
										_UpperCamelCase			:					str     =    {
										    '''editing_prompt''': ['''sunglasses'''],
										    '''reverse_editing_direction''': [False],
										    '''edit_warmup_steps''': 1_0,
										    '''edit_guidance_scale''': 6,
										    '''edit_threshold''': 0.95,
										    '''edit_momentum_scale''': 0.5,
										    '''edit_mom_beta''': 0.6,
										}

										_UpperCamelCase			:					Tuple     =    3
										_UpperCamelCase			:					List[str]     =    7

										# no sega enabled
										_UpperCamelCase			:					Any     =    torch.Generator(lowerCAmelCase__							)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					int     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, )

										_UpperCamelCase			:					Union[str, Any]     =    output.images
										_UpperCamelCase			:					Union[str, Any]     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					List[Any]     =    [
										    0.34_887_695,
										    0.3_876_953,
										    0.375,
										    0.34_423_828,
										    0.3_581_543,
										    0.35_717_773,
										    0.3_383_789,
										    0.34_570_312,
										    0.359_375,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

										# with sega enabled
										# generator = torch.manual_seed(seed)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[int]     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, **lowerCAmelCase__, )

										_UpperCamelCase			:					Tuple     =    output.images
										_UpperCamelCase			:					int     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					Optional[Any]     =    [
										    0.42_285_156,
										    0.36_914_062,
										    0.29_077_148,
										    0.42_041_016,
										    0.41_918_945,
										    0.35_498_047,
										    0.3_618_164,
										    0.4_423_828,
										    0.43_115_234,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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



