


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    _UpperCAmelCase      ( unittest.TestCase			):

				"""simple docstring"""




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




				@property
				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    1
								lowercase__     				=    3
								lowercase__     				=    (32, 32)

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




				@property
				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								torch.manual_seed(0						)
								lowercase__     				=    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        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								torch.manual_seed(0						)
								lowercase__     				=    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        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								torch.manual_seed(0						)
								lowercase__     				=    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(lowerCamelCase						)




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

								'''simple docstring'''
								def extract(*lowerCamelCase		:					Union[str, Any],						**lowerCamelCase		:					Optional[Any]						):
												class    _UpperCAmelCase      :

																"""simple docstring"""




																def __init__(  self		:					Optional[int]						):

																				'''simple docstring'''
																				lowercase__     				=    torch.ones([0]						)




																def        lowercase__     (  self		:					Union[str, Any],						lowerCamelCase		:					List[str]						):

																				'''simple docstring'''
																				self.pixel_values.to(lowerCamelCase						)
																				return self



												return Out()

								return extract




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
								lowercase__     				=    self.dummy_cond_unet
								lowercase__     				=    DDIMScheduler(
								    beta_start=0.00085,						beta_end=0.012,						beta_schedule='''scaled_linear''',						clip_sample=lowerCamelCase,						set_alpha_to_one=lowerCamelCase,						)

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

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

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

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

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

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

								assert image.shape == (1, 64, 64, 3)
								lowercase__     				=    np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864]						)

								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        lowercase__     (  self		:					Optional[int]						):

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

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

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

								lowercase__     				=    output.images

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

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

								assert image.shape == (1, 64, 64, 3)
								lowercase__     				=    np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993]						)

								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        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    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

								lowercase__     				=    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						)
												lowercase__     				=    StableDiffusionPipeline.from_pretrained(lowerCamelCase						)

								# sanity check that the pipeline still works
								assert pipe.safety_checker is None
								lowercase__     				=    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        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    self.dummy_cond_unet
								lowercase__     				=    PNDMScheduler(skip_prk_steps=lowerCamelCase						)
								lowercase__     				=    self.dummy_vae
								lowercase__     				=    self.dummy_text_encoder
								lowercase__     				=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

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

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

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

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



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

				"""simple docstring"""




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




				def        lowercase__     (  self		:					Dict						):

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

								lowercase__     				=    (
								    '''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 '''
								)
								lowercase__     				=    4_003_660_346
								lowercase__     				=    7

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

								lowercase__     				=    output.images
								lowercase__     				=    image[0, -3:, -3:, -1]
								lowercase__     				=    [0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

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

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

								# without safety guidance (strong configuration)
								lowercase__     				=    torch.manual_seed(lowerCamelCase						)
								lowercase__     				=    sd_pipe(
								    [prompt],						generator=lowerCamelCase,						guidance_scale=lowerCamelCase,						num_inference_steps=50,						output_type='''np''',						width=512,						height=512,						sld_guidance_scale=2_000,						sld_warmup_steps=7,						sld_threshold=0.025,						sld_momentum_scale=0.5,						sld_mom_beta=0.7,						)

								lowercase__     				=    output.images
								lowercase__     				=    image[0, -3:, -3:, -1]
								lowercase__     				=    [0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

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

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




				def        lowercase__     (  self		:					Any						):

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

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

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

								lowercase__     				=    output.images
								lowercase__     				=    image[0, -3:, -3:, -1]
								lowercase__     				=    [0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]

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

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

								lowercase__     				=    torch.manual_seed(lowerCamelCase						)
								lowercase__     				=    sd_pipe(
								    [prompt],						generator=lowerCamelCase,						guidance_scale=lowerCamelCase,						num_inference_steps=50,						output_type='''np''',						width=512,						height=512,						sld_guidance_scale=2_000,						sld_warmup_steps=7,						sld_threshold=0.025,						sld_momentum_scale=0.5,						sld_mom_beta=0.7,						)

								lowercase__     				=    output.images
								lowercase__     				=    image[0, -3:, -3:, -1]
								lowercase__     				=    [0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]

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

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




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''						)
								lowercase__     				=    sd_pipe.to(lowerCamelCase						)
								sd_pipe.set_progress_bar_config(disable=lowerCamelCase						)

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

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

								lowercase__     				=    output.images
								lowercase__     				=    image[0, -3:, -3:, -1]
								lowercase__     				=    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

								lowercase__     				=    torch.manual_seed(lowerCamelCase						)
								lowercase__     				=    sd_pipe(
								    [prompt],						generator=lowerCamelCase,						guidance_scale=lowerCamelCase,						num_inference_steps=50,						output_type='''np''',						width=512,						height=512,						sld_guidance_scale=2_000,						sld_warmup_steps=7,						sld_threshold=0.025,						sld_momentum_scale=0.5,						sld_mom_beta=0.7,						)

								lowercase__     				=    output.images
								lowercase__     				=    image[0, -3:, -3:, -1]
								lowercase__     				=    np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561]						)
								assert image.shape == (1, 512, 512, 3)

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












from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= DistilBertTokenizer
				lowercase__       							= DistilBertTokenizerFast
				lowercase__       							= True




				@slow
				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								lowercase__     				=    DistilBertTokenizer.from_pretrained('''distilbert-base-uncased'''						)

								lowercase__     				=    tokenizer.encode('''sequence builders''',						add_special_tokens=lowerCamelCase						)
								lowercase__     				=    tokenizer.encode('''multi-sequence build''',						add_special_tokens=lowerCamelCase						)

								lowercase__     				=    tokenizer.build_inputs_with_special_tokens(lowerCamelCase						)
								lowercase__     				=    tokenizer.build_inputs_with_special_tokens(lowerCamelCase,						lowerCamelCase						)

								assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
								assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
								    tokenizer.sep_token_id
								]




