



"""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.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 				lowercase__       (   unittest.TestCase				):







       '''simple docstring'''


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


             '''simple docstring'''




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



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


             '''simple docstring'''




             UpperCAmelCase_      			=							1
             UpperCAmelCase_      			=							3
             UpperCAmelCase_      			=							(32, 32)

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



       @property
       def 					lowercase__    (							self      :  Optional[Any]   )			->						int:


             '''simple docstring'''




             torch.manual_seed(0   )
             UpperCAmelCase_      			=							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      :  List[Any]   )			->						Optional[Any]:


             '''simple docstring'''




             torch.manual_seed(0   )
             UpperCAmelCase_      			=							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      :  Optional[Any]   )			->						Union[str, Any]:


             '''simple docstring'''




             torch.manual_seed(0   )
             UpperCAmelCase_      			=							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=1000		,    )
             return CLIPTextModel(_UpperCAmelCase   )



       @property
       def 					lowercase__    (							self      :  Tuple   )			->						Dict:


             '''simple docstring'''




             def extract(*_UpperCAmelCase      :  Tuple		,    **_UpperCAmelCase      :  Tuple   ):
                   class 				lowercase__       :







                          '''simple docstring'''


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


                                '''simple docstring'''




                                UpperCAmelCase_      			=							torch.ones([0]   )







                          def 					lowercase__    (							self      :  Optional[Any]		,    _UpperCAmelCase      :  Union[str, Any]   )			->						List[str]:


                                '''simple docstring'''




                                self.pixel_values.to(_UpperCAmelCase   )
                                return self



                   return Out()

             return extract



       def 					lowercase__    (							self      :  int   )			->						int:


             '''simple docstring'''




             UpperCAmelCase_      			=							"cpu"  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase_      			=							self.dummy_cond_unet
             UpperCAmelCase_      			=							DDIMScheduler(
                 beta_start=0.0_0085		,    beta_end=0.012		,    beta_schedule="scaled_linear"		,    clip_sample=_UpperCAmelCase		,    set_alpha_to_one=_UpperCAmelCase		,    )

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

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

             UpperCAmelCase_      			=							"A painting of a squirrel eating a burger"

             UpperCAmelCase_      			=							torch.Generator(device=_UpperCAmelCase   ).manual_seed(0   )
             UpperCAmelCase_      			=							sd_pipe([prompt]		,    generator=_UpperCAmelCase		,    guidance_scale=6.0		,    num_inference_steps=2		,    output_type="np"   )
             UpperCAmelCase_      			=							output.images

             UpperCAmelCase_      			=							torch.Generator(device=_UpperCAmelCase   ).manual_seed(0   )
             UpperCAmelCase_      			=							sd_pipe(
                 [prompt]		,    generator=_UpperCAmelCase		,    guidance_scale=6.0		,    num_inference_steps=2		,    output_type="np"		,    return_dict=_UpperCAmelCase		,    )[0]

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

             assert image.shape == (1, 64, 64, 3)
             UpperCAmelCase_      			=							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[Any]   )			->						int:


             '''simple docstring'''




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

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

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

             UpperCAmelCase_      			=							output.images

             UpperCAmelCase_      			=							torch.Generator(device=_UpperCAmelCase   ).manual_seed(0   )
             UpperCAmelCase_      			=							sd_pipe(
                 [prompt]		,    generator=_UpperCAmelCase		,    guidance_scale=6.0		,    num_inference_steps=2		,    output_type="np"		,    return_dict=_UpperCAmelCase		,    )[0]

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

             assert image.shape == (1, 64, 64, 3)
             UpperCAmelCase_      			=							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      :  str   )			->						str:


             '''simple docstring'''




             UpperCAmelCase_      			=							StableDiffusionPipeline.from_pretrained(
                 "hf-internal-testing/tiny-stable-diffusion-lms-pipe"		,    safety_checker=_UpperCAmelCase   )
             assert isinstance(_UpperCAmelCase		,    _UpperCAmelCase   )
             assert isinstance(pipe.scheduler		,    _UpperCAmelCase   )
             assert pipe.safety_checker is None

             UpperCAmelCase_      			=							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(_UpperCAmelCase   )
                   UpperCAmelCase_      			=							StableDiffusionPipeline.from_pretrained(_UpperCAmelCase   )

             # sanity check that the pipeline still works
             assert pipe.safety_checker is None
             UpperCAmelCase_      			=							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      :  Any   )			->						List[str]:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.dummy_cond_unet
             UpperCAmelCase_      			=							PNDMScheduler(skip_prk_steps=_UpperCAmelCase   )
             UpperCAmelCase_      			=							self.dummy_vae
             UpperCAmelCase_      			=							self.dummy_text_encoder
             UpperCAmelCase_      			=							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )

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

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

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

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



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







       '''simple docstring'''


       def 					lowercase__    (							self      :  Dict   )			->						Optional[int]:


             '''simple docstring'''




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



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


             '''simple docstring'''




             UpperCAmelCase_      			=							StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"		,    safety_checker=_UpperCAmelCase   )
             UpperCAmelCase_      			=							LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config   )
             UpperCAmelCase_      			=							sd_pipe.to(_UpperCAmelCase   )
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase   )

             UpperCAmelCase_      			=							(
                 "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 "
             )
             UpperCAmelCase_      			=							4003660346
             UpperCAmelCase_      			=							7

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

             UpperCAmelCase_      			=							output.images
             UpperCAmelCase_      			=							image[0, -3:, -3:, -1]
             UpperCAmelCase_      			=							[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)
             UpperCAmelCase_      			=							torch.manual_seed(_UpperCAmelCase   )
             UpperCAmelCase_      			=							sd_pipe(
                 [prompt]		,    generator=_UpperCAmelCase		,    guidance_scale=_UpperCAmelCase		,    num_inference_steps=50		,    output_type="np"		,    width=512		,    height=512		,    sld_guidance_scale=2000		,    sld_warmup_steps=7		,    sld_threshold=0.025		,    sld_momentum_scale=0.5		,    sld_mom_beta=0.7		,    )

             UpperCAmelCase_      			=							output.images
             UpperCAmelCase_      			=							image[0, -3:, -3:, -1]
             UpperCAmelCase_      			=							[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   )			->						Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"		,    safety_checker=_UpperCAmelCase   )
             UpperCAmelCase_      			=							LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config   )
             UpperCAmelCase_      			=							sd_pipe.to(_UpperCAmelCase   )
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase   )

             UpperCAmelCase_      			=							"padme amidala taking a bath artwork, safe for work, no nudity"
             UpperCAmelCase_      			=							2734971755
             UpperCAmelCase_      			=							7

             UpperCAmelCase_      			=							torch.manual_seed(_UpperCAmelCase   )
             UpperCAmelCase_      			=							sd_pipe(
                 [prompt]		,    generator=_UpperCAmelCase		,    guidance_scale=_UpperCAmelCase		,    num_inference_steps=50		,    output_type="np"		,    width=512		,    height=512		,    sld_guidance_scale=0		,    )

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

             UpperCAmelCase_      			=							torch.manual_seed(_UpperCAmelCase   )
             UpperCAmelCase_      			=							sd_pipe(
                 [prompt]		,    generator=_UpperCAmelCase		,    guidance_scale=_UpperCAmelCase		,    num_inference_steps=50		,    output_type="np"		,    width=512		,    height=512		,    sld_guidance_scale=2000		,    sld_warmup_steps=7		,    sld_threshold=0.025		,    sld_momentum_scale=0.5		,    sld_mom_beta=0.7		,    )

             UpperCAmelCase_      			=							output.images
             UpperCAmelCase_      			=							image[0, -3:, -3:, -1]
             UpperCAmelCase_      			=							[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[Any]   )			->						str:


             '''simple docstring'''




             UpperCAmelCase_      			=							StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"   )
             UpperCAmelCase_      			=							sd_pipe.to(_UpperCAmelCase   )
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase   )

             UpperCAmelCase_      			=							(
                 "the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."
                 " leyendecker"
             )
             UpperCAmelCase_      			=							1044355234
             UpperCAmelCase_      			=							12

             UpperCAmelCase_      			=							torch.manual_seed(_UpperCAmelCase   )
             UpperCAmelCase_      			=							sd_pipe(
                 [prompt]		,    generator=_UpperCAmelCase		,    guidance_scale=_UpperCAmelCase		,    num_inference_steps=50		,    output_type="np"		,    width=512		,    height=512		,    sld_guidance_scale=0		,    )

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

             UpperCAmelCase_      			=							torch.manual_seed(_UpperCAmelCase   )
             UpperCAmelCase_      			=							sd_pipe(
                 [prompt]		,    generator=_UpperCAmelCase		,    guidance_scale=_UpperCAmelCase		,    num_inference_steps=50		,    output_type="np"		,    width=512		,    height=512		,    sld_guidance_scale=2000		,    sld_warmup_steps=7		,    sld_threshold=0.025		,    sld_momentum_scale=0.5		,    sld_mom_beta=0.7		,    )

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









"""simple docstring"""





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
  import torch

  from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class 				lowercase__       (   unittest.TestCase				):







       '''simple docstring'''


       @slow
       def 					lowercase__    (							self      :  Tuple   )			->						str:


             '''simple docstring'''




             UpperCAmelCase_      			=							XLMRobertaModel.from_pretrained("xlm-roberta-base"   )
             UpperCAmelCase_      			=							torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]   )
             # The dog is cute and lives in the garden house

             UpperCAmelCase_      			=							torch.Size((1, 12, 768)   )  # batch_size, sequence_length, embedding_vector_dim
             UpperCAmelCase_      			=							torch.tensor(
                 [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]   )
             #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
             #  xlmr.eval()
             #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
             with torch.no_grad():
                   UpperCAmelCase_      			=							model(_UpperCAmelCase   )["last_hidden_state"].detach()
             self.assertEqual(output.shape		,    _UpperCAmelCase   )
             # compare the actual values for a slice of last dim
             self.assertTrue(torch.allclose(output[:, :, -1]		,    _UpperCAmelCase		,    atol=1e-3   )   )







       @slow
       def 					lowercase__    (							self      :  List[Any]   )			->						Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_      			=							XLMRobertaModel.from_pretrained("xlm-roberta-large"   )
             UpperCAmelCase_      			=							torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]   )
             # The dog is cute and lives in the garden house

             UpperCAmelCase_      			=							torch.Size((1, 12, 1024)   )  # batch_size, sequence_length, embedding_vector_dim
             UpperCAmelCase_      			=							torch.tensor(
                 [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]   )
             #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
             #  xlmr.eval()
             #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
             with torch.no_grad():
                   UpperCAmelCase_      			=							model(_UpperCAmelCase   )["last_hidden_state"].detach()
             self.assertEqual(output.shape		,    _UpperCAmelCase   )
             # compare the actual values for a slice of last dim
             self.assertTrue(torch.allclose(output[:, :, -1]		,    _UpperCAmelCase		,    atol=1e-3   )   )




