






'''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 						SCREAMING_SNAKE_CASE       (       unittest.TestCase							):






			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      List[str]:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






			@property
			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]       )      ->      Any:
							a_		:	Optional[int]									=   1
							a_		:	List[str]									=   3
							a_		:	Union[str, Any]									=   (32, 32)

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






			@property
			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      List[str]:
							torch.manual_seed(0       )
							a_		:	Any									=   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       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      Dict:
							torch.manual_seed(0       )
							a_		:	List[Any]									=   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       SCREAMING_SNAKE_CASE   (			self   :      Union[str, Any]       )      ->      Optional[Any]:
							torch.manual_seed(0       )
							a_		:	Optional[int]									=   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(__SCREAMING_SNAKE_CASE       )






			@property
			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]       )      ->      Tuple:
							def extract(*__SCREAMING_SNAKE_CASE   :      str	,    **__SCREAMING_SNAKE_CASE   :      Optional[int]       ):





											class 						SCREAMING_SNAKE_CASE       :






														def __init__(			self   :      int       )      ->      str:
																		a_		:	List[Any]									=   torch.ones([0]       )







														def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]       )      ->      Union[str, Any]:
																		self.pixel_values.to(__SCREAMING_SNAKE_CASE       )
																		return self





											return Out()

							return extract






			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      int:
							a_		:	Optional[Any]									=   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							a_		:	Any									=   self.dummy_cond_unet
							a_		:	Optional[Any]									=   DDIMScheduler(
							    beta_start=0.0_0085	,    beta_end=0.012	,    beta_schedule='''scaled_linear'''	,    clip_sample=__SCREAMING_SNAKE_CASE	,    set_alpha_to_one=__SCREAMING_SNAKE_CASE	,    )

							a_		:	int									=   self.dummy_vae
							a_		:	Optional[Any]									=   self.dummy_text_encoder
							a_		:	Optional[int]									=   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

							# make sure here that pndm scheduler skips prk
							a_		:	str									=   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_		:	int									=   sd_pipe.to(__SCREAMING_SNAKE_CASE       )
							sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

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

							a_		:	Union[str, Any]									=   torch.Generator(device=__SCREAMING_SNAKE_CASE       ).manual_seed(0       )
							a_		:	Optional[int]									=   sd_pipe([prompt]	,    generator=__SCREAMING_SNAKE_CASE	,    guidance_scale=6.0	,    num_inference_steps=2	,    output_type='''np'''       )
							a_		:	Tuple									=   output.images

							a_		:	int									=   torch.Generator(device=__SCREAMING_SNAKE_CASE       ).manual_seed(0       )
							a_		:	List[str]									=   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_		:	Union[str, Any]									=   image[0, -3:, -3:, -1]
							a_		:	Union[str, Any]									=   image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							a_		:	Optional[int]									=   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       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]       )      ->      Optional[Any]:
							a_		:	str									=   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							a_		:	Optional[Any]									=   self.dummy_cond_unet
							a_		:	Union[str, Any]									=   PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE       )
							a_		:	str									=   self.dummy_vae
							a_		:	Tuple									=   self.dummy_text_encoder
							a_		:	List[str]									=   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

							# make sure here that pndm scheduler skips prk
							a_		:	Optional[int]									=   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_		:	List[Any]									=   sd_pipe.to(__SCREAMING_SNAKE_CASE       )
							sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

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

							a_		:	int									=   output.images

							a_		:	Dict									=   torch.Generator(device=__SCREAMING_SNAKE_CASE       ).manual_seed(0       )
							a_		:	Dict									=   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_		:	str									=   image[0, -3:, -3:, -1]
							a_		:	Union[str, Any]									=   image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							a_		:	Optional[int]									=   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       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]       )      ->      List[Any]:
							a_		:	List[str]									=   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_		:	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(__SCREAMING_SNAKE_CASE       )
											a_		:	int									=   StableDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE       )

							# sanity check that the pipeline still works
							assert pipe.safety_checker is None
							a_		:	List[str]									=   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       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      Dict:
							a_		:	str									=   self.dummy_cond_unet
							a_		:	Union[str, Any]									=   PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE       )
							a_		:	Dict									=   self.dummy_vae
							a_		:	Union[str, Any]									=   self.dummy_text_encoder
							a_		:	List[Any]									=   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

							# put models in fp16
							a_		:	Dict									=   unet.half()
							a_		:	List[str]									=   vae.half()
							a_		:	Optional[int]									=   bert.half()

							# make sure here that pndm scheduler skips prk
							a_		:	str									=   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_		:	Dict									=   sd_pipe.to(__SCREAMING_SNAKE_CASE       )
							sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

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

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





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






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]       )      ->      str:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      List[str]:
							a_		:	List[str]									=   StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''	,    safety_checker=__SCREAMING_SNAKE_CASE       )
							a_		:	List[str]									=   LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config       )
							a_		:	Optional[Any]									=   sd_pipe.to(__SCREAMING_SNAKE_CASE       )
							sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

							a_		:	Tuple									=   (
							    '''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_		:	Any									=   40_0366_0346
							a_		:	Dict									=   7

							# without safety guidance (sld_guidance_scale = 0)
							a_		:	Optional[Any]									=   torch.manual_seed(__SCREAMING_SNAKE_CASE       )
							a_		:	Any									=   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_		:	Any									=   output.images
							a_		:	Union[str, Any]									=   image[0, -3:, -3:, -1]
							a_		:	Optional[Any]									=   [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)
							a_		:	Union[str, Any]									=   torch.manual_seed(__SCREAMING_SNAKE_CASE       )
							a_		:	Tuple									=   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=2000	,    sld_warmup_steps=7	,    sld_threshold=0.025	,    sld_momentum_scale=0.5	,    sld_mom_beta=0.7	,    )

							a_		:	Union[str, Any]									=   output.images
							a_		:	Tuple									=   image[0, -3:, -3:, -1]
							a_		:	Optional[Any]									=   [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       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]       )      ->      List[str]:
							a_		:	Optional[int]									=   StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''	,    safety_checker=__SCREAMING_SNAKE_CASE       )
							a_		:	Tuple									=   LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config       )
							a_		:	Optional[Any]									=   sd_pipe.to(__SCREAMING_SNAKE_CASE       )
							sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

							a_		:	str									=   '''padme amidala taking a bath artwork, safe for work, no nudity'''
							a_		:	Any									=   27_3497_1755
							a_		:	Any									=   7

							a_		:	Union[str, Any]									=   torch.manual_seed(__SCREAMING_SNAKE_CASE       )
							a_		:	Union[str, Any]									=   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_		:	Tuple									=   output.images
							a_		:	int									=   image[0, -3:, -3:, -1]
							a_		:	List[str]									=   [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

							a_		:	Any									=   torch.manual_seed(__SCREAMING_SNAKE_CASE       )
							a_		:	Optional[Any]									=   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=2000	,    sld_warmup_steps=7	,    sld_threshold=0.025	,    sld_momentum_scale=0.5	,    sld_mom_beta=0.7	,    )

							a_		:	Optional[int]									=   output.images
							a_		:	int									=   image[0, -3:, -3:, -1]
							a_		:	Optional[Any]									=   [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       SCREAMING_SNAKE_CASE   (			self   :      Union[str, Any]       )      ->      List[str]:
							a_		:	Any									=   StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''       )
							a_		:	int									=   sd_pipe.to(__SCREAMING_SNAKE_CASE       )
							sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

							a_		:	str									=   (
							    '''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
							    ''' leyendecker'''
							)
							a_		:	List[Any]									=   10_4435_5234
							a_		:	Optional[int]									=   12

							a_		:	List[Any]									=   torch.manual_seed(__SCREAMING_SNAKE_CASE       )
							a_		:	Union[str, Any]									=   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_		:	int									=   output.images
							a_		:	Any									=   image[0, -3:, -3:, -1]
							a_		:	Optional[Any]									=   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_		:	Optional[int]									=   torch.manual_seed(__SCREAMING_SNAKE_CASE       )
							a_		:	List[str]									=   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=2000	,    sld_warmup_steps=7	,    sld_threshold=0.025	,    sld_momentum_scale=0.5	,    sld_mom_beta=0.7	,    )

							a_		:	List[str]									=   output.images
							a_		:	str									=   image[0, -3:, -3:, -1]
							a_		:	int									=   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'''







# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

__lowerCAmelCase									=   'facebook/wmt19-en-de'
__lowerCAmelCase									=   FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__lowerCAmelCase									=   FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

__lowerCAmelCase									=   FSMTForConditionalGeneration(config)
print(F"""num of params {tiny_model.num_parameters()}""")

# Test
__lowerCAmelCase									=   tokenizer(['Making tiny model'], return_tensors='pt')
__lowerCAmelCase									=   tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
__lowerCAmelCase									=   'tiny-wmt19-en-de'
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"""Generated {mname_tiny}""")

# Upload
# transformers-cli upload tiny-wmt19-en-de




