






"""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 					_UpperCAmelCase       ( unittest.TestCase							):




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




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

							lowercase_ : Optional[int]							       =   floats_tensor((batch_size, num_channels) + sizes    ,						rng=random.Random(0   )   ).to(A   )
							return image




					@property
					def   A  (				self     :						int   )  ->  Optional[Any]:
							torch.manual_seed(0   )
							lowercase_ : List[str]							       =   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   A  (				self     :						Optional[int]   )  ->  Optional[Any]:
							torch.manual_seed(0   )
							lowercase_ : Dict							       =   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   A  (				self     :						Optional[int]   )  ->  Optional[int]:
							torch.manual_seed(0   )
							lowercase_ : List[Any]							       =   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=10_00    ,						)
							return CLIPTextModel(A   )




					@property
					def   A  (				self     :						Optional[Any]   )  ->  Union[str, Any]:
							def extract(*A     :						int    ,						**A     :						List[str]   ):





									class 					_UpperCAmelCase       :




														def __init__(				self     :						List[Any]   )  ->  Tuple:
																lowercase_ : Dict							       =   torch.ones([0]   )






														def   A  (				self     :						str    ,						A     :						Any   )  ->  str:
																self.pixel_values.to(A   )
																return self
									return Out()

							return extract




					def   A  (				self     :						int   )  ->  List[str]:
							lowercase_ : Any							       =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							lowercase_ : Optional[Any]							       =   self.dummy_cond_unet
							lowercase_ : Any							       =   DDIMScheduler(
							    beta_start=0.00085    ,						beta_end=0.012    ,						beta_schedule='''scaled_linear'''    ,						clip_sample=A    ,						set_alpha_to_one=A    ,						)

							lowercase_ : List[str]							       =   self.dummy_vae
							lowercase_ : Dict							       =   self.dummy_text_encoder
							lowercase_ : List[Any]							       =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

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

							lowercase_ : Union[str, Any]							       =   '''A painting of a squirrel eating a burger'''

							lowercase_ : Tuple							       =   torch.Generator(device=A   ).manual_seed(0   )
							lowercase_ : Tuple							       =   sd_pipe([prompt]    ,						generator=A    ,						guidance_scale=6.0    ,						num_inference_steps=2    ,						output_type='''np'''   )
							lowercase_ : int							       =   output.images

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

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

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

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

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

							lowercase_ : Any							       =   output.images

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

							lowercase_ : Dict							       =   image[0, -3:, -3:, -1]
							lowercase_ : Optional[Any]							       =   image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							lowercase_ : Tuple							       =   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   A  (				self     :						Tuple   )  ->  Any:
							lowercase_ : Any							       =   StableDiffusionPipeline.from_pretrained(
							    '''hf-internal-testing/tiny-stable-diffusion-lms-pipe'''    ,						safety_checker=A   )
							assert isinstance(A    ,						A   )
							assert isinstance(pipe.scheduler    ,						A   )
							assert pipe.safety_checker is None

							lowercase_ : str							       =   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(A   )
									lowercase_ : List[str]							       =   StableDiffusionPipeline.from_pretrained(A   )

							# sanity check that the pipeline still works
							assert pipe.safety_checker is None
							lowercase_ : Dict							       =   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   A  (				self     :						Tuple   )  ->  Any:
							lowercase_ : Optional[int]							       =   self.dummy_cond_unet
							lowercase_ : Dict							       =   PNDMScheduler(skip_prk_steps=A   )
							lowercase_ : List[Any]							       =   self.dummy_vae
							lowercase_ : Any							       =   self.dummy_text_encoder
							lowercase_ : Tuple							       =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

							# put models in fp16
							lowercase_ : int							       =   unet.half()
							lowercase_ : str							       =   vae.half()
							lowercase_ : str							       =   bert.half()

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

							lowercase_ : Optional[int]							       =   '''A painting of a squirrel eating a burger'''
							lowercase_ : Union[str, Any]							       =   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							):




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




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

							lowercase_ : Optional[int]							       =   (
							    '''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_ : Dict							       =   40_03_66_03_46
							lowercase_ : int							       =   7

							# without safety guidance (sld_guidance_scale = 0)
							lowercase_ : Optional[int]							       =   torch.manual_seed(A   )
							lowercase_ : Optional[int]							       =   sd_pipe(
							    [prompt]    ,						generator=A    ,						guidance_scale=A    ,						num_inference_steps=50    ,						output_type='''np'''    ,						width=5_12    ,						height=5_12    ,						sld_guidance_scale=0    ,						)

							lowercase_ : Union[str, Any]							       =   output.images
							lowercase_ : Tuple							       =   image[0, -3:, -3:, -1]
							lowercase_ : Any							       =   [0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

							assert image.shape == (1, 5_12, 5_12, 3)

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

							# without safety guidance (strong configuration)
							lowercase_ : Union[str, Any]							       =   torch.manual_seed(A   )
							lowercase_ : Dict							       =   sd_pipe(
							    [prompt]    ,						generator=A    ,						guidance_scale=A    ,						num_inference_steps=50    ,						output_type='''np'''    ,						width=5_12    ,						height=5_12    ,						sld_guidance_scale=20_00    ,						sld_warmup_steps=7    ,						sld_threshold=0.025    ,						sld_momentum_scale=0.5    ,						sld_mom_beta=0.7    ,						)

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

							assert image.shape == (1, 5_12, 5_12, 3)

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




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

							lowercase_ : Union[str, Any]							       =   '''padme amidala taking a bath artwork, safe for work, no nudity'''
							lowercase_ : Tuple							       =   27_34_97_17_55
							lowercase_ : str							       =   7

							lowercase_ : Optional[int]							       =   torch.manual_seed(A   )
							lowercase_ : List[str]							       =   sd_pipe(
							    [prompt]    ,						generator=A    ,						guidance_scale=A    ,						num_inference_steps=50    ,						output_type='''np'''    ,						width=5_12    ,						height=5_12    ,						sld_guidance_scale=0    ,						)

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

							assert image.shape == (1, 5_12, 5_12, 3)

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

							lowercase_ : List[str]							       =   torch.manual_seed(A   )
							lowercase_ : str							       =   sd_pipe(
							    [prompt]    ,						generator=A    ,						guidance_scale=A    ,						num_inference_steps=50    ,						output_type='''np'''    ,						width=5_12    ,						height=5_12    ,						sld_guidance_scale=20_00    ,						sld_warmup_steps=7    ,						sld_threshold=0.025    ,						sld_momentum_scale=0.5    ,						sld_mom_beta=0.7    ,						)

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

							assert image.shape == (1, 5_12, 5_12, 3)

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






					def   A  (				self     :						Tuple   )  ->  Optional[Any]:
							lowercase_ : Optional[int]							       =   StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''   )
							lowercase_ : Dict							       =   sd_pipe.to(A   )
							sd_pipe.set_progress_bar_config(disable=A   )

							lowercase_ : Dict							       =   (
							    '''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
							    ''' leyendecker'''
							)
							lowercase_ : Tuple							       =   10_44_35_52_34
							lowercase_ : int							       =   12

							lowercase_ : Tuple							       =   torch.manual_seed(A   )
							lowercase_ : List[Any]							       =   sd_pipe(
							    [prompt]    ,						generator=A    ,						guidance_scale=A    ,						num_inference_steps=50    ,						output_type='''np'''    ,						width=5_12    ,						height=5_12    ,						sld_guidance_scale=0    ,						)

							lowercase_ : List[str]							       =   output.images
							lowercase_ : Any							       =   image[0, -3:, -3:, -1]
							lowercase_ : List[str]							       =   np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]   )

							assert image.shape == (1, 5_12, 5_12, 3)

							assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-7

							lowercase_ : Tuple							       =   torch.manual_seed(A   )
							lowercase_ : Any							       =   sd_pipe(
							    [prompt]    ,						generator=A    ,						guidance_scale=A    ,						num_inference_steps=50    ,						output_type='''np'''    ,						width=5_12    ,						height=5_12    ,						sld_guidance_scale=20_00    ,						sld_warmup_steps=7    ,						sld_threshold=0.025    ,						sld_momentum_scale=0.5    ,						sld_mom_beta=0.7    ,						)

							lowercase_ : Tuple							       =   output.images
							lowercase_ : Optional[Any]							       =   image[0, -3:, -3:, -1]
							lowercase_ : Optional[Any]							       =   np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561]   )
							assert image.shape == (1, 5_12, 5_12, 3)

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











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__A		:       Union[str, Any]  				=		{}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:       Dict  				=		['''NllbTokenizer''']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:       Dict  				=		['''NllbTokenizerFast''']


if TYPE_CHECKING:
						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_nllb import NllbTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_nllb_fast import NllbTokenizerFast

else:
						import sys

						__A		:       List[Any]  				=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



