
'''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  A				(							unittest.TestCase						):





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






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





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






         """simple docstring"""






         A       :   Optional[int]					=						1
         A       :   str					=						3
         A       :   str					=						(32, 32)

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





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






         """simple docstring"""






         torch.manual_seed(0	)
         A       :   int					=						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 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         torch.manual_seed(0	)
         A       :   Optional[int]					=						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 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         torch.manual_seed(0	)
         A       :   Tuple					=						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 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         def extract(*SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	):





             class  A				:





                  def __init__(							self	) ->				str:






                      """simple docstring"""






                      A       :   str					=						torch.ones([0]	)





                  def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				Optional[Any]:






                      """simple docstring"""






                      self.pixel_values.to(SCREAMING_SNAKE_CASE__	)
                      return self





             return Out()

         return extract





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






         """simple docstring"""






         A       :   List[Any]					=						'''cpu'''  # ensure determinism for the device-dependent torch.Generator
         A       :   Tuple					=						self.dummy_cond_unet
         A       :   List[str]					=						DDIMScheduler(
             beta_start=0.00_085						,			beta_end=0.012						,			beta_schedule='''scaled_linear'''						,			clip_sample=SCREAMING_SNAKE_CASE__						,			set_alpha_to_one=SCREAMING_SNAKE_CASE__						,			)

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

         # make sure here that pndm scheduler skips prk
         A       :   List[Any]					=						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       :   Optional[Any]					=						sd_pipe.to(SCREAMING_SNAKE_CASE__	)
         sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__	)

         A       :   str					=						'''A painting of a squirrel eating a burger'''

         A       :   str					=						torch.Generator(device=SCREAMING_SNAKE_CASE__	).manual_seed(0	)
         A       :   Optional[Any]					=						sd_pipe([prompt]						,			generator=SCREAMING_SNAKE_CASE__						,			guidance_scale=6.0						,			num_inference_steps=2						,			output_type='''np'''	)
         A       :   Dict					=						output.images

         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'''						,			return_dict=SCREAMING_SNAKE_CASE__						,			)[0]

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

         assert image.shape == (1, 64, 64, 3)
         A       :   Optional[int]					=						np.array([0.5_756, 0.6_118, 0.5_005, 0.5_041, 0.5_471, 0.4_726, 0.4_976, 0.4_865, 0.4_864]	)

         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 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






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

         # make sure here that pndm scheduler skips prk
         A       :   Any					=						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       :   str					=						sd_pipe.to(SCREAMING_SNAKE_CASE__	)
         sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__	)

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

         A       :   Optional[Any]					=						output.images

         A       :   str					=						torch.Generator(device=SCREAMING_SNAKE_CASE__	).manual_seed(0	)
         A       :   Optional[Any]					=						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       :   Optional[int]					=						image[0, -3:, -3:, -1]
         A       :   Optional[Any]					=						image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         A       :   int					=						np.array([0.5_125, 0.5_716, 0.4_828, 0.5_060, 0.5_650, 0.4_768, 0.5_185, 0.4_895, 0.4_993]	)

         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 				__lowerCAmelCase							(							self	) ->				List[Any]:






         """simple docstring"""






         A       :   Dict					=						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       :   Tuple					=						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       :   List[str]					=						StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE__	)

         # sanity check that the pipeline still works
         assert pipe.safety_checker is None
         A       :   Optional[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 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






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

         # put models in fp16
         A       :   Optional[int]					=						unet.half()
         A       :   Dict					=						vae.half()
         A       :   Optional[Any]					=						bert.half()

         # make sure here that pndm scheduler skips prk
         A       :   List[Any]					=						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       :   str					=						'''A painting of a squirrel eating a burger'''
         A       :   Any					=						sd_pipe([prompt]						,			num_inference_steps=2						,			output_type='''np'''	).images

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





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





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






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





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






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

         A       :   str					=						(
             '''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       :   Union[str, Any]					=						4003660346
         A       :   Any					=						7

         # without safety guidance (sld_guidance_scale = 0)
         A       :   List[Any]					=						torch.manual_seed(SCREAMING_SNAKE_CASE__	)
         A       :   List[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       :   str					=						output.images
         A       :   Any					=						image[0, -3:, -3:, -1]
         A       :   Optional[Any]					=						[0.2_278, 0.2_231, 0.2_249, 0.2_333, 0.2_303, 0.1_885, 0.2_273, 0.2_144, 0.2_176]

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

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

         # without safety guidance (strong configuration)
         A       :   List[Any]					=						torch.manual_seed(SCREAMING_SNAKE_CASE__	)
         A       :   List[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       :   Union[str, Any]					=						output.images
         A       :   Optional[Any]					=						image[0, -3:, -3:, -1]
         A       :   List[str]					=						[0.2_383, 0.2_276, 0.236, 0.2_192, 0.2_186, 0.2_053, 0.1_971, 0.1_901, 0.1_719]

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

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





     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






         A       :   int					=						StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''						,			safety_checker=SCREAMING_SNAKE_CASE__	)
         A       :   Dict					=						LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config	)
         A       :   Dict					=						sd_pipe.to(SCREAMING_SNAKE_CASE__	)
         sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__	)

         A       :   List[Any]					=						'''padme amidala taking a bath artwork, safe for work, no nudity'''
         A       :   List[str]					=						2734971755
         A       :   Union[str, Any]					=						7

         A       :   int					=						torch.manual_seed(SCREAMING_SNAKE_CASE__	)
         A       :   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=0						,			)

         A       :   Dict					=						output.images
         A       :   str					=						image[0, -3:, -3:, -1]
         A       :   Dict					=						[0.3_502, 0.3_622, 0.3_396, 0.3_642, 0.3_478, 0.3_318, 0.35, 0.3_348, 0.3_297]

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

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

         A       :   Dict					=						torch.manual_seed(SCREAMING_SNAKE_CASE__	)
         A       :   Dict					=						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       :   Any					=						output.images
         A       :   Union[str, Any]					=						image[0, -3:, -3:, -1]
         A       :   Tuple					=						[0.5_531, 0.5_206, 0.4_895, 0.5_156, 0.5_182, 0.4_751, 0.4_802, 0.4_803, 0.4_443]

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

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





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         A       :   int					=						StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''	)
         A       :   Tuple					=						sd_pipe.to(SCREAMING_SNAKE_CASE__	)
         sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__	)

         A       :   Optional[int]					=						(
             '''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
             ''' leyendecker'''
         )
         A       :   Dict					=						1044355234
         A       :   List[Any]					=						12

         A       :   Optional[int]					=						torch.manual_seed(SCREAMING_SNAKE_CASE__	)
         A       :   Dict					=						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       :   Optional[int]					=						output.images
         A       :   str					=						image[0, -3:, -3:, -1]
         A       :   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, 512, 512, 3)

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

         A       :   Tuple					=						torch.manual_seed(SCREAMING_SNAKE_CASE__	)
         A       :   int					=						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       :   Dict					=						image[0, -3:, -3:, -1]
         A       :   Optional[Any]					=						np.array([0.5_818, 0.6_285, 0.6_835, 0.6_019, 0.625, 0.6_754, 0.6_096, 0.6_334, 0.6_561]	)
         assert image.shape == (1, 512, 512, 3)

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











"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_								=		logging.get_logger(__name__)

lowerCamelCase_								=		{
    "google/vivit-b-16x2-kinetics400": (
        "https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}






class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      List[str]							=  '''vivit'''




       def __init__(						self		,SCREAMING_SNAKE_CASE__=2_24		,SCREAMING_SNAKE_CASE__=32		,SCREAMING_SNAKE_CASE__=[2, 16, 16]		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=7_68		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=30_72		,SCREAMING_SNAKE_CASE__="gelu_fast"		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=1E-06		,SCREAMING_SNAKE_CASE__=True		,**SCREAMING_SNAKE_CASE__		,)			->      Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :int						=  hidden_size
             __SCREAMING_SNAKE_CASE   :List[Any]						=  num_hidden_layers
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  num_attention_heads
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  intermediate_size
             __SCREAMING_SNAKE_CASE   :Any						=  hidden_act
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  hidden_dropout_prob
             __SCREAMING_SNAKE_CASE   :str						=  attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE   :Any						=  initializer_range
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  layer_norm_eps

             __SCREAMING_SNAKE_CASE   :Optional[int]						=  image_size
             __SCREAMING_SNAKE_CASE   :List[str]						=  num_frames
             __SCREAMING_SNAKE_CASE   :Any						=  tubelet_size
             __SCREAMING_SNAKE_CASE   :str						=  num_channels
             __SCREAMING_SNAKE_CASE   :Any						=  qkv_bias

             super().__init__(**SCREAMING_SNAKE_CASE__							)