





"""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     _UpperCamelCase     (						self							)			->      Optional[int]:





             """simple docstring"""






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




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





             """simple docstring"""






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

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




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





             """simple docstring"""






             torch.manual_seed(0							)
             __SCREAMING_SNAKE_CASE   :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     _UpperCamelCase     (						self							)			->      int:





             """simple docstring"""






             torch.manual_seed(0							)
             __SCREAMING_SNAKE_CASE   :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     _UpperCamelCase     (						self							)			->      Tuple:





             """simple docstring"""






             torch.manual_seed(0							)
             __SCREAMING_SNAKE_CASE   :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=10_00		,)
             return CLIPTextModel(SCREAMING_SNAKE_CASE__							)




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





             """simple docstring"""






             def extract(*SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							):






                   class 					_SCREAMING_SNAKE_CASE:




                          def __init__(						self							)			->      str:





                                """simple docstring"""






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




                          def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__							)			->      Optional[Any]:





                                """simple docstring"""






                                self.pixel_values.to(SCREAMING_SNAKE_CASE__							)
                                return self




                   return Out()

             return extract




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :List[Any]						=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
             __SCREAMING_SNAKE_CASE   :Tuple						=  self.dummy_cond_unet
             __SCREAMING_SNAKE_CASE   :List[str]						=  DDIMScheduler(
                 beta_start=0.0_0_0_8_5		,beta_end=0.0_1_2		,beta_schedule='''scaled_linear'''		,clip_sample=SCREAMING_SNAKE_CASE__		,set_alpha_to_one=SCREAMING_SNAKE_CASE__		,)

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

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

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

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

             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  torch.Generator(device=SCREAMING_SNAKE_CASE__							).manual_seed(0							)
             __SCREAMING_SNAKE_CASE   :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]

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

             assert image.shape == (1, 64, 64, 3)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  np.array([0.5_7_5_6, 0.6_1_1_8, 0.5_0_0_5, 0.5_0_4_1, 0.5_4_7_1, 0.4_7_2_6, 0.4_9_7_6, 0.4_8_6_5, 0.4_8_6_4]							)

             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     _UpperCamelCase     (						self							)			->      Tuple:





             """simple docstring"""






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

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

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

             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  output.images

             __SCREAMING_SNAKE_CASE   :str						=  torch.Generator(device=SCREAMING_SNAKE_CASE__							).manual_seed(0							)
             __SCREAMING_SNAKE_CASE   :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]

             __SCREAMING_SNAKE_CASE   :Optional[int]						=  image[0, -3:, -3:, -1]
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 64, 64, 3)
             __SCREAMING_SNAKE_CASE   :int						=  np.array([0.5_1_2_5, 0.5_7_1_6, 0.4_8_2_8, 0.5_0_6_0, 0.5_6_5_0, 0.4_7_6_8, 0.5_1_8_5, 0.4_8_9_5, 0.4_9_9_3]							)

             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     _UpperCamelCase     (						self							)			->      List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :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

             __SCREAMING_SNAKE_CASE   :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__							)
                   __SCREAMING_SNAKE_CASE   :List[str]						=  StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE__							)

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





             """simple docstring"""






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

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

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

             __SCREAMING_SNAKE_CASE   :str						=  '''A painting of a squirrel eating a burger'''
             __SCREAMING_SNAKE_CASE   :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     _UpperCamelCase     (						self							)			->      Tuple:





             """simple docstring"""






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




       def     _UpperCamelCase     (						self							)			->      Any:





             """simple docstring"""






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

             __SCREAMING_SNAKE_CASE   :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 '''
             )
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  40_03_66_03_46
             __SCREAMING_SNAKE_CASE   :Any						=  7

             # without safety guidance (sld_guidance_scale = 0)
             __SCREAMING_SNAKE_CASE   :List[Any]						=  torch.manual_seed(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :List[Any]						=  sd_pipe(
                 [prompt]		,generator=SCREAMING_SNAKE_CASE__		,guidance_scale=SCREAMING_SNAKE_CASE__		,num_inference_steps=50		,output_type='''np'''		,width=5_12		,height=5_12		,sld_guidance_scale=0		,)

             __SCREAMING_SNAKE_CASE   :str						=  output.images
             __SCREAMING_SNAKE_CASE   :Any						=  image[0, -3:, -3:, -1]
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  [0.2_2_7_8, 0.2_2_3_1, 0.2_2_4_9, 0.2_3_3_3, 0.2_3_0_3, 0.1_8_8_5, 0.2_2_7_3, 0.2_1_4_4, 0.2_1_7_6]

             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)
             __SCREAMING_SNAKE_CASE   :List[Any]						=  torch.manual_seed(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :List[Any]						=  sd_pipe(
                 [prompt]		,generator=SCREAMING_SNAKE_CASE__		,guidance_scale=SCREAMING_SNAKE_CASE__		,num_inference_steps=50		,output_type='''np'''		,width=5_12		,height=5_12		,sld_guidance_scale=20_00		,sld_warmup_steps=7		,sld_threshold=0.0_2_5		,sld_momentum_scale=0.5		,sld_mom_beta=0.7		,)

             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  output.images
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  image[0, -3:, -3:, -1]
             __SCREAMING_SNAKE_CASE   :List[str]						=  [0.2_3_8_3, 0.2_2_7_6, 0.2_3_6, 0.2_1_9_2, 0.2_1_8_6, 0.2_0_5_3, 0.1_9_7_1, 0.1_9_0_1, 0.1_7_1_9]

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

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




       def     _UpperCamelCase     (						self							)			->      str:





             """simple docstring"""






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

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

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

             __SCREAMING_SNAKE_CASE   :Dict						=  output.images
             __SCREAMING_SNAKE_CASE   :str						=  image[0, -3:, -3:, -1]
             __SCREAMING_SNAKE_CASE   :Dict						=  [0.3_5_0_2, 0.3_6_2_2, 0.3_3_9_6, 0.3_6_4_2, 0.3_4_7_8, 0.3_3_1_8, 0.3_5, 0.3_3_4_8, 0.3_2_9_7]

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

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

             __SCREAMING_SNAKE_CASE   :Dict						=  torch.manual_seed(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Dict						=  sd_pipe(
                 [prompt]		,generator=SCREAMING_SNAKE_CASE__		,guidance_scale=SCREAMING_SNAKE_CASE__		,num_inference_steps=50		,output_type='''np'''		,width=5_12		,height=5_12		,sld_guidance_scale=20_00		,sld_warmup_steps=7		,sld_threshold=0.0_2_5		,sld_momentum_scale=0.5		,sld_mom_beta=0.7		,)

             __SCREAMING_SNAKE_CASE   :Any						=  output.images
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  image[0, -3:, -3:, -1]
             __SCREAMING_SNAKE_CASE   :Tuple						=  [0.5_5_3_1, 0.5_2_0_6, 0.4_8_9_5, 0.5_1_5_6, 0.5_1_8_2, 0.4_7_5_1, 0.4_8_0_2, 0.4_8_0_3, 0.4_4_4_3]

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

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




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





             """simple docstring"""






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

             __SCREAMING_SNAKE_CASE   :Optional[int]						=  (
                 '''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
                 ''' leyendecker'''
             )
             __SCREAMING_SNAKE_CASE   :Dict						=  10_44_35_52_34
             __SCREAMING_SNAKE_CASE   :List[Any]						=  12

             __SCREAMING_SNAKE_CASE   :Optional[int]						=  torch.manual_seed(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Dict						=  sd_pipe(
                 [prompt]		,generator=SCREAMING_SNAKE_CASE__		,guidance_scale=SCREAMING_SNAKE_CASE__		,num_inference_steps=50		,output_type='''np'''		,width=5_12		,height=5_12		,sld_guidance_scale=0		,)

             __SCREAMING_SNAKE_CASE   :Optional[int]						=  output.images
             __SCREAMING_SNAKE_CASE   :str						=  image[0, -3:, -3:, -1]
             __SCREAMING_SNAKE_CASE   :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

             __SCREAMING_SNAKE_CASE   :Tuple						=  torch.manual_seed(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :int						=  sd_pipe(
                 [prompt]		,generator=SCREAMING_SNAKE_CASE__		,guidance_scale=SCREAMING_SNAKE_CASE__		,num_inference_steps=50		,output_type='''np'''		,width=5_12		,height=5_12		,sld_guidance_scale=20_00		,sld_warmup_steps=7		,sld_threshold=0.0_2_5		,sld_momentum_scale=0.5		,sld_mom_beta=0.7		,)

             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  output.images
             __SCREAMING_SNAKE_CASE   :Dict						=  image[0, -3:, -3:, -1]
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  np.array([0.5_8_1_8, 0.6_2_8_5, 0.6_8_3_5, 0.6_0_1_9, 0.6_2_5, 0.6_7_5_4, 0.6_0_9_6, 0.6_3_3_4, 0.6_5_6_1]							)
             assert image.shape == (1, 5_12, 5_12, 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__							)