

"""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.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()





class      __UpperCamelCase					(  unittest.TestCase		):


		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							A     =   1
							A     =   3
							A     =   (3_2, 3_2)

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


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							torch.manual_seed(0)
							A     =   UNetaDConditionModel(
							    block_out_channels=(3_2, 6_4)	,	layers_per_block=2	,	sample_size=3_2	,	in_channels=4	,	out_channels=4	,	down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,	up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,	cross_attention_dim=3_2	,	)
							return model


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[int]):
							torch.manual_seed(0)
							A     =   AutoencoderKL(
							    block_out_channels=[3_2, 6_4]	,	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					:						Dict):
							torch.manual_seed(0)
							A     =   CLIPTextConfig(
							    bos_token_id=0	,	eos_token_id=2	,	hidden_size=3_2	,	intermediate_size=3_7	,	layer_norm_eps=1E-05	,	num_attention_heads=4	,	num_hidden_layers=5	,	pad_token_id=1	,	vocab_size=1_0_0_0	,	)
							return CLIPTextModel(__SCREAMING_SNAKE_CASE)


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							def extract(*__SCREAMING_SNAKE_CASE					:						List[Any]	,	**__SCREAMING_SNAKE_CASE					:						Optional[int]):





												class      __UpperCamelCase					:


														def __init__(self					:						Any):
																			A     =   torch.ones([0])


														def    SCREAMING_SNAKE_CASE__  (self					:						Dict	,	__SCREAMING_SNAKE_CASE					:						Tuple):
																			self.pixel_values.to(__SCREAMING_SNAKE_CASE)
																			return self

												return Out()

							return extract


		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							A     =   "cpu"  # ensure determinism for the device-dependent torch.Generator
							A     =   self.dummy_cond_unet
							A     =   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	,	)

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

							# make sure here that pndm scheduler skips prk
							A     =   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     =   sd_pipe.to(__SCREAMING_SNAKE_CASE)
							sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

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

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

							A     =   torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(0)
							A     =   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     =   image[0, -3:, -3:, -1]
							A     =   image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 6_4, 6_4, 3)
							A     =   np.array([0.5_7_5_3, 0.6_1_1_4, 0.5_0_0_1, 0.5_0_3_4, 0.5_4_7_0, 0.4_7_2_9, 0.4_9_7_1, 0.4_8_6_7, 0.4_8_6_7])

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

							# make sure here that pndm scheduler skips prk
							A     =   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     =   sd_pipe.to(__SCREAMING_SNAKE_CASE)
							sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

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

							A     =   output.images

							A     =   torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(0)
							A     =   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     =   image[0, -3:, -3:, -1]
							A     =   image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 6_4, 6_4, 3)
							A     =   np.array([0.5_1_2_2, 0.5_7_1_2, 0.4_8_2_5, 0.5_0_5_3, 0.5_6_4_6, 0.4_7_6_9, 0.5_1_7_9, 0.4_8_9_4, 0.4_9_9_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    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							A     =   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     =   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     =   StableDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE)

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

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

							# make sure here that pndm scheduler skips prk
							A     =   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     =   sd_pipe.to(__SCREAMING_SNAKE_CASE)
							sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

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

							assert image.shape == (1, 6_4, 6_4, 3)





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


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


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							A     =   "cuda"
							A     =   StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
							A     =   pipe.to(__SCREAMING_SNAKE_CASE)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

							A     =   "a photo of a cat"
							A     =   {
							    "editing_prompt": ["sunglasses"],
							    "reverse_editing_direction": [False],
							    "edit_warmup_steps": 1_0,
							    "edit_guidance_scale": 6,
							    "edit_threshold": 0.9_5,
							    "edit_momentum_scale": 0.5,
							    "edit_mom_beta": 0.6,
							}

							A     =   3
							A     =   7

							# no sega enabled
							A     =   torch.Generator(__SCREAMING_SNAKE_CASE)
							generator.manual_seed(__SCREAMING_SNAKE_CASE)
							A     =   pipe(
							    [prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=__SCREAMING_SNAKE_CASE	,	num_inference_steps=5_0	,	output_type="np"	,	width=5_1_2	,	height=5_1_2	,	)

							A     =   output.images
							A     =   image[0, -3:, -3:, -1]
							A     =   [
							    0.3_4_6_7_3_1_1_3,
							    0.3_8_4_9_2_7_3_3,
							    0.3_7_5_9_7_3_5_2,
							    0.3_4_0_8_6_3_3_5,
							    0.3_5_6_5_0_7_4_8,
							    0.3_5_5_7_9_2_0_5,
							    0.3_3_8_4_7_6_3,
							    0.3_4_3_4_0_2_3_6,
							    0.3_5_7_3_2_7_1,
							]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

							# with sega enabled
							# generator = torch.manual_seed(seed)
							generator.manual_seed(__SCREAMING_SNAKE_CASE)
							A     =   pipe(
							    [prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=__SCREAMING_SNAKE_CASE	,	num_inference_steps=5_0	,	output_type="np"	,	width=5_1_2	,	height=5_1_2	,	**__SCREAMING_SNAKE_CASE	,	)

							A     =   output.images
							A     =   image[0, -3:, -3:, -1]
							A     =   [
							    0.4_1_8_8_7_8_2_6,
							    0.3_7_7_2_8_7_6_6,
							    0.3_0_1_3_8_2_7_2,
							    0.4_1_4_1_6_3_3_5,
							    0.4_1_6_6_4_9_8_5,
							    0.3_6_2_8_3_3_9_2,
							    0.3_6_1_9_1_2_4_6,
							    0.4_3_3_6_4_4_6_5,
							    0.4_3_0_0_1_7_3_2,
							]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)

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


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							A     =   "cuda"
							A     =   StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
							A     =   pipe.to(__SCREAMING_SNAKE_CASE)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

							A     =   "an image of a crowded boulevard, realistic, 4k"
							A     =   {
							    "editing_prompt": "crowd, crowded, people",
							    "reverse_editing_direction": True,
							    "edit_warmup_steps": 1_0,
							    "edit_guidance_scale": 8.3,
							    "edit_threshold": 0.9,
							    "edit_momentum_scale": 0.5,
							    "edit_mom_beta": 0.6,
							}

							A     =   9
							A     =   7

							# no sega enabled
							A     =   torch.Generator(__SCREAMING_SNAKE_CASE)
							generator.manual_seed(__SCREAMING_SNAKE_CASE)
							A     =   pipe(
							    [prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=__SCREAMING_SNAKE_CASE	,	num_inference_steps=5_0	,	output_type="np"	,	width=5_1_2	,	height=5_1_2	,	)

							A     =   output.images
							A     =   image[0, -3:, -3:, -1]
							A     =   [
							    0.4_3_4_9_7_9_9_8,
							    0.9_1_8_1_4_0_6_5,
							    0.7_5_4_0_7_3_9,
							    0.5_5_5_8_0_2_0_5,
							    0.8_4_6_7_2_6_5,
							    0.5_3_8_9_6_9_1,
							    0.6_2_5_7_4_5_0_6,
							    0.5_8_8_9_7_7_6_3,
							    0.5_0_9_2_6_7_5_7,
							]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

							# with sega enabled
							# generator = torch.manual_seed(seed)
							generator.manual_seed(__SCREAMING_SNAKE_CASE)
							A     =   pipe(
							    [prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=__SCREAMING_SNAKE_CASE	,	num_inference_steps=5_0	,	output_type="np"	,	width=5_1_2	,	height=5_1_2	,	**__SCREAMING_SNAKE_CASE	,	)

							A     =   output.images
							A     =   image[0, -3:, -3:, -1]
							A     =   [
							    0.3_0_8_9_7_1_9,
							    0.3_0_5_0_0_1_4_4,
							    0.2_9_0_1_6_0_4_2,
							    0.3_0_6_3_0_9_6_4,
							    0.3_2_5_6_8_7,
							    0.2_9_4_1_9_2_2_5,
							    0.2_9_0_8_0_9_1,
							    0.2_8_7_2_3_5_9_8,
							    0.2_7_6_9_6_2_9_4,
							]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)

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


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							A     =   "cuda"
							A     =   StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
							A     =   pipe.to(__SCREAMING_SNAKE_CASE)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

							A     =   "a castle next to a river"
							A     =   {
							    "editing_prompt": ["boat on a river, boat", "monet, impression, sunrise"],
							    "reverse_editing_direction": False,
							    "edit_warmup_steps": [1_5, 1_8],
							    "edit_guidance_scale": 6,
							    "edit_threshold": [0.9, 0.8],
							    "edit_momentum_scale": 0.5,
							    "edit_mom_beta": 0.6,
							}

							A     =   4_8
							A     =   7

							# no sega enabled
							A     =   torch.Generator(__SCREAMING_SNAKE_CASE)
							generator.manual_seed(__SCREAMING_SNAKE_CASE)
							A     =   pipe(
							    [prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=__SCREAMING_SNAKE_CASE	,	num_inference_steps=5_0	,	output_type="np"	,	width=5_1_2	,	height=5_1_2	,	)

							A     =   output.images
							A     =   image[0, -3:, -3:, -1]
							A     =   [
							    0.7_5_1_6_3_5_5_5,
							    0.7_6_0_3_7_1_4_5,
							    0.6_1_7_8_5,
							    0.9_1_8_9_6_7_3,
							    0.8_6_2_7_7_0_1,
							    0.8_5_1_8_9_6_9_4,
							    0.8_5_1_2_8_1_3,
							    0.8_7_0_1_2_0_7_6,
							    0.8_3_1_2_8_5_7,
							]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

							# with sega enabled
							# generator = torch.manual_seed(seed)
							generator.manual_seed(__SCREAMING_SNAKE_CASE)
							A     =   pipe(
							    [prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=__SCREAMING_SNAKE_CASE	,	num_inference_steps=5_0	,	output_type="np"	,	width=5_1_2	,	height=5_1_2	,	**__SCREAMING_SNAKE_CASE	,	)

							A     =   output.images
							A     =   image[0, -3:, -3:, -1]
							A     =   [
							    0.7_3_5_5_3_3_6_5,
							    0.7_5_3_7_2_7_1,
							    0.7_4_3_4_1_9_0_5,
							    0.6_6_4_8_0_3_5_6,
							    0.6_4_7_2_9_2_5,
							    0.6_3_0_3_9_4_1_6,
							    0.6_4_8_1_2_9_0_5,
							    0.6_7_4_9_7_1_7,
							    0.6_5_1_7_1_0_2,
							]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)

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


		def    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							A     =   "cuda"
							A     =   StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"	,	torch_dtype=torch.floataa)
							A     =   pipe.to(__SCREAMING_SNAKE_CASE)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

							A     =   "a photo of a cat"
							A     =   {
							    "editing_prompt": ["sunglasses"],
							    "reverse_editing_direction": [False],
							    "edit_warmup_steps": 1_0,
							    "edit_guidance_scale": 6,
							    "edit_threshold": 0.9_5,
							    "edit_momentum_scale": 0.5,
							    "edit_mom_beta": 0.6,
							}

							A     =   3
							A     =   7

							# no sega enabled
							A     =   torch.Generator(__SCREAMING_SNAKE_CASE)
							generator.manual_seed(__SCREAMING_SNAKE_CASE)
							A     =   pipe(
							    [prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=__SCREAMING_SNAKE_CASE	,	num_inference_steps=5_0	,	output_type="np"	,	width=5_1_2	,	height=5_1_2	,	)

							A     =   output.images
							A     =   image[0, -3:, -3:, -1]
							A     =   [
							    0.3_4_8_8_7_6_9_5,
							    0.3_8_7_6_9_5_3,
							    0.3_7_5,
							    0.3_4_4_2_3_8_2_8,
							    0.3_5_8_1_5_4_3,
							    0.3_5_7_1_7_7_7_3,
							    0.3_3_8_3_7_8_9,
							    0.3_4_5_7_0_3_1_2,
							    0.3_5_9_3_7_5,
							]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

							# with sega enabled
							# generator = torch.manual_seed(seed)
							generator.manual_seed(__SCREAMING_SNAKE_CASE)
							A     =   pipe(
							    [prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=__SCREAMING_SNAKE_CASE	,	num_inference_steps=5_0	,	output_type="np"	,	width=5_1_2	,	height=5_1_2	,	**__SCREAMING_SNAKE_CASE	,	)

							A     =   output.images
							A     =   image[0, -3:, -3:, -1]
							A     =   [
							    0.4_2_2_8_5_1_5_6,
							    0.3_6_9_1_4_0_6_2,
							    0.2_9_0_7_7_1_4_8,
							    0.4_2_0_4_1_0_1_6,
							    0.4_1_9_1_8_9_4_5,
							    0.3_5_4_9_8_0_4_7,
							    0.3_6_1_8_1_6_4,
							    0.4_4_2_3_8_2_8,
							    0.4_3_1_1_5_2_3_4,
							]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)

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










import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation
def   UpperCAmelCase							(		A__				,  A__				,  A__ = False				,  A__ = 1				,  A__ = 1				,  A__ = 1_00_00				,  )							->					Optional[int]:
	assert len(timesteps.shape     ) == 1, "Timesteps should be a 1d-array"

	_snake_case					:							Tuple					     =   embedding_dim // 2
	_snake_case					:							Optional[Any]					     =   -math.log(A__     ) * torch.arange(
	    start=0				,  end=A__				,  dtype=torch.floataa				,  device=timesteps.device     )
	_snake_case					:							Dict					     =   exponent / (half_dim - downscale_freq_shift)

	_snake_case					:							Optional[int]					     =   torch.exp(A__     )
	_snake_case					:							List[str]					     =   timesteps[:, None].float() * emb[None, :]

	# scale embeddings
	_snake_case					:							Optional[int]					     =   scale * emb

	# concat sine and cosine embeddings
	_snake_case					:							Union[str, Any]					     =   torch.cat([torch.sin(A__     ), torch.cos(A__     )]				,  dim=-1     )

	# flip sine and cosine embeddings
	if flip_sin_to_cos:
		_snake_case					:							Optional[Any]					     =   torch.cat([emb[:, half_dim:], emb[:, :half_dim]]				,  dim=-1     )

	# zero pad
	if embedding_dim % 2 == 1:
		_snake_case					:							Optional[Any]					     =   torch.nn.functional.pad(A__				,  (0, 1, 0, 0)     )
	return emb
def   UpperCAmelCase							(		A__				,  A__				,  A__=False				,  A__=0     )							->					Optional[Any]:
	_snake_case					:							str					     =   np.arange(A__				,  dtype=np.floataa     )
	_snake_case					:							int					     =   np.arange(A__				,  dtype=np.floataa     )
	_snake_case					:							Union[str, Any]					     =   np.meshgrid(A__				,  A__     )  # here w goes first
	_snake_case					:							Optional[int]					     =   np.stack(A__				,  axis=0     )

	_snake_case					:							List[Any]					     =   grid.reshape([2, 1, grid_size, grid_size]     )
	_snake_case					:							Optional[int]					     =   get_ad_sincos_pos_embed_from_grid(A__				,  A__     )
	if cls_token and extra_tokens > 0:
		_snake_case					:							Any					     =   np.concatenate([np.zeros([extra_tokens, embed_dim]     ), pos_embed]				,  axis=0     )
	return pos_embed
def   UpperCAmelCase							(		A__				,  A__     )							->					List[str]:
	if embed_dim % 2 != 0:
		raise ValueError("""embed_dim must be divisible by 2"""     )

	# use half of dimensions to encode grid_h
	_snake_case					:							int					     =   get_ad_sincos_pos_embed_from_grid(embed_dim // 2				,  grid[0]     )  # (H*W, D/2)
	_snake_case					:							List[str]					     =   get_ad_sincos_pos_embed_from_grid(embed_dim // 2				,  grid[1]     )  # (H*W, D/2)

	_snake_case					:							List[Any]					     =   np.concatenate([emb_h, emb_w]				,  axis=1     )  # (H*W, D)
	return emb
def   UpperCAmelCase							(		A__				,  A__     )							->					str:
	if embed_dim % 2 != 0:
		raise ValueError("""embed_dim must be divisible by 2"""     )

	_snake_case					:							Union[str, Any]					     =   np.arange(embed_dim // 2				,  dtype=np.floataa     )
	omega /= embed_dim / 2.0
	_snake_case					:							Optional[Any]					     =   1.0 / 1_00_00**omega  # (D/2,)

	_snake_case					:							Union[str, Any]					     =   pos.reshape(-1     )  # (M,)
	_snake_case					:							Dict					     =   np.einsum("""m,d->md"""				,  A__				,  A__     )  # (M, D/2), outer product

	_snake_case					:							Union[str, Any]					     =   np.sin(A__     )  # (M, D/2)
	_snake_case					:							Tuple					     =   np.cos(A__     )  # (M, D/2)

	_snake_case					:							Union[str, Any]					     =   np.concatenate([emb_sin, emb_cos]				,  axis=1     )  # (M, D)
	return emb





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__=2_24						,	SCREAMING_SNAKE_CASE__=2_24						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=3						,	SCREAMING_SNAKE_CASE__=7_68						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	):






					"""simple docstring"""

					super().__init__()

					_snake_case					:							Tuple					     =   (height // patch_size) * (width // patch_size)
					_snake_case					:							Union[str, Any]					     =   flatten
					_snake_case					:							str					     =   layer_norm

					_snake_case					:							List[Any]					     =   nn.Convad(
					    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	kernel_size=(patch_size, patch_size)						,	stride=SCREAMING_SNAKE_CASE__						,	bias=SCREAMING_SNAKE_CASE__      )
					if layer_norm:
						_snake_case					:							Dict					     =   nn.LayerNorm(SCREAMING_SNAKE_CASE__						,	elementwise_affine=SCREAMING_SNAKE_CASE__						,	eps=1e-6      )
					else:
						_snake_case					:							Union[str, Any]					     =   None

					_snake_case					:							Tuple					     =   get_ad_sincos_pos_embed(SCREAMING_SNAKE_CASE__						,	int(num_patches**0.5      )      )
					self.register_buffer("""pos_embed"""						,	torch.from_numpy(SCREAMING_SNAKE_CASE__      ).float().unsqueeze(0      )						,	persistent=SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							List[str]					     =   self.proj(SCREAMING_SNAKE_CASE__      )
					if self.flatten:
						_snake_case					:							Any					     =   latent.flatten(2      ).transpose(1						,	2      )  # BCHW -> BNC
					if self.layer_norm:
						_snake_case					:							Any					     =   self.norm(SCREAMING_SNAKE_CASE__      )
					return latent + self.pos_embed





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = "silu"						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__=None						,	):






					"""simple docstring"""

					super().__init__()

					_snake_case					:							List[Any]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )

					if cond_proj_dim is not None:
						_snake_case					:							Dict					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	bias=SCREAMING_SNAKE_CASE__      )
					else:
						_snake_case					:							Tuple					     =   None

					_snake_case					:							int					     =   get_activation(SCREAMING_SNAKE_CASE__      )

					if out_dim is not None:
						_snake_case					:							Any					     =   out_dim
					else:
						_snake_case					:							int					     =   time_embed_dim
					_snake_case					:							str					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )

					if post_act_fn is None:
						_snake_case					:							Tuple					     =   None
					else:
						_snake_case					:							Optional[Any]					     =   get_activation(SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=None      ):






					"""simple docstring"""

					if condition is not None:
						_snake_case					:							int					     =   sample + self.cond_proj(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Dict					     =   self.linear_a(SCREAMING_SNAKE_CASE__      )

					if self.act is not None:
						_snake_case					:							Optional[int]					     =   self.act(SCREAMING_SNAKE_CASE__      )

					_snake_case					:							Any					     =   self.linear_a(SCREAMING_SNAKE_CASE__      )

					if self.post_act is not None:
						_snake_case					:							str					     =   self.post_act(SCREAMING_SNAKE_CASE__      )
					return sample





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					super().__init__()
					_snake_case					:							List[str]					     =   num_channels
					_snake_case					:							List[Any]					     =   flip_sin_to_cos
					_snake_case					:							List[str]					     =   downscale_freq_shift







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Any					     =   get_timestep_embedding(
					    SCREAMING_SNAKE_CASE__						,	self.num_channels						,	flip_sin_to_cos=self.flip_sin_to_cos						,	downscale_freq_shift=self.downscale_freq_shift						,	)
					return t_emb





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__ = 2_56						,	SCREAMING_SNAKE_CASE__ = 1.0						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=False      ):






					"""simple docstring"""

					super().__init__()
					_snake_case					:							Any					     =   nn.Parameter(torch.randn(SCREAMING_SNAKE_CASE__      ) * scale						,	requires_grad=SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Union[str, Any]					     =   log
					_snake_case					:							Any					     =   flip_sin_to_cos

					if set_W_to_weight:
						# to delete later
						_snake_case					:							int					     =   nn.Parameter(torch.randn(SCREAMING_SNAKE_CASE__      ) * scale						,	requires_grad=SCREAMING_SNAKE_CASE__      )

						_snake_case					:							List[Any]					     =   self.W







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					if self.log:
						_snake_case					:							Any					     =   torch.log(SCREAMING_SNAKE_CASE__      )

					_snake_case					:							Any					     =   x[:, None] * self.weight[None, :] * 2 * np.pi

					if self.flip_sin_to_cos:
						_snake_case					:							Dict					     =   torch.cat([torch.cos(SCREAMING_SNAKE_CASE__      ), torch.sin(SCREAMING_SNAKE_CASE__      )]						,	dim=-1      )
					else:
						_snake_case					:							int					     =   torch.cat([torch.sin(SCREAMING_SNAKE_CASE__      ), torch.cos(SCREAMING_SNAKE_CASE__      )]						,	dim=-1      )
					return out





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	):






					"""simple docstring"""

					super().__init__()

					_snake_case					:							List[Any]					     =   height
					_snake_case					:							Optional[int]					     =   width
					_snake_case					:							Any					     =   num_embed
					_snake_case					:							int					     =   embed_dim

					_snake_case					:							Optional[Any]					     =   nn.Embedding(self.num_embed						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Any					     =   nn.Embedding(self.height						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							List[Any]					     =   nn.Embedding(self.width						,	SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Any					     =   self.emb(SCREAMING_SNAKE_CASE__      )

					_snake_case					:							List[str]					     =   self.height_emb(torch.arange(self.height						,	device=index.device      ).view(1						,	self.height      )      )

					# 1 x H x D -> 1 x H x 1 x D
					_snake_case					:							List[str]					     =   height_emb.unsqueeze(2      )

					_snake_case					:							List[Any]					     =   self.width_emb(torch.arange(self.width						,	device=index.device      ).view(1						,	self.width      )      )

					# 1 x W x D -> 1 x 1 x W x D
					_snake_case					:							List[str]					     =   width_emb.unsqueeze(1      )

					_snake_case					:							int					     =   height_emb + width_emb

					# 1 x H x W x D -> 1 x L xD
					_snake_case					:							int					     =   pos_emb.view(1						,	self.height * self.width						,	-1      )

					_snake_case					:							str					     =   emb + pos_emb[:, : emb.shape[1], :]

					return emb





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					super().__init__()
					_snake_case					:							Any					     =   dropout_prob > 0
					_snake_case					:							Optional[int]					     =   nn.Embedding(num_classes + use_cfg_embedding						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Union[str, Any]					     =   num_classes
					_snake_case					:							Tuple					     =   dropout_prob







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=None      ):






					"""simple docstring"""

					if force_drop_ids is None:
						_snake_case					:							Dict					     =   torch.rand(labels.shape[0]						,	device=labels.device      ) < self.dropout_prob
					else:
						_snake_case					:							List[str]					     =   torch.tensor(force_drop_ids == 1      )
					_snake_case					:							List[Any]					     =   torch.where(SCREAMING_SNAKE_CASE__						,	self.num_classes						,	SCREAMING_SNAKE_CASE__      )
					return labels







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=None      ):






					"""simple docstring"""

					_snake_case					:							Union[str, Any]					     =   self.dropout_prob > 0
					if (self.training and use_dropout) or (force_drop_ids is not None):
						_snake_case					:							Dict					     =   self.token_drop(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[Any]					     =   self.embedding_table(SCREAMING_SNAKE_CASE__      )
					return embeddings





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__ = 10_24						,	SCREAMING_SNAKE_CASE__ = 7_68						,	SCREAMING_SNAKE_CASE__ = 7_68						,	SCREAMING_SNAKE_CASE__ = 10						,	):






					"""simple docstring"""

					super().__init__()

					_snake_case					:							str					     =   num_image_text_embeds
					_snake_case					:							Optional[Any]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	self.num_image_text_embeds * cross_attention_dim      )
					_snake_case					:							Optional[Any]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Optional[int]					     =   text_embeds.shape[0]

					# image
					_snake_case					:							Optional[Any]					     =   self.image_embeds(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							List[str]					     =   image_text_embeds.reshape(SCREAMING_SNAKE_CASE__						,	self.num_image_text_embeds						,	-1      )

					# text
					_snake_case					:							Dict					     =   self.text_proj(SCREAMING_SNAKE_CASE__      )

					return torch.cat([image_text_embeds, text_embeds]						,	dim=1      )





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__ = 7_68						,	SCREAMING_SNAKE_CASE__ = 7_68						,	SCREAMING_SNAKE_CASE__ = 32						,	):






					"""simple docstring"""

					super().__init__()

					_snake_case					:							Dict					     =   num_image_text_embeds
					_snake_case					:							int					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	self.num_image_text_embeds * cross_attention_dim      )
					_snake_case					:							Optional[int]					     =   nn.LayerNorm(SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Tuple					     =   image_embeds.shape[0]

					# image
					_snake_case					:							Tuple					     =   self.image_embeds(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Tuple					     =   image_embeds.reshape(SCREAMING_SNAKE_CASE__						,	self.num_image_text_embeds						,	-1      )
					_snake_case					:							Union[str, Any]					     =   self.norm(SCREAMING_SNAKE_CASE__      )
					return image_embeds





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=0.1      ):






					"""simple docstring"""

					super().__init__()

					_snake_case					:							Optional[Any]					     =   Timesteps(num_channels=2_56						,	flip_sin_to_cos=SCREAMING_SNAKE_CASE__						,	downscale_freq_shift=1      )
					_snake_case					:							Optional[Any]					     =   TimestepEmbedding(in_channels=2_56						,	time_embed_dim=SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Dict					     =   LabelEmbedding(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=None      ):






					"""simple docstring"""

					_snake_case					:							Optional[int]					     =   self.time_proj(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[int]					     =   self.timestep_embedder(timesteps_proj.to(dtype=SCREAMING_SNAKE_CASE__      )      )  # (N, D)

					_snake_case					:							Tuple					     =   self.class_embedder(SCREAMING_SNAKE_CASE__      )  # (N, D)

					_snake_case					:							Tuple					     =   timesteps_emb + class_labels  # (N, D)

					return conditioning





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = 64      ):






					"""simple docstring"""

					super().__init__()
					_snake_case					:							List[Any]					     =   nn.LayerNorm(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Any					     =   AttentionPooling(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							List[Any]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[Any]					     =   nn.LayerNorm(SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							str					     =   self.norma(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Any					     =   self.pool(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[Any]					     =   self.proj(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Tuple					     =   self.norma(SCREAMING_SNAKE_CASE__      )
					return hidden_states





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__ = 7_68						,	SCREAMING_SNAKE_CASE__ = 7_68						,	SCREAMING_SNAKE_CASE__ = 15_36      ):






					"""simple docstring"""

					super().__init__()
					_snake_case					:							Optional[Any]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							str					     =   nn.LayerNorm(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[int]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							int					     =   self.text_proj(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Union[str, Any]					     =   self.text_norm(SCREAMING_SNAKE_CASE__      )

					# image
					_snake_case					:							Any					     =   self.image_proj(SCREAMING_SNAKE_CASE__      )

					return time_image_embeds + time_text_embeds





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__ = 7_68						,	SCREAMING_SNAKE_CASE__ = 15_36      ):






					"""simple docstring"""

					super().__init__()
					_snake_case					:							Union[str, Any]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Tuple					     =   nn.LayerNorm(SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Optional[int]					     =   self.image_proj(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Any					     =   self.image_norm(SCREAMING_SNAKE_CASE__      )
					return time_image_embeds





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__ = 7_68						,	SCREAMING_SNAKE_CASE__ = 15_36      ):






					"""simple docstring"""

					super().__init__()
					_snake_case					:							Optional[Any]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[int]					     =   nn.LayerNorm(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Any					     =   nn.Sequential(
					    nn.Convad(3						,	16						,	3						,	padding=1      )						,	nn.SiLU()						,	nn.Convad(16						,	16						,	3						,	padding=1      )						,	nn.SiLU()						,	nn.Convad(16						,	32						,	3						,	padding=1						,	stride=2      )						,	nn.SiLU()						,	nn.Convad(32						,	32						,	3						,	padding=1      )						,	nn.SiLU()						,	nn.Convad(32						,	96						,	3						,	padding=1						,	stride=2      )						,	nn.SiLU()						,	nn.Convad(96						,	96						,	3						,	padding=1      )						,	nn.SiLU()						,	nn.Convad(96						,	2_56						,	3						,	padding=1						,	stride=2      )						,	nn.SiLU()						,	nn.Convad(2_56						,	4						,	3						,	padding=1      )						,	)







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Dict					     =   self.image_proj(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							int					     =   self.image_norm(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[Any]					     =   self.input_hint_block(SCREAMING_SNAKE_CASE__      )
					return time_image_embeds, hint





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=None      ):






					"""simple docstring"""

					super().__init__()
					_snake_case					:							Union[str, Any]					     =   dtype
					_snake_case					:							Optional[int]					     =   nn.Parameter(torch.randn(1						,	SCREAMING_SNAKE_CASE__      ) / embed_dim**0.5      )
					_snake_case					:							Optional[int]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	dtype=self.dtype      )
					_snake_case					:							Dict					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	dtype=self.dtype      )
					_snake_case					:							Optional[int]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	dtype=self.dtype      )
					_snake_case					:							Dict					     =   num_heads
					_snake_case					:							Tuple					     =   embed_dim // self.num_heads







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case     , _snake_case     , _snake_case					:							Dict					     =   x.size()

					def shape(SCREAMING_SNAKE_CASE__      ):
						# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
						_snake_case					:							Tuple					     =   x.view(SCREAMING_SNAKE_CASE__						,	-1						,	self.num_heads						,	self.dim_per_head      )
						# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
						_snake_case					:							Optional[int]					     =   x.transpose(1						,	2      )
						# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
						_snake_case					:							Optional[int]					     =   x.reshape(bs * self.num_heads						,	-1						,	self.dim_per_head      )
						# (bs*n_heads, length, dim_per_head) --> (bs*n_heads, dim_per_head, length)
						_snake_case					:							Optional[int]					     =   x.transpose(1						,	2      )
						return x

					_snake_case					:							int					     =   x.mean(dim=1						,	keepdim=SCREAMING_SNAKE_CASE__      ) + self.positional_embedding.to(x.dtype      )
					_snake_case					:							Any					     =   torch.cat([class_token, x]						,	dim=1      )  # (bs, length+1, width)

					# (bs*n_heads, class_token_length, dim_per_head)
					_snake_case					:							Optional[int]					     =   shape(self.q_proj(SCREAMING_SNAKE_CASE__      )      )
					# (bs*n_heads, length+class_token_length, dim_per_head)
					_snake_case					:							str					     =   shape(self.k_proj(SCREAMING_SNAKE_CASE__      )      )
					_snake_case					:							List[str]					     =   shape(self.v_proj(SCREAMING_SNAKE_CASE__      )      )

					# (bs*n_heads, class_token_length, length+class_token_length):
					_snake_case					:							Dict					     =   1 / math.sqrt(math.sqrt(self.dim_per_head      )      )
					_snake_case					:							Any					     =   torch.einsum("""bct,bcs->bts"""						,	q * scale						,	k * scale      )  # More stable with f16 than dividing afterwards
					_snake_case					:							Tuple					     =   torch.softmax(weight.float()						,	dim=-1      ).type(weight.dtype      )

					# (bs*n_heads, dim_per_head, class_token_length)
					_snake_case					:							Optional[int]					     =   torch.einsum("""bts,bcs->bct"""						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )

					# (bs, length+1, width)
					_snake_case					:							Tuple					     =   a.reshape(SCREAMING_SNAKE_CASE__						,	-1						,	1      ).transpose(1						,	2      )

					return a[:, 0, :]  # cls_token





