

import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
					import onnxruntime as ort

class 				a  (	__lowerCAmelCase							, unittest.TestCase				):
						_lowercase    						=			'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''


						def        _UpperCAmelCase				(       self						,					A_=0	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    np.random.RandomState(lowerCAmelCase_	)
										_UpperCAmelCase       :		Optional[int] 		=    {
										    "prompt": "A painting of a squirrel eating a burger",
										    "generator": generator,
										    "num_inference_steps": 2,
										    "guidance_scale": 7.5,
										    "output_type": "numpy",
										}
										return inputs


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint						,					provider="CPUExecutionProvider"	)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

										_UpperCAmelCase       :		int 		=    self.get_dummy_inputs()
										_UpperCAmelCase       :		Union[str, Any] 		=    pipe(**lowerCAmelCase_	).images
										_UpperCAmelCase       :		str 		=    image[0, -3:, -3:, -1]

										assert image.shape == (1, 128, 128, 3)
										_UpperCAmelCase       :		List[Any] 		=    np.array([0.6_50_72, 0.5_84_92, 0.4_82_19, 0.5_55_21, 0.5_31_80, 0.5_59_39, 0.5_06_97, 0.3_98_00, 0.4_64_55]	)

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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint						,					provider="CPUExecutionProvider"	)
										_UpperCAmelCase       :		Optional[int] 		=    PNDMScheduler.from_config(pipe.scheduler.config						,					skip_prk_steps=lowerCAmelCase_	)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

										_UpperCAmelCase       :		List[str] 		=    self.get_dummy_inputs()
										_UpperCAmelCase       :		List[Any] 		=    pipe(**lowerCAmelCase_	).images
										_UpperCAmelCase       :		Tuple 		=    image[0, -3:, -3:, -1]

										assert image.shape == (1, 128, 128, 3)
										_UpperCAmelCase       :		List[Any] 		=    np.array([0.6_58_63, 0.5_94_25, 0.4_93_26, 0.5_63_13, 0.5_38_75, 0.5_66_27, 0.5_10_65, 0.3_97_77, 0.4_63_30]	)

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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint						,					provider="CPUExecutionProvider"	)
										_UpperCAmelCase       :		Optional[Any] 		=    LMSDiscreteScheduler.from_config(pipe.scheduler.config	)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

										_UpperCAmelCase       :		List[str] 		=    self.get_dummy_inputs()
										_UpperCAmelCase       :		int 		=    pipe(**lowerCAmelCase_	).images
										_UpperCAmelCase       :		Any 		=    image[0, -3:, -3:, -1]

										assert image.shape == (1, 128, 128, 3)
										_UpperCAmelCase       :		List[str] 		=    np.array([0.5_37_55, 0.6_07_86, 0.4_74_02, 0.4_94_88, 0.5_18_69, 0.4_98_19, 0.4_79_85, 0.3_89_57, 0.4_42_79]	)

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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint						,					provider="CPUExecutionProvider"	)
										_UpperCAmelCase       :		Optional[int] 		=    EulerDiscreteScheduler.from_config(pipe.scheduler.config	)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

										_UpperCAmelCase       :		Tuple 		=    self.get_dummy_inputs()
										_UpperCAmelCase       :		Any 		=    pipe(**lowerCAmelCase_	).images
										_UpperCAmelCase       :		List[str] 		=    image[0, -3:, -3:, -1]

										assert image.shape == (1, 128, 128, 3)
										_UpperCAmelCase       :		str 		=    np.array([0.5_37_55, 0.6_07_86, 0.4_74_02, 0.4_94_88, 0.5_18_69, 0.4_98_19, 0.4_79_85, 0.3_89_57, 0.4_42_79]	)

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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint						,					provider="CPUExecutionProvider"	)
										_UpperCAmelCase       :		Any 		=    EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config	)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

										_UpperCAmelCase       :		Dict 		=    self.get_dummy_inputs()
										_UpperCAmelCase       :		int 		=    pipe(**lowerCAmelCase_	).images
										_UpperCAmelCase       :		Union[str, Any] 		=    image[0, -3:, -3:, -1]

										assert image.shape == (1, 128, 128, 3)
										_UpperCAmelCase       :		Dict 		=    np.array([0.5_38_17, 0.6_08_12, 0.4_73_84, 0.4_95_30, 0.5_18_94, 0.4_98_14, 0.4_79_84, 0.3_89_58, 0.4_42_71]	)

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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint						,					provider="CPUExecutionProvider"	)
										_UpperCAmelCase       :		str 		=    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

										_UpperCAmelCase       :		Any 		=    self.get_dummy_inputs()
										_UpperCAmelCase       :		Tuple 		=    pipe(**lowerCAmelCase_	).images
										_UpperCAmelCase       :		Dict 		=    image[0, -3:, -3:, -1]

										assert image.shape == (1, 128, 128, 3)
										_UpperCAmelCase       :		Dict 		=    np.array([0.5_38_95, 0.6_08_08, 0.4_79_33, 0.4_96_08, 0.5_18_86, 0.4_99_50, 0.4_80_53, 0.3_89_57, 0.4_42_00]	)

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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint						,					provider="CPUExecutionProvider"	)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

										_UpperCAmelCase       :		str 		=    self.get_dummy_inputs()
										_UpperCAmelCase       :		int 		=    3 * [inputs["prompt"]]

										# forward
										_UpperCAmelCase       :		Any 		=    pipe(**lowerCAmelCase_	)
										_UpperCAmelCase       :		Optional[int] 		=    output.images[0, -3:, -3:, -1]

										_UpperCAmelCase       :		Dict 		=    self.get_dummy_inputs()
										_UpperCAmelCase       :		Any 		=    3 * [inputs.pop("prompt"	)]

										_UpperCAmelCase       :		List[Any] 		=    pipe.tokenizer(
										    lowerCAmelCase_						,					padding="max_length"						,					max_length=pipe.tokenizer.model_max_length						,					truncation=lowerCAmelCase_						,					return_tensors="np"						,					)
										_UpperCAmelCase       :		int 		=    text_inputs["input_ids"]

										_UpperCAmelCase       :		List[Any] 		=    pipe.text_encoder(input_ids=text_inputs.astype(np.intaa	)	)[0]

										_UpperCAmelCase       :		List[Any] 		=    prompt_embeds

										# forward
										_UpperCAmelCase       :		str 		=    pipe(**lowerCAmelCase_	)
										_UpperCAmelCase       :		Any 		=    output.images[0, -3:, -3:, -1]

										assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()	).max() < 1e-4





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint						,					provider="CPUExecutionProvider"	)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

										_UpperCAmelCase       :		Any 		=    self.get_dummy_inputs()
										_UpperCAmelCase       :		List[Any] 		=    3 * ["this is a negative prompt"]
										_UpperCAmelCase       :		int 		=    negative_prompt
										_UpperCAmelCase       :		Tuple 		=    3 * [inputs["prompt"]]

										# forward
										_UpperCAmelCase       :		Tuple 		=    pipe(**lowerCAmelCase_	)
										_UpperCAmelCase       :		int 		=    output.images[0, -3:, -3:, -1]

										_UpperCAmelCase       :		List[Any] 		=    self.get_dummy_inputs()
										_UpperCAmelCase       :		Any 		=    3 * [inputs.pop("prompt"	)]

										_UpperCAmelCase       :		Tuple 		=    []
										for p in [prompt, negative_prompt]:
														_UpperCAmelCase       :		List[str] 		=    pipe.tokenizer(
														    lowerCAmelCase_						,					padding="max_length"						,					max_length=pipe.tokenizer.model_max_length						,					truncation=lowerCAmelCase_						,					return_tensors="np"						,					)
														_UpperCAmelCase       :		Any 		=    text_inputs["input_ids"]

														embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa	)	)[0]	)

										_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[int] 		=    embeds

										# forward
										_UpperCAmelCase       :		Any 		=    pipe(**lowerCAmelCase_	)
										_UpperCAmelCase       :		int 		=    output.images[0, -3:, -3:, -1]

										assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()	).max() < 1e-4







@nightly
@require_onnxruntime
@require_torch_gpu
class 				a  (	unittest.TestCase				):


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return (
										    "CUDAExecutionProvider",
										    {
										        "gpu_mem_limit": "15000000000",  # 15GB
										        "arena_extend_strategy": "kSameAsRequested",
										    },
										)


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    ort.SessionOptions()
										_UpperCAmelCase       :		Union[str, Any] 		=    False
										return options


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    OnnxStableDiffusionPipeline.from_pretrained(
										    "CompVis/stable-diffusion-v1-4"						,					revision="onnx"						,					safety_checker=lowerCAmelCase_						,					feature_extractor=lowerCAmelCase_						,					provider=self.gpu_provider						,					sess_options=self.gpu_options						,					)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

										_UpperCAmelCase       :		Dict 		=    "A painting of a squirrel eating a burger"
										np.random.seed(0	)
										_UpperCAmelCase       :		Any 		=    sd_pipe([prompt]						,					guidance_scale=6.0						,					num_inference_steps=10						,					output_type="np"	)
										_UpperCAmelCase       :		Union[str, Any] 		=    output.images

										_UpperCAmelCase       :		List[str] 		=    image[0, -3:, -3:, -1]

										assert image.shape == (1, 512, 512, 3)
										_UpperCAmelCase       :		Any 		=    np.array([0.04_52, 0.03_90, 0.00_87, 0.03_50, 0.06_17, 0.03_64, 0.05_44, 0.05_23, 0.07_20]	)

										assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    DDIMScheduler.from_pretrained(
										    "runwayml/stable-diffusion-v1-5"						,					subfolder="scheduler"						,					revision="onnx"	)
										_UpperCAmelCase       :		Optional[int] 		=    OnnxStableDiffusionPipeline.from_pretrained(
										    "runwayml/stable-diffusion-v1-5"						,					revision="onnx"						,					scheduler=lowerCAmelCase_						,					safety_checker=lowerCAmelCase_						,					feature_extractor=lowerCAmelCase_						,					provider=self.gpu_provider						,					sess_options=self.gpu_options						,					)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

										_UpperCAmelCase       :		Optional[int] 		=    "open neural network exchange"
										_UpperCAmelCase       :		Optional[int] 		=    np.random.RandomState(0	)
										_UpperCAmelCase       :		Dict 		=    sd_pipe([prompt]						,					guidance_scale=7.5						,					num_inference_steps=10						,					generator=lowerCAmelCase_						,					output_type="np"	)
										_UpperCAmelCase       :		Tuple 		=    output.images
										_UpperCAmelCase       :		Union[str, Any] 		=    image[0, -3:, -3:, -1]

										assert image.shape == (1, 512, 512, 3)
										_UpperCAmelCase       :		Tuple 		=    np.array([0.28_67, 0.19_74, 0.14_81, 0.72_94, 0.72_51, 0.66_67, 0.41_94, 0.56_42, 0.64_86]	)

										assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    LMSDiscreteScheduler.from_pretrained(
										    "runwayml/stable-diffusion-v1-5"						,					subfolder="scheduler"						,					revision="onnx"	)
										_UpperCAmelCase       :		List[Any] 		=    OnnxStableDiffusionPipeline.from_pretrained(
										    "runwayml/stable-diffusion-v1-5"						,					revision="onnx"						,					scheduler=lowerCAmelCase_						,					safety_checker=lowerCAmelCase_						,					feature_extractor=lowerCAmelCase_						,					provider=self.gpu_provider						,					sess_options=self.gpu_options						,					)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

										_UpperCAmelCase       :		Optional[int] 		=    "open neural network exchange"
										_UpperCAmelCase       :		Optional[Any] 		=    np.random.RandomState(0	)
										_UpperCAmelCase       :		Optional[Any] 		=    sd_pipe([prompt]						,					guidance_scale=7.5						,					num_inference_steps=10						,					generator=lowerCAmelCase_						,					output_type="np"	)
										_UpperCAmelCase       :		Union[str, Any] 		=    output.images
										_UpperCAmelCase       :		int 		=    image[0, -3:, -3:, -1]

										assert image.shape == (1, 512, 512, 3)
										_UpperCAmelCase       :		Tuple 		=    np.array([0.23_06, 0.19_59, 0.15_93, 0.65_49, 0.63_94, 0.54_08, 0.50_65, 0.60_10, 0.61_61]	)

										assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    0

										def test_callback_fn(A_						,					A_						,					A_	) -> None:
														_UpperCAmelCase       :		Any 		=    True
														nonlocal number_of_steps
														number_of_steps += 1
														if step == 0:
																		assert latents.shape == (1, 4, 64, 64)
																		_UpperCAmelCase       :		Tuple 		=    latents[0, -3:, -3:, -1]
																		_UpperCAmelCase       :		Optional[Any] 		=    np.array(
																		    [-0.67_72, -0.38_35, -1.24_56, 0.19_05, -1.09_74, 0.69_67, -1.93_53, 0.01_78, 1.01_67]	)

																		assert np.abs(latents_slice.flatten() - expected_slice	).max() < 1e-3
														elif step == 5:
																		assert latents.shape == (1, 4, 64, 64)
																		_UpperCAmelCase       :		Dict 		=    latents[0, -3:, -3:, -1]
																		_UpperCAmelCase       :		Tuple 		=    np.array(
																		    [-0.33_51, 0.22_41, -0.18_37, -0.23_25, -0.65_77, 0.33_93, -0.02_41, 0.58_99, 1.38_75]	)

																		assert np.abs(latents_slice.flatten() - expected_slice	).max() < 1e-3

										_UpperCAmelCase       :		Any 		=    False

										_UpperCAmelCase       :		Any 		=    OnnxStableDiffusionPipeline.from_pretrained(
										    "runwayml/stable-diffusion-v1-5"						,					revision="onnx"						,					safety_checker=lowerCAmelCase_						,					feature_extractor=lowerCAmelCase_						,					provider=self.gpu_provider						,					sess_options=self.gpu_options						,					)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

										_UpperCAmelCase       :		Optional[Any] 		=    "Andromeda galaxy in a bottle"

										_UpperCAmelCase       :		List[Any] 		=    np.random.RandomState(0	)
										pipe(
										    prompt=lowerCAmelCase_						,					num_inference_steps=5						,					guidance_scale=7.5						,					generator=lowerCAmelCase_						,					callback=lowerCAmelCase_						,					callback_steps=1						,					)
										assert test_callback_fn.has_been_called
										assert number_of_steps == 6





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    OnnxStableDiffusionPipeline.from_pretrained(
										    "runwayml/stable-diffusion-v1-5"						,					revision="onnx"						,					safety_checker=lowerCAmelCase_						,					feature_extractor=lowerCAmelCase_						,					provider=self.gpu_provider						,					sess_options=self.gpu_options						,					)
										assert isinstance(lowerCAmelCase_						,					lowerCAmelCase_	)
										assert pipe.safety_checker is None

										_UpperCAmelCase       :		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(lowerCAmelCase_	)
														_UpperCAmelCase       :		Optional[Any] 		=    OnnxStableDiffusionPipeline.from_pretrained(lowerCAmelCase_	)

										# sanity check that the pipeline still works
										assert pipe.safety_checker is None
										_UpperCAmelCase       :		str 		=    pipe("example prompt"						,					num_inference_steps=2	).images[0]
										assert image is not None





import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE_        =      'scheduler_config.json'

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			1
						_lowercase    						=			2
						_lowercase    						=			3
						_lowercase    						=			4
						_lowercase    						=			5







@dataclass
class 				a  (	UpperCAmelCase				):
						_lowercase    						=			42







class 				a  :
						_lowercase    						=			SCHEDULER_CONFIG_NAME
						_lowercase    						=			["dtype"]
						_lowercase    						=			[]
						_lowercase    						=			True


						@classmethod
						def        _UpperCAmelCase				(       cls						,					A_ = None						,					A_ = None						,					A_=False						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase       :		str 		=    cls.load_config(
										    pretrained_model_name_or_path=A_						,					subfolder=A_						,					return_unused_kwargs=A_						,					**A_						,					)
										_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[int] 		=    cls.from_config(A_						,					return_unused_kwargs=A_						,					**A_	)

										if hasattr(A_						,					"create_state"	) and getattr(A_						,					"has_state"						,					A_	):
														_UpperCAmelCase       :		Union[str, Any] 		=    scheduler.create_state()

										if return_unused_kwargs:
														return scheduler, state, unused_kwargs

										return scheduler, state


						def        _UpperCAmelCase				(       self						,					A_						,					A_ = False						,					**A_	):







										'''simple docstring'''


										self.save_config(save_directory=A_						,					push_to_hub=A_						,					**A_	)


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return self._get_compatibles()





						@classmethod
						def        _UpperCAmelCase				(       cls	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    list(set([cls.__name__] + cls._compatibles	)	)
										_UpperCAmelCase       :		Optional[Any] 		=    importlib.import_module(__name__.split("."	)[0]	)
										_UpperCAmelCase       :		Dict 		=    [
										    getattr(A_						,					A_	) for c in compatible_classes_str if hasattr(A_						,					A_	)
										]
										return compatible_classes







def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  Tuple[int] )   ->       jnp.ndarray:
				assert len(lowerCAmelCase ) >= x.ndim
				return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCAmelCase ) - x.ndim) )  ,      lowerCAmelCase )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  int  ,      lowerCAmelCase:  Tuple=0.999  ,      lowerCAmelCase:  int=jnp.floataa )   ->       jnp.ndarray:

				def alpha_bar(lowerCAmelCase:  Union[str, Any] ):
								return math.cos((time_step + 0.008) / 1.008 * math.pi / 2 ) ** 2

				_UpperCAmelCase       :		str 		=    []
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		Optional[int] 		=    i / num_diffusion_timesteps
								_UpperCAmelCase       :		str 		=    (i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar(lowerCAmelCase ) / alpha_bar(lowerCAmelCase )  ,      lowerCAmelCase ) )
				return jnp.array(lowerCAmelCase  ,      dtype=lowerCAmelCase )

@flax.struct.dataclass
class 				a  :
						_lowercase    						=			42
						_lowercase    						=			42
						_lowercase    						=			42


						@classmethod
						def        _UpperCAmelCase				(       cls						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    scheduler.config

										if config.trained_betas is not None:
														_UpperCAmelCase       :		List[Any] 		=    jnp.asarray(config.trained_betas						,					dtype=scheduler.dtype	)
										elif config.beta_schedule == "linear":
														_UpperCAmelCase       :		List[Any] 		=    jnp.linspace(config.beta_start						,					config.beta_end						,					config.num_train_timesteps						,					dtype=scheduler.dtype	)
										elif config.beta_schedule == "scaled_linear":
														# this schedule is very specific to the latent diffusion model.
														_UpperCAmelCase       :		List[str] 		=    (
														    jnp.linspace(
														        config.beta_start**0.5						,					config.beta_end**0.5						,					config.num_train_timesteps						,					dtype=scheduler.dtype	)
														    ** 2
														)
										elif config.beta_schedule == "squaredcos_cap_v2":
														# Glide cosine schedule
														_UpperCAmelCase       :		str 		=    betas_for_alpha_bar(config.num_train_timesteps						,					dtype=scheduler.dtype	)
										else:
														raise NotImplementedError(
														    f'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'	)

										_UpperCAmelCase       :		Optional[int] 		=    1.0 - betas

										_UpperCAmelCase       :		int 		=    jnp.cumprod(A_						,					axis=0	)

										return cls(
										    alphas=A_						,					betas=A_						,					alphas_cumprod=A_						,					)







def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  CommonSchedulerState  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray )   ->       Union[str, Any]:
				_UpperCAmelCase       :		Optional[int] 		=    state.alphas_cumprod

				_UpperCAmelCase       :		Optional[Any] 		=    alphas_cumprod[timesteps] ** 0.5
				_UpperCAmelCase       :		str 		=    sqrt_alpha_prod.flatten()
				_UpperCAmelCase       :		List[Any] 		=    broadcast_to_shape_from_left(lowerCAmelCase  ,      original_samples.shape )

				_UpperCAmelCase       :		Optional[int] 		=    (1 - alphas_cumprod[timesteps]) ** 0.5
				_UpperCAmelCase       :		List[Any] 		=    sqrt_one_minus_alpha_prod.flatten()
				_UpperCAmelCase       :		int 		=    broadcast_to_shape_from_left(lowerCAmelCase  ,      original_samples.shape )

				return sqrt_alpha_prod, sqrt_one_minus_alpha_prod

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  CommonSchedulerState  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray )   ->       List[Any]:
				_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[Any] 		=    get_sqrt_alpha_prod(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase )
				_UpperCAmelCase       :		Any 		=    sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
				return noisy_samples





def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  CommonSchedulerState  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray )   ->       Dict:
				_UpperCAmelCase		,							_UpperCAmelCase       :		int 		=    get_sqrt_alpha_prod(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase )
				_UpperCAmelCase       :		Tuple 		=    sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
				return velocity


