


'''simple docstring'''







# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput


@dataclass
class 			__lowerCamelCase			(	a_	):
    """simple docstring"""
    a													=   42
    a													=   42




class 			__lowerCamelCase			(	a_    , a_	):
    """simple docstring"""
    a													=   1


    @register_to_config
    def __init__(  self      :							int				,							SCREAMING_SNAKE_CASE      :							int = 2000				,							SCREAMING_SNAKE_CASE      :							float = 0.15				,							SCREAMING_SNAKE_CASE      :							float = 0.01				,							SCREAMING_SNAKE_CASE      :							float = 1348.0				,							SCREAMING_SNAKE_CASE      :							float = 1e-5				,							SCREAMING_SNAKE_CASE      :							int = 1				,							):
           # standard deviation of the initial noise distribution
           _A					:     str     = sigma_max

           # setable values
           _A					:     Optional[int]     = None

           self.set_sigmas(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int				,							SCREAMING_SNAKE_CASE      :							torch.FloatTensor				,							SCREAMING_SNAKE_CASE      :							Optional[int] = None):
           return sample


    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							float = None				,							SCREAMING_SNAKE_CASE      :							Union[str, torch.device] = None):
           _A					:     str     = sampling_eps if sampling_eps is not None else self.config.sampling_eps

           _A					:     str     = torch.linspace(1				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							device=SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							float = None				,							SCREAMING_SNAKE_CASE      :							float = None				,							SCREAMING_SNAKE_CASE      :							float = None):
           _A					:     Tuple     = sigma_min if sigma_min is not None else self.config.sigma_min
           _A					:     str     = sigma_max if sigma_max is not None else self.config.sigma_max
           _A					:     int     = sampling_eps if sampling_eps is not None else self.config.sampling_eps
           if self.timesteps is None:
                  self.set_timesteps(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)

           _A					:     Any     = sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
           _A					:     List[str]     = torch.exp(torch.linspace(math.log(SCREAMING_SNAKE_CASE)				,							math.log(SCREAMING_SNAKE_CASE)				,							SCREAMING_SNAKE_CASE))
           _A					:     str     = torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps])


    def        A   (  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							Optional[Any]):
           return torch.where(
               timesteps == 0				,							torch.zeros_like(t.to(timesteps.device))				,							self.discrete_sigmas[timesteps - 1].to(timesteps.device)				,							)


    def        A   (  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							torch.FloatTensor				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							torch.FloatTensor				,							SCREAMING_SNAKE_CASE      :							Optional[torch.Generator] = None				,							SCREAMING_SNAKE_CASE      :							bool = True				,							):
           if self.timesteps is None:
                  raise ValueError(
                      '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler')

           _A					:     str     = timestep * torch.ones(
               sample.shape[0]				,							device=sample.device)  # torch.repeat_interleave(timestep, sample.shape[0])
           _A					:     Tuple     = (timestep * (len(self.timesteps) - 1)).long()

           # mps requires indices to be in the same device, so we use cpu as is the default with cuda
           _A					:     str     = timesteps.to(self.discrete_sigmas.device)

           _A					:     Union[str, Any]     = self.discrete_sigmas[timesteps].to(sample.device)
           _A					:     str     = self.get_adjacent_sigma(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE).to(sample.device)
           _A					:     Optional[int]     = torch.zeros_like(SCREAMING_SNAKE_CASE)
           _A					:     Any     = (sigma**2 - adjacent_sigma**2) ** 0.5

           # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
           # also equation 47 shows the analog from SDE models to ancestral sampling methods
           _A					:     Optional[Any]     = diffusion.flatten()
           while len(diffusion.shape) < len(sample.shape):
                  _A					:     str     = diffusion.unsqueeze(-1)
           _A					:     Union[str, Any]     = drift - diffusion**2 * model_output

           #  equation 6: sample noise for the diffusion term of
           _A					:     Tuple     = randn_tensor(
               sample.shape				,							layout=sample.layout				,							generator=SCREAMING_SNAKE_CASE				,							device=sample.device				,							dtype=sample.dtype)
           _A					:     int     = sample - drift  # subtract because `dt` is a small negative timestep
           # TODO is the variable diffusion the correct scaling term for the noise?
           _A					:     Optional[int]     = prev_sample_mean + diffusion * noise  # add impact of diffusion field g

           if not return_dict:
                  return (prev_sample, prev_sample_mean)

           return SdeVeOutput(prev_sample=SCREAMING_SNAKE_CASE				,							prev_sample_mean=SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							torch.FloatTensor				,							SCREAMING_SNAKE_CASE      :							torch.FloatTensor				,							SCREAMING_SNAKE_CASE      :							Optional[torch.Generator] = None				,							SCREAMING_SNAKE_CASE      :							bool = True				,							):
           if self.timesteps is None:
                  raise ValueError(
                      '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler')

           # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
           # sample noise for correction
           _A					:     str     = randn_tensor(sample.shape				,							layout=sample.layout				,							generator=SCREAMING_SNAKE_CASE).to(sample.device)

           # compute step size from the model_output, the noise, and the snr
           _A					:     Optional[int]     = torch.norm(model_output.reshape(model_output.shape[0]				,							-1)				,							dim=-1).mean()
           _A					:     Dict     = torch.norm(noise.reshape(noise.shape[0]				,							-1)				,							dim=-1).mean()
           _A					:     List[str]     = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
           _A					:     Optional[Any]     = step_size * torch.ones(sample.shape[0]).to(sample.device)
           # self.repeat_scalar(step_size, sample.shape[0])

           # compute corrected sample: model_output term and noise term
           _A					:     int     = step_size.flatten()
           while len(step_size.shape) < len(sample.shape):
                  _A					:     Optional[int]     = step_size.unsqueeze(-1)
           _A					:     str     = sample + step_size * model_output
           _A					:     Dict     = prev_sample_mean + ((step_size * 2) ** 0.5) * noise

           if not return_dict:
                  return (prev_sample,)

           return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int				,							SCREAMING_SNAKE_CASE      :							torch.FloatTensor				,							SCREAMING_SNAKE_CASE      :							torch.FloatTensor				,							SCREAMING_SNAKE_CASE      :							torch.FloatTensor				,							):
           # Make sure sigmas and timesteps have the same device and dtype as original_samples
           _A					:     Any     = timesteps.to(original_samples.device)
           _A					:     int     = self.discrete_sigmas.to(original_samples.device)[timesteps]
           _A					:     Optional[Any]     = (
               noise * sigmas[:, None, None, None]
               if noise is not None
               else torch.randn_like(SCREAMING_SNAKE_CASE) * sigmas[:, None, None, None]
           )
           _A					:     List[Any]     = noise + original_samples
           return noisy_samples


    def __len__(  self      :							Dict):
           return self.config.num_train_timesteps










'''simple docstring'''







import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 			__lowerCamelCase			(	a_    , unittest.TestCase	):
    """simple docstring"""
    a													=   KandinskyVaaPriorPipeline
    a													=   ["prompt"]
    a													=   ["prompt", "negative_prompt"]
    a													=   [
        "num_images_per_prompt",
        "generator",
        "num_inference_steps",
        "latents",
        "negative_prompt",
        "guidance_scale",
        "output_type",
        "return_dict",
    ]
    a													=   False


    @property
    def        A   (  self      :							List[str]):
           return 32


    @property
    def        A   (  self      :							List[Any]):
           return 32


    @property
    def        A   (  self      :							Dict):
           return self.time_input_dim


    @property
    def        A   (  self      :							Tuple):
           return self.time_input_dim * 4


    @property
    def        A   (  self      :							Optional[int]):
           return 100


    @property
    def        A   (  self      :							Dict):
           _A					:     Tuple     = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
           return tokenizer


    @property
    def        A   (  self      :							Optional[Any]):
           torch.manual_seed(0)
           _A					:     Any     = CLIPTextConfig(
               bos_token_id=0				,							eos_token_id=2				,							hidden_size=self.text_embedder_hidden_size				,							projection_dim=self.text_embedder_hidden_size				,							intermediate_size=37				,							layer_norm_eps=1e-05				,							num_attention_heads=4				,							num_hidden_layers=5				,							pad_token_id=1				,							vocab_size=1000				,							)
           return CLIPTextModelWithProjection(SCREAMING_SNAKE_CASE)


    @property
    def        A   (  self      :							List[Any]):
           torch.manual_seed(0)

           _A					:     Optional[Any]     = {
               'num_attention_heads': 2,
               'attention_head_dim': 12,
               'embedding_dim': self.text_embedder_hidden_size,
               'num_layers': 1,
           }

           _A					:     Any     = PriorTransformer(**SCREAMING_SNAKE_CASE)
           # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
           _A					:     str     = nn.Parameter(torch.ones(model.clip_std.shape))
           return model


    @property
    def        A   (  self      :							List[str]):
           torch.manual_seed(0)
           _A					:     List[Any]     = CLIPVisionConfig(
               hidden_size=self.text_embedder_hidden_size				,							image_size=224				,							projection_dim=self.text_embedder_hidden_size				,							intermediate_size=37				,							num_attention_heads=4				,							num_channels=3				,							num_hidden_layers=5				,							patch_size=14				,							)

           _A					:     Union[str, Any]     = CLIPVisionModelWithProjection(SCREAMING_SNAKE_CASE)
           return model


    @property
    def        A   (  self      :							int):
           _A					:     Optional[Any]     = CLIPImageProcessor(
               crop_size=224				,							do_center_crop=SCREAMING_SNAKE_CASE				,							do_normalize=SCREAMING_SNAKE_CASE				,							do_resize=SCREAMING_SNAKE_CASE				,							image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]				,							image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]				,							resample=3				,							size=224				,							)

           return image_processor


    def        A   (  self      :							Optional[Any]):
           _A					:     Optional[int]     = self.dummy_prior
           _A					:     Dict     = self.dummy_image_encoder
           _A					:     Dict     = self.dummy_text_encoder
           _A					:     str     = self.dummy_tokenizer
           _A					:     Optional[Any]     = self.dummy_image_processor

           _A					:     Optional[Any]     = UnCLIPScheduler(
               variance_type='fixed_small_log'				,							prediction_type='sample'				,							num_train_timesteps=1000				,							clip_sample=SCREAMING_SNAKE_CASE				,							clip_sample_range=10.0				,							)

           _A					:     Dict     = {
               'prior': prior,
               'image_encoder': image_encoder,
               'text_encoder': text_encoder,
               'tokenizer': tokenizer,
               'scheduler': scheduler,
               'image_processor': image_processor,
           }

           return components


    def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							List[str]=0):
           if str(SCREAMING_SNAKE_CASE).startswith('mps'):
                  _A					:     Dict     = torch.manual_seed(SCREAMING_SNAKE_CASE)
           else:
                  _A					:     int     = torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(SCREAMING_SNAKE_CASE)
           _A					:     List[Any]     = {
               'prompt': 'horse',
               'generator': generator,
               'guidance_scale': 4.0,
               'num_inference_steps': 2,
               'output_type': 'np',
           }
           return inputs


    def        A   (  self      :							List[Any]):
           _A					:     str     = 'cpu'

           _A					:     Tuple     = self.get_dummy_components()

           _A					:     List[Any]     = self.pipeline_class(**SCREAMING_SNAKE_CASE)
           _A					:     Any     = pipe.to(SCREAMING_SNAKE_CASE)

           pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

           _A					:     Dict     = pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE))
           _A					:     str     = output.image_embeds

           _A					:     Optional[int]     = pipe(
               **self.get_dummy_inputs(SCREAMING_SNAKE_CASE)				,							return_dict=SCREAMING_SNAKE_CASE				,							)[0]

           _A					:     Optional[int]     = image[0, -10:]
           _A					:     int     = image_from_tuple[0, -10:]

           assert image.shape == (1, 32)

           _A					:     Dict     = np.array(
               [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156])

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


    @skip_mps
    def        A   (  self      :							Any):
           _A					:     Tuple     = torch_device == 'cpu'
           _A					:     Optional[int]     = True
           _A					:     Tuple     = False

           self._test_inference_batch_single_identical(
               test_max_difference=SCREAMING_SNAKE_CASE				,							relax_max_difference=SCREAMING_SNAKE_CASE				,							test_mean_pixel_difference=SCREAMING_SNAKE_CASE				,							)


    @skip_mps
    def        A   (  self      :							int):
           _A					:     Tuple     = torch_device == 'cpu'
           _A					:     Optional[Any]     = False

           self._test_attention_slicing_forward_pass(
               test_max_difference=SCREAMING_SNAKE_CASE				,							test_mean_pixel_difference=SCREAMING_SNAKE_CASE				,							)






