"""simple docstring"""





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

import numpy as np
import torch

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


_UpperCamelCase       :						Union[str, Any]     =   logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
class 					UpperCAmelCase_     (   _a):
      lowerCamelCase__		:   torch.FloatTensor





class 					UpperCAmelCase_     (   _a			,							_a):
      lowerCamelCase__		:   List[str]			  =  1


      @register_to_config
      def __init__(       self		,      a = 4_0		,      a = 0.002		,      a = 80.0		,      a = 0.5		,      a = 1.0		,      a = 7.0		,      a = True		,      )	-> Tuple:
             # standard deviation of the initial noise distribution
             lowercase__      :		Optional[int]          =     sigma_max

             lowercase__      :		int          =     np.linspace(0		,      1		,      a							)
             lowercase__      :		Any          =     self._convert_to_karras(a							)
             lowercase__      :		Optional[Any]          =     self.sigma_to_t(a							)

             # setable values
             lowercase__      :		Union[str, Any]          =     None
             lowercase__      :		int          =     torch.from_numpy(a							)
             lowercase__      :		Any          =     torch.from_numpy(a							)
             lowercase__      :		str          =     False
             lowercase__      :		List[Any]          =     False


      def 		_UpperCAmelCase	(       self		,      a		,      a=None							)	-> List[str]:
             if schedule_timesteps is None:
                    lowercase__      :		List[Any]          =     self.timesteps

             lowercase__      :		Tuple          =     (schedule_timesteps == timestep).nonzero()
             return indices.item()


      def 		_UpperCAmelCase	(       self		,      a		,      a							)	-> torch.FloatTensor:
             # Get sigma corresponding to timestep
             if isinstance(a		,      torch.Tensor							):
                    lowercase__      :		Tuple          =     timestep.to(self.timesteps.device							)
             lowercase__      :		Dict          =     self.index_for_timestep(a							)
             lowercase__      :		Union[str, Any]          =     self.sigmas[step_idx]

             lowercase__      :		Optional[int]          =     sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

             lowercase__      :		Tuple          =     True
             return sample


      def 		_UpperCAmelCase	(       self		,      a							)	-> List[Any]:
             if not isinstance(a		,      np.ndarray							):
                    lowercase__      :		Dict          =     np.array(a		,      dtype=np.floataa							)

             lowercase__      :		Optional[int]          =     1_0_0_0 * 0.25 * np.log(sigmas + 1e-44							)

             return timesteps


      def 		_UpperCAmelCase	(       self		,      a = None		,      a = None		,      a = None		,      )	-> Optional[int]:
             if num_inference_steps is None and timesteps is None:
                    raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.'							)

             if num_inference_steps is not None and timesteps is not None:
                    raise ValueError('Can only pass one of `num_inference_steps` or `timesteps`.'							)

             # Follow DDPMScheduler custom timesteps logic
             if timesteps is not None:
                    for i in range(1		,      len(a							)							):
                           if timesteps[i] >= timesteps[i - 1]:
                                  raise ValueError('`timesteps` must be in descending order.'							)

                    if timesteps[0] >= self.config.num_train_timesteps:
                           raise ValueError(
                               f"""`timesteps` must start before `self.config.train_timesteps`:"""
                               f""" {self.config.num_train_timesteps}."""							)

                    lowercase__      :		List[Any]          =     np.array(a		,      dtype=np.intaa							)
                    lowercase__      :		Optional[int]          =     True
             else:
                    if num_inference_steps > self.config.num_train_timesteps:
                           raise ValueError(
                               f"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
                               f""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
                               f""" maximal {self.config.num_train_timesteps} timesteps."""							)

                    lowercase__      :		Dict          =     num_inference_steps

                    lowercase__      :		Optional[int]          =     self.config.num_train_timesteps // self.num_inference_steps
                    lowercase__      :		int          =     (np.arange(0		,      a							) * step_ratio).round()[::-1].copy().astype(np.intaa							)
                    lowercase__      :		Tuple          =     False

             # Map timesteps to Karras sigmas directly for multistep sampling
             # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
             lowercase__      :		List[Any]          =     self.config.num_train_timesteps
             lowercase__      :		str          =     timesteps[::-1].copy()
             lowercase__      :		List[Any]          =     ramp / (num_train_timesteps - 1)
             lowercase__      :		str          =     self._convert_to_karras(a							)
             lowercase__      :		Optional[Any]          =     self.sigma_to_t(a							)

             lowercase__      :		List[Any]          =     np.concatenate([sigmas, [self.sigma_min]]							).astype(np.floataa							)
             lowercase__      :		Dict          =     torch.from_numpy(a							).to(device=a							)

             if str(a							).startswith('mps'							):
                    # mps does not support float64
                    lowercase__      :		Any          =     torch.from_numpy(a							).to(a		,      dtype=torch.floataa							)
             else:
                    lowercase__      :		Optional[Any]          =     torch.from_numpy(a							).to(device=a							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> int:
             lowercase__      :		float          =     self.config.sigma_min
             lowercase__      :		float          =     self.config.sigma_max

             lowercase__      :		int          =     self.config.rho
             lowercase__      :		List[str]          =     sigma_min ** (1 / rho)
             lowercase__      :		Union[str, Any]          =     sigma_max ** (1 / rho)
             lowercase__      :		List[Any]          =     (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
             return sigmas


      def 		_UpperCAmelCase	(       self		,      a							)	-> int:
             lowercase__      :		List[str]          =     self.config.sigma_data

             lowercase__      :		Optional[Any]          =     sigma_data**2 / (sigma**2 + sigma_data**2)
             lowercase__      :		List[str]          =     sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
             return c_skip, c_out


      def 		_UpperCAmelCase	(       self		,      a							)	-> Optional[Any]:
             lowercase__      :		Union[str, Any]          =     self.config.sigma_min
             lowercase__      :		List[Any]          =     self.config.sigma_data

             lowercase__      :		List[str]          =     sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
             lowercase__      :		Dict          =     (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
             return c_skip, c_out


      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      a = None		,      a = True		,      )	-> Union[CMStochasticIterativeSchedulerOutput, Tuple]:

             if (
                 isinstance(a		,      a							)
                 or isinstance(a		,      torch.IntTensor							)
                 or isinstance(a		,      torch.LongTensor							)
             ):
                    raise ValueError(
                        (
                            'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
                            f""" `{self.__class__}.step()` is not supported. Make sure to pass"""
                            ' one of the `scheduler.timesteps` as a timestep.'
                        )		,      )

             if not self.is_scale_input_called:
                    logger.warning(
                        'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
                        'See `StableDiffusionPipeline` for a usage example.'							)

             if isinstance(a		,      torch.Tensor							):
                    lowercase__      :		Dict          =     timestep.to(self.timesteps.device							)

             lowercase__      :		Tuple          =     self.config.sigma_min
             lowercase__      :		Tuple          =     self.config.sigma_max

             lowercase__      :		Union[str, Any]          =     self.index_for_timestep(a							)

             # sigma_next corresponds to next_t in original implementation
             lowercase__      :		Optional[Any]          =     self.sigmas[step_index]
             if step_index + 1 < self.config.num_train_timesteps:
                    lowercase__      :		Union[str, Any]          =     self.sigmas[step_index + 1]
             else:
                    # Set sigma_next to sigma_min
                    lowercase__      :		Tuple          =     self.sigmas[-1]

             # Get scalings for boundary conditions
             lowercase__   ,				lowercase__      :		Union[str, Any]          =     self.get_scalings_for_boundary_condition(a							)

             # 1. Denoise model output using boundary conditions
             lowercase__      :		Any          =     c_out * model_output + c_skip * sample
             if self.config.clip_denoised:
                    lowercase__      :		List[Any]          =     denoised.clamp(-1		,      1							)

             # 2. Sample z ~ N(0, s_noise^2 * I)
             # Noise is not used for onestep sampling.
             if len(self.timesteps							) > 1:
                    lowercase__      :		str          =     randn_tensor(
                        model_output.shape		,      dtype=model_output.dtype		,      device=model_output.device		,      generator=a							)
             else:
                    lowercase__      :		List[str]          =     torch.zeros_like(a							)
             lowercase__      :		Tuple          =     noise * self.config.s_noise

             lowercase__      :		Tuple          =     sigma_next.clamp(min=a		,      max=a							)

             # 3. Return noisy sample
             # tau = sigma_hat, eps = sigma_min
             lowercase__      :		Tuple          =     denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

             if not return_dict:
                    return (prev_sample,)

             return CMStochasticIterativeSchedulerOutput(prev_sample=a							)


      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      )	-> torch.FloatTensor:
             # Make sure sigmas and timesteps have the same device and dtype as original_samples
             lowercase__      :		Optional[Any]          =     self.sigmas.to(device=original_samples.device		,      dtype=original_samples.dtype							)
             if original_samples.device.type == "mps" and torch.is_floating_point(a							):
                    # mps does not support float64
                    lowercase__      :		List[Any]          =     self.timesteps.to(original_samples.device		,      dtype=torch.floataa							)
                    lowercase__      :		List[Any]          =     timesteps.to(original_samples.device		,      dtype=torch.floataa							)
             else:
                    lowercase__      :		List[str]          =     self.timesteps.to(original_samples.device							)
                    lowercase__      :		Optional[Any]          =     timesteps.to(original_samples.device							)

             lowercase__      :		Optional[int]          =     [(schedule_timesteps == t).nonzero().item() for t in timesteps]

             lowercase__      :		Tuple          =     sigmas[step_indices].flatten()
             while len(sigma.shape							) < len(original_samples.shape							):
                    lowercase__      :		List[str]          =     sigma.unsqueeze(-1							)

             lowercase__      :		int          =     original_samples + noise * sigma
             return noisy_samples


      def __len__(       self							)	-> Tuple:
             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 KandinskyPriorPipeline, 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 					UpperCAmelCase_     (   _a			,							unittest.TestCase):
      lowerCamelCase__		:   Tuple			  =  KandinskyPriorPipeline
      lowerCamelCase__		:   Tuple			  =  ["prompt"]
      lowerCamelCase__		:   List[str]			  =  ["prompt", "negative_prompt"]
      lowerCamelCase__		:   List[Any]			  =  [
          "num_images_per_prompt",
          "generator",
          "num_inference_steps",
          "latents",
          "negative_prompt",
          "guidance_scale",
          "output_type",
          "return_dict",
      ]
      lowerCamelCase__		:   Optional[Any]			  =  False


      @property
      def 		_UpperCAmelCase	(       self							)	-> Union[str, Any]:
             return 3_2


      @property
      def 		_UpperCAmelCase	(       self							)	-> str:
             return 3_2


      @property
      def 		_UpperCAmelCase	(       self							)	-> Optional[Any]:
             return self.time_input_dim


      @property
      def 		_UpperCAmelCase	(       self							)	-> Any:
             return self.time_input_dim * 4


      @property
      def 		_UpperCAmelCase	(       self							)	-> Optional[int]:
             return 1_0_0


      @property
      def 		_UpperCAmelCase	(       self							)	-> List[Any]:
             lowercase__      :		Tuple          =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)
             return tokenizer


      @property
      def 		_UpperCAmelCase	(       self							)	-> Any:
             torch.manual_seed(0							)
             lowercase__      :		Optional[int]          =     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=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 CLIPTextModelWithProjection(a							)


      @property
      def 		_UpperCAmelCase	(       self							)	-> Union[str, Any]:
             torch.manual_seed(0							)

             lowercase__      :		int          =     {
                 'num_attention_heads': 2,
                 'attention_head_dim': 1_2,
                 'embedding_dim': self.text_embedder_hidden_size,
                 'num_layers': 1,
             }

             lowercase__      :		Tuple          =     PriorTransformer(**a							)
             # 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
             lowercase__      :		int          =     nn.Parameter(torch.ones(model.clip_std.shape							)							)
             return model


      @property
      def 		_UpperCAmelCase	(       self							)	-> Optional[int]:
             torch.manual_seed(0							)
             lowercase__      :		Optional[int]          =     CLIPVisionConfig(
                 hidden_size=self.text_embedder_hidden_size		,      image_size=2_2_4		,      projection_dim=self.text_embedder_hidden_size		,      intermediate_size=3_7		,      num_attention_heads=4		,      num_channels=3		,      num_hidden_layers=5		,      patch_size=1_4		,      )

             lowercase__      :		Optional[Any]          =     CLIPVisionModelWithProjection(a							)
             return model


      @property
      def 		_UpperCAmelCase	(       self							)	-> Tuple:
             lowercase__      :		Dict          =     CLIPImageProcessor(
                 crop_size=2_2_4		,      do_center_crop=a		,      do_normalize=a		,      do_resize=a		,      image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]		,      image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]		,      resample=3		,      size=2_2_4		,      )

             return image_processor


      def 		_UpperCAmelCase	(       self							)	-> Dict:
             lowercase__      :		List[str]          =     self.dummy_prior
             lowercase__      :		Tuple          =     self.dummy_image_encoder
             lowercase__      :		Optional[Any]          =     self.dummy_text_encoder
             lowercase__      :		List[str]          =     self.dummy_tokenizer
             lowercase__      :		Optional[int]          =     self.dummy_image_processor

             lowercase__      :		List[Any]          =     UnCLIPScheduler(
                 variance_type='fixed_small_log'		,      prediction_type='sample'		,      num_train_timesteps=1_0_0_0		,      clip_sample=a		,      clip_sample_range=10.0		,      )

             lowercase__      :		List[str]          =     {
                 'prior': prior,
                 'image_encoder': image_encoder,
                 'text_encoder': text_encoder,
                 'tokenizer': tokenizer,
                 'scheduler': scheduler,
                 'image_processor': image_processor,
             }

             return components


      def 		_UpperCAmelCase	(       self		,      a		,      a=0							)	-> Union[str, Any]:
             if str(a							).startswith('mps'							):
                    lowercase__      :		Optional[Any]          =     torch.manual_seed(a							)
             else:
                    lowercase__      :		Any          =     torch.Generator(device=a							).manual_seed(a							)
             lowercase__      :		str          =     {
                 'prompt': 'horse',
                 'generator': generator,
                 'guidance_scale': 4.0,
                 'num_inference_steps': 2,
                 'output_type': 'np',
             }
             return inputs


      def 		_UpperCAmelCase	(       self							)	-> Dict:
             lowercase__      :		Optional[int]          =     'cpu'

             lowercase__      :		Optional[Any]          =     self.get_dummy_components()

             lowercase__      :		Optional[Any]          =     self.pipeline_class(**a							)
             lowercase__      :		Optional[int]          =     pipe.to(a							)

             pipe.set_progress_bar_config(disable=a							)

             lowercase__      :		Optional[int]          =     pipe(**self.get_dummy_inputs(a							)							)
             lowercase__      :		Optional[Any]          =     output.image_embeds

             lowercase__      :		Dict          =     pipe(
                 **self.get_dummy_inputs(a							)		,      return_dict=a		,      )[0]

             lowercase__      :		List[str]          =     image[0, -1_0:]
             lowercase__      :		Optional[Any]          =     image_from_tuple[0, -1_0:]

             assert image.shape == (1, 3_2)

             lowercase__      :		Union[str, Any]          =     np.array(
                 [-0.0_532, 1.7_120, 0.3_656, -1.0_852, -0.8_946, -1.1_756, 0.4_348, 0.2_482, 0.5_146, -0.1_156]							)

             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 		_UpperCAmelCase	(       self							)	-> List[Any]:
             lowercase__      :		Tuple          =     torch_device == 'cpu'
             lowercase__      :		Union[str, Any]          =     True
             lowercase__      :		int          =     False

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


      @skip_mps
      def 		_UpperCAmelCase	(       self							)	-> int:
             lowercase__      :		Tuple          =     torch_device == 'cpu'
             lowercase__      :		Any          =     False

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

