




'''simple docstring'''





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

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





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_=0.999   ,	UpperCAmelCase_="cosine"   ,	):
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(UpperCAmelCase_						):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(UpperCAmelCase_						):
            return math.exp(t * -12.0						)

    else:
        raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''						)

    _a				:							Tuple							       =      []
    for i in range(UpperCAmelCase_						):
        _a				:							int							       =      i / num_diffusion_timesteps
        _a				:							List[str]							       =      (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(UpperCAmelCase_						) / alpha_bar_fn(UpperCAmelCase_						)   ,	UpperCAmelCase_						)						)
    return torch.tensor(UpperCAmelCase_   ,	dtype=torch.floataa						)





class      UpperCamelCase       (	snake_case__      ,		snake_case__       ):
  a     :Any          =					[e.name for e in KarrasDiffusionSchedulers]
  a     :List[Any]          =					2



  @register_to_config
  def __init__(			self		,     __lowerCAmelCase = 1000		,     __lowerCAmelCase = 0.00085		,     __lowerCAmelCase = 0.012		,     __lowerCAmelCase = "linear"		,     __lowerCAmelCase = None		,     __lowerCAmelCase = "epsilon"		,     __lowerCAmelCase = "linspace"		,     __lowerCAmelCase = 0		,     ):







      '''simple docstring'''


      if trained_betas is not None:
          _a				:							List[str]							       =      torch.tensor(__lowerCAmelCase		,     dtype=torch.floataa )
      elif beta_schedule == "linear":
          _a				:							Any							       =      torch.linspace(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     dtype=torch.floataa )
      elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          _a				:							Dict							       =      (
              torch.linspace(beta_start**0.5		,     beta_end**0.5		,     __lowerCAmelCase		,     dtype=torch.floataa ) ** 2
          )
      elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          _a				:							int							       =      betas_for_alpha_bar(__lowerCAmelCase )
      else:
          raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''' )

      _a				:							int							       =      1.0 - self.betas
      _a				:							Any							       =      torch.cumprod(self.alphas		,     dim=0 )

      #  set all values
      self.set_timesteps(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase=None ):







      '''simple docstring'''


      if schedule_timesteps is None:
          _a				:							List[Any]							       =      self.timesteps

      _a				:							Tuple							       =      (schedule_timesteps == timestep).nonzero()

      # The sigma index that is taken for the **very** first `step`
      # is always the second index (or the last index if there is only 1)
      # This way we can ensure we don't accidentally skip a sigma in
      # case we start in the middle of the denoising schedule (e.g. for image-to-image)
      if len(self._index_counter ) == 0:
          _a				:							str							       =      1 if len(__lowerCAmelCase ) > 1 else 0
      else:
          _a				:							Optional[Any]							       =      timestep.cpu().item() if torch.is_tensor(__lowerCAmelCase ) else timestep
          _a				:							Any							       =      self._index_counter[timestep_int]

      return indices[pos].item()



  @property
  def     a_							(			self ):







      '''simple docstring'''


      if self.config.timestep_spacing in ["linspace", "trailing"]:
          return self.sigmas.max()

      return (self.sigmas.max() ** 2 + 1) ** 0.5



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     ):







      '''simple docstring'''


      _a				:							List[str]							       =      self.index_for_timestep(__lowerCAmelCase )

      if self.state_in_first_order:
          _a				:							Any							       =      self.sigmas[step_index]
      else:
          _a				:							Optional[int]							       =      self.sigmas_interpol[step_index - 1]

      _a				:							Optional[Any]							       =      sample / ((sigma**2 + 1) ** 0.5)
      return sample



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase = None		,     __lowerCAmelCase = None		,     ):







      '''simple docstring'''


      _a				:							Any							       =      num_inference_steps

      _a				:							List[Any]							       =      num_train_timesteps or self.config.num_train_timesteps

      # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
      if self.config.timestep_spacing == "linspace":
          _a				:							Tuple							       =      np.linspace(0		,     num_train_timesteps - 1		,     __lowerCAmelCase		,     dtype=__lowerCAmelCase )[::-1].copy()
      elif self.config.timestep_spacing == "leading":
          _a				:							Any							       =      num_train_timesteps // self.num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # casting to int to avoid issues when num_inference_step is power of 3
          _a				:							int							       =      (np.arange(0		,     __lowerCAmelCase ) * step_ratio).round()[::-1].copy().astype(__lowerCAmelCase )
          timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
          _a				:							List[str]							       =      num_train_timesteps / self.num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # casting to int to avoid issues when num_inference_step is power of 3
          _a				:							int							       =      (np.arange(__lowerCAmelCase		,     0		,     -step_ratio )).round().copy().astype(__lowerCAmelCase )
          timesteps -= 1
      else:
          raise ValueError(
              f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''' )

      _a				:							Dict							       =      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
      _a				:							Optional[Any]							       =      torch.from_numpy(np.log(__lowerCAmelCase ) ).to(__lowerCAmelCase )

      _a				:							List[Any]							       =      np.interp(__lowerCAmelCase		,     np.arange(0		,     len(__lowerCAmelCase ) )		,     __lowerCAmelCase )
      _a				:							Optional[Any]							       =      np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
      _a				:							List[str]							       =      torch.from_numpy(__lowerCAmelCase ).to(device=__lowerCAmelCase )

      # compute up and down sigmas
      _a				:							Dict							       =      sigmas.roll(-1 )
      _a				:							Dict							       =      0.0
      _a				:							Optional[Any]							       =      (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
      _a				:							Optional[Any]							       =      (sigmas_next**2 - sigmas_up**2) ** 0.5
      _a				:							Any							       =      0.0

      # compute interpolated sigmas
      _a				:							Any							       =      sigmas.log().lerp(sigmas_down.log()		,     0.5 ).exp()
      _a				:							Any							       =      0.0

      # set sigmas
      _a				:							Optional[Any]							       =      torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
      _a				:							Optional[int]							       =      torch.cat(
          [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
      _a				:							Tuple							       =      torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2 ), sigmas_up[-1:]] )
      _a				:							str							       =      torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2 ), sigmas_down[-1:]] )

      if str(__lowerCAmelCase ).startswith("""mps""" ):
          # mps does not support float64
          _a				:							int							       =      torch.from_numpy(__lowerCAmelCase ).to(__lowerCAmelCase		,     dtype=torch.floataa )
      else:
          _a				:							str							       =      torch.from_numpy(__lowerCAmelCase ).to(__lowerCAmelCase )

      _a				:							str							       =      self.sigma_to_t(__lowerCAmelCase ).to(__lowerCAmelCase		,     dtype=timesteps.dtype )
      _a				:							int							       =      torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])		,     dim=-1 ).flatten()

      _a				:							Optional[Any]							       =      torch.cat([timesteps[:1], interleaved_timesteps] )

      _a				:							Optional[int]							       =      None

      # for exp beta schedules, such as the one for `pipeline_shap_e.py`
      # we need an index counter
      _a				:							Any							       =      defaultdict(__lowerCAmelCase )



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							str							       =      sigma.log()

      # get distribution
      _a				:							Optional[Any]							       =      log_sigma - self.log_sigmas[:, None]

      # get sigmas range
      _a				:							Optional[int]							       =      dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
      _a				:							str							       =      low_idx + 1

      _a				:							Dict							       =      self.log_sigmas[low_idx]
      _a				:							Dict							       =      self.log_sigmas[high_idx]

      # interpolate sigmas
      _a				:							Dict							       =      (low - log_sigma) / (low - high)
      _a				:							Optional[int]							       =      w.clamp(0		,     1 )

      # transform interpolation to time range
      _a				:							Optional[Any]							       =      (1 - w) * low_idx + w * high_idx
      _a				:							int							       =      t.view(sigma.shape )
      return t



  @property
  def     a_							(			self ):







      '''simple docstring'''


      return self.sample is None



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase = None		,     __lowerCAmelCase = True		,     ):







      '''simple docstring'''


      _a				:							str							       =      self.index_for_timestep(__lowerCAmelCase )

      # advance index counter by 1
      _a				:							Dict							       =      timestep.cpu().item() if torch.is_tensor(__lowerCAmelCase ) else timestep
      self._index_counter[timestep_int] += 1

      if self.state_in_first_order:
          _a				:							str							       =      self.sigmas[step_index]
          _a				:							List[str]							       =      self.sigmas_interpol[step_index]
          _a				:							Dict							       =      self.sigmas_up[step_index]
          _a				:							Any							       =      self.sigmas_down[step_index - 1]
      else:
          # 2nd order / KPDM2's method
          _a				:							Union[str, Any]							       =      self.sigmas[step_index - 1]
          _a				:							List[str]							       =      self.sigmas_interpol[step_index - 1]
          _a				:							Union[str, Any]							       =      self.sigmas_up[step_index - 1]
          _a				:							List[Any]							       =      self.sigmas_down[step_index - 1]

      # currently only gamma=0 is supported. This usually works best anyways.
      # We can support gamma in the future but then need to scale the timestep before
      # passing it to the model which requires a change in API
      _a				:							Any							       =      0
      _a				:							Tuple							       =      sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

      _a				:							Any							       =      model_output.device
      _a				:							Tuple							       =      randn_tensor(model_output.shape		,     dtype=model_output.dtype		,     device=__lowerCAmelCase		,     generator=__lowerCAmelCase )

      # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
      if self.config.prediction_type == "epsilon":
          _a				:							Dict							       =      sigma_hat if self.state_in_first_order else sigma_interpol
          _a				:							int							       =      sample - sigma_input * model_output
      elif self.config.prediction_type == "v_prediction":
          _a				:							Optional[int]							       =      sigma_hat if self.state_in_first_order else sigma_interpol
          _a				:							List[str]							       =      model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
              sample / (sigma_input**2 + 1)
          )
      elif self.config.prediction_type == "sample":
          raise NotImplementedError("""prediction_type not implemented yet: sample""" )
      else:
          raise ValueError(
              f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`''' )

      if self.state_in_first_order:
          # 2. Convert to an ODE derivative for 1st order
          _a				:							Tuple							       =      (sample - pred_original_sample) / sigma_hat
          # 3. delta timestep
          _a				:							List[str]							       =      sigma_interpol - sigma_hat

          # store for 2nd order step
          _a				:							Optional[int]							       =      sample
          _a				:							str							       =      dt
          _a				:							Optional[int]							       =      sample + derivative * dt
      else:
          # DPM-Solver-2
          # 2. Convert to an ODE derivative for 2nd order
          _a				:							Any							       =      (sample - pred_original_sample) / sigma_interpol
          # 3. delta timestep
          _a				:							Union[str, Any]							       =      sigma_down - sigma_hat

          _a				:							Tuple							       =      self.sample
          _a				:							List[Any]							       =      None

          _a				:							Tuple							       =      sample + derivative * dt
          _a				:							int							       =      prev_sample + noise * sigma_up

      if not return_dict:
          return (prev_sample,)

      return SchedulerOutput(prev_sample=__lowerCAmelCase )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     ):







      '''simple docstring'''


      _a				:							List[Any]							       =      self.sigmas.to(device=original_samples.device		,     dtype=original_samples.dtype )
      if original_samples.device.type == "mps" and torch.is_floating_point(__lowerCAmelCase ):
          # mps does not support float64
          _a				:							Union[str, Any]							       =      self.timesteps.to(original_samples.device		,     dtype=torch.floataa )
          _a				:							Optional[int]							       =      timesteps.to(original_samples.device		,     dtype=torch.floataa )
      else:
          _a				:							Optional[int]							       =      self.timesteps.to(original_samples.device )
          _a				:							str							       =      timesteps.to(original_samples.device )

      _a				:							str							       =      [self.index_for_timestep(__lowerCAmelCase		,     __lowerCAmelCase ) for t in timesteps]

      _a				:							str							       =      sigmas[step_indices].flatten()
      while len(sigma.shape ) < len(original_samples.shape ):
          _a				:							Union[str, Any]							       =      sigma.unsqueeze(-1 )

      _a				:							int							       =      original_samples + noise * sigma
      return noisy_samples



  def __len__(			self ):







      '''simple docstring'''


      return self.config.num_train_timesteps



"""simple docstring"""






import random
import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class 							snake_case			(					__UpperCAmelCase ,       unittest.TestCase							):




					"""simple docstring"""

					snake_case__							       =					KandinskyVaaPriorEmbaEmbPipeline
					snake_case__							       =					["prompt", "image"]
					snake_case__							       =					["prompt", "image"]
					snake_case__							       =					[
					    "num_images_per_prompt",
					    "strength",
					    "generator",
					    "num_inference_steps",
					    "latents",
					    "negative_prompt",
					    "guidance_scale",
					    "output_type",
					    "return_dict",
					]
					snake_case__							       =					False







					@property
					def 						__lowerCAmelCase		(							self  :      Tuple     ):
									return 32







					@property
					def 						__lowerCAmelCase		(							self  :      Union[str, Any]     ):
									return 32







					@property
					def 						__lowerCAmelCase		(							self  :      str     ):
									return self.time_input_dim







					@property
					def 						__lowerCAmelCase		(							self  :      Union[str, Any]     ):
									return self.time_input_dim * 4







					@property
					def 						__lowerCAmelCase		(							self  :      Optional[Any]     ):
									return 100







					@property
					def 						__lowerCAmelCase		(							self  :      int     ):
									UpperCAmelCase__			 =				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )
									return tokenizer







					@property
					def 						__lowerCAmelCase		(							self  :      str     ):
									torch.manual_seed(0     )
									UpperCAmelCase__			 =				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=1_000       ,)
									return CLIPTextModelWithProjection(lowerCamelCase__     )







					@property
					def 						__lowerCAmelCase		(							self  :      str     ):
									torch.manual_seed(0     )

									UpperCAmelCase__			 =				{
									    'num_attention_heads': 2,
									    'attention_head_dim': 12,
									    'embedding_dim': self.text_embedder_hidden_size,
									    'num_layers': 1,
									}

									UpperCAmelCase__			 =				PriorTransformer(**lowerCamelCase__     )
									# 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
									UpperCAmelCase__			 =				nn.Parameter(torch.ones(model.clip_std.shape     )     )
									return model







					@property
					def 						__lowerCAmelCase		(							self  :      Optional[int]     ):
									torch.manual_seed(0     )
									UpperCAmelCase__			 =				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       ,)

									UpperCAmelCase__			 =				CLIPVisionModelWithProjection(lowerCamelCase__     )
									return model







					@property
					def 						__lowerCAmelCase		(							self  :      List[str]     ):
									UpperCAmelCase__			 =				CLIPImageProcessor(
									    crop_size=224       ,do_center_crop=lowerCamelCase__       ,do_normalize=lowerCamelCase__       ,do_resize=lowerCamelCase__       ,image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]       ,image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]       ,resample=3       ,size=224       ,)

									return image_processor







					def 						__lowerCAmelCase		(							self  :      List[Any]     ):
									UpperCAmelCase__			 =				self.dummy_prior
									UpperCAmelCase__			 =				self.dummy_image_encoder
									UpperCAmelCase__			 =				self.dummy_text_encoder
									UpperCAmelCase__			 =				self.dummy_tokenizer
									UpperCAmelCase__			 =				self.dummy_image_processor

									UpperCAmelCase__			 =				UnCLIPScheduler(
									    variance_type='fixed_small_log'       ,prediction_type='sample'       ,num_train_timesteps=1_000       ,clip_sample=lowerCamelCase__       ,clip_sample_range=1_0.0       ,)

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

									return components







					def 						__lowerCAmelCase		(							self  :      Optional[int]       ,lowerCamelCase__  :      Any       ,lowerCamelCase__  :      str=0     ):
									if str(lowerCamelCase__     ).startswith('mps'     ):
													UpperCAmelCase__			 =				torch.manual_seed(lowerCamelCase__     )
									else:
													UpperCAmelCase__			 =				torch.Generator(device=lowerCamelCase__     ).manual_seed(lowerCamelCase__     )

									UpperCAmelCase__			 =				floats_tensor((1, 3, 64, 64)       ,rng=random.Random(lowerCamelCase__     )     ).to(lowerCamelCase__     )
									UpperCAmelCase__			 =				image.cpu().permute(0       ,2       ,3       ,1     )[0]
									UpperCAmelCase__			 =				Image.fromarray(np.uinta(lowerCamelCase__     )     ).convert('RGB'     ).resize((256, 256)     )

									UpperCAmelCase__			 =				{
									    'prompt': 'horse',
									    'image': init_image,
									    'strength': 0.5,
									    'generator': generator,
									    'guidance_scale': 4.0,
									    'num_inference_steps': 2,
									    'output_type': 'np',
									}
									return inputs







					def 						__lowerCAmelCase		(							self  :      List[Any]     ):
									UpperCAmelCase__			 =				'cpu'

									UpperCAmelCase__			 =				self.get_dummy_components()

									UpperCAmelCase__			 =				self.pipeline_class(**lowerCamelCase__     )
									UpperCAmelCase__			 =				pipe.to(lowerCamelCase__     )

									pipe.set_progress_bar_config(disable=lowerCamelCase__     )

									UpperCAmelCase__			 =				pipe(**self.get_dummy_inputs(lowerCamelCase__     )     )
									UpperCAmelCase__			 =				output.image_embeds

									UpperCAmelCase__			 =				pipe(
									    **self.get_dummy_inputs(lowerCamelCase__     )       ,return_dict=lowerCamelCase__       ,)[0]

									UpperCAmelCase__			 =				image[0, -10:]
									UpperCAmelCase__			 =				image_from_tuple[0, -10:]

									assert image.shape == (1, 32)

									UpperCAmelCase__			 =				np.array(
									    [
									        0.1_0_7_1_2_8_4,
									        1.3_3_3_0_2_7_1,
									        0.6_1_2_6_0_2_2_3,
									        -0.6_6_9_1_0_6_5,
									        -0.3_8_4_6_8_5_2,
									        -1.0_3_0_3_6_6_1,
									        0.2_2_7_1_6_1_1_1,
									        0.0_3_3_4_8_9_0_1,
									        0.3_0_0_4_0_6_7_5,
									        -0.2_4_8_0_5_0_2_9,
									    ]     )

									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 						__lowerCAmelCase		(							self  :      Optional[int]     ):
									UpperCAmelCase__			 =				torch_device == 'cpu'
									UpperCAmelCase__			 =				True
									UpperCAmelCase__			 =				False

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







					@skip_mps
					def 						__lowerCAmelCase		(							self  :      int     ):
									UpperCAmelCase__			 =				torch_device == 'cpu'
									UpperCAmelCase__			 =				False

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



