





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 .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput



def        _a						(    UpperCAmelCase			,  UpperCAmelCase=0.9_99			,  UpperCAmelCase="cosine"			,  )				->				Dict:







   """simple docstring"""
   if alpha_transform_type == "cosine":

      def alpha_bar_fn(UpperCAmelCase     ):
         return math.cos((t + 0.0_08) / 1.0_08 * 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}"     )

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




class    __SCREAMING_SNAKE_CASE			( lowerCAmelCase_     ,lowerCAmelCase_					):
       _UpperCAmelCase      :  Dict     =    [e.name for e in KarrasDiffusionSchedulers]
       _UpperCAmelCase      :  Dict     =    2







       @register_to_config
       def __init__(      self     :	str		,					A     :	int = 1_0_0_0		,					A     :	float = 0.0_00_85		,					A     :	float = 0.0_12		,					A     :	str = "linear"		,					A     :	Optional[Union[np.ndarray, List[float]]] = None		,					A     :	str = "epsilon"		,					A     :	str = "linspace"		,					A     :	int = 0		,					)   ->str:







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

          lowerCamelCase__   :						Optional[int]					   =							1.0 - self.betas
          lowerCamelCase__   :						Union[str, Any]					   =							torch.cumprod(self.alphas		,					dim=0			)

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







       def 						__lowerCamelCase    (      self     :	List[str]		,					A     :	Tuple		,					A     :	int=None			)   ->Union[str, Any]:







          if schedule_timesteps is None:
             lowerCamelCase__   :						Optional[Any]					   =							self.timesteps

          lowerCamelCase__   :						Optional[int]					   =							(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:
             lowerCamelCase__   :						List[str]					   =							1 if len(A			) > 1 else 0
          else:
             lowerCamelCase__   :						str					   =							timestep.cpu().item() if torch.is_tensor(A			) else timestep
             lowerCamelCase__   :						int					   =							self._index_counter[timestep_int]

          return indices[pos].item()







       @property
       def 						__lowerCamelCase    (      self     :	List[str]			)   ->List[Any]:







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

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







       def 						__lowerCamelCase    (      self     :	Any		,					A     :	torch.FloatTensor		,					A     :	Union[float, torch.FloatTensor]		,					)   ->Optional[Any]:
          lowerCamelCase__   :						Tuple					   =							self.index_for_timestep(A			)

          if self.state_in_first_order:
             lowerCamelCase__   :						Dict					   =							self.sigmas[step_index]
          else:
             lowerCamelCase__   :						List[str]					   =							self.sigmas_interpol[step_index]

          lowerCamelCase__   :						Union[str, Any]					   =							sample / ((sigma**2 + 1) ** 0.5)
          return sample







       def 						__lowerCamelCase    (      self     :	Optional[int]		,					A     :	int		,					A     :	Union[str, torch.device] = None		,					A     :	Optional[int] = None		,					)   ->str:
          lowerCamelCase__   :						Union[str, Any]					   =							num_inference_steps

          lowerCamelCase__   :						Union[str, 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":
             lowerCamelCase__   :						Union[str, Any]					   =							np.linspace(0		,					num_train_timesteps - 1		,					A		,					dtype=A			)[::-1].copy()
          elif self.config.timestep_spacing == "leading":
             lowerCamelCase__   :						Union[str, 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
             lowerCamelCase__   :						Any					   =							(np.arange(0		,					A			) * step_ratio).round()[::-1].copy().astype(A			)
             timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
             lowerCamelCase__   :						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
             lowerCamelCase__   :						List[str]					   =							(np.arange(A		,					0		,					-step_ratio			)).round().copy().astype(A			)
             timesteps -= 1
          else:
             raise ValueError(
                 F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'."			)

          lowerCamelCase__   :						Dict					   =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
          lowerCamelCase__   :						int					   =							torch.from_numpy(np.log(A			)			).to(A			)

          lowerCamelCase__   :						str					   =							np.interp(A		,					np.arange(0		,					len(A			)			)		,					A			)
          lowerCamelCase__   :						Union[str, Any]					   =							np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
          lowerCamelCase__   :						str					   =							torch.from_numpy(A			).to(device=A			)

          # interpolate sigmas
          lowerCamelCase__   :						Optional[int]					   =							sigmas.log().lerp(sigmas.roll(1			).log()		,					0.5			).exp()

          lowerCamelCase__   :						List[Any]					   =							torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2			), sigmas[-1:]]			)
          lowerCamelCase__   :						Tuple					   =							torch.cat(
              [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2			), sigmas_interpol[-1:]]			)

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

          # interpolate timesteps
          lowerCamelCase__   :						Any					   =							self.sigma_to_t(A			).to(A		,					dtype=timesteps.dtype			)
          lowerCamelCase__   :						Tuple					   =							torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])		,					dim=-1			).flatten()

          lowerCamelCase__   :						Dict					   =							torch.cat([timesteps[:1], interleaved_timesteps]			)

          lowerCamelCase__   :						Dict					   =							None

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







       def 						__lowerCamelCase    (      self     :	Union[str, Any]		,					A     :	Dict			)   ->Dict:
          lowerCamelCase__   :						Dict					   =							sigma.log()

          # get distribution
          lowerCamelCase__   :						int					   =							log_sigma - self.log_sigmas[:, None]

          # get sigmas range
          lowerCamelCase__   :						Tuple					   =							dists.ge(0			).cumsum(dim=0			).argmax(dim=0			).clamp(max=self.log_sigmas.shape[0] - 2			)
          lowerCamelCase__   :						Dict					   =							low_idx + 1

          lowerCamelCase__   :						Optional[int]					   =							self.log_sigmas[low_idx]
          lowerCamelCase__   :						Tuple					   =							self.log_sigmas[high_idx]

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

          # transform interpolation to time range
          lowerCamelCase__   :						List[Any]					   =							(1 - w) * low_idx + w * high_idx
          lowerCamelCase__   :						Union[str, Any]					   =							t.view(sigma.shape			)
          return t







       @property
       def 						__lowerCamelCase    (      self     :	Dict			)   ->List[Any]:
          return self.sample is None







       def 						__lowerCamelCase    (      self     :	List[str]		,					A     :	Union[torch.FloatTensor, np.ndarray]		,					A     :	Union[float, torch.FloatTensor]		,					A     :	Union[torch.FloatTensor, np.ndarray]		,					A     :	bool = True		,					)   ->Union[str, Any]:
          lowerCamelCase__   :						Optional[int]					   =							self.index_for_timestep(A			)

          # advance index counter by 1
          lowerCamelCase__   :						Optional[int]					   =							timestep.cpu().item() if torch.is_tensor(A			) else timestep
          self._index_counter[timestep_int] += 1

          if self.state_in_first_order:
             lowerCamelCase__   :						Dict					   =							self.sigmas[step_index]
             lowerCamelCase__   :						int					   =							self.sigmas_interpol[step_index + 1]
             lowerCamelCase__   :						Tuple					   =							self.sigmas[step_index + 1]
          else:
             # 2nd order / KDPM2's method
             lowerCamelCase__   :						str					   =							self.sigmas[step_index - 1]
             lowerCamelCase__   :						Union[str, Any]					   =							self.sigmas_interpol[step_index]
             lowerCamelCase__   :						Tuple					   =							self.sigmas[step_index]

          # 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
          lowerCamelCase__   :						Any					   =							0
          lowerCamelCase__   :						List[Any]					   =							sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
             lowerCamelCase__   :						Tuple					   =							sigma_hat if self.state_in_first_order else sigma_interpol
             lowerCamelCase__   :						Dict					   =							sample - sigma_input * model_output
          elif self.config.prediction_type == "v_prediction":
             lowerCamelCase__   :						Any					   =							sigma_hat if self.state_in_first_order else sigma_interpol
             lowerCamelCase__   :						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
             lowerCamelCase__   :						List[Any]					   =							(sample - pred_original_sample) / sigma_hat
             # 3. delta timestep
             lowerCamelCase__   :						List[str]					   =							sigma_interpol - sigma_hat

             # store for 2nd order step
             lowerCamelCase__   :						Dict					   =							sample
          else:
             # DPM-Solver-2
             # 2. Convert to an ODE derivative for 2nd order
             lowerCamelCase__   :						Optional[int]					   =							(sample - pred_original_sample) / sigma_interpol

             # 3. delta timestep
             lowerCamelCase__   :						List[str]					   =							sigma_next - sigma_hat

             lowerCamelCase__   :						Tuple					   =							self.sample
             lowerCamelCase__   :						Dict					   =							None

          lowerCamelCase__   :						Any					   =							sample + derivative * dt

          if not return_dict:
             return (prev_sample,)

          return SchedulerOutput(prev_sample=A			)







       def 						__lowerCamelCase    (      self     :	List[Any]		,					A     :	torch.FloatTensor		,					A     :	torch.FloatTensor		,					A     :	torch.FloatTensor		,					)   ->int:
          lowerCamelCase__   :						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
             lowerCamelCase__   :						Optional[Any]					   =							self.timesteps.to(original_samples.device		,					dtype=torch.floataa			)
             lowerCamelCase__   :						List[str]					   =							timesteps.to(original_samples.device		,					dtype=torch.floataa			)
          else:
             lowerCamelCase__   :						str					   =							self.timesteps.to(original_samples.device			)
             lowerCamelCase__   :						int					   =							timesteps.to(original_samples.device			)

          lowerCamelCase__   :						str					   =							[self.index_for_timestep(A		,					A			) for t in timesteps]

          lowerCamelCase__   :						Dict					   =							sigmas[step_indices].flatten()
          while len(sigma.shape			) < len(original_samples.shape			):
             lowerCamelCase__   :						Dict					   =							sigma.unsqueeze(-1			)

          lowerCamelCase__   :						Union[str, Any]					   =							original_samples + noise * sigma
          return noisy_samples







       def __len__(      self     :	List[str]			)   ->Any:
          return self.config.num_train_timesteps









import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference

class 		__lowerCAmelCase	(	__magic_name__      ,       unittest.TestCase ):
							UpperCamelCase__					     =  ShapEImgaImgPipeline
							UpperCamelCase__					     =  ['''image''']
							UpperCamelCase__					     =  ['''image''']
							UpperCamelCase__					     =  [
							    '''num_images_per_prompt''',
							    '''num_inference_steps''',
							    '''generator''',
							    '''latents''',
							    '''guidance_scale''',
							    '''frame_size''',
							    '''output_type''',
							    '''return_dict''',
							]
							UpperCamelCase__					     =  False
							@property
							def       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								return 32
							@property
							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								return 32
							@property
							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								return self.time_input_dim * 4
							@property
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								return 8
							@property
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								torch.manual_seed(0				)
								a										=							CLIPVisionConfig(
								    hidden_size=self.text_embedder_hidden_size		,      image_size=64		,      projection_dim=self.text_embedder_hidden_size		,      intermediate_size=37		,      num_attention_heads=4		,      num_channels=3		,      num_hidden_layers=5		,      patch_size=1		,      )

								a										=							CLIPVisionModel(__magic_name__				)
								return model
							@property
							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								a										=							CLIPImageProcessor(
								    crop_size=224		,      do_center_crop=__magic_name__		,      do_normalize=__magic_name__		,      do_resize=__magic_name__		,      image_mean=[0.48145466, 0.4578275, 0.40821073]		,      image_std=[0.26862954, 0.26130258, 0.27577711]		,      resample=3		,      size=224		,      )

								return image_processor
							@property
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								torch.manual_seed(0				)

								a										=							{
								    """num_attention_heads""": 2,
								    """attention_head_dim""": 16,
								    """embedding_dim""": self.time_input_dim,
								    """num_embeddings""": 32,
								    """embedding_proj_dim""": self.text_embedder_hidden_size,
								    """time_embed_dim""": self.time_embed_dim,
								    """num_layers""": 1,
								    """clip_embed_dim""": self.time_input_dim * 2,
								    """additional_embeddings""": 0,
								    """time_embed_act_fn""": """gelu""",
								    """norm_in_type""": """layer""",
								    """embedding_proj_norm_type""": """layer""",
								    """encoder_hid_proj_type""": None,
								    """added_emb_type""": None,
								}

								a										=							PriorTransformer(**__magic_name__				)
								return model
							@property
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								torch.manual_seed(0				)

								a										=							{
								    """param_shapes""": (
								        (self.renderer_dim, 93),
								        (self.renderer_dim, 8),
								        (self.renderer_dim, 8),
								        (self.renderer_dim, 8),
								    ),
								    """d_latent""": self.time_input_dim,
								    """d_hidden""": self.renderer_dim,
								    """n_output""": 12,
								    """background""": (
								        0.1,
								        0.1,
								        0.1,
								    ),
								}
								a										=							ShapERenderer(**__magic_name__				)
								return model
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								a										=							self.dummy_prior
								a										=							self.dummy_image_encoder
								a										=							self.dummy_image_processor
								a										=							self.dummy_renderer

								a										=							HeunDiscreteScheduler(
								    beta_schedule="""exp"""		,      num_train_timesteps=1024		,      prediction_type="""sample"""		,      use_karras_sigmas=__magic_name__		,      clip_sample=__magic_name__		,      clip_sample_range=1.0		,      )
								a										=							{
								    """prior""": prior,
								    """image_encoder""": image_encoder,
								    """image_processor""": image_processor,
								    """renderer""": renderer,
								    """scheduler""": scheduler,
								}

								return components
							def       lowerCamelCase__							(						self						:List[Any]		,      __magic_name__						:str		,      __magic_name__						:Tuple=0				):







								'''simple docstring'''







								a										=							floats_tensor((1, 3, 64, 64)		,      rng=random.Random(__magic_name__				)				).to(__magic_name__				)

								if str(__magic_name__				).startswith("""mps"""				):
									a										=							torch.manual_seed(__magic_name__				)
								else:
									a										=							torch.Generator(device=__magic_name__				).manual_seed(__magic_name__				)
								a										=							{
								    """image""": input_image,
								    """generator""": generator,
								    """num_inference_steps""": 1,
								    """frame_size""": 32,
								    """output_type""": """np""",
								}
								return inputs
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								a										=							"""cpu"""

								a										=							self.get_dummy_components()

								a										=							self.pipeline_class(**__magic_name__				)
								a										=							pipe.to(__magic_name__				)

								pipe.set_progress_bar_config(disable=__magic_name__				)

								a										=							pipe(**self.get_dummy_inputs(__magic_name__				)				)
								a										=							output.images[0]
								a										=							image[0, -3:, -3:, -1]

								assert image.shape == (20, 32, 32, 3)

								a										=							np.array(
								    [
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								    ]				)

								assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								self._test_inference_batch_consistent(batch_sizes=[1, 2]				)
							def       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







								a										=							torch_device == """cpu"""
								a										=							True
								self._test_inference_batch_single_identical(
								    batch_size=2		,      test_max_difference=__magic_name__		,      relax_max_difference=__magic_name__		,      )





							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								a										=							self.get_dummy_components()
								a										=							self.pipeline_class(**__magic_name__				)
								a										=							pipe.to(__magic_name__				)
								pipe.set_progress_bar_config(disable=__magic_name__				)

								a										=							1
								a										=							2

								a										=							self.get_dummy_inputs(__magic_name__				)

								for key in inputs.keys():
									if key in self.batch_params:
										a										=							batch_size * [inputs[key]]

								a										=							pipe(**__magic_name__		,      num_images_per_prompt=__magic_name__				)[0]

								assert images.shape[0] == batch_size * num_images_per_prompt







@slow
@require_torch_gpu
class 		__lowerCAmelCase	(	unittest.TestCase ):
							def       lowerCamelCase__							(						self						:Optional[int]				):







								'''simple docstring'''







								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()





							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								a										=							load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png"""				)
								a										=							load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/shap_e/test_shap_e_img2img_out.npy"""				)
								a										=							ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img"""				)
								a										=							pipe.to(__magic_name__				)
								pipe.set_progress_bar_config(disable=__magic_name__				)

								a										=							torch.Generator(device=__magic_name__				).manual_seed(0				)

								a										=							pipe(
								    __magic_name__		,      generator=__magic_name__		,      guidance_scale=3.0		,      num_inference_steps=64		,      frame_size=64		,      output_type="""np"""		,      ).images[0]

								assert images.shape == (20, 64, 64, 3)

								assert_mean_pixel_difference(__magic_name__		,      __magic_name__				)

