

'''simple docstring'''







from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


__lowerCamelCase		       =    8
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__=BITS				)			->			Any:
       A_						=      x.device

       A_						=      (x * 2_55).int().clamp(0,       2_55				)

       A_						=      2 ** torch.arange(bits - 1,       -1,       -1,       device=UpperCAmelCase__				)
       A_						=      rearrange(UpperCAmelCase__,       """d -> d 1 1"""				)
       A_						=      rearrange(UpperCAmelCase__,       """b c h w -> b c 1 h w"""				)

       A_						=      ((x & mask) != 0).float()
       A_						=      rearrange(UpperCAmelCase__,       """b c d h w -> b (c d) h w"""				)
       A_						=      bits * 2 - 1
       return bits
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__=BITS				)			->			List[str]:
       A_						=      x.device

       A_						=      (x > 0).int()
       A_						=      2 ** torch.arange(bits - 1,       -1,       -1,       device=UpperCAmelCase__,       dtype=torch.intaa				)

       A_						=      rearrange(UpperCAmelCase__,       """d -> d 1 1"""				)
       A_						=      rearrange(UpperCAmelCase__,       """b (c d) h w -> b c d h w""",       d=8				)
       A_						=      reduce(x * mask,       """b c d h w -> b c h w""",       """sum"""				)
       return (dec / 2_55).clamp(0.0,       1.0				)
def       UpperCAmelCase__    (      self,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__ = 0.0,       UpperCAmelCase__ = True,       UpperCAmelCase__=None,       UpperCAmelCase__ = True,       )			->			Union[DDIMSchedulerOutput, Tuple]:
       if self.num_inference_steps is None:
              raise ValueError(
                  """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""				)

       # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
       # Ideally, read DDIM paper in-detail understanding

       # Notation (<variable name> -> <name in paper>
       # - pred_noise_t -> e_theta(x_t, t)
       # - pred_original_sample -> f_theta(x_t, t) or x_0
       # - std_dev_t -> sigma_t
       # - eta -> η
       # - pred_sample_direction -> "direction pointing to x_t"
       # - pred_prev_sample -> "x_t-1"

       # 1. get previous step value (=t-1)
       A_						=      timestep - self.config.num_train_timesteps // self.num_inference_steps

       # 2. compute alphas, betas
       A_						=      self.alphas_cumprod[timestep]
       A_						=      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

       A_						=      1 - alpha_prod_t

       # 3. compute predicted original sample from predicted noise also called
       # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       A_						=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

       # 4. Clip "predicted x_0"
       A_						=      self.bit_scale
       if self.config.clip_sample:
              A_						=      torch.clamp(UpperCAmelCase__,       -scale,       UpperCAmelCase__				)

       # 5. compute variance: "sigma_t(η)" -> see formula (16)
       # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
       A_						=      self._get_variance(UpperCAmelCase__,       UpperCAmelCase__				)
       A_						=      eta * variance ** 0.5

       if use_clipped_model_output:
              # the model_output is always re-derived from the clipped x_0 in Glide
              A_						=      (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

       # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       A_						=      (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

       # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       A_						=      alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

       if eta > 0:
              # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
              A_						=      model_output.device if torch.is_tensor(UpperCAmelCase__				) else """cpu"""
              A_						=      torch.randn(model_output.shape,       dtype=model_output.dtype,       generator=UpperCAmelCase__				).to(UpperCAmelCase__				)
              A_						=      self._get_variance(UpperCAmelCase__,       UpperCAmelCase__				) ** 0.5 * eta * noise

              A_						=      prev_sample + variance

       if not return_dict:
              return (prev_sample,)

       return DDIMSchedulerOutput(prev_sample=UpperCAmelCase__,       pred_original_sample=UpperCAmelCase__				)
def       UpperCAmelCase__    (      self,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__="epsilon",       UpperCAmelCase__=None,       UpperCAmelCase__ = True,       )			->			Union[DDPMSchedulerOutput, Tuple]:
       A_						=      timestep

       if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
              A_					,			A_						=      torch.split(UpperCAmelCase__,       sample.shape[1],       dim=1				)
       else:
              A_						=      None

       # 1. compute alphas, betas
       A_						=      self.alphas_cumprod[t]
       A_						=      self.alphas_cumprod[t - 1] if t > 0 else self.one
       A_						=      1 - alpha_prod_t
       A_						=      1 - alpha_prod_t_prev

       # 2. compute predicted original sample from predicted noise also called
       # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
       if prediction_type == "epsilon":
              A_						=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       elif prediction_type == "sample":
              A_						=      model_output
       else:
              raise ValueError(F'''Unsupported prediction_type {prediction_type}.'''				)

       # 3. Clip "predicted x_0"
       A_						=      self.bit_scale
       if self.config.clip_sample:
              A_						=      torch.clamp(UpperCAmelCase__,       -scale,       UpperCAmelCase__				)

       # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
       # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
       A_						=      (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
       A_						=      self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

       # 5. Compute predicted previous sample µ_t
       # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
       A_						=      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

       # 6. Add noise
       A_						=      0
       if t > 0:
              A_						=      torch.randn(
                  model_output.size(),       dtype=model_output.dtype,       layout=model_output.layout,       generator=UpperCAmelCase__				).to(model_output.device				)
              A_						=      (self._get_variance(UpperCAmelCase__,       predicted_variance=UpperCAmelCase__				) ** 0.5) * noise

       A_						=      pred_prev_sample + variance

       if not return_dict:
              return (pred_prev_sample,)

       return DDPMSchedulerOutput(prev_sample=UpperCAmelCase__,       pred_original_sample=UpperCAmelCase__				)







class 					A__      (      _snake_case			):



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__ = 1.0		,				)						->      str:





           '''simple docstring'''
           super().__init__()
           A_						=      bit_scale
           A_						=      (
               ddim_bit_scheduler_step if isinstance(UpperCamelCase__		,				UpperCamelCase__       ) else ddpm_bit_scheduler_step
           )

           self.register_modules(unet=UpperCamelCase__		,				scheduler=UpperCamelCase__       )







    @torch.no_grad()
    def __call__(					self		,				UpperCamelCase__ = 256		,				UpperCamelCase__ = 256		,				UpperCamelCase__ = 50		,				UpperCamelCase__ = None		,				UpperCamelCase__ = 1		,				UpperCamelCase__ = "pil"		,				UpperCamelCase__ = True		,				**UpperCamelCase__		,				)						->      Union[Tuple, ImagePipelineOutput]:





           '''simple docstring'''
           A_						=      torch.randn(
               (batch_size, self.unet.config.in_channels, height, width)		,				generator=UpperCamelCase__		,				)
           A_						=      decimal_to_bits(UpperCamelCase__       ) * self.bit_scale
           A_						=      latents.to(self.device       )

           self.scheduler.set_timesteps(UpperCamelCase__       )

           for t in self.progress_bar(self.scheduler.timesteps       ):
                  # predict the noise residual
                  A_						=      self.unet(UpperCamelCase__		,				UpperCamelCase__       ).sample

                  # compute the previous noisy sample x_t -> x_t-1
                  A_						=      self.scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       ).prev_sample

           A_						=      bits_to_decimal(UpperCamelCase__       )

           if output_type == "pil":
                  A_						=      self.numpy_to_pil(UpperCamelCase__       )

           if not return_dict:
                  return (image,)

           return ImagePipelineOutput(images=UpperCamelCase__       )



'''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 					A__      (      _snake_case						,						unittest.TestCase			):
    lowercase						=		KandinskyVaaPriorPipeline
    lowercase						=		["prompt"]
    lowercase						=		["prompt", "negative_prompt"]
    lowercase						=		[
        "num_images_per_prompt",
        "generator",
        "num_inference_steps",
        "latents",
        "negative_prompt",
        "guidance_scale",
        "output_type",
        "return_dict",
    ]
    lowercase						=		False



    @property
    def 					snake_case_			(					self       )						->      Any:





           '''simple docstring'''
           return 32



    @property
    def 					snake_case_			(					self       )						->      Optional[int]:





           '''simple docstring'''
           return 32



    @property
    def 					snake_case_			(					self       )						->      Optional[Any]:





           '''simple docstring'''
           return self.time_input_dim



    @property
    def 					snake_case_			(					self       )						->      str:





           '''simple docstring'''
           return self.time_input_dim * 4



    @property
    def 					snake_case_			(					self       )						->      int:





           '''simple docstring'''
           return 100



    @property
    def 					snake_case_			(					self       )						->      Optional[Any]:





           '''simple docstring'''
           A_						=      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )
           return tokenizer



    @property
    def 					snake_case_			(					self       )						->      Union[str, Any]:





           '''simple docstring'''
           torch.manual_seed(0       )
           A_						=      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-0_5		,				num_attention_heads=4		,				num_hidden_layers=5		,				pad_token_id=1		,				vocab_size=1000		,				)
           return CLIPTextModelWithProjection(UpperCamelCase__       )



    @property
    def 					snake_case_			(					self       )						->      Tuple:





           '''simple docstring'''
           torch.manual_seed(0       )

           A_						=      {
               """num_attention_heads""": 2,
               """attention_head_dim""": 12,
               """embedding_dim""": self.text_embedder_hidden_size,
               """num_layers""": 1,
           }

           A_						=      PriorTransformer(**UpperCamelCase__       )
           # 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_						=      nn.Parameter(torch.ones(model.clip_std.shape       )       )
           return model



    @property
    def 					snake_case_			(					self       )						->      str:





           '''simple docstring'''
           torch.manual_seed(0       )
           A_						=      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_						=      CLIPVisionModelWithProjection(UpperCamelCase__       )
           return model



    @property
    def 					snake_case_			(					self       )						->      str:





           '''simple docstring'''
           A_						=      CLIPImageProcessor(
               crop_size=224		,				do_center_crop=UpperCamelCase__		,				do_normalize=UpperCamelCase__		,				do_resize=UpperCamelCase__		,				image_mean=[0.48145466, 0.4578275, 0.40821073]		,				image_std=[0.26862954, 0.26130258, 0.27577711]		,				resample=3		,				size=224		,				)

           return image_processor



    def 					snake_case_			(					self       )						->      Union[str, Any]:





           '''simple docstring'''
           A_						=      self.dummy_prior
           A_						=      self.dummy_image_encoder
           A_						=      self.dummy_text_encoder
           A_						=      self.dummy_tokenizer
           A_						=      self.dummy_image_processor

           A_						=      UnCLIPScheduler(
               variance_type="""fixed_small_log"""		,				prediction_type="""sample"""		,				num_train_timesteps=1000		,				clip_sample=UpperCamelCase__		,				clip_sample_range=10.0		,				)

           A_						=      {
               """prior""": prior,
               """image_encoder""": image_encoder,
               """text_encoder""": text_encoder,
               """tokenizer""": tokenizer,
               """scheduler""": scheduler,
               """image_processor""": image_processor,
           }

           return components



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__=0       )						->      Optional[int]:





           '''simple docstring'''
           if str(UpperCamelCase__       ).startswith("""mps"""       ):
                  A_						=      torch.manual_seed(UpperCamelCase__       )
           else:
                  A_						=      torch.Generator(device=UpperCamelCase__       ).manual_seed(UpperCamelCase__       )
           A_						=      {
               """prompt""": """horse""",
               """generator""": generator,
               """guidance_scale""": 4.0,
               """num_inference_steps""": 2,
               """output_type""": """np""",
           }
           return inputs



    def 					snake_case_			(					self       )						->      List[str]:





           '''simple docstring'''
           A_						=      """cpu"""

           A_						=      self.get_dummy_components()

           A_						=      self.pipeline_class(**UpperCamelCase__       )
           A_						=      pipe.to(UpperCamelCase__       )

           pipe.set_progress_bar_config(disable=UpperCamelCase__       )

           A_						=      pipe(**self.get_dummy_inputs(UpperCamelCase__       )       )
           A_						=      output.image_embeds

           A_						=      pipe(
               **self.get_dummy_inputs(UpperCamelCase__       )		,				return_dict=UpperCamelCase__		,				)[0]

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

           assert image.shape == (1, 32)

           A_						=      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 					snake_case_			(					self       )						->      int:





           '''simple docstring'''
           A_						=      torch_device == """cpu"""
           A_						=      True
           A_						=      False

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







    @skip_mps
    def 					snake_case_			(					self       )						->      Union[str, Any]:





           '''simple docstring'''
           A_						=      torch_device == """cpu"""
           A_						=      False

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