
"""simple docstring"""





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Optional[int]       =		(DDIMParallelScheduler,)
  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		(("""eta""", 0.0), ("""num_inference_steps""", 50))







  def        UpperCamelCase__					(		self				,   **lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    {
         "num_train_timesteps": 1000,
         "beta_start": 0.00_01,
         "beta_end": 0.02,
         "beta_schedule": "linear",
         "clip_sample": True,
     }

     config.update(**lowercase_  )
     return config







  def        UpperCamelCase__					(		self				,   **lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    self.scheduler_classes[0]
     UpperCAmelCase_					:					Union[str, Any]			     =    self.get_scheduler_config(**lowercase_  )
     UpperCAmelCase_					:					int			     =    scheduler_class(**lowercase_  )

     UpperCAmelCase_     ,   UpperCAmelCase_					:					str			     =    10, 0.0

     UpperCAmelCase_					:					Optional[int]			     =    self.dummy_model()
     UpperCAmelCase_					:					str			     =    self.dummy_sample_deter

     scheduler.set_timesteps(lowercase_  )

     for t in scheduler.timesteps:
        UpperCAmelCase_					:					Dict			     =    model(lowercase_				,   lowercase_  )
        UpperCAmelCase_					:					Dict			     =    scheduler.step(lowercase_				,   lowercase_				,   lowercase_				,   lowercase_  ).prev_sample

     return sample







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for timesteps in [100, 500, 1000]:
        self.check_over_configs(num_train_timesteps=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for steps_offset in [0, 1]:
        self.check_over_configs(steps_offset=lowercase_  )

     UpperCAmelCase_					:					str			     =    self.scheduler_classes[0]
     UpperCAmelCase_					:					List[str]			     =    self.get_scheduler_config(steps_offset=1  )
     UpperCAmelCase_					:					List[str]			     =    scheduler_class(**lowercase_  )
     scheduler.set_timesteps(5  )
     assert torch.equal(scheduler.timesteps				,   torch.LongTensor([801, 601, 401, 201, 1]  )  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]				,   [0.0_02, 0.02, 0.2, 2]  ):
        self.check_over_configs(beta_start=lowercase_				,   beta_end=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for schedule in ["linear", "squaredcos_cap_v2"]:
        self.check_over_configs(beta_schedule=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for timestep_spacing in ["trailing", "leading"]:
        self.check_over_configs(timestep_spacing=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for rescale_betas_zero_snr in [True, False]:
        self.check_over_configs(rescale_betas_zero_snr=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     self.check_over_configs(thresholding=lowercase_  )
     for threshold in [0.5, 1.0, 2.0]:
        for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(
               thresholding=lowercase_				,   prediction_type=lowercase_				,   sample_max_value=lowercase_				,   )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for t in [1, 10, 49]:
        self.check_over_forward(time_step=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for t, num_inference_steps in zip([1, 10, 50]				,   [10, 50, 500]  ):
        self.check_over_forward(time_step=lowercase_				,   num_inference_steps=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for t, eta in zip([1, 10, 49]				,   [0.0, 0.5, 1.0]  ):
        self.check_over_forward(time_step=lowercase_				,   eta=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    self.scheduler_classes[0]
     UpperCAmelCase_					:					List[str]			     =    self.get_scheduler_config()
     UpperCAmelCase_					:					List[str]			     =    scheduler_class(**lowercase_  )

     assert torch.sum(torch.abs(scheduler._get_variance(0				,   0  ) - 0.0  )  ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(420				,   400  ) - 0.1_47_71  )  ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(980				,   960  ) - 0.3_24_60  )  ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(0				,   0  ) - 0.0  )  ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(487				,   486  ) - 0.0_09_79  )  ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(999				,   998  ) - 0.02  )  ) < 1E-5







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    self.scheduler_classes[0]
     UpperCAmelCase_					:					Optional[int]			     =    self.get_scheduler_config()
     UpperCAmelCase_					:					List[str]			     =    scheduler_class(**lowercase_  )

     UpperCAmelCase_     ,   UpperCAmelCase_					:					Tuple			     =    10, 0.0
     scheduler.set_timesteps(lowercase_  )

     UpperCAmelCase_					:					Union[str, Any]			     =    self.dummy_model()
     UpperCAmelCase_					:					List[str]			     =    self.dummy_sample_deter
     UpperCAmelCase_					:					Any			     =    self.dummy_sample_deter + 0.1
     UpperCAmelCase_					:					int			     =    self.dummy_sample_deter - 0.1

     UpperCAmelCase_					:					List[Any]			     =    samplea.shape[0]
     UpperCAmelCase_					:					int			     =    torch.stack([samplea, samplea, samplea]				,   dim=0  )
     UpperCAmelCase_					:					int			     =    torch.arange(lowercase_  )[0:3, None].repeat(1				,   lowercase_  )

     UpperCAmelCase_					:					int			     =    model(samples.flatten(0				,   1  )				,   timesteps.flatten(0				,   1  )  )
     UpperCAmelCase_					:					Optional[Any]			     =    scheduler.batch_step_no_noise(lowercase_				,   timesteps.flatten(0				,   1  )				,   samples.flatten(0				,   1  )				,   lowercase_  )

     UpperCAmelCase_					:					List[Any]			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					str			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 11_47.79_04  ) < 1E-2
     assert abs(result_mean.item() - 0.49_82  ) < 1E-3







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    self.full_loop()

     UpperCAmelCase_					:					int			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					List[str]			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 1_72.00_67  ) < 1E-2
     assert abs(result_mean.item() - 0.22_39_67  ) < 1E-3







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    self.full_loop(prediction_type="v_prediction"  )

     UpperCAmelCase_					:					str			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					Dict			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 52.53_02  ) < 1E-2
     assert abs(result_mean.item() - 0.06_84  ) < 1E-3







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     # We specify different beta, so that the first alpha is 0.99
     UpperCAmelCase_					:					List[str]			     =    self.full_loop(set_alpha_to_one=lowercase_				,   beta_start=0.01  )
     UpperCAmelCase_					:					Dict			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					Tuple			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 1_49.82_95  ) < 1E-2
     assert abs(result_mean.item() - 0.19_51  ) < 1E-3







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     # We specify different beta, so that the first alpha is 0.99
     UpperCAmelCase_					:					int			     =    self.full_loop(set_alpha_to_one=lowercase_				,   beta_start=0.01  )
     UpperCAmelCase_					:					List[Any]			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					Dict			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 1_49.07_84  ) < 1E-2
     assert abs(result_mean.item() - 0.19_41  ) < 1E-3









'''simple docstring'''

from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


lowercase_					= logging.get_logger(__name__)  # pylint: disable=invalid-name

lowercase_					= """
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")
        >>> pipe_prior.to(\"cuda\")
        >>> prompt = \"red cat, 4k photo\"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")
        >>> pipe.to(\"cuda\")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save(\"cat.png\")
        ```
"""





def lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    List[Any]=8    )						->Tuple:
    _SCREAMING_SNAKE_CASE									=			height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    _SCREAMING_SNAKE_CASE									=			width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor






class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def __init__(		self ,		A ,		A ,		A ,		)		->   Union[str, Any]:
        super().__init__()

        self.register_modules(
            unet=A ,		scheduler=A ,		movq=A ,		)
        _SCREAMING_SNAKE_CASE									=			2 ** (len(self.movq.config.block_out_channels       ) - 1)


    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A ,		A       )		->   Union[str, Any]:
        if latents is None:
            _SCREAMING_SNAKE_CASE									=			randn_tensor(A ,		generator=A ,		device=A ,		dtype=A       )
        else:
            if latents.shape != shape:
                raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'       )
            _SCREAMING_SNAKE_CASE									=			latents.to(A       )

        _SCREAMING_SNAKE_CASE									=			latents * scheduler.init_noise_sigma
        return latents


    def 			snake_case_(		self ,		A=0       )		->   Dict:
        if is_accelerate_available():
            from accelerate import cpu_offload
        else:
            raise ImportError("""Please install accelerate via `pip install accelerate`"""       )

        _SCREAMING_SNAKE_CASE									=			torch.device(f'cuda:{gpu_id}'       )

        _SCREAMING_SNAKE_CASE									=			[
            self.unet,
            self.movq,
        ]
        for cpu_offloaded_model in models:
            if cpu_offloaded_model is not None:
                cpu_offload(A ,		A       )


    def 			snake_case_(		self ,		A=0       )		->   str:
        if is_accelerate_available() and is_accelerate_version(""">=""" ,		"""0.17.0.dev0"""       ):
            from accelerate import cpu_offload_with_hook
        else:
            raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""       )

        _SCREAMING_SNAKE_CASE									=			torch.device(f'cuda:{gpu_id}'       )

        if self.device.type != "cpu":
            self.to("""cpu""" ,		silence_dtype_warnings=A       )
            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

        _SCREAMING_SNAKE_CASE									=			None
        for cpu_offloaded_model in [self.unet, self.movq]:
            _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			cpu_offload_with_hook(A ,		A ,		prev_module_hook=A       )

        # We'll offload the last model manually.
        _SCREAMING_SNAKE_CASE									=			hook


    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def 			snake_case_(		self       )		->   Tuple:
        if not hasattr(self.unet ,		"""_hf_hook"""       ):
            return self.device
        for module in self.unet.modules():
            if (
                hasattr(A ,		"""_hf_hook"""       )
                and hasattr(module._hf_hook ,		"""execution_device"""       )
                and module._hf_hook.execution_device is not None
            ):
                return torch.device(module._hf_hook.execution_device       )
        return self.device





    @torch.no_grad()
    @replace_example_docstring(A       )
    def __call__(		self ,		A ,		A ,		A = 512 ,		A = 512 ,		A = 100 ,		A = 4.0 ,		A = 1 ,		A = None ,		A = None ,		A = "pil" ,		A = True ,		)		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			self._execution_device

        _SCREAMING_SNAKE_CASE									=			guidance_scale > 1.0

        if isinstance(A ,		A       ):
            _SCREAMING_SNAKE_CASE									=			torch.cat(A ,		dim=0       )
        _SCREAMING_SNAKE_CASE									=			image_embeds.shape[0] * num_images_per_prompt
        if isinstance(A ,		A       ):
            _SCREAMING_SNAKE_CASE									=			torch.cat(A ,		dim=0       )

        if do_classifier_free_guidance:
            _SCREAMING_SNAKE_CASE									=			image_embeds.repeat_interleave(A ,		dim=0       )
            _SCREAMING_SNAKE_CASE									=			negative_image_embeds.repeat_interleave(A ,		dim=0       )

        _SCREAMING_SNAKE_CASE									=			torch.cat([negative_image_embeds, image_embeds] ,		dim=0       ).to(dtype=self.unet.dtype ,		device=A       )

        self.scheduler.set_timesteps(A ,		device=A       )
        _SCREAMING_SNAKE_CASE									=			self.scheduler.timesteps

        _SCREAMING_SNAKE_CASE									=			self.unet.config.in_channels

        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			downscale_height_and_width(A ,		A ,		self.movq_scale_factor       )

        # create initial latent
        _SCREAMING_SNAKE_CASE									=			self.prepare_latents(
            (batch_size, num_channels_latents, height, width) ,		image_embeds.dtype ,		A ,		A ,		A ,		self.scheduler ,		)

        for i, t in enumerate(self.progress_bar(A       )       ):
            # expand the latents if we are doing classifier free guidance
            _SCREAMING_SNAKE_CASE									=			torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents

            _SCREAMING_SNAKE_CASE									=			{"""image_embeds""": image_embeds}
            _SCREAMING_SNAKE_CASE									=			self.unet(
                sample=A ,		timestep=A ,		encoder_hidden_states=A ,		added_cond_kwargs=A ,		return_dict=A ,		)[0]

            if do_classifier_free_guidance:
                _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			noise_pred.split(latents.shape[1] ,		dim=1       )
                _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			noise_pred.chunk(2       )
                _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			variance_pred.chunk(2       )
                _SCREAMING_SNAKE_CASE									=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                _SCREAMING_SNAKE_CASE									=			torch.cat([noise_pred, variance_pred_text] ,		dim=1       )

            if not (
                hasattr(self.scheduler.config ,		"""variance_type"""       )
                and self.scheduler.config.variance_type in ["learned", "learned_range"]
            ):
                _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			noise_pred.split(latents.shape[1] ,		dim=1       )

            # compute the previous noisy sample x_t -> x_t-1
            _SCREAMING_SNAKE_CASE									=			self.scheduler.step(
                A ,		A ,		A ,		generator=A ,		)[0]
        # post-processing
        _SCREAMING_SNAKE_CASE									=			self.movq.decode(A ,		force_not_quantize=A       )["""sample"""]

        if output_type not in ["pt", "np", "pil"]:
            raise ValueError(f'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'       )

        if output_type in ["np", "pil"]:
            _SCREAMING_SNAKE_CASE									=			image * 0.5 + 0.5
            _SCREAMING_SNAKE_CASE									=			image.clamp(0 ,		1       )
            _SCREAMING_SNAKE_CASE									=			image.cpu().permute(0 ,		2 ,		3 ,		1       ).float().numpy()

        if output_type == "pil":
            _SCREAMING_SNAKE_CASE									=			self.numpy_to_pil(A       )

        if not return_dict:
            return (image,)

        return ImagePipelineOutput(images=A       )



