


'''simple docstring'''




from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_a      :      List[str]       				=      logging.get_logger(__name__)  # pylint: disable=invalid-name


_a      :      Optional[int]       				=      '\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = "cuda"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample.png")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")\n        ```\n'




class  _lowercase   (  lowerCAmelCase_       ):
   def __init__(	self					:			Optional[Any]					,      SCREAMING_SNAKE_CASE_					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]			)   ->       Tuple:
        super().__init__()

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

        __snake_case    			=	None
   def a     (	self					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			Dict=0			)   ->       Union[str, Any]:





        if is_accelerate_available() and is_accelerate_version('>='					,      '0.14.0'			):
             from accelerate import cpu_offload
        else:
             raise ImportError('`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher'			)

        __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)

        for cpu_offloaded_model in [self.unet]:
             cpu_offload(__a					,      __a			)

        if self.safety_checker is not None:
             cpu_offload(self.safety_checker					,      execution_device=__a					,      offload_buffers=__a			)
   def a     (	self					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			List[Any]=0			)   ->       Tuple:





        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.'			)

        __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)

        __snake_case    			=	None
        for cpu_offloaded_model in [self.unet]:
             __snake_case    			=	cpu_offload_with_hook(__a					,      __a					,      prev_module_hook=__a			)

        if self.safety_checker is not None:
             __snake_case    			=	cpu_offload_with_hook(self.safety_checker					,      __a					,      prev_module_hook=__a			)

        # We'll offload the last model manually.
        __snake_case    			=	hook
   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def a     (	self					:			Union[str, Any]			)   ->       List[str]:





        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
   def a     (	self					:			int					,      SCREAMING_SNAKE_CASE_					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			Tuple					,      SCREAMING_SNAKE_CASE_					:			Tuple					,      SCREAMING_SNAKE_CASE_					:			int					,      SCREAMING_SNAKE_CASE_					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			Any					,      SCREAMING_SNAKE_CASE_					:			Dict=None			)   ->       List[str]:
        __snake_case    			=	(batch_size, num_channels, height, width)
        if isinstance(__a					,      __a			) and len(__a			) != batch_size:
             raise ValueError(
                 f'You have passed a list of generators of length {len(__a			)}, but requested an effective batch'
                 f' size of {batch_size}. Make sure the batch size matches the length of the generators.'			)

        if latents is None:
             __snake_case    			=	randn_tensor(__a					,      generator=__a					,      device=__a					,      dtype=__a			)
        else:
             __snake_case    			=	latents.to(device=__a					,      dtype=__a			)

        # scale the initial noise by the standard deviation required by the scheduler
        __snake_case    			=	latents * self.scheduler.init_noise_sigma
        return latents
   def a     (	self					:			int					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]					,      SCREAMING_SNAKE_CASE_					:			str = "pil"			)   ->       Optional[Any]:





        if output_type not in ["pt", "np", "pil"]:
             raise ValueError(
                 f'output_type={output_type} is not supported. Make sure to choose one of [\'pt\', \'np\', or \'pil\']'			)

        # Equivalent to diffusers.VaeImageProcessor.denormalize
        __snake_case    			=	(sample / 2 + 0.5).clamp(0					,      1			)
        if output_type == "pt":
             return sample

        # Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
        __snake_case    			=	sample.cpu().permute(0					,      2					,      3					,      1			).numpy()
        if output_type == "np":
             return sample

        # Output_type must be 'pil'
        __snake_case    			=	self.numpy_to_pil(__a			)
        return sample
   def a     (	self					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=None			)   ->       List[str]:





        if self.unet.config.num_class_embeds is not None:
             if isinstance(__a					,      __a			):
                  __snake_case    			=	torch.tensor(__a					,      dtype=torch.int			)
             elif isinstance(__a					,      __a			):
                  assert batch_size == 1, "Batch size must be 1 if classes is an int"
                  __snake_case    			=	torch.tensor([class_labels]					,      dtype=torch.int			)
             elif class_labels is None:
                  # Randomly generate batch_size class labels
                  # TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
                  __snake_case    			=	torch.randint(0					,      self.unet.config.num_class_embeds					,      size=(batch_size,)			)
             __snake_case    			=	class_labels.to(__a			)
        else:
             __snake_case    			=	None
        return class_labels
   def a     (	self					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			int					,      SCREAMING_SNAKE_CASE_					:			Dict					,      SCREAMING_SNAKE_CASE_					:			Any					,      SCREAMING_SNAKE_CASE_					:			int					,      SCREAMING_SNAKE_CASE_					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			str			)   ->       List[Any]:





        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:
             logger.warning(
                 f'Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;'
                 ' `timesteps` will be used over `num_inference_steps`.'			)

        if latents is not None:
             __snake_case    			=	(batch_size, 3, img_size, img_size)
             if latents.shape != expected_shape:
                  raise ValueError(f'The shape of latents is {latents.shape} but is expected to be {expected_shape}.'			)

        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(__a					,      __a			) or callback_steps <= 0)
        ):
             raise ValueError(
                 f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
                 f' {type(__a			)}.'			)





   @torch.no_grad()
   @replace_example_docstring(__a			)
   def __call__(	self					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			int = 1					,      SCREAMING_SNAKE_CASE_					:			List[Any] = None					,      SCREAMING_SNAKE_CASE_					:			List[Any] = 1					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any] = None					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any] = None					,      SCREAMING_SNAKE_CASE_					:			List[Any] = None					,      SCREAMING_SNAKE_CASE_					:			int = "pil"					,      SCREAMING_SNAKE_CASE_					:			int = True					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any] = None					,      SCREAMING_SNAKE_CASE_					:			int = 1					,      )   ->       Tuple:
        __snake_case    			=	self.unet.config.sample_size
        __snake_case    			=	self._execution_device

        # 1. Check inputs
        self.check_inputs(__a					,      __a					,      __a					,      __a					,      __a					,      __a			)

        # 2. Prepare image latents
        # Sample image latents x_0 ~ N(0, sigma_0^2 * I)
        __snake_case    			=	self.prepare_latents(
            batch_size=__a					,      num_channels=self.unet.config.in_channels					,      height=__a					,      width=__a					,      dtype=self.unet.dtype					,      device=__a					,      generator=__a					,      latents=__a					,      )

        # 3. Handle class_labels for class-conditional models
        __snake_case    			=	self.prepare_class_labels(__a					,      __a					,      class_labels=__a			)

        # 4. Prepare timesteps
        if timesteps is not None:
             self.scheduler.set_timesteps(timesteps=__a					,      device=__a			)
             __snake_case    			=	self.scheduler.timesteps
             __snake_case    			=	len(__a			)
        else:
             self.scheduler.set_timesteps(__a			)
             __snake_case    			=	self.scheduler.timesteps

        # 5. Denoising loop
        # Multistep sampling: implements Algorithm 1 in the paper
        with self.progress_bar(total=__a			) as progress_bar:
             for i, t in enumerate(__a			):
                  __snake_case    			=	self.scheduler.scale_model_input(__a					,      __a			)
                  __snake_case    			=	self.unet(__a					,      __a					,      class_labels=__a					,      return_dict=__a			)[0]

                  __snake_case    			=	self.scheduler.step(__a					,      __a					,      __a					,      generator=__a			)[0]

                  # call the callback, if provided
                  progress_bar.update()
                  if callback is not None and i % callback_steps == 0:
                       callback(__a					,      __a					,      __a			)

        # 6. Post-process image sample
        __snake_case    			=	self.postprocess_image(__a					,      output_type=__a			)

        # Offload last model to CPU
        if hasattr(self					,      'final_offload_hook'			) and self.final_offload_hook is not None:
             self.final_offload_hook.offload()

        if not return_dict:
             return (image,)

        return ImagePipelineOutput(images=__a			)








'''simple docstring'''




_a      :      List[Any]       				=      [0.33, 0.44, 0.55, 0.44, 0.33]
_a      :      Union[str, Any]       				=      3
_a      :      Optional[Any]       				=      5

# Buffer Space (with Parameters Space)
_a      :      list[float]       				=      []
_a      :      list[float]       				=      []

# Machine Time
_a      :      str       				=      0





def      _a		(lowercase__ :      Optional[int]	) -> List[str]:






     """simple docstring"""
     global buffer_space, params_space, machine_time, K, m, t

     # Choosing Dynamical Systems (All)
     for key, value in enumerate(lowercase__	):
          # Evolution Parameter
          __snake_case    			=	float(seed / value	)

          # Control Theory: Orbit Change
          __snake_case    			=	(buffer_space[(key + 1) % m] + e) % 1

          # Control Theory: Trajectory Change
          __snake_case    			=	(params_space[key] + e) % 1 + 3

          # Modification (Transition Function) - Jumps
          __snake_case    			=	round(float(r * value * (1 - value)	)       ,      1_0	)
          __snake_case    			=	r  # Saving to Parameters Space

     # Logistic Map
     assert max(lowercase__	) < 1
     assert max(lowercase__	) < 4

     # Machine Time
     machine_time += 1





def      _a		() -> Tuple:






     """simple docstring"""
     global buffer_space, params_space, machine_time, K, m, t

     # PRNG (Xorshift by George Marsaglia)
     def xorshift(lowercase__ :      Tuple       ,      lowercase__ :      int	):
          x ^= y >> 1_3
          y ^= x << 1_7
          x ^= y >> 5
          return x

     # Choosing Dynamical Systems (Increment)
     __snake_case    			=	machine_time % m

     # Evolution (Time Length)
     for _ in range(0       ,      lowercase__	):
          # Variables (Position + Parameters)
          __snake_case    			=	params_space[key]
          __snake_case    			=	buffer_space[key]

          # Modification (Transition Function) - Flow
          __snake_case    			=	round(float(r * value * (1 - value)	)       ,      1_0	)
          __snake_case    			=	(machine_time * 0.01 + r * 1.01) % 1 + 3

     # Choosing Chaotic Data
     __snake_case    			=	int(buffer_space[(key + 2) % m] * (1_0**1_0)	)
     __snake_case    			=	int(buffer_space[(key - 2) % m] * (1_0**1_0)	)

     # Machine Time
     machine_time += 1

     return xorshift(lowercase__       ,      lowercase__	) % 0xF_F_F_F_F_F_F_F





def      _a		() -> List[str]:






     """simple docstring"""
     global buffer_space, params_space, machine_time, K, m, t

     __snake_case    			=	K
     __snake_case    			=	[0] * m
     __snake_case    			=	0


if __name__ == "__main__":
       # Initialization
       reset()

       # Pushing Data (Input)
       import random

       _a      :      Tuple       				=      random.sample(range(0xFFFF_FFFF), 100)
       for chunk in message:
              push(chunk)

       # for controlling
       _a      :      List[Any]       				=      ""

       # Pulling Data (Output)
       while inp in ("e", "E"):
              print(f'''{format(pull(), "#04x")}''')
              print(buffer_space)
              print(params_space)
              _a      :      List[str]       				=      input("(e)exit? ").strip()




