

'''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


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


lowerCAmelCase__      				=			"\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    (	a_			):


							def __init__(      self     ,      _snake_case     ,      _snake_case)				->					None:
								super().__init__()

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

								UpperCAmelCase_		:   List[str]													=       None


							def     _snake_case				(      self     ,      _snake_case=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')

								UpperCAmelCase_		:   Optional[Any]													=       torch.device(F"""cuda:{gpu_id}""")

								if self.device.type != "cpu":
									self.to('cpu'     ,      silence_dtype_warnings=_snake_case)
									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(_snake_case     ,      _snake_case)

								if self.safety_checker is not None:
									cpu_offload(self.safety_checker     ,      execution_device=_snake_case     ,      offload_buffers=_snake_case)


							def     _snake_case				(      self     ,      _snake_case=0)				->					Any:
								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.')

								UpperCAmelCase_		:   List[Any]													=       torch.device(F"""cuda:{gpu_id}""")

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

								UpperCAmelCase_		:   List[Any]													=       None
								for cpu_offloaded_model in [self.unet]:
									UpperCAmelCase_						,     UpperCAmelCase_		:   Dict													=       cpu_offload_with_hook(_snake_case     ,      _snake_case     ,      prev_module_hook=_snake_case)

								if self.safety_checker is not None:
									UpperCAmelCase_						,     UpperCAmelCase_		:   Tuple													=       cpu_offload_with_hook(self.safety_checker     ,      _snake_case     ,      prev_module_hook=_snake_case)

								# We'll offload the last model manually.
								UpperCAmelCase_		:   Optional[Any]													=       hook


							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def     _snake_case				(      self)				->					int:
								if not hasattr(self.unet     ,      '_hf_hook'):
									return self.device
								for module in self.unet.modules():
									if (
									    hasattr(_snake_case     ,      '_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     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case=None)				->					str:
								UpperCAmelCase_		:   Dict													=       (batch_size, num_channels, height, width)
								if isinstance(_snake_case     ,      _snake_case) and len(_snake_case) != batch_size:
									raise ValueError(
									    F"""You have passed a list of generators of length {len(_snake_case)}, 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:
									UpperCAmelCase_		:   Any													=       randn_tensor(_snake_case     ,      generator=_snake_case     ,      device=_snake_case     ,      dtype=_snake_case)
								else:
									UpperCAmelCase_		:   List[Any]													=       latents.to(device=_snake_case     ,      dtype=_snake_case)

								# scale the initial noise by the standard deviation required by the scheduler
								UpperCAmelCase_		:   Union[str, Any]													=       latents * self.scheduler.init_noise_sigma
								return latents


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = "pil")				->					str:
								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
								UpperCAmelCase_		:   Union[str, Any]													=       (sample / 2 + 0.5).clamp(0     ,      1)
								if output_type == "pt":
									return sample

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

								# Output_type must be 'pil'
								UpperCAmelCase_		:   Dict													=       self.numpy_to_pil(_snake_case)
								return sample


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case=None)				->					Tuple:
								if self.unet.config.num_class_embeds is not None:
									if isinstance(_snake_case     ,      _snake_case):
										UpperCAmelCase_		:   str													=       torch.tensor(_snake_case     ,      dtype=torch.int)
									elif isinstance(_snake_case     ,      _snake_case):
										assert batch_size == 1, "Batch size must be 1 if classes is an int"
										UpperCAmelCase_		:   int													=       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
										UpperCAmelCase_		:   List[str]													=       torch.randint(0     ,      self.unet.config.num_class_embeds     ,      size=(batch_size,))
									UpperCAmelCase_		:   Optional[int]													=       class_labels.to(_snake_case)
								else:
									UpperCAmelCase_		:   List[str]													=       None
								return class_labels


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case)				->					Optional[int]:
								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:
									UpperCAmelCase_		:   int													=       (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(_snake_case     ,      _snake_case) or callback_steps <= 0)
								):
									raise ValueError(
									    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
									    F""" {type(_snake_case)}.""")





							@torch.no_grad()
							@replace_example_docstring(_snake_case)
							def __call__(      self     ,      _snake_case = 1     ,      _snake_case = None     ,      _snake_case = 1     ,      _snake_case = None     ,      _snake_case = None     ,      _snake_case = None     ,      _snake_case = "pil"     ,      _snake_case = True     ,      _snake_case = None     ,      _snake_case = 1     ,      )				->					Tuple:
								UpperCAmelCase_		:   Dict													=       self.unet.config.sample_size
								UpperCAmelCase_		:   Optional[Any]													=       self._execution_device

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

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

								# 3. Handle class_labels for class-conditional models
								UpperCAmelCase_		:   List[str]													=       self.prepare_class_labels(_snake_case     ,      _snake_case     ,      class_labels=_snake_case)

								# 4. Prepare timesteps
								if timesteps is not None:
									self.scheduler.set_timesteps(timesteps=_snake_case     ,      device=_snake_case)
									UpperCAmelCase_		:   Optional[Any]													=       self.scheduler.timesteps
									UpperCAmelCase_		:   str													=       len(_snake_case)
								else:
									self.scheduler.set_timesteps(_snake_case)
									UpperCAmelCase_		:   List[str]													=       self.scheduler.timesteps

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

										UpperCAmelCase_		:   Union[str, Any]													=       self.scheduler.step(_snake_case     ,      _snake_case     ,      _snake_case     ,      generator=_snake_case)[0]

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

        # 6. Post-process image sample
								UpperCAmelCase_		:   str													=       self.postprocess_image(_snake_case     ,      output_type=_snake_case)

								# 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=_snake_case)









'''simple docstring'''


import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
						import torch



class        lowercase    (	unittest.TestCase,       a_			):


							def     _snake_case				(      self)				->					Optional[int]:
								UpperCAmelCase_		:   int													=       load_tool('speech-to-text')
								self.tool.setup()


							def     _snake_case				(      self)				->					Any:
								UpperCAmelCase_		:   str													=       self.tool(torch.ones(3000))
								self.assertEqual(_snake_case     ,      ' you')





							def     _snake_case				(      self)				->					str:
								UpperCAmelCase_		:   List[Any]													=       self.tool(audio=torch.ones(3000))
								self.assertEqual(_snake_case     ,      ' you')






