



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


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


UpperCAmelCase								=   '\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 	__snake_case (				A__):







							'''simple docstring'''






							def __init__(							self					,					a_					,					a_						):
											super().__init__()

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

											a__			 =						None


							def _a (							self					,					a_=0						):
											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"""						)

											a__			 =						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					,					a_=0						):
											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."""						)

											a__			 =						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)

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

											if self.safety_checker is not None:
															a__ ,     a__			 =						cpu_offload_with_hook(self.safety_checker					,					a_					,					prev_module_hook=a_						)

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


							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def _a (							self						):
											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					,					a_					,					a_					,					a_					,					a_					,					a_					,					a_					,					a_					,					a_=None						):
											a__			 =						(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:
															a__			 =						randn_tensor(a_					,					generator=a_					,					device=a_					,					dtype=a_						)
											else:
															a__			 =						latents.to(device=a_					,					dtype=a_						)

											# scale the initial noise by the standard deviation required by the scheduler
											a__			 =						latents * self.scheduler.init_noise_sigma
											return latents


							def _a (							self					,					a_					,					a_ = "pil"						):
											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
											a__			 =						(sample / 2 + 0.5).clamp(0					,					1						)
											if output_type == "pt":
															return sample

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

											# Output_type must be 'pil'
											a__			 =						self.numpy_to_pil(a_						)
											return sample


							def _a (							self					,					a_					,					a_					,					a_=None						):
											if self.unet.config.num_class_embeds is not None:
															if isinstance(a_					,					a_						):
																			a__			 =						torch.tensor(a_					,					dtype=torch.int						)
															elif isinstance(a_					,					a_						):
																			assert batch_size == 1, "Batch size must be 1 if classes is an int"
																			a__			 =						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
																			a__			 =						torch.randint(0					,					self.unet.config.num_class_embeds					,					size=(batch_size,)						)
															a__			 =						class_labels.to(a_						)
											else:
															a__			 =						None
											return class_labels


							def _a (							self					,					a_					,					a_					,					a_					,					a_					,					a_					,					a_						):
											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:
															a__			 =						(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					,					a_ = 1					,					a_ = None					,					a_ = 1					,					a_ = None					,					a_ = None					,					a_ = None					,					a_ = "pil"					,					a_ = True					,					a_ = None					,					a_ = 1					,					):
											a__			 =						self.unet.config.sample_size
											a__			 =						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)
											a__			 =						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
											a__			 =						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_						)
															a__			 =						self.scheduler.timesteps
															a__			 =						len(a_						)
											else:
															self.scheduler.set_timesteps(a_						)
															a__			 =						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_						):
																			a__			 =						self.scheduler.scale_model_input(a_					,					a_						)
																			a__			 =						self.unet(a_					,					a_					,					class_labels=a_					,					return_dict=a_						)[0]

																			a__			 =						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
											a__			 =						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_						)






import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property

class 	__snake_case (				unittest.TestCase):







							'''simple docstring'''






							@cached_property
							def _a (							self						):
											return BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""						)


							@cached_property
							def _a (							self						):
											return BlenderbotTokenizerFast.from_pretrained("""facebook/blenderbot-3B"""						)


							def _a (							self						):
											a__			 =						self.tokenizer_ab
											a__			 =						""" I am a small frog."""
											a__			 =						tok([src_text]					,					padding=a_					,					truncation=a_						)["""input_ids"""]
											a__			 =						tok.batch_decode(a_					,					skip_special_tokens=a_					,					clean_up_tokenization_spaces=a_						)[0]
											assert src_text == decoded


							def _a (							self						):
											a__			 =						self.rust_tokenizer_ab
											a__			 =						""" I am a small frog."""
											a__			 =						tok([src_text]					,					padding=a_					,					truncation=a_						)["""input_ids"""]
											a__			 =						tok.batch_decode(a_					,					skip_special_tokens=a_					,					clean_up_tokenization_spaces=a_						)[0]
											assert src_text == decoded


							def _a (							self						):
											assert self.tokenizer_ab.add_prefix_space
											assert self.tokenizer_ab([""" Sam""", """Sam"""]						).input_ids == [[5_502, 2], [5_502, 2]]







							def _a (							self						):
											assert self.rust_tokenizer_ab.add_prefix_space
											assert self.rust_tokenizer_ab([""" Sam""", """Sam"""]						).input_ids == [[5_502, 2], [5_502, 2]]

