"""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


UpperCAmelCase       :    Optional[Any]									=			logging.get_logger(__name__)  # pylint: disable=invalid-name


UpperCAmelCase       :    List[Any]									=			"\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        __SCREAMING_SNAKE_CASE     (   _lowerCAmelCase							):







				def __init__(    self    :List[str]      ,__UpperCAmelCase    :UNetaDModel      ,__UpperCAmelCase    :CMStochasticIterativeScheduler  )      ->					None:




									"""simple docstring"""



									super().__init__()

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

									lowerCamelCase__		:   Optional[int]  				=					None







				def   lowercase_		(    self    :int      ,__UpperCAmelCase    :Dict=0  )      ->					Dict:




									"""simple docstring"""



									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'''  )

									lowerCamelCase__		:   Dict  				=					torch.device(F"""cuda:{gpu_id}"""  )

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

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







				def   lowercase_		(    self    :Optional[Any]      ,__UpperCAmelCase    :Any=0  )      ->					Optional[int]:




									"""simple docstring"""



									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.'''  )

									lowerCamelCase__		:   Tuple  				=					torch.device(F"""cuda:{gpu_id}"""  )

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

									lowerCamelCase__		:   str  				=					None
									for cpu_offloaded_model in [self.unet]:
														lowerCamelCase__				,						lowerCamelCase__		:   str  				=					cpu_offload_with_hook(__UpperCAmelCase      ,__UpperCAmelCase      ,prev_module_hook=__UpperCAmelCase  )

									if self.safety_checker is not None:
														lowerCamelCase__				,						lowerCamelCase__		:   Optional[int]  				=					cpu_offload_with_hook(self.safety_checker      ,__UpperCAmelCase      ,prev_module_hook=__UpperCAmelCase  )

									# We'll offload the last model manually.
									lowerCamelCase__		:   List[str]  				=					hook







				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def   lowercase_		(    self    :Tuple  )      ->					str:




									"""simple docstring"""



									if not hasattr(self.unet      ,'''_hf_hook'''  ):
														return self.device
									for module in self.unet.modules():
														if (
														    hasattr(__UpperCAmelCase      ,'''_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   lowercase_		(    self    :Tuple      ,__UpperCAmelCase    :Optional[int]      ,__UpperCAmelCase    :List[Any]      ,__UpperCAmelCase    :str      ,__UpperCAmelCase    :Optional[Any]      ,__UpperCAmelCase    :Dict      ,__UpperCAmelCase    :Union[str, Any]      ,__UpperCAmelCase    :str      ,__UpperCAmelCase    :Union[str, Any]=None  )      ->					List[Any]:




									"""simple docstring"""



									lowerCamelCase__		:   Tuple  				=					(batch_size, num_channels, height, width)
									if isinstance(__UpperCAmelCase      ,__UpperCAmelCase  ) and len(__UpperCAmelCase  ) != batch_size:
														raise ValueError(
														    F"""You have passed a list of generators of length {len(__UpperCAmelCase  )}, 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:
														lowerCamelCase__		:   List[Any]  				=					randn_tensor(__UpperCAmelCase      ,generator=__UpperCAmelCase      ,device=__UpperCAmelCase      ,dtype=__UpperCAmelCase  )
									else:
														lowerCamelCase__		:   Any  				=					latents.to(device=__UpperCAmelCase      ,dtype=__UpperCAmelCase  )

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







				def   lowercase_		(    self    :Any      ,__UpperCAmelCase    :torch.FloatTensor      ,__UpperCAmelCase    :str = "pil"  )      ->					Dict:




									"""simple docstring"""



									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
									lowerCamelCase__		:   Union[str, Any]  				=					(sample / 2 + 0.5).clamp(0      ,1  )
									if output_type == "pt":
														return sample

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

									# Output_type must be 'pil'
									lowerCamelCase__		:   int  				=					self.numpy_to_pil(__UpperCAmelCase  )
									return sample







				def   lowercase_		(    self    :Optional[int]      ,__UpperCAmelCase    :List[Any]      ,__UpperCAmelCase    :Any      ,__UpperCAmelCase    :Any=None  )      ->					List[Any]:




									"""simple docstring"""



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







				def   lowercase_		(    self    :int      ,__UpperCAmelCase    :Optional[Any]      ,__UpperCAmelCase    :Optional[Any]      ,__UpperCAmelCase    :Tuple      ,__UpperCAmelCase    :str      ,__UpperCAmelCase    :Tuple      ,__UpperCAmelCase    :List[Any]  )      ->					str:




									"""simple docstring"""



									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:
														lowerCamelCase__		:   str  				=					(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(__UpperCAmelCase      ,__UpperCAmelCase  ) or callback_steps <= 0)
									):
														raise ValueError(
														    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
														    F""" {type(__UpperCAmelCase  )}."""  )







				@torch.no_grad()
				@replace_example_docstring(__UpperCAmelCase  )
				def __call__(    self    :Optional[Any]      ,__UpperCAmelCase    :int = 1      ,__UpperCAmelCase    :Optional[Union[torch.Tensor, List[int], int]] = None      ,__UpperCAmelCase    :int = 1      ,__UpperCAmelCase    :List[int] = None      ,__UpperCAmelCase    :Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,__UpperCAmelCase    :Optional[torch.FloatTensor] = None      ,__UpperCAmelCase    :Optional[str] = "pil"      ,__UpperCAmelCase    :bool = True      ,__UpperCAmelCase    :Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,__UpperCAmelCase    :int = 1      ,)      ->					int:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					self.unet.config.sample_size
									lowerCamelCase__		:   List[Any]  				=					self._execution_device

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

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

									# 3. Handle class_labels for class-conditional models
									lowerCamelCase__		:   List[Any]  				=					self.prepare_class_labels(__UpperCAmelCase      ,__UpperCAmelCase      ,class_labels=__UpperCAmelCase  )

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

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

																			lowerCamelCase__		:   Any  				=					self.scheduler.step(__UpperCAmelCase      ,__UpperCAmelCase      ,__UpperCAmelCase      ,generator=__UpperCAmelCase  )[0]

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

        # 6. Post-process image sample
									lowerCamelCase__		:   Any  				=					self.postprocess_image(__UpperCAmelCase      ,output_type=__UpperCAmelCase  )

									# 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=__UpperCAmelCase  )







"""simple docstring"""




import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
					import torch

					from transformers import GPTaLMHeadModel

@require_torch
@require_sentencepiece
@require_tokenizers
class        __SCREAMING_SNAKE_CASE     (   unittest.TestCase							):







				@slow
				@unittest.skip('''Model is not available.'''  )
				def   lowercase_		(    self    :Union[str, Any]  )      ->					Optional[int]:




									"""simple docstring"""



									lowerCamelCase__		:   Any  				=					'''nvidia/megatron-gpt2-345m/'''
									if "MYDIR" in os.environ:
														lowerCamelCase__		:   str  				=					os.path.join(os.environ['''MYDIR''']      ,__UpperCAmelCase  )
									lowerCamelCase__		:   Dict  				=					GPTaLMHeadModel.from_pretrained(__UpperCAmelCase  )
									model.to(__UpperCAmelCase  )
									model.half()

									lowerCamelCase__		:   Dict  				=					torch.tensor(
									    [[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]]      ,device=__UpperCAmelCase      ,dtype=torch.long      ,)

									with torch.no_grad():
														lowerCamelCase__		:   Tuple  				=					model(__UpperCAmelCase  ).logits

									lowerCamelCase__		:   List[str]  				=					torch.Size((1, 9, 5_02_57)  )
									self.assertEqual(output.shape      ,__UpperCAmelCase  )

									lowerCamelCase__		:   Tuple  				=					torch.tensor(
									    [
									        4.9_414,
									        -0.2_920,
									        -1.2_148,
									        -4.0_273,
									        -0.5_161,
									        -5.2_109,
									        -1.2_412,
									        -1.8_301,
									        -1.7_734,
									        -4.7_148,
									        -0.2_317,
									        -1.0_811,
									        -2.1_777,
									        0.4_141,
									        -3.7_969,
									        -4.0_586,
									        -2.5_332,
									        -3.3_809,
									        4.3_867,
									    ]      ,device=__UpperCAmelCase      ,dtype=torch.half      ,)

									for i in range(19  ):
														lowerCamelCase__				,						lowerCamelCase__		:   Dict  				=					8 * i // 17, 27_92 * i  # along the diagonal
														lowerCamelCase__				,						lowerCamelCase__		:   Optional[int]  				=					output[0, r, c], expected_diag[i]
														lowerCamelCase__		:   Dict  				=					F"""row={r} col={c} computed={computed} expected={expected}"""
														self.assertAlmostEqual(__UpperCAmelCase      ,__UpperCAmelCase      ,delta=1E-4      ,msg=__UpperCAmelCase  )






