


'''simple docstring'''



# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import json
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import yaml

from ...utils import ComputeEnvironment, DistributedType, SageMakerDistributedType
from ...utils.constants import SAGEMAKER_PYTHON_VERSION, SAGEMAKER_PYTORCH_VERSION, SAGEMAKER_TRANSFORMERS_VERSION


__lowercase			:		Any =			os.path.expanduser(
    os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
__lowercase			:		Union[str, Any] =			os.path.join(hf_cache_home, 'accelerate')
__lowercase			:		str =			os.path.join(cache_dir, 'default_config.yaml')
__lowercase			:		Optional[int] =			os.path.join(cache_dir, 'default_config.yaml')

# For backward compatibility: the default config is the json one if it's the only existing file.
if os.path.isfile(default_yaml_config_file) or not os.path.isfile(default_json_config_file):
	__lowercase			:		Union[str, Any] =			default_yaml_config_file
else:
	__lowercase			:		str =			default_json_config_file






def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Dict     ):
					if config_file is not None:
										if not os.path.isfile(_SCREAMING_SNAKE_CASE     ):
															raise FileNotFoundError(
															    F"""The passed configuration file `{config_file}` does not exist. """
															    'Please pass an existing file to `accelerate launch`, or use the the default one '
															    'created through `accelerate config` and run `accelerate launch` '
															    'without the `--config_file` argument.'     )
					else:
										__a    : str      					=						default_config_file
					with open(_SCREAMING_SNAKE_CASE		,   'r'		,   encoding='utf-8'     ) as f:
										if config_file.endswith('.json'     ):
															if (
															    json.load(_SCREAMING_SNAKE_CASE     ).get('compute_environment'		,   ComputeEnvironment.LOCAL_MACHINE     )
															    == ComputeEnvironment.LOCAL_MACHINE
															):
																				__a    : str      					=						ClusterConfig
															else:
																				__a    : List[Any]      					=						SageMakerConfig
															return config_class.from_json_file(json_file=_SCREAMING_SNAKE_CASE     )
										else:
															if (
															    yaml.safe_load(_SCREAMING_SNAKE_CASE     ).get('compute_environment'		,   ComputeEnvironment.LOCAL_MACHINE     )
															    == ComputeEnvironment.LOCAL_MACHINE
															):
																				__a    : List[Any]      					=						ClusterConfig
															else:
																				__a    : Dict      					=						SageMakerConfig
															return config_class.from_yaml_file(yaml_file=_SCREAMING_SNAKE_CASE     )





@dataclass
class 							__UpperCamelCase       :
	A_			    =					42
	A_			    =					42
	A_			    =					42
	A_			    =					42




	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						__a    : Optional[Any]      					=						self.__dict__
						# For serialization, it's best to convert Enums to strings (or their underlying value type).
						for key, value in result.items():
											if isinstance(__a		,       __a							):
																__a    : Optional[Any]      					=						value.value
											if isinstance(__a		,       __a							) and not bool(__a							):
																__a    : str      					=						None
						__a    : Tuple      					=						{k: v for k, v in result.items() if v is not None}
						return result




	@classmethod
	def    __UpperCAmelCase							(     cls		,       __a=None							):

						'''simple docstring'''





						__a    : Tuple      					=						default_json_config_file if json_file is None else json_file
						with open(__a		,       'r'		,       encoding='utf-8'							) as f:
											__a    : Optional[Any]      					=						json.load(__a							)
						if "compute_environment" not in config_dict:
											__a    : List[str]      					=						ComputeEnvironment.LOCAL_MACHINE
						if "mixed_precision" not in config_dict:
											__a    : Union[str, Any]      					=						'fp16' if ('fp16' in config_dict and config_dict['fp16']) else None
						if "fp16" in config_dict:  # Convert the config to the new format.
											del config_dict["fp16"]
						if "dynamo_backend" in config_dict:  # Convert the config to the new format.
											__a    : Optional[Any]      					=						config_dict.pop('dynamo_backend'							)
											__a    : int      					=						{} if dynamo_backend == 'NO' else {'dynamo_backend': dynamo_backend}
						if "use_cpu" not in config_dict:
											__a    : str      					=						False
						return cls(**__a							)




	def    __UpperCAmelCase							(     self		,       __a							):

						'''simple docstring'''





						with open(__a		,       'w'		,       encoding='utf-8'							) as f:
											__a    : Dict      					=						json.dumps(self.to_dict()		,       indent=2		,       sort_keys=__a							) + '\n'
											f.write(__a							)




	@classmethod
	def    __UpperCAmelCase							(     cls		,       __a=None							):

						'''simple docstring'''





						__a    : List[Any]      					=						default_yaml_config_file if yaml_file is None else yaml_file
						with open(__a		,       'r'		,       encoding='utf-8'							) as f:
											__a    : Union[str, Any]      					=						yaml.safe_load(__a							)
						if "compute_environment" not in config_dict:
											__a    : int      					=						ComputeEnvironment.LOCAL_MACHINE

						if "mixed_precision" not in config_dict:
											__a    : Any      					=						'fp16' if ('fp16' in config_dict and config_dict['fp16']) else None
						if isinstance(config_dict['mixed_precision']		,       __a							) and not config_dict["mixed_precision"]:
											__a    : Any      					=						'no'
						if "fp16" in config_dict:  # Convert the config to the new format.
											del config_dict["fp16"]
						if "dynamo_backend" in config_dict:  # Convert the config to the new format.
											__a    : Optional[int]      					=						config_dict.pop('dynamo_backend'							)
											__a    : List[Any]      					=						{} if dynamo_backend == 'NO' else {'dynamo_backend': dynamo_backend}
						if "use_cpu" not in config_dict:
											__a    : List[str]      					=						False
						return cls(**__a							)




	def    __UpperCAmelCase							(     self		,       __a							):

						'''simple docstring'''





						with open(__a		,       'w'		,       encoding='utf-8'							) as f:
											yaml.safe_dump(self.to_dict()		,       __a							)





	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						if isinstance(self.compute_environment		,       __a							):
											__a    : str      					=						ComputeEnvironment(self.compute_environment							)
						if isinstance(self.distributed_type		,       __a							):
											if self.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
																__a    : Union[str, Any]      					=						SageMakerDistributedType(self.distributed_type							)
											else:
																__a    : int      					=						DistributedType(self.distributed_type							)
						if self.dynamo_config is None:
											__a    : Any      					=						{}





@dataclass
class 							__UpperCamelCase       (	lowerCAmelCase_      ):
	A_			    =					42
	A_			    =					0
	A_			    =					1
	A_			    =					None
	A_			    =					None
	A_			    =					None
	A_			    =					"static"
	A_			    =					False
	A_			    =					"main"

	# args for deepspeed_plugin
	A_			    =					None
	# args for fsdp
	A_			    =					None
	# args for megatron_lm
	A_			    =					None
	# args for ipex
	A_			    =					None
	# args for TPU
	A_			    =					False

	# args for TPU pods
	A_			    =					None
	A_			    =					None
	A_			    =					False
	A_			    =					False
	A_			    =					None
	A_			    =					None
	A_			    =					None
	A_			    =					None

	# args for dynamo
	A_			    =					None




	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						if self.deepspeed_config is None:
											__a    : Dict      					=						{}
						if self.fsdp_config is None:
											__a    : List[Any]      					=						{}
						if self.megatron_lm_config is None:
											__a    : Union[str, Any]      					=						{}
						if self.ipex_config is None:
											__a    : Optional[Any]      					=						{}
						return super().__post_init__()





@dataclass
class 							__UpperCamelCase       (	lowerCAmelCase_      ):
	A_			    =					42
	A_			    =					42
	A_			    =					None
	A_			    =					None
	A_			    =					"us-east-1"
	A_			    =					1
	A_			    =					"all"
	A_			    =					f"""accelerate-sagemaker-{num_machines}"""
	A_			    =					SAGEMAKER_PYTORCH_VERSION
	A_			    =					SAGEMAKER_TRANSFORMERS_VERSION
	A_			    =					SAGEMAKER_PYTHON_VERSION
	A_			    =					None
	A_			    =					None
	A_			    =					None
	A_			    =					None







'''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


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


__lowercase			:		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 							__UpperCamelCase       (	lowerCAmelCase_      ):




	def __init__(     self		,       __a		,       __a							):

						'''simple docstring'''





						super().__init__()

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

						__a    : List[str]      					=						None




	def    __UpperCAmelCase							(     self		,       __a=0							):

						'''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'							)

						__a    : str      					=						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    __UpperCAmelCase							(     self		,       __a=0							):

						'''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.'							)

						__a    : Tuple      					=						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    : List[Any]      					=						None
						for cpu_offloaded_model in [self.unet]:
											__a					,       __a    : List[str]      					=						cpu_offload_with_hook(__a		,       __a		,       prev_module_hook=__a							)

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

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




	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						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    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a		,       __a		,       __a		,       __a		,       __a		,       __a=None							):

						'''simple docstring'''





						__a    : str      					=						(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    : Optional[Any]      					=						randn_tensor(__a		,       generator=__a		,       device=__a		,       dtype=__a							)
						else:
											__a    : Dict      					=						latents.to(device=__a		,       dtype=__a							)

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




	def    __UpperCAmelCase							(     self		,       __a		,       __a = "pil"							):

						'''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
						__a    : List[Any]      					=						(sample / 2 + 0.5).clamp(0		,       1							)
						if output_type == "pt":
											return sample

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

						# Output_type must be 'pil'
						__a    : Union[str, Any]      					=						self.numpy_to_pil(__a							)
						return sample




	def    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a=None							):

						'''simple docstring'''





						if self.unet.config.num_class_embeds is not None:
											if isinstance(__a		,       __a							):
																__a    : Tuple      					=						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    : 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
																__a    : str      					=						torch.randint(0		,       self.unet.config.num_class_embeds		,       size=(batch_size,)							)
											__a    : str      					=						class_labels.to(__a							)
						else:
											__a    : Optional[int]      					=						None
						return class_labels




	def    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a		,       __a		,       __a		,       __a							):

						'''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:
											__a    : Optional[Any]      					=						(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		,       ):

						'''simple docstring'''





						__a    : Dict      					=						self.unet.config.sample_size
						__a    : Union[str, Any]      					=						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    : Union[str, Any]      					=						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    : Optional[Any]      					=						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    : Union[str, Any]      					=						self.scheduler.timesteps
											__a    : Tuple      					=						len(__a							)
						else:
											self.scheduler.set_timesteps(__a							)
											__a    : Tuple      					=						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    : Optional[int]      					=						self.scheduler.scale_model_input(__a		,       __a							)
																__a    : List[str]      					=						self.unet(__a		,       __a		,       class_labels=__a		,       return_dict=__a							)[0]

																__a    : int      					=						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    : Tuple      					=						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							)



