


lowerCAmelCase	=							'0.18.2'

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


try:
			if not is_onnx_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from .utils.dummy_onnx_objects import *  # noqa F403
else:
			from .pipelines import OnnxRuntimeModel

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from .utils.dummy_pt_objects import *  # noqa F403
else:
			from .models import (
			    AutoencoderKL,
			    ControlNetModel,
			    ModelMixin,
			    PriorTransformer,
			    TaFilmDecoder,
			    TransformeraDModel,
			    UNetaDModel,
			    UNetaDConditionModel,
			    UNetaDModel,
			    UNetaDConditionModel,
			    VQModel,
			)
			from .optimization import (
			    get_constant_schedule,
			    get_constant_schedule_with_warmup,
			    get_cosine_schedule_with_warmup,
			    get_cosine_with_hard_restarts_schedule_with_warmup,
			    get_linear_schedule_with_warmup,
			    get_polynomial_decay_schedule_with_warmup,
			    get_scheduler,
			)
			from .pipelines import (
			    AudioPipelineOutput,
			    ConsistencyModelPipeline,
			    DanceDiffusionPipeline,
			    DDIMPipeline,
			    DDPMPipeline,
			    DiffusionPipeline,
			    DiTPipeline,
			    ImagePipelineOutput,
			    KarrasVePipeline,
			    LDMPipeline,
			    LDMSuperResolutionPipeline,
			    PNDMPipeline,
			    RePaintPipeline,
			    ScoreSdeVePipeline,
			)
			from .schedulers import (
			    CMStochasticIterativeScheduler,
			    DDIMInverseScheduler,
			    DDIMParallelScheduler,
			    DDIMScheduler,
			    DDPMParallelScheduler,
			    DDPMScheduler,
			    DEISMultistepScheduler,
			    DPMSolverMultistepInverseScheduler,
			    DPMSolverMultistepScheduler,
			    DPMSolverSinglestepScheduler,
			    EulerAncestralDiscreteScheduler,
			    EulerDiscreteScheduler,
			    HeunDiscreteScheduler,
			    IPNDMScheduler,
			    KarrasVeScheduler,
			    KDPMaAncestralDiscreteScheduler,
			    KDPMaDiscreteScheduler,
			    PNDMScheduler,
			    RePaintScheduler,
			    SchedulerMixin,
			    ScoreSdeVeScheduler,
			    UnCLIPScheduler,
			    UniPCMultistepScheduler,
			    VQDiffusionScheduler,
			)
			from .training_utils import EMAModel

try:
			if not (is_torch_available() and is_scipy_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
			from .schedulers import LMSDiscreteScheduler

try:
			if not (is_torch_available() and is_torchsde_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
			from .schedulers import DPMSolverSDEScheduler

try:
			if not (is_torch_available() and is_transformers_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
			from .pipelines import (
			    AltDiffusionImgaImgPipeline,
			    AltDiffusionPipeline,
			    AudioLDMPipeline,
			    CycleDiffusionPipeline,
			    IFImgaImgPipeline,
			    IFImgaImgSuperResolutionPipeline,
			    IFInpaintingPipeline,
			    IFInpaintingSuperResolutionPipeline,
			    IFPipeline,
			    IFSuperResolutionPipeline,
			    ImageTextPipelineOutput,
			    KandinskyImgaImgPipeline,
			    KandinskyInpaintPipeline,
			    KandinskyPipeline,
			    KandinskyPriorPipeline,
			    KandinskyVaaControlnetImgaImgPipeline,
			    KandinskyVaaControlnetPipeline,
			    KandinskyVaaImgaImgPipeline,
			    KandinskyVaaInpaintPipeline,
			    KandinskyVaaPipeline,
			    KandinskyVaaPriorEmbaEmbPipeline,
			    KandinskyVaaPriorPipeline,
			    LDMTextToImagePipeline,
			    PaintByExamplePipeline,
			    SemanticStableDiffusionPipeline,
			    ShapEImgaImgPipeline,
			    ShapEPipeline,
			    StableDiffusionAttendAndExcitePipeline,
			    StableDiffusionControlNetImgaImgPipeline,
			    StableDiffusionControlNetInpaintPipeline,
			    StableDiffusionControlNetPipeline,
			    StableDiffusionDepthaImgPipeline,
			    StableDiffusionDiffEditPipeline,
			    StableDiffusionImageVariationPipeline,
			    StableDiffusionImgaImgPipeline,
			    StableDiffusionInpaintPipeline,
			    StableDiffusionInpaintPipelineLegacy,
			    StableDiffusionInstructPixaPixPipeline,
			    StableDiffusionLatentUpscalePipeline,
			    StableDiffusionLDMaDPipeline,
			    StableDiffusionModelEditingPipeline,
			    StableDiffusionPanoramaPipeline,
			    StableDiffusionParadigmsPipeline,
			    StableDiffusionPipeline,
			    StableDiffusionPipelineSafe,
			    StableDiffusionPixaPixZeroPipeline,
			    StableDiffusionSAGPipeline,
			    StableDiffusionUpscalePipeline,
			    StableUnCLIPImgaImgPipeline,
			    StableUnCLIPPipeline,
			    TextToVideoSDPipeline,
			    TextToVideoZeroPipeline,
			    UnCLIPImageVariationPipeline,
			    UnCLIPPipeline,
			    UniDiffuserModel,
			    UniDiffuserPipeline,
			    UniDiffuserTextDecoder,
			    VersatileDiffusionDualGuidedPipeline,
			    VersatileDiffusionImageVariationPipeline,
			    VersatileDiffusionPipeline,
			    VersatileDiffusionTextToImagePipeline,
			    VideoToVideoSDPipeline,
			    VQDiffusionPipeline,
			)

try:
			if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
			from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
			if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
			from .pipelines import StableDiffusionKDiffusionPipeline

try:
			if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from .utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
			from .pipelines import (
			    OnnxStableDiffusionImgaImgPipeline,
			    OnnxStableDiffusionInpaintPipeline,
			    OnnxStableDiffusionInpaintPipelineLegacy,
			    OnnxStableDiffusionPipeline,
			    OnnxStableDiffusionUpscalePipeline,
			    StableDiffusionOnnxPipeline,
			)

try:
			if not (is_torch_available() and is_librosa_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from .utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
			from .pipelines import AudioDiffusionPipeline, Mel

try:
			if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from .utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
			from .pipelines import SpectrogramDiffusionPipeline

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from .utils.dummy_flax_objects import *  # noqa F403
else:
			from .models.controlnet_flax import FlaxControlNetModel
			from .models.modeling_flax_utils import FlaxModelMixin
			from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
			from .models.vae_flax import FlaxAutoencoderKL
			from .pipelines import FlaxDiffusionPipeline
			from .schedulers import (
			    FlaxDDIMScheduler,
			    FlaxDDPMScheduler,
			    FlaxDPMSolverMultistepScheduler,
			    FlaxKarrasVeScheduler,
			    FlaxLMSDiscreteScheduler,
			    FlaxPNDMScheduler,
			    FlaxSchedulerMixin,
			    FlaxScoreSdeVeScheduler,
			)


try:
			if not (is_flax_available() and is_transformers_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from .utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
			from .pipelines import (
			    FlaxStableDiffusionControlNetPipeline,
			    FlaxStableDiffusionImgaImgPipeline,
			    FlaxStableDiffusionInpaintPipeline,
			    FlaxStableDiffusionPipeline,
			)

try:
			if not (is_note_seq_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from .utils.dummy_note_seq_objects import *  # noqa F403
else:
			from .pipelines import MidiProcessor






# 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.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


lowerCAmelCase	=							'Create a default config file for Accelerate with only a few flags set.'
def       _a			(				SCREAMING_SNAKE_CASE="no"						,    SCREAMING_SNAKE_CASE = default_json_config_file						,    SCREAMING_SNAKE_CASE = False					):


		"""simple docstring"""




		lowercase__						=				Path(SCREAMING_SNAKE_CASE					)
		path.parent.mkdir(parents=SCREAMING_SNAKE_CASE						,    exist_ok=SCREAMING_SNAKE_CASE					)
		if path.exists():
				print(
				    f'Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'					)
				return False
		lowercase__						=				mixed_precision.lower()
		if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
				raise ValueError(
				    f'`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'					)
		lowercase__						=				{
		    '''compute_environment''': '''LOCAL_MACHINE''',
		    '''mixed_precision''': mixed_precision,
		}
		if torch.cuda.is_available():
				lowercase__						=				torch.cuda.device_count()
				lowercase__						=				num_gpus
				lowercase__						=				False
				if num_gpus > 1:
						lowercase__						=				'''MULTI_GPU'''
				else:
						lowercase__						=				'''NO'''
		elif is_xpu_available() and use_xpu:
				lowercase__						=				torch.xpu.device_count()
				lowercase__						=				num_xpus
				lowercase__						=				False
				if num_xpus > 1:
						lowercase__						=				'''MULTI_XPU'''
				else:
						lowercase__						=				'''NO'''
		elif is_npu_available():
				lowercase__						=				torch.npu.device_count()
				lowercase__						=				num_npus
				lowercase__						=				False
				if num_npus > 1:
						lowercase__						=				'''MULTI_NPU'''
				else:
						lowercase__						=				'''NO'''
		else:
				lowercase__						=				0
				lowercase__						=				True
				lowercase__						=				1
				lowercase__						=				'''NO'''
		lowercase__						=				ClusterConfig(**SCREAMING_SNAKE_CASE					)
		config.to_json_file(SCREAMING_SNAKE_CASE					)
		return path
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				parser.add_parser('''default'''						,    parents=SCREAMING_SNAKE_CASE						,    help=SCREAMING_SNAKE_CASE						,    formatter_class=SCREAMING_SNAKE_CASE					)
		parser.add_argument(
		    '''--config_file'''						,    default=SCREAMING_SNAKE_CASE						,    help=(
		        '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
		        '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
		        '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
		        '''with \'huggingface\'.'''
		    )						,    dest='''save_location'''						,    )

		parser.add_argument(
		    '''--mixed_precision'''						,    choices=['''no''', '''fp16''', '''bf16''']						,    type=SCREAMING_SNAKE_CASE						,    help='''Whether or not to use mixed precision training. '''
		    '''Choose between FP16 and BF16 (bfloat16) training. '''
		    '''BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.'''						,    default='''no'''						,    )
		parser.set_defaults(func=SCREAMING_SNAKE_CASE					)
		return parser
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				write_basic_config(args.mixed_precision						,    args.save_location					)
		if config_file:
				print(f'accelerate configuration saved at {config_file}'					)


