




from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
		import tensorflow as tf


_lowerCamelCase  :			List[str]        =       logging.get_logger(__name__)

@dataclass
class        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    Optional[Any]         =   [
				    """no_inference""",
				    """no_cuda""",
				    """no_tpu""",
				    """no_speed""",
				    """no_memory""",
				    """no_env_print""",
				    """no_multi_process""",
				]







				def __init__(							self   :					Union[str, Any]      ,     **_lowerCamelCase   :					Optional[int]       ):



					"""simple docstring"""

					for deprecated_arg in self.deprecated_args:
						if deprecated_arg in kwargs:
							A_  :				Optional[Any]	   =		deprecated_arg[3:]
							A_  :				List[str]	   =		not kwargs.pop(_lowerCamelCase       )
							logger.warning(
							    F"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
							    F""" {positive_arg}={kwargs[positive_arg]}"""       )
					A_  :				Dict	   =		kwargs.pop('''tpu_name'''      ,     self.tpu_name       )
					A_  :				Dict	   =		kwargs.pop('''device_idx'''      ,     self.device_idx       )
					A_  :				List[Any]	   =		kwargs.pop('''eager_mode'''      ,     self.eager_mode       )
					A_  :				Tuple	   =		kwargs.pop('''use_xla'''      ,     self.use_xla       )
					super().__init__(**_lowerCamelCase       )

				__lowerCAmelCase    :    str         =   field(
				    default=__UpperCAmelCase     ,	metadata={"""help""": """Name of TPU"""}     ,	)
				__lowerCAmelCase    :    int         =   field(
				    default=0     ,	metadata={"""help""": """CPU / GPU device index. Defaults to 0."""}     ,	)
				__lowerCAmelCase    :    bool         =   field(default=__UpperCAmelCase     ,	metadata={"""help""": """Benchmark models in eager model."""})
				__lowerCAmelCase    :    bool         =   field(
				    default=__UpperCAmelCase     ,	metadata={
				        """help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
				    }     ,	)







				@cached_property
				def 					a_  (							self   :					Optional[Any]       ):



					"""simple docstring"""

					requires_backends(self      ,     ['''tf''']       )
					A_  :				Optional[Any]	   =		None
					if self.tpu:
						try:
							if self.tpu_name:
								A_  :				Tuple	   =		tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name       )
							else:
								A_  :				Tuple	   =		tf.distribute.cluster_resolver.TPUClusterResolver()
						except ValueError:
							A_  :				int	   =		None
					return tpu







				@cached_property
				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					requires_backends(self      ,     ['''tf''']       )
					if self.is_tpu:
						tf.config.experimental_connect_to_cluster(self._setup_tpu       )
						tf.tpu.experimental.initialize_tpu_system(self._setup_tpu       )

						A_  :				Optional[Any]	   =		tf.distribute.TPUStrategy(self._setup_tpu       )
					else:
						# currently no multi gpu is allowed
						if self.is_gpu:
							# TODO: Currently only single GPU is supported
							tf.config.set_visible_devices(self.gpu_list[self.device_idx]      ,     '''GPU'''       )
							A_  :				Any	   =		tf.distribute.OneDeviceStrategy(device=F"""/gpu:{self.device_idx}"""       )
						else:
							tf.config.set_visible_devices([]      ,     '''GPU'''       )  # disable GPU
							A_  :				Dict	   =		tf.distribute.OneDeviceStrategy(device=F"""/cpu:{self.device_idx}"""       )

					return strategy







				@property
				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					requires_backends(self      ,     ['''tf''']       )
					return self._setup_tpu is not None







				@property
				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					requires_backends(self      ,     ['''tf''']       )
					return self._setup_strategy







				@property
				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					requires_backends(self      ,     ['''tf''']       )
					return tf.config.list_physical_devices('''GPU'''       )







				@property
				def 					a_  (							self   :					Tuple       ):



					"""simple docstring"""

					requires_backends(self      ,     ['''tf''']       )
					if self.cuda:
						return len(self.gpu_list       )
					return 0







				@property
				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

					return self.n_gpu > 0











"""simple docstring"""

import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
		_lowerCamelCase  :			Optional[int]        =       argparse.ArgumentParser()

		parser.add_argument(
		    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
		)
		# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
		parser.add_argument(
		    '--original_config_file',
		    default=None,
		    type=str,
		    help='The YAML config file corresponding to the original architecture.',
		)
		parser.add_argument(
		    '--num_in_channels',
		    default=None,
		    type=int,
		    help='The number of input channels. If `None` number of input channels will be automatically inferred.',
		)
		parser.add_argument(
		    '--scheduler_type',
		    default='pndm',
		    type=str,
		    help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
		)
		parser.add_argument(
		    '--pipeline_type',
		    default=None,
		    type=str,
		    help=(
		        'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
		        '. If `None` pipeline will be automatically inferred.'
		    ),
		)
		parser.add_argument(
		    '--image_size',
		    default=None,
		    type=int,
		    help=(
		        'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
		        ' Base. Use 768 for Stable Diffusion v2.'
		    ),
		)
		parser.add_argument(
		    '--prediction_type',
		    default=None,
		    type=str,
		    help=(
		        'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
		        ' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'
		    ),
		)
		parser.add_argument(
		    '--extract_ema',
		    action='store_true',
		    help=(
		        'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
		        ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
		        ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
		    ),
		)
		parser.add_argument(
		    '--upcast_attention',
		    action='store_true',
		    help=(
		        'Whether the attention computation should always be upcasted. This is necessary when running stable'
		        ' diffusion 2.1.'
		    ),
		)
		parser.add_argument(
		    '--from_safetensors',
		    action='store_true',
		    help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
		)
		parser.add_argument(
		    '--to_safetensors',
		    action='store_true',
		    help='Whether to store pipeline in safetensors format or not.',
		)
		parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
		parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
		parser.add_argument(
		    '--stable_unclip',
		    type=str,
		    default=None,
		    required=False,
		    help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
		)
		parser.add_argument(
		    '--stable_unclip_prior',
		    type=str,
		    default=None,
		    required=False,
		    help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
		)
		parser.add_argument(
		    '--clip_stats_path',
		    type=str,
		    help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
		    required=False,
		)
		parser.add_argument(
		    '--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
		)
		parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
		parser.add_argument(
		    '--vae_path',
		    type=str,
		    default=None,
		    required=False,
		    help='Set to a path, hub id to an already converted vae to not convert it again.',
		)
		_lowerCamelCase  :			Optional[Any]        =       parser.parse_args()

		_lowerCamelCase  :			Optional[Any]        =       download_from_original_stable_diffusion_ckpt(
		    checkpoint_path=args.checkpoint_path,
		    original_config_file=args.original_config_file,
		    image_size=args.image_size,
		    prediction_type=args.prediction_type,
		    model_type=args.pipeline_type,
		    extract_ema=args.extract_ema,
		    scheduler_type=args.scheduler_type,
		    num_in_channels=args.num_in_channels,
		    upcast_attention=args.upcast_attention,
		    from_safetensors=args.from_safetensors,
		    device=args.device,
		    stable_unclip=args.stable_unclip,
		    stable_unclip_prior=args.stable_unclip_prior,
		    clip_stats_path=args.clip_stats_path,
		    controlnet=args.controlnet,
		    vae_path=args.vae_path,
		)

		if args.half:
				pipe.to(torch_dtype=torch.floataa)

		if args.controlnet:
				# only save the controlnet model
				pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
		else:
				pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)





