'''simple docstring'''




import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
  __UpperCAmelCase     							=argparse.ArgumentParser()

  parser.add_argument(
      "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
  )
  parser.add_argument(
      "--original_config_file",
      type=str,
      required=True,
      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(
      "--image_size",
      default=5_1_2,
      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(
      "--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.)")






  def __lowerCAmelCase (							UpperCamelCase__				)	->    Tuple:
      if string == "True":
          return True
      elif string == "False":
          return False
      else:
          raise ValueError(f"""could not parse string as bool {string}"""				)

  parser.add_argument(
      "--use_linear_projection", help="Override for use linear projection", required=False, type=parse_bool
  )

  parser.add_argument("--cross_attention_dim", help="Override for cross attention_dim", required=False, type=int)

  __UpperCAmelCase     							=parser.parse_args()

  __UpperCAmelCase     							=download_controlnet_from_original_ckpt(
      checkpoint_path=args.checkpoint_path,
      original_config_file=args.original_config_file,
      image_size=args.image_size,
      extract_ema=args.extract_ema,
      num_in_channels=args.num_in_channels,
      upcast_attention=args.upcast_attention,
      from_safetensors=args.from_safetensors,
      device=args.device,
      use_linear_projection=args.use_linear_projection,
      cross_attention_dim=args.cross_attention_dim,
  )



  controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a  :     Any       =  {
    """configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a  :     Optional[int]       =  ["""BloomTokenizerFast"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a  :     str       =  [
		    """BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """BloomForCausalLM""",
		    """BloomModel""",
		    """BloomPreTrainedModel""",
		    """BloomForSequenceClassification""",
		    """BloomForTokenClassification""",
		    """BloomForQuestionAnswering""",
		]

if TYPE_CHECKING:
		from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_bloom_fast import BloomTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bloom import (
				    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BloomForCausalLM,
				    BloomForQuestionAnswering,
				    BloomForSequenceClassification,
				    BloomForTokenClassification,
				    BloomModel,
				    BloomPreTrainedModel,
				)

else:
		import sys

		__a  :     Dict       =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)