



'''simple docstring'''



import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
       lowerCAmelCase_     :       List[str]								=						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=512,
           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 					UpperCAmelCase   (						A  :				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)

       lowerCAmelCase_     :       int								=						parser.parse_args()

       lowerCAmelCase_     :       Any								=						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)









'''simple docstring'''



from typing import TYPE_CHECKING

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


lowerCAmelCase_     :       Tuple								=						{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_     :       Dict								=						[
							    'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TapasForMaskedLM',
							    'TapasForQuestionAnswering',
							    'TapasForSequenceClassification',
							    'TapasModel',
							    'TapasPreTrainedModel',
							    'load_tf_weights_in_tapas',
							]
try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_     :       str								=						[
							    'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFTapasForMaskedLM',
							    'TFTapasForQuestionAnswering',
							    'TFTapasForSequenceClassification',
							    'TFTapasModel',
							    'TFTapasPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
							from .tokenization_tapas import TapasTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tapas import (
														    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TapasForMaskedLM,
														    TapasForQuestionAnswering,
														    TapasForSequenceClassification,
														    TapasModel,
														    TapasPreTrainedModel,
														    load_tf_weights_in_tapas,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_tapas import (
														    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFTapasForMaskedLM,
														    TFTapasForQuestionAnswering,
														    TFTapasForSequenceClassification,
														    TFTapasModel,
														    TFTapasPreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase_     :       List[Any]								=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




