



"""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    :Dict     =					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    :Tuple     =					parser.parse_args()

  _lowerCAmelCase    :Tuple     =					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)







"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

_lowerCAmelCase    :Any     =					{
    'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
    'tiiuae/falcon-7b': 'https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json',
}
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     a__									='''falcon'''
     a__									=['''past_key_values''']





     def __init__(					self       ,	A=6_5_0_2_4       ,	A=4_5_4_4       ,	A=3_2       ,	A=7_1       ,	A=1E-5       ,	A=0.02       ,	A=True       ,	A=0.0       ,	A=0.0       ,	A=None       ,	A=False       ,	A=False       ,	A=True       ,	A=True       ,	A=False       ,	A=1_1       ,	A=1_1       ,	**A       ,	)							->					Any:
         _UpperCAmelCase							:  int											=    vocab_size
         # Backward compatibility with n_embed kwarg
         _UpperCAmelCase							:  Optional[Any]											=    kwargs.pop('''n_embed'''       ,	A		)
         _UpperCAmelCase							:  int											=    hidden_size if n_embed is None else n_embed
         _UpperCAmelCase							:  List[str]											=    num_hidden_layers
         _UpperCAmelCase							:  Tuple											=    num_attention_heads
         _UpperCAmelCase							:  Optional[int]											=    layer_norm_epsilon
         _UpperCAmelCase							:  Tuple											=    initializer_range
         _UpperCAmelCase							:  Optional[int]											=    use_cache
         _UpperCAmelCase							:  Any											=    hidden_dropout
         _UpperCAmelCase							:  Dict											=    attention_dropout

         _UpperCAmelCase							:  Any											=    bos_token_id
         _UpperCAmelCase							:  List[Any]											=    eos_token_id
         _UpperCAmelCase							:  Tuple											=    num_attention_heads if num_kv_heads is None else num_kv_heads
         _UpperCAmelCase							:  Dict											=    alibi
         _UpperCAmelCase							:  Optional[int]											=    new_decoder_architecture
         _UpperCAmelCase							:  str											=    multi_query  # Ignored when new_decoder_architecture is True
         _UpperCAmelCase							:  Optional[int]											=    parallel_attn
         _UpperCAmelCase							:  Optional[int]											=    bias

         super().__init__(bos_token_id=A       ,	eos_token_id=A       ,	**A		)





     @property
     def  __lowerCAmelCase	(					self		)							->					List[str]:
         return self.hidden_size // self.num_attention_heads





     @property
     def  __lowerCAmelCase	(					self		)							->					List[Any]:
         return not self.alibi


