'''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)

'''simple docstring'''




import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase												=logging.get_logger(__name__)

__UpperCAmelCase												={
    "microsoft/unispeech-sat-base-100h-libri-ft": (
        "https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json"
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}




class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					List[Any]							="unispeech-sat"






     def __init__(			self		:		Dict  ,    a		:		str=32  ,    a		:		Any=7_68  ,    a		:		Optional[Any]=12  ,    a		:		Optional[int]=12  ,    a		:		int=30_72  ,    a		:		int="gelu"  ,    a		:		Dict=0.1  ,    a		:		Dict=0.1  ,    a		:		List[Any]=0.1  ,    a		:		Tuple=0.0  ,    a		:		Optional[Any]=0.0  ,    a		:		Tuple=0.1  ,    a		:		List[Any]=0.1  ,    a		:		str=0.02  ,    a		:		List[Any]=1e-5  ,    a		:		int="group"  ,    a		:		Union[str, Any]="gelu"  ,    a		:		Optional[int]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)  ,    a		:		List[Any]=(5, 2, 2, 2, 2, 2, 2)  ,    a		:		int=(10, 3, 3, 3, 3, 2, 2)  ,    a		:		Optional[Any]=False  ,    a		:		Any=1_28  ,    a		:		Tuple=16  ,    a		:		str=False  ,    a		:		Optional[Any]=True  ,    a		:		Dict=0.05  ,    a		:		List[Any]=10  ,    a		:		Any=2  ,    a		:		Optional[Any]=0.0  ,    a		:		Optional[Any]=10  ,    a		:		Any=0  ,    a		:		Any=3_20  ,    a		:		str=2  ,    a		:		List[str]=0.1  ,    a		:		List[str]=1_00  ,    a		:		List[str]=2_56  ,    a		:		str=2_56  ,    a		:		Dict=0.1  ,    a		:		Optional[Any]="mean"  ,    a		:		str=False  ,    a		:		Tuple=False  ,    a		:		Optional[Any]=2_56  ,    a		:		int=(5_12, 5_12, 5_12, 5_12, 15_00)  ,    a		:		int=(5, 3, 3, 1, 1)  ,    a		:		Any=(1, 2, 3, 1, 1)  ,    a		:		Union[str, Any]=5_12  ,    a		:		Optional[int]=0  ,    a		:		Optional[int]=1  ,    a		:		Optional[int]=2  ,    a		:		int=5_04  ,    **a		:		Dict  ,    ):





         """simple docstring"""



         super().__init__(**a  ,    pad_token_id=a  ,    bos_token_id=a  ,    eos_token_id=a   )
         __lowerCamelCase          =  hidden_size
         __lowerCamelCase          =  feat_extract_norm
         __lowerCamelCase          =  feat_extract_activation
         __lowerCamelCase          =  list(a   )
         __lowerCamelCase          =  list(a   )
         __lowerCamelCase          =  list(a   )
         __lowerCamelCase          =  conv_bias
         __lowerCamelCase          =  num_conv_pos_embeddings
         __lowerCamelCase          =  num_conv_pos_embedding_groups
         __lowerCamelCase          =  len(self.conv_dim   )
         __lowerCamelCase          =  num_hidden_layers
         __lowerCamelCase          =  intermediate_size
         __lowerCamelCase          =  hidden_act
         __lowerCamelCase          =  num_attention_heads
         __lowerCamelCase          =  hidden_dropout
         __lowerCamelCase          =  attention_dropout
         __lowerCamelCase          =  activation_dropout
         __lowerCamelCase          =  feat_proj_dropout
         __lowerCamelCase          =  final_dropout
         __lowerCamelCase          =  layerdrop
         __lowerCamelCase          =  layer_norm_eps
         __lowerCamelCase          =  initializer_range
         __lowerCamelCase          =  vocab_size
         __lowerCamelCase          =  num_clusters
         __lowerCamelCase          =  do_stable_layer_norm
         __lowerCamelCase          =  use_weighted_layer_sum

         if (
             (len(self.conv_stride   ) != self.num_feat_extract_layers)
             or (len(self.conv_kernel   ) != self.num_feat_extract_layers)
             or (len(self.conv_dim   ) != self.num_feat_extract_layers)
         ):
             raise ValueError(
                 '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
                 ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
                 f""" {len(self.conv_dim   )}`, `len(config.conv_stride) = {len(self.conv_stride   )}`,"""
                 f""" `len(config.conv_kernel) = {len(self.conv_kernel   )}`."""   )

         # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
         __lowerCamelCase          =  apply_spec_augment
         __lowerCamelCase          =  mask_time_prob
         __lowerCamelCase          =  mask_time_length
         __lowerCamelCase          =  mask_time_min_masks
         __lowerCamelCase          =  mask_feature_prob
         __lowerCamelCase          =  mask_feature_length
         __lowerCamelCase          =  mask_feature_min_masks

         # parameters for pretraining with codevector quantized representations
         __lowerCamelCase          =  num_codevectors_per_group
         __lowerCamelCase          =  num_codevector_groups
         __lowerCamelCase          =  contrastive_logits_temperature
         __lowerCamelCase          =  feat_quantizer_dropout
         __lowerCamelCase          =  num_negatives
         __lowerCamelCase          =  codevector_dim
         __lowerCamelCase          =  proj_codevector_dim
         __lowerCamelCase          =  diversity_loss_weight

         # ctc loss
         __lowerCamelCase          =  ctc_loss_reduction
         __lowerCamelCase          =  ctc_zero_infinity

         # SequenceClassification-specific parameter. Feel free to ignore for other classes.
         __lowerCamelCase          =  classifier_proj_size

         # XVector-specific parameters. Feel free to ignore for other classes.
         __lowerCamelCase          =  list(a   )
         __lowerCamelCase          =  list(a   )
         __lowerCamelCase          =  list(a   )
         __lowerCamelCase          =  xvector_output_dim






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		Any   ):





         """simple docstring"""



         return functools.reduce(operator.mul  ,    self.conv_stride  ,    1   )
