
import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
			_A     =  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_12,
			    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__       (       __lowerCAmelCase						:						Optional[int]       ):




						"""simple docstring"""


						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)

			_A     =  parser.parse_args()

			_A     =  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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A     =  logging.get_logger(__name__)

_A     =  {
    "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/config.json",
    "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/config.json",
    "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/config.json",
    "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/config.json",
    "bert-base-multilingual-uncased": "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json",
    "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json",
    "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/config.json",
    "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/config.json",
    "bert-large-uncased-whole-word-masking": (
        "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"
    ),
    "bert-large-cased-whole-word-masking": (
        "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"
    ),
    "bert-large-uncased-whole-word-masking-finetuned-squad": (
        "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"
    ),
    "bert-large-cased-whole-word-masking-finetuned-squad": (
        "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"
    ),
    "bert-base-cased-finetuned-mrpc": "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json",
    "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json",
    "bert-base-german-dbmdz-uncased": "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json",
    "cl-tohoku/bert-base-japanese": "https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json",
    "cl-tohoku/bert-base-japanese-whole-word-masking": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"
    ),
    "cl-tohoku/bert-base-japanese-char": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"
    ),
    "cl-tohoku/bert-base-japanese-char-whole-word-masking": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"
    ),
    "TurkuNLP/bert-base-finnish-cased-v1": (
        "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"
    ),
    "TurkuNLP/bert-base-finnish-uncased-v1": (
        "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"
    ),
    "wietsedv/bert-base-dutch-cased": "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json",
    # See all BERT models at https://huggingface.co/models?filter=bert
}





class 			_lowerCAmelCase      (			__a ):
				_lowercase    	='''bert'''




				def __init__(      self       ,       _UpperCamelCase=30_522       ,       _UpperCamelCase=768       ,       _UpperCamelCase=12       ,       _UpperCamelCase=12       ,       _UpperCamelCase=3_072       ,       _UpperCamelCase="gelu"       ,       _UpperCamelCase=0.1       ,       _UpperCamelCase=0.1       ,       _UpperCamelCase=512       ,       _UpperCamelCase=2       ,       _UpperCamelCase=0.02       ,       _UpperCamelCase=1e-1_2       ,       _UpperCamelCase=0       ,       _UpperCamelCase="absolute"       ,       _UpperCamelCase=True       ,       _UpperCamelCase=None       ,       **_UpperCamelCase       ,       )       ->   str:
							super().__init__(pad_token_id=_UpperCamelCase       ,       **_UpperCamelCase  )

							lowerCAmelCase_      			=			vocab_size
							lowerCAmelCase_      			=			hidden_size
							lowerCAmelCase_      			=			num_hidden_layers
							lowerCAmelCase_      			=			num_attention_heads
							lowerCAmelCase_      			=			hidden_act
							lowerCAmelCase_      			=			intermediate_size
							lowerCAmelCase_      			=			hidden_dropout_prob
							lowerCAmelCase_      			=			attention_probs_dropout_prob
							lowerCAmelCase_      			=			max_position_embeddings
							lowerCAmelCase_      			=			type_vocab_size
							lowerCAmelCase_      			=			initializer_range
							lowerCAmelCase_      			=			layer_norm_eps
							lowerCAmelCase_      			=			position_embedding_type
							lowerCAmelCase_      			=			use_cache
							lowerCAmelCase_      			=			classifier_dropout





class 			_lowerCAmelCase      (			__a ):




				@property
				def   __a		(      self  )       ->   Mapping[str, Mapping[int, str]]:
							if self.task == "multiple-choice":
										lowerCAmelCase_      			=			{0: "batch", 1: "choice", 2: "sequence"}
							else:
										lowerCAmelCase_      			=			{0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("input_ids", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							        ("token_type_ids", dynamic_axis),
							    ]  )



