

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=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        lowerCamelCase__       (    _a):
				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 __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


_UpperCAmelCase	:     Dict   ={
    """susnato/ernie-m-base_pytorch""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json""",
    """susnato/ernie-m-large_pytorch""": """https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json""",
}







class  snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__					:     Dict    =							"""ernie_m"""
						SCREAMING_SNAKE_CASE__					:     Dict[str, str]    =							{"dropout": "classifier_dropout", "num_classes": "num_labels"}




						def __init__( self      ,  __lowercase = 2_5_0_0_0_2      ,  __lowercase = 7_6_8      ,  __lowercase = 1_2      ,  __lowercase = 1_2      ,  __lowercase = 3_0_7_2      ,  __lowercase = "gelu"      ,  __lowercase = 0.1      ,  __lowercase = 0.1      ,  __lowercase = 5_1_4      ,  __lowercase = 0.02      ,  __lowercase = 1      ,  __lowercase = 1e-05      ,  __lowercase=None      ,  __lowercase=False      ,  __lowercase=0.0      ,  **__lowercase      ,  )    ->			Tuple:
										super().__init__(pad_token_id=__lowercase      ,  **__lowercase							)
										lowerCAmelCase_       :					Tuple			 = vocab_size
										lowerCAmelCase_       :					Dict			 = hidden_size
										lowerCAmelCase_       :					Tuple			 = num_hidden_layers
										lowerCAmelCase_       :					int			 = num_attention_heads
										lowerCAmelCase_       :					Dict			 = intermediate_size
										lowerCAmelCase_       :					int			 = hidden_act
										lowerCAmelCase_       :					Union[str, Any]			 = hidden_dropout_prob
										lowerCAmelCase_       :					Any			 = attention_probs_dropout_prob
										lowerCAmelCase_       :					Union[str, Any]			 = max_position_embeddings
										lowerCAmelCase_       :					str			 = initializer_range
										lowerCAmelCase_       :					List[str]			 = layer_norm_eps
										lowerCAmelCase_       :					List[Any]			 = classifier_dropout
										lowerCAmelCase_       :					Any			 = is_decoder
										lowerCAmelCase_       :					List[Any]			 = act_dropout