


"""simple docstring"""
from typing import List, Optional, Union

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


SCREAMING_SNAKE_CASE :    Union[str, Any]									=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :    Dict									=				{
    """huggingface/informer-tourism-monthly""": (
        """https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"""
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}



class 			_UpperCAmelCase (							_UpperCAmelCase				):






		'''simple docstring'''

		lowerCamelCase__				="""informer"""
		lowerCamelCase__				={
		    """hidden_size""": """d_model""",
		    """num_attention_heads""": """encoder_attention_heads""",
		    """num_hidden_layers""": """encoder_layers""",
		}



		def __init__(self				,       a_ = None				,       a_ = None				,       a_ = "student_t"				,       a_ = "nll"				,       a_ = 1				,       a_ = None				,       a_ = "mean"				,       a_ = 0				,       a_ = 0				,       a_ = 0				,       a_ = 0				,       a_ = None				,       a_ = None				,       a_ = 64				,       a_ = 32				,       a_ = 32				,       a_ = 2				,       a_ = 2				,       a_ = 2				,       a_ = 2				,       a_ = True				,       a_ = "gelu"				,       a_ = 0.05				,       a_ = 0.1				,       a_ = 0.1				,       a_ = 0.1				,       a_ = 0.1				,       a_ = 1_00				,       a_ = 0.02				,       a_=True				,       a_ = "prob"				,       a_ = 5				,       a_ = True				,       **a_				,       ):





			'''simple docstring'''

			__snake_case	:						Dict						=				prediction_length
			__snake_case	:						Any						=				context_length or prediction_length
			__snake_case	:						int						=				distribution_output
			__snake_case	:						int						=				loss
			__snake_case	:						Any						=				input_size
			__snake_case	:						Union[str, Any]						=				num_time_features
			__snake_case	:						int						=				lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
			__snake_case	:						Union[str, Any]						=				scaling
			__snake_case	:						List[Any]						=				num_dynamic_real_features
			__snake_case	:						Dict						=				num_static_real_features
			__snake_case	:						Optional[int]						=				num_static_categorical_features

			# set cardinality
			if cardinality and num_static_categorical_features > 0:
				if len(UpperCAmelCase_					) != num_static_categorical_features:
					raise ValueError(
					    '''The cardinality should be a list of the same length as `num_static_categorical_features`'''					)
				__snake_case	:						int						=				cardinality
			else:
				__snake_case	:						List[str]						=				[0]

			# set embedding_dimension
			if embedding_dimension and num_static_categorical_features > 0:
				if len(UpperCAmelCase_					) != num_static_categorical_features:
					raise ValueError(
					    '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''					)
				__snake_case	:						str						=				embedding_dimension
			else:
				__snake_case	:						str						=				[min(50				,       (cat + 1) // 2					) for cat in self.cardinality]

			__snake_case	:						Optional[Any]						=				num_parallel_samples

			# Transformer architecture configuration
			__snake_case	:						Optional[Any]						=				input_size * len(self.lags_sequence					) + self._number_of_features
			__snake_case	:						str						=				d_model
			__snake_case	:						Tuple						=				encoder_attention_heads
			__snake_case	:						Dict						=				decoder_attention_heads
			__snake_case	:						List[str]						=				encoder_ffn_dim
			__snake_case	:						Tuple						=				decoder_ffn_dim
			__snake_case	:						Optional[int]						=				encoder_layers
			__snake_case	:						List[Any]						=				decoder_layers

			__snake_case	:						List[str]						=				dropout
			__snake_case	:						Optional[Any]						=				attention_dropout
			__snake_case	:						int						=				activation_dropout
			__snake_case	:						List[Any]						=				encoder_layerdrop
			__snake_case	:						Tuple						=				decoder_layerdrop

			__snake_case	:						str						=				activation_function
			__snake_case	:						List[str]						=				init_std

			__snake_case	:						Dict						=				use_cache

			# Informer
			__snake_case	:						Optional[Any]						=				attention_type
			__snake_case	:						Any						=				sampling_factor
			__snake_case	:						Dict						=				distil

			super().__init__(is_encoder_decoder=UpperCAmelCase_				,       **UpperCAmelCase_					)



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return (
			    sum(self.embedding_dimension					)
			    + self.num_dynamic_real_features
			    + self.num_time_features
			    + self.num_static_real_features
			    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
			)







import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
				__snake_case								=  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 							_lowercase							(     UpperCamelCase_		)		->		Dict:
									'''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)

				__snake_case								=  parser.parse_args()

				__snake_case								=  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)





