




from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

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


if TYPE_CHECKING:
					from ... import FeatureExtractionMixin, TensorType

UpperCamelCase__         =      logging.get_logger(__name__)

UpperCamelCase__         =      {
    """openai/imagegpt-small""": """""",
    """openai/imagegpt-medium""": """""",
    """openai/imagegpt-large""": """""",
}
class       a__   ( snake_case__				):
						_a							:   List[str] =   """imagegpt"""
						_a							:   List[str] =   ["""past_key_values"""]
						_a							:   int =   {
						    """hidden_size""": """n_embd""",
						    """max_position_embeddings""": """n_positions""",
						    """num_attention_heads""": """n_head""",
						    """num_hidden_layers""": """n_layer""",
						}






						def __init__(       self		,      _A=5_1_2 + 1		,      _A=3_2 * 3_2		,      _A=5_1_2		,      _A=2_4		,      _A=8		,      _A=None		,      _A="quick_gelu"		,      _A=0.1		,      _A=0.1		,      _A=0.1		,      _A=1E-5		,      _A=0.02		,      _A=True		,      _A=True		,      _A=False		,      _A=False		,      _A=False		,      **_A		,      ):




											"""simple docstring"""



											__lowerCAmelCase           =	vocab_size
											__lowerCAmelCase           =	n_positions
											__lowerCAmelCase           =	n_embd
											__lowerCAmelCase           =	n_layer
											__lowerCAmelCase           =	n_head
											__lowerCAmelCase           =	n_inner
											__lowerCAmelCase           =	activation_function
											__lowerCAmelCase           =	resid_pdrop
											__lowerCAmelCase           =	embd_pdrop
											__lowerCAmelCase           =	attn_pdrop
											__lowerCAmelCase           =	layer_norm_epsilon
											__lowerCAmelCase           =	initializer_range
											__lowerCAmelCase           =	scale_attn_weights
											__lowerCAmelCase           =	use_cache
											__lowerCAmelCase           =	scale_attn_by_inverse_layer_idx
											__lowerCAmelCase           =	reorder_and_upcast_attn
											__lowerCAmelCase           =	tie_word_embeddings

											super().__init__(tie_word_embeddings=_A		,      **_A      )






class       a__   ( snake_case__				):






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return OrderedDict(
											    [
											        ("input_ids", {0: "batch", 1: "sequence"}),
											    ]      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A = 1		,      _A = -1		,      _A = False		,      _A = None		,      _A = 3		,      _A = 3_2		,      _A = 3_2		,      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self._generate_dummy_images(_A		,      _A		,      _A		,      _A      )
											__lowerCAmelCase           =	dict(preprocessor(images=_A		,      return_tensors=_A      )      )

											return inputs










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=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      _a				(						SCREAMING_SNAKE_CASE_							:    List[Any]					):
										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)




