



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

_lowercase						  =		logging.get_logger(__name__)

_lowercase						  =		{
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}



class     _UpperCAmelCase			(       __SCREAMING_SNAKE_CASE     ):
     UpperCamelCase__		    =				'''imagegpt'''
     UpperCamelCase__		    =				['''past_key_values''']
     UpperCamelCase__		    =				{
         '''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__=0.0_2 ,						a__=True ,						a__=True ,						a__=False ,						a__=False ,						a__=False ,						**a__ ,						):
         A__		 =					vocab_size
         A__		 =					n_positions
         A__		 =					n_embd
         A__		 =					n_layer
         A__		 =					n_head
         A__		 =					n_inner
         A__		 =					activation_function
         A__		 =					resid_pdrop
         A__		 =					embd_pdrop
         A__		 =					attn_pdrop
         A__		 =					layer_norm_epsilon
         A__		 =					initializer_range
         A__		 =					scale_attn_weights
         A__		 =					use_cache
         A__		 =					scale_attn_by_inverse_layer_idx
         A__		 =					reorder_and_upcast_attn
         A__		 =					tie_word_embeddings

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







class     _UpperCAmelCase			(       __SCREAMING_SNAKE_CASE     ):




     @property
     def 		snake_case_		(				self):
         return OrderedDict(
             [
                 ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
             ])




     def 		snake_case_		(				self ,						a__ ,						a__ = 1 ,						a__ = -1 ,						a__ = False ,						a__ = None ,						a__ = 3 ,						a__ = 3_2 ,						a__ = 3_2 ,						):
         A__		 =					self._generate_dummy_images(_a ,						_a ,						_a ,						_a)
         A__		 =					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__":
   _lowercase						  =		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__    (					UpperCamelCase_				:       Any	)->  Union[str, 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)

   _lowercase						  =		parser.parse_args()

   _lowercase						  =		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)
