


import copy
import os
from typing import Union

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


_lowerCAmelCase:						List[str]   				=       logging.get_logger(__name__)

_lowerCAmelCase:						Any   				=       {
    'google/pix2struct-textcaps-base': (
        'https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'
    ),
}




class       lowercase_					(lowercase__   ):
				snake_case	    ='pix2struct_text_model'
				snake_case	    =['past_key_values']
				snake_case	    ={
				    'hidden_size': 'hidden_size',
				    'num_attention_heads': 'num_heads',
				    'num_hidden_layers': 'num_layers',
				}





				def __init__(				self       ,  lowercase_=50244       ,  lowercase_=768       ,  lowercase_=64       ,  lowercase_=2048       ,  lowercase_=12       ,  lowercase_=12       ,  lowercase_=32       ,  lowercase_=128       ,  lowercase_=0.1       ,  lowercase_=1e-6       ,  lowercase_=1.0       ,  lowercase_="gelu_new"       ,  lowercase_=0       ,  lowercase_=False       ,  lowercase_=0       ,  lowercase_=1       ,  lowercase_=False       ,  lowercase_=True       ,  **lowercase_       ,  )  -> str:
											a__      =vocab_size
											a__      =hidden_size
											a__      =d_kv
											a__      =d_ff
											a__      =num_layers
											a__      =num_heads
											a__      =relative_attention_num_buckets
											a__      =relative_attention_max_distance
											a__      =dropout_rate
											a__      =layer_norm_epsilon
											a__      =initializer_factor
											a__      =use_cache

											a__      =eos_token_id
											a__      =decoder_start_token_id

											# for backwards compatibility
											a__      =dense_act_fn

											super().__init__(
											    pad_token_id=lowercase_       ,  eos_token_id=lowercase_       ,  decoder_start_token_id=lowercase_       ,  tie_word_embeddings=lowercase_       ,  is_decoder=lowercase_       ,  **lowercase_       ,  )





				@classmethod
				def 						__UpperCamelCase    (				cls       ,  lowercase_       ,  **lowercase_)  -> "PretrainedConfig":
											cls._set_token_in_kwargs(lowercase_)

											a__    ,     a__      =cls.get_config_dict(lowercase_       ,  **lowercase_)

											# get the text config dict if we are loading from Pix2StructConfig
											if config_dict.get('model_type') == "pix2struct":
																		a__      =config_dict['text_config']

											if "model_type" in config_dict and hasattr(cls       ,  'model_type') and config_dict["model_type"] != cls.model_type:
																		logger.warning(
																		    F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
																		    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""")

											return cls.from_dict(lowercase_       ,  **lowercase_)







class       lowercase_					(lowercase__   ):
				snake_case	    ='pix2struct_vision_model'





				def __init__(				self       ,  lowercase_=768       ,  lowercase_=768       ,  lowercase_=2048       ,  lowercase_=64       ,  lowercase_=12       ,  lowercase_=12       ,  lowercase_="gelu_new"       ,  lowercase_=1e-6       ,  lowercase_=0.0       ,  lowercase_=0.0       ,  lowercase_=1e-10       ,  lowercase_=1.0       ,  lowercase_=4096       ,  lowercase_=32       ,  lowercase_=128       ,  **lowercase_       ,  )  -> Optional[Any]:
											super().__init__(**lowercase_)

											a__      =hidden_size
											a__      =patch_embed_hidden_size
											a__      =d_ff
											a__      =dropout_rate
											a__      =num_hidden_layers
											a__      =num_attention_heads
											a__      =initializer_range
											a__      =initializer_factor
											a__      =attention_dropout
											a__      =layer_norm_eps
											a__      =dense_act_fn
											a__      =seq_len
											a__      =relative_attention_num_buckets
											a__      =relative_attention_max_distance
											a__      =d_kv





				@classmethod
				def 						__UpperCamelCase    (				cls       ,  lowercase_       ,  **lowercase_)  -> "PretrainedConfig":
											cls._set_token_in_kwargs(lowercase_)

											a__    ,     a__      =cls.get_config_dict(lowercase_       ,  **lowercase_)

											# get the vision config dict if we are loading from Pix2StructConfig
											if config_dict.get('model_type') == "pix2struct":
																		a__      =config_dict['vision_config']

											if "model_type" in config_dict and hasattr(cls       ,  'model_type') and config_dict["model_type"] != cls.model_type:
																		logger.warning(
																		    F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
																		    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""")

											return cls.from_dict(lowercase_       ,  **lowercase_)







class       lowercase_					(lowercase__   ):
				snake_case	    ='pix2struct'
				snake_case	    =True





				def __init__(				self       ,  lowercase_=None       ,  lowercase_=None       ,  lowercase_=1.0       ,  lowercase_=0.02       ,  lowercase_=False       ,  lowercase_=False       ,  lowercase_=True       ,  **lowercase_       ,  )  -> str:
											super().__init__(tie_word_embeddings=lowercase_       ,  is_encoder_decoder=lowercase_       ,  **lowercase_)

											if text_config is None:
																		a__      ={}
																		logger.info('text_config is None. Initializing the Pix2StructTextConfig with default values.')

											if vision_config is None:
																		a__      ={}
																		logger.info('vision_config is None. Initializing the Pix2StructVisionConfig with default values.')

											a__      =PixaStructTextConfig(**lowercase_)
											a__      =PixaStructVisionConfig(**lowercase_)

											a__      =self.text_config.decoder_start_token_id
											a__      =self.text_config.pad_token_id
											a__      =self.text_config.eos_token_id

											a__      =initializer_factor
											a__      =initializer_range

											a__      =self.initializer_range
											a__      =self.initializer_range

											a__      =is_vqa





				@classmethod
				def 						__UpperCamelCase    (				cls       ,  lowercase_       ,  lowercase_       ,  **lowercase_)  -> Any:
											return cls(text_config=text_config.to_dict()       ,  vision_config=vision_config.to_dict()       ,  **lowercase_)





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											a__      =copy.deepcopy(self.__dict__)
											a__      =self.text_config.to_dict()
											a__      =self.vision_config.to_dict()
											a__      =self.__class__.model_type
											return output





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
	_lowerCAmelCase:						int   				=       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   _lowercase(					__a				:						Optional[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)

	_lowerCAmelCase:						str   				=       parser.parse_args()

	_lowerCAmelCase:						Tuple   				=       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)

