


import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
			lowerCAmelCase	=							argparse.ArgumentParser()

			parser.add_argument(
			    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
			)
			# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
			parser.add_argument(
			    '--original_config_file',
			    default=None,
			    type=str,
			    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(
			    '--scheduler_type',
			    default='pndm',
			    type=str,
			    help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
			)
			parser.add_argument(
			    '--pipeline_type',
			    default=None,
			    type=str,
			    help=(
			        'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
			        '. If `None` pipeline will be automatically inferred.'
			    ),
			)
			parser.add_argument(
			    '--image_size',
			    default=None,
			    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(
			    '--prediction_type',
			    default=None,
			    type=str,
			    help=(
			        'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
			        ' Diffusion v2 Base. Use \'v_prediction\' 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.)')
			parser.add_argument(
			    '--stable_unclip',
			    type=str,
			    default=None,
			    required=False,
			    help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
			)
			parser.add_argument(
			    '--stable_unclip_prior',
			    type=str,
			    default=None,
			    required=False,
			    help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
			)
			parser.add_argument(
			    '--clip_stats_path',
			    type=str,
			    help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
			    required=False,
			)
			parser.add_argument(
			    '--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
			)
			parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
			parser.add_argument(
			    '--vae_path',
			    type=str,
			    default=None,
			    required=False,
			    help='Set to a path, hub id to an already converted vae to not convert it again.',
			)
			lowerCAmelCase	=							parser.parse_args()

			lowerCAmelCase	=							download_from_original_stable_diffusion_ckpt(
			    checkpoint_path=args.checkpoint_path,
			    original_config_file=args.original_config_file,
			    image_size=args.image_size,
			    prediction_type=args.prediction_type,
			    model_type=args.pipeline_type,
			    extract_ema=args.extract_ema,
			    scheduler_type=args.scheduler_type,
			    num_in_channels=args.num_in_channels,
			    upcast_attention=args.upcast_attention,
			    from_safetensors=args.from_safetensors,
			    device=args.device,
			    stable_unclip=args.stable_unclip,
			    stable_unclip_prior=args.stable_unclip_prior,
			    clip_stats_path=args.clip_stats_path,
			    controlnet=args.controlnet,
			    vae_path=args.vae_path,
			)

			if args.half:
						pipe.to(torch_dtype=torch.floataa)

			if args.controlnet:
						# only save the controlnet model
						pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
			else:
						pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)






import copy
import os
from typing import Union

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


lowerCAmelCase	=							logging.get_logger(__name__)

lowerCAmelCase	=							{
    'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config.json',
}


class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      int  				=			'''git_vision_model'''

							def __init__(							self:     Tuple   ,						UpperCamelCase_:     Optional[int]=768   ,						UpperCamelCase_:     Optional[int]=3_072   ,						UpperCamelCase_:     Optional[int]=12   ,						UpperCamelCase_:     Union[str, Any]=12   ,						UpperCamelCase_:     Any=3   ,						UpperCamelCase_:     str=224   ,						UpperCamelCase_:     int=16   ,						UpperCamelCase_:     Any="quick_gelu"   ,						UpperCamelCase_:     Union[str, Any]=1E-5   ,						UpperCamelCase_:     Tuple=0.0   ,						UpperCamelCase_:     Tuple=0.02   ,						**UpperCamelCase_:     str   ,						)      ->       Optional[int]:





									"""simple docstring"""







									super().__init__(**UpperCamelCase_							)

									lowercase__						=				hidden_size
									lowercase__						=				intermediate_size
									lowercase__						=				num_hidden_layers
									lowercase__						=				num_attention_heads
									lowercase__						=				num_channels
									lowercase__						=				patch_size
									lowercase__						=				image_size
									lowercase__						=				initializer_range
									lowercase__						=				attention_dropout
									lowercase__						=				layer_norm_eps
									lowercase__						=				hidden_act


							@classmethod
							def 				lowerCamelCase_			(							cls:     Optional[Any]   ,						UpperCamelCase_:     Union[str, os.PathLike]   ,						**UpperCamelCase_:     List[Any]							)      ->       "PretrainedConfig":





									"""simple docstring"""







									cls._set_token_in_kwargs(UpperCamelCase_							)

									lowercase__					,		lowercase__						=				cls.get_config_dict(UpperCamelCase_   ,						**UpperCamelCase_							)

									# get the vision config dict if we are loading from GITConfig
									if config_dict.get('''model_type'''							) == "git":
											lowercase__						=				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(UpperCamelCase_   ,						**UpperCamelCase_							)


class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      Optional[int]  				=			'''git'''

							def __init__(							self:     Optional[Any]   ,						UpperCamelCase_:     Optional[Any]=None   ,						UpperCamelCase_:     List[str]=30_522   ,						UpperCamelCase_:     List[Any]=768   ,						UpperCamelCase_:     List[Any]=6   ,						UpperCamelCase_:     Union[str, Any]=12   ,						UpperCamelCase_:     Any=3_072   ,						UpperCamelCase_:     Any="gelu"   ,						UpperCamelCase_:     List[Any]=0.1   ,						UpperCamelCase_:     Dict=0.1   ,						UpperCamelCase_:     str=1_024   ,						UpperCamelCase_:     Any=0.02   ,						UpperCamelCase_:     str=1E-1_2   ,						UpperCamelCase_:     Optional[int]=0   ,						UpperCamelCase_:     Optional[Any]="absolute"   ,						UpperCamelCase_:     Optional[int]=True   ,						UpperCamelCase_:     List[Any]=False   ,						UpperCamelCase_:     List[Any]=101   ,						UpperCamelCase_:     int=102   ,						UpperCamelCase_:     List[str]=None   ,						**UpperCamelCase_:     int   ,						)      ->       Union[str, Any]:





									"""simple docstring"""







									super().__init__(bos_token_id=UpperCamelCase_   ,						eos_token_id=UpperCamelCase_   ,						pad_token_id=UpperCamelCase_   ,						**UpperCamelCase_							)

									if vision_config is None:
											lowercase__						=				{}
											logger.info('''vision_config is None. initializing the GitVisionConfig with default values.'''							)

									lowercase__						=				GitVisionConfig(**UpperCamelCase_							)
									lowercase__						=				vocab_size
									lowercase__						=				hidden_size
									lowercase__						=				num_hidden_layers
									lowercase__						=				num_attention_heads
									lowercase__						=				hidden_act
									lowercase__						=				intermediate_size
									lowercase__						=				hidden_dropout_prob
									lowercase__						=				attention_probs_dropout_prob
									lowercase__						=				max_position_embeddings
									lowercase__						=				initializer_range
									lowercase__						=				layer_norm_eps
									lowercase__						=				position_embedding_type
									lowercase__						=				use_cache
									lowercase__						=				tie_word_embeddings
									lowercase__						=				num_image_with_embedding

									lowercase__						=				bos_token_id
									lowercase__						=				eos_token_id


							def 				lowerCamelCase_			(							self:     Dict							)      ->       Tuple:





									"""simple docstring"""







									lowercase__						=				copy.deepcopy(self.__dict__							)
									lowercase__						=				self.vision_config.to_dict()
									lowercase__						=				self.__class__.model_type
									return output


