






import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


a_		:    Tuple   							=     logging.get_logger(__name__)

a_		:    Dict   							=     {
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}

class 		_snake_case	(	A__       ):
			_lowercase		:     List[str]        =				'''conditional_detr'''
			_lowercase		:     Optional[Any]        =				['''past_key_values''']
			_lowercase		:     Optional[Any]        =				{
			    '''hidden_size''': '''d_model''',
			    '''num_attention_heads''': '''encoder_attention_heads''',
			}






			def __init__(							self		,       a=True		,       a=None		,       a=3		,       a=300		,       a=6		,       a=2048		,       a=8		,       a=6		,       a=2048		,       a=8		,       a=0.0		,       a=0.0		,       a=True		,       a="relu"		,       a=256		,       a=0.1		,       a=0.0		,       a=0.0		,       a=0.02		,       a=1.0		,       a=False		,       a="sine"		,       a="resnet50"		,       a=True		,       a=False		,       a=2		,       a=5		,       a=2		,       a=1		,       a=1		,       a=2		,       a=5		,       a=2		,       a=0.25		,       **a		,       )   ->							List[str]:
						if backbone_config is not None and use_timm_backbone:
									raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.')

						if not use_timm_backbone:
									if backbone_config is None:
												logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.')
												SCREAMING_SNAKE_CASE							=			CONFIG_MAPPING['resnet'](out_features=['stage4'])
									elif isinstance(a		,       a):
												SCREAMING_SNAKE_CASE							=			backbone_config.get('model_type')
												SCREAMING_SNAKE_CASE							=			CONFIG_MAPPING[backbone_model_type]
												SCREAMING_SNAKE_CASE							=			config_class.from_dict(a)

						SCREAMING_SNAKE_CASE							=			use_timm_backbone
						SCREAMING_SNAKE_CASE							=			backbone_config
						SCREAMING_SNAKE_CASE							=			num_channels
						SCREAMING_SNAKE_CASE							=			num_queries
						SCREAMING_SNAKE_CASE							=			d_model
						SCREAMING_SNAKE_CASE							=			encoder_ffn_dim
						SCREAMING_SNAKE_CASE							=			encoder_layers
						SCREAMING_SNAKE_CASE							=			encoder_attention_heads
						SCREAMING_SNAKE_CASE							=			decoder_ffn_dim
						SCREAMING_SNAKE_CASE							=			decoder_layers
						SCREAMING_SNAKE_CASE							=			decoder_attention_heads
						SCREAMING_SNAKE_CASE							=			dropout
						SCREAMING_SNAKE_CASE							=			attention_dropout
						SCREAMING_SNAKE_CASE							=			activation_dropout
						SCREAMING_SNAKE_CASE							=			activation_function
						SCREAMING_SNAKE_CASE							=			init_std
						SCREAMING_SNAKE_CASE							=			init_xavier_std
						SCREAMING_SNAKE_CASE							=			encoder_layerdrop
						SCREAMING_SNAKE_CASE							=			decoder_layerdrop
						SCREAMING_SNAKE_CASE							=			encoder_layers
						SCREAMING_SNAKE_CASE							=			auxiliary_loss
						SCREAMING_SNAKE_CASE							=			position_embedding_type
						SCREAMING_SNAKE_CASE							=			backbone
						SCREAMING_SNAKE_CASE							=			use_pretrained_backbone
						SCREAMING_SNAKE_CASE							=			dilation
						# Hungarian matcher
						SCREAMING_SNAKE_CASE							=			class_cost
						SCREAMING_SNAKE_CASE							=			bbox_cost
						SCREAMING_SNAKE_CASE							=			giou_cost
						# Loss coefficients
						SCREAMING_SNAKE_CASE							=			mask_loss_coefficient
						SCREAMING_SNAKE_CASE							=			dice_loss_coefficient
						SCREAMING_SNAKE_CASE							=			cls_loss_coefficient
						SCREAMING_SNAKE_CASE							=			bbox_loss_coefficient
						SCREAMING_SNAKE_CASE							=			giou_loss_coefficient
						SCREAMING_SNAKE_CASE							=			focal_alpha
						super().__init__(is_encoder_decoder=a		,       **a)






			@property
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							int:
						return self.encoder_attention_heads






			@property
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							int:
						return self.d_model







			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Any:
						SCREAMING_SNAKE_CASE							=			copy.deepcopy(self.__dict__)
						if self.backbone_config is not None:
									SCREAMING_SNAKE_CASE							=			self.backbone_config.to_dict()
						SCREAMING_SNAKE_CASE							=			self.__class__.model_type
						return output



class 		_snake_case	(	A__       ):
			_lowercase		:     int        =				version.parse('''1.11'''       )






			@property
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Mapping[str, Mapping[int, str]]:
						return OrderedDict(
						    [
						        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
						        ('pixel_mask', {0: 'batch'}),
						    ])






			@property
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							float:
						return 1E-5







			@property
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							int:
						return 12








'''simple docstring'''

import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_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.'
 )
 # !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				:  str    		=      parser.parse_args()

 lowerCamelCase				:  Tuple    		=      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)




