code: 
  
  
  '''simple docstring'''
  
  
  
  import argparse
  import collections
  
  import jax
  import jax.numpy as jnp
  import torch
  import torch.nn as nn
  from clip.model import CLIP
  from flax.training import checkpoints
  from huggingface_hub import Repository
  
  from transformers import (
      CLIPTokenizer,
      OwlViTConfig,
      OwlViTForObjectDetection,
      OwlViTImageProcessor,
      OwlViTModel,
      OwlViTProcessor,
  )
  
  
  __lowercase			:		Optional[Any] =			{
      'vit_b32': {
          'embed_dim': 5_12,
          'image_resolution': 7_68,
          'context_length': 16,
          'vocab_size': 4_94_08,
          'vision_layers': 12,
          'vision_width': 7_68,
          'vision_patch_size': 32,
          'transformer_width': 5_12,
          'transformer_heads': 8,
          'transformer_layers': 12,
      },
      'vit_b16': {
          'embed_dim': 5_12,
          'image_resolution': 7_68,
          'context_length': 16,
          'vocab_size': 4_94_08,
          'vision_layers': 12,
          'vision_width': 7_68,
          'vision_patch_size': 16,
          'transformer_width': 5_12,
          'transformer_heads': 8,
          'transformer_layers': 12,
      },
      'vit_l14': {
          'embed_dim': 7_68,
          'image_resolution': 8_40,
          'context_length': 16,
          'vocab_size': 4_94_08,
          'vision_layers': 24,
          'vision_width': 10_24,
          'vision_patch_size': 14,
          'transformer_width': 7_68,
          'transformer_heads': 12,
          'transformer_layers': 12,
      },
  }
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Optional[Any]		,   _SCREAMING_SNAKE_CASE   :     Dict=""		,   _SCREAMING_SNAKE_CASE   :     int="/"     ):
  					__a    : List[str]      					=						[]
  
  					for k, v in params.items():
  										__a    : Tuple      					=						parent_key + sep + k if parent_key else k
  
  										if isinstance(_SCREAMING_SNAKE_CASE		,   collections.MutableMapping     ):
  															items.extend(flatten_nested_dict(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   sep=_SCREAMING_SNAKE_CASE     ).items()     )
  										else:
  															items.append((new_key, v)     )
  					return dict(_SCREAMING_SNAKE_CASE     )
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Tuple     ):
  					return jax.tree_util.tree_map(lambda _SCREAMING_SNAKE_CASE     : x.astype(jnp.floataa     ) if x.dtype == jnp.bfloataa else x		,   _SCREAMING_SNAKE_CASE     )
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Any		,   _SCREAMING_SNAKE_CASE   :     Optional[Any]     ):
  					__a					,       __a					,       __a    : str      					=						pt_attn_layer.in_proj_weight.chunk(3		,   dim=0     )
  					__a					,       __a					,       __a    : Any      					=						pt_attn_layer.in_proj_bias.chunk(3		,   dim=0     )
  
  					__a    : str      					=						pt_attn_layer.out_proj.weight
  					__a    : str      					=						pt_attn_layer.out_proj.bias
  
  					__a    : List[Any]      					=						q_proj
  					__a    : Dict      					=						q_proj_bias
  
  					__a    : Optional[int]      					=						k_proj
  					__a    : Tuple      					=						k_proj_bias
  
  					__a    : str      					=						v_proj
  					__a    : Tuple      					=						v_proj_bias
  
  					__a    : List[str]      					=						out_proj_weights
  					__a    : Optional[Any]      					=						out_proj_bias
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Optional[int]		,   _SCREAMING_SNAKE_CASE   :     Union[str, Any]     ):
  					copy_linear(hf_mlp.fca		,   pt_mlp.c_fc     )
  					copy_linear(hf_mlp.fca		,   pt_mlp.c_proj     )
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     List[str]		,   _SCREAMING_SNAKE_CASE   :     Optional[int]     ):
  					__a    : Dict      					=						pt_linear.weight
  					__a    : Optional[int]      					=						pt_linear.bias
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Optional[Any]		,   _SCREAMING_SNAKE_CASE   :     List[str]     ):
  					# copy layer norms
  					copy_linear(hf_layer.layer_norma		,   pt_layer.ln_a     )
  					copy_linear(hf_layer.layer_norma		,   pt_layer.ln_a     )
  
  					# copy MLP
  					copy_mlp(hf_layer.mlp		,   pt_layer.mlp     )
  
  					# copy attn
  					copy_attn_layer(hf_layer.self_attn		,   pt_layer.attn     )
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Any		,   _SCREAMING_SNAKE_CASE   :     Dict     ):
  					for hf_layer, pt_layer in zip(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     ):
  										copy_layer(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Dict		,   _SCREAMING_SNAKE_CASE   :     Dict     ):
  					# copy  embeds
  					__a    : Optional[int]      					=						pt_model.token_embedding.weight
  					__a    : int      					=						pt_model.positional_embedding
  
  					# copy layer norm
  					copy_linear(hf_encoder.final_layer_norm		,   pt_model.ln_final     )
  
  					# copy hidden layers
  					copy_layers(hf_encoder.encoder.layers		,   pt_model.transformer.resblocks     )
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     str		,   _SCREAMING_SNAKE_CASE   :     Optional[Any]     ):
  					# copy projection
  					__a    : List[Any]      					=						pt_model.text_projection.data.T
  
  					# copy text encoder
  					copy_encoder(hf_model.text_model		,   _SCREAMING_SNAKE_CASE     )
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     List[str]		,   _SCREAMING_SNAKE_CASE   :     Union[str, Any]     ):
  					# copy projection
  					__a    : Optional[Any]      					=						pt_model.visual.proj.data.T
  
  					# copy layer norms
  					copy_linear(hf_model.vision_model.pre_layernorm		,   pt_model.visual.ln_pre     )
  					copy_linear(hf_model.vision_model.post_layernorm		,   pt_model.visual.ln_post     )
  
  					# copy embeds
  					__a    : Optional[int]      					=						pt_model.visual.conva.weight.data
  					__a    : Union[str, Any]      					=						pt_model.visual.class_embedding
  					__a    : Union[str, Any]      					=						pt_model.visual.positional_embedding.data
  
  					# copy encoder
  					copy_layers(hf_model.vision_model.encoder.layers		,   pt_model.visual.transformer.resblocks     )
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     List[str]		,   _SCREAMING_SNAKE_CASE   :     List[Any]     ):
  					__a    : Dict      					=						flatten_nested_dict(flax_params['backbone']['merged_class_token']     )
  
  					__a    : Union[str, Any]      					=						torch.from_numpy(flax_class_token_params['scale']     )
  					__a    : Any      					=						torch.from_numpy(flax_class_token_params['bias']     )
  					__a    : int      					=						nn.Parameter(_SCREAMING_SNAKE_CASE     )
  					__a    : List[str]      					=						nn.Parameter(_SCREAMING_SNAKE_CASE     )
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Optional[int]		,   _SCREAMING_SNAKE_CASE   :     List[Any]     ):
  					__a    : int      					=						hf_model.state_dict()
  					__a    : Union[str, Any]      					=						{}
  
  					# Rename class prediction head flax params to pytorch HF
  					__a    : str      					=						flatten_nested_dict(flax_params['class_head']     )
  
  					for flax_key, v in flax_class_params.items():
  										__a    : Union[str, Any]      					=						flax_key.replace('/'		,   '.'     )
  										__a    : str      					=						torch_key.replace('.kernel'		,   '.weight'     )
  										__a    : str      					=						torch_key.replace('Dense_0'		,   'dense0'     )
  										__a    : Optional[Any]      					=						'class_head.' + torch_key
  
  										if "weight" in torch_key and v.ndim == 2:
  															__a    : Union[str, Any]      					=						v.T
  
  										__a    : List[Any]      					=						nn.Parameter(torch.from_numpy(_SCREAMING_SNAKE_CASE     )     )
  
  					# Rename box prediction box flax params to pytorch HF
  					__a    : Union[str, Any]      					=						flatten_nested_dict(flax_params['obj_box_head']     )
  
  					for flax_key, v in flax_box_params.items():
  										__a    : str      					=						flax_key.replace('/'		,   '.'     )
  										__a    : Dict      					=						torch_key.replace('.kernel'		,   '.weight'     )
  										__a    : Optional[Any]      					=						torch_key.replace('_'		,   ''     ).lower()
  										__a    : Optional[int]      					=						'box_head.' + torch_key
  
  										if "weight" in torch_key and v.ndim == 2:
  															__a    : Optional[int]      					=						v.T
  
  										__a    : List[str]      					=						nn.Parameter(torch.from_numpy(_SCREAMING_SNAKE_CASE     )     )
  
  					# Copy flax params to PyTorch params
  					for name, param in new_params.items():
  										if name in pt_params.keys():
  															pt_params[name].copy_(_SCREAMING_SNAKE_CASE     )
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Any		,   _SCREAMING_SNAKE_CASE   :     Dict     ):
  					for k, v in flax_attn_params.items():
  										if k.startswith('transformer'     ):
  															__a    : int      					=						k.replace('transformer.resblocks'		,   'text_model.encoder.layers'     )
  										else:
  															__a    : Any      					=						k.replace('visual.transformer.resblocks'		,   'vision_model.encoder.layers'     )
  
  										__a    : Optional[Any]      					=						torch_key.replace('attn'		,   'self_attn'     )
  										__a    : Optional[int]      					=						torch_key.replace('key'		,   'k_proj'     )
  										__a    : int      					=						torch_key.replace('value'		,   'v_proj'     )
  										__a    : Union[str, Any]      					=						torch_key.replace('query'		,   'q_proj'     )
  										__a    : Tuple      					=						torch_key.replace('out'		,   'out_proj'     )
  
  										if "bias" in torch_key and v.ndim == 2:
  															__a    : int      					=						v.shape[0] * v.shape[1]
  															__a    : Optional[int]      					=						v.reshape(_SCREAMING_SNAKE_CASE     )
  
  										if "weight" in torch_key and "out" in torch_key:
  															__a    : Any      					=						(v.shape[0] * v.shape[1], v.shape[2])
  															__a    : Any      					=						v.reshape(_SCREAMING_SNAKE_CASE     ).T
  
  										if "weight" in torch_key and "out" not in torch_key:
  															__a    : Any      					=						(v.shape[0], v.shape[1] * v.shape[2])
  															__a    : Union[str, Any]      					=						v.reshape(_SCREAMING_SNAKE_CASE     ).T
  
  										# Copy flax CLIP attn params to HF PyTorch params
  										__a    : int      					=						torch.from_numpy(_SCREAMING_SNAKE_CASE     )
  										hf_backbone.state_dict()[torch_key].copy_(_SCREAMING_SNAKE_CASE     )
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Optional[Any]     ):
  					__a    : List[Any]      					=						{}
  					__a    : Union[str, Any]      					=						[]
  
  					for k, v in params.items():
  										if "attn." in k:
  															__a    : List[str]      					=						k[: k.rindex('attn.'     ) + 5]
  															if base in processed_attn_layers:
  																				continue
  
  															processed_attn_layers.append(_SCREAMING_SNAKE_CASE     )
  															__a    : Union[str, Any]      					=						params[base + 'out.weight'].shape[-1]
  															__a    : Tuple      					=						params[base + 'out.weight'].reshape(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     ).T
  															__a    : Optional[int]      					=						params[base + 'out.bias']
  										else:
  															__a    : Tuple      					=						v
  					return new_params
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Dict		,   _SCREAMING_SNAKE_CASE   :     Union[str, Any]     ):
  					__a    : Union[str, Any]      					=						CLIP(**_SCREAMING_SNAKE_CASE     )
  					torch_model.eval()
  					__a    : Optional[Any]      					=						torch_model.state_dict()
  
  					__a    : int      					=						flatten_nested_dict(flax_params['backbone']['clip']     )
  					__a    : Optional[int]      					=						{}
  
  					for flax_key, v in flax_clip_params.items():
  										__a    : str      					=						flax_key.replace('/'		,   '.'     )
  										__a    : int      					=						torch_key.replace('text.token_embedding.embedding'		,   'token_embedding.kernel'     )
  
  										if (
  										    torch_key.startswith('text.transformer'     )
  										    or torch_key.startswith('text.text_projection'     )
  										    or torch_key.startswith('text.ln_final'     )
  										    or torch_key.startswith('text.positional_embedding'     )
  										):
  															__a    : Union[str, Any]      					=						torch_key[5:]
  
  										__a    : Optional[int]      					=						torch_key.replace('text_projection.kernel'		,   'text_projection'     )
  										__a    : List[Any]      					=						torch_key.replace('visual.proj.kernel'		,   'visual.proj'     )
  										__a    : int      					=						torch_key.replace('.scale'		,   '.weight'     )
  										__a    : List[Any]      					=						torch_key.replace('.kernel'		,   '.weight'     )
  
  										if "conv" in torch_key or "downsample.0.weight" in torch_key:
  															__a    : Tuple      					=						v.transpose(3		,   2		,   0		,   1     )
  
  										elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
  															# Fully connected layers are transposed, embeddings are not
  															__a    : Optional[int]      					=						v.T
  
  										__a    : List[str]      					=						v
  
  					__a    : Union[str, Any]      					=						_convert_attn_layers(_SCREAMING_SNAKE_CASE     )
  					new_torch_params.update(_SCREAMING_SNAKE_CASE     )
  					__a    : str      					=						{}
  
  					# Copy flax CLIP backbone params to PyTorch params
  					for name, param in new_torch_params.items():
  										if name in torch_clip_params.keys():
  															__a    : List[Any]      					=						torch.from_numpy(new_torch_params[name]     )
  															torch_clip_params[name].copy_(_SCREAMING_SNAKE_CASE     )
  										else:
  															__a    : List[Any]      					=						param
  
  					return torch_clip_params, torch_model, attn_params
  
  
  
  
  
  
  @torch.no_grad()
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Any		,   _SCREAMING_SNAKE_CASE   :     Optional[int]		,   _SCREAMING_SNAKE_CASE   :     Optional[int]		,   _SCREAMING_SNAKE_CASE   :     Tuple		,   _SCREAMING_SNAKE_CASE   :     List[Any]=None     ):
  					__a    : int      					=						Repository(_SCREAMING_SNAKE_CASE		,   clone_from=F"""google/{pytorch_dump_folder_path}"""     )
  					repo.git_pull()
  
  					if config_path is not None:
  										__a    : Optional[Any]      					=						OwlViTConfig.from_pretrained(_SCREAMING_SNAKE_CASE     )
  					else:
  										__a    : List[Any]      					=						OwlViTConfig()
  
  					__a    : Union[str, Any]      					=						OwlViTModel(_SCREAMING_SNAKE_CASE     ).eval()
  					__a    : Dict      					=						OwlViTForObjectDetection(_SCREAMING_SNAKE_CASE     ).eval()
  
  					copy_text_model_and_projection(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
  					copy_vision_model_and_projection(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
  					__a    : Optional[int]      					=						pt_backbone.logit_scale
  					copy_flax_attn_params(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
  
  					__a    : Any      					=						hf_backbone
  					copy_class_merge_token(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
  					copy_class_box_heads(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
  
  					# Save HF model
  					hf_model.save_pretrained(repo.local_dir     )
  
  					# Initialize image processor
  					__a    : Any      					=						OwlViTImageProcessor(
  					    size=config.vision_config.image_size		,   crop_size=config.vision_config.image_size     )
  					# Initialize tokenizer
  					__a    : Any      					=						CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'		,   pad_token='!'		,   model_max_length=16     )
  
  					# Initialize processor
  					__a    : Union[str, Any]      					=						OwlViTProcessor(image_processor=_SCREAMING_SNAKE_CASE		,   tokenizer=_SCREAMING_SNAKE_CASE     )
  					image_processor.save_pretrained(repo.local_dir     )
  					processor.save_pretrained(repo.local_dir     )
  
  					repo.git_add()
  					repo.git_commit('Upload model and processor'     )
  					repo.git_push()
  
  
  if __name__ == "__main__":
  	__lowercase			:		Optional[Any] =			argparse.ArgumentParser()
  	# Required parameters
  	parser.add_argument(
  	    '--owlvit_version',
  	    default=None,
  	    type=str,
  	    required=True,
  	    help='OWL-ViT model name [clip_b16, clip_b32, clip_l14].',
  	)
  	parser.add_argument(
  	    '--owlvit_checkpoint', default=None, type=str, required=True, help='Path to flax model checkpoint.'
  	)
  	parser.add_argument('--hf_config', default=None, type=str, required=True, help='Path to HF model config.')
  	parser.add_argument(
  	    '--pytorch_dump_folder_path', default='hf_model', type=str, help='Path to the output PyTorch model.'
  	)
  	__lowercase			:		List[Any] =			parser.parse_args()
  
  	# Initialize PyToch clip model
  	__lowercase			:		Tuple =			args.owlvit_version
  	if model_name == "clip_b16":
  		__lowercase			:		Tuple =			CONFIGS['vit_b16']
  	elif model_name == "clip_b32":
  		__lowercase			:		Optional[int] =			CONFIGS['vit_b32']
  	elif model_name == "clip_l14":
  		__lowercase			:		List[Any] =			CONFIGS['vit_l14']
  
  	# Load from checkpoint and convert params to float-32
  	__lowercase			:		Optional[Any] =			checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)['optimizer']['target']
  	__lowercase			:		List[Any] =			jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
  	del variables
  
  	# Convert CLIP backbone
  	__lowercase ,   __lowercase ,   __lowercase			:		str =			convert_clip_backbone(flax_params, torch_config)
  
  	convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  import argparse
  
  import torch
  
  from diffusers import UNetaDConditionModel
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     List[str]		,   _SCREAMING_SNAKE_CASE   :     Dict		,   _SCREAMING_SNAKE_CASE   :     Any		,   _SCREAMING_SNAKE_CASE   :     Tuple=None		,   _SCREAMING_SNAKE_CASE   :     Any=None		,   _SCREAMING_SNAKE_CASE   :     List[Any]=None     ):
  					assert isinstance(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     ), "Paths should be a list of dicts containing 'old' and 'new' keys."
  
  					# Splits the attention layers into three variables.
  					if attention_paths_to_split is not None:
  										for path, path_map in attention_paths_to_split.items():
  															__a    : Union[str, Any]      					=						old_checkpoint[path]
  															__a    : List[str]      					=						old_tensor.shape[0] // 3
  
  															__a    : Dict      					=						(-1, channels) if len(old_tensor.shape     ) == 3 else (-1)
  
  															__a    : List[str]      					=						old_tensor.shape[0] // config['num_head_channels'] // 3
  
  															__a    : str      					=						old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]     )
  															__a					,       __a					,       __a    : Dict      					=						old_tensor.split(channels // num_heads		,   dim=1     )
  
  															__a    : Optional[int]      					=						query.reshape(_SCREAMING_SNAKE_CASE     )
  															__a    : Tuple      					=						key.reshape(_SCREAMING_SNAKE_CASE     )
  															__a    : List[str]      					=						value.reshape(_SCREAMING_SNAKE_CASE     )
  
  					for path in paths:
  										__a    : Tuple      					=						path['new']
  
  										# These have already been assigned
  										if attention_paths_to_split is not None and new_path in attention_paths_to_split:
  															continue
  
  										if additional_replacements is not None:
  															for replacement in additional_replacements:
  																				__a    : int      					=						new_path.replace(replacement['old']		,   replacement['new']     )
  
          # proj_attn.weight has to be converted from conv 1D to linear
  										__a    : Any      					=						old_checkpoint[path['old']]
  										__a    : Tuple      					=						['proj_attn.weight']
  										__a    : Tuple      					=						['proj_out.weight', 'proj_in.weight']
  										if any(k in new_path for k in names     ):
  															__a    : Tuple      					=						weight[:, :, 0]
  										elif any(k in new_path for k in names_a     ) and len(weight.shape     ) > 2 and ".attentions." not in new_path:
  															__a    : int      					=						weight[:, :, 0]
  										else:
  															__a    : Union[str, Any]      					=						weight
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Optional[int]		,   _SCREAMING_SNAKE_CASE   :     str=0     ):
  					__a    : Tuple      					=						[]
  					for old_item in old_list:
  										__a    : Dict      					=						old_item
  
  										#         new_item = new_item.replace('norm.weight', 'group_norm.weight')
  										#         new_item = new_item.replace('norm.bias', 'group_norm.bias')
  
  										#         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
  										#         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')
  
  										#         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
  
  										mapping.append({'old': old_item, 'new': new_item}     )
  
  					return mapping
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Optional[Any]		,   _SCREAMING_SNAKE_CASE   :     Optional[int]=1     ):
  					if n_shave_prefix_segments >= 0:
  										return ".".join(path.split('.'     )[n_shave_prefix_segments:]     )
  					else:
  										return ".".join(path.split('.'     )[:n_shave_prefix_segments]     )
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     List[str]		,   _SCREAMING_SNAKE_CASE   :     str=0     ):
  					__a    : Optional[Any]      					=						[]
  					for old_item in old_list:
  										mapping.append({'old': old_item, 'new': old_item}     )
  
  					return mapping
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Any		,   _SCREAMING_SNAKE_CASE   :     Union[str, Any]=0     ):
  					__a    : Tuple      					=						[]
  					for old_item in old_list:
  										__a    : Optional[int]      					=						old_item.replace('in_layers.0'		,   'norm1'     )
  										__a    : Optional[Any]      					=						new_item.replace('in_layers.2'		,   'conv1'     )
  
  										__a    : str      					=						new_item.replace('out_layers.0'		,   'norm2'     )
  										__a    : str      					=						new_item.replace('out_layers.3'		,   'conv2'     )
  
  										__a    : List[Any]      					=						new_item.replace('emb_layers.1'		,   'time_emb_proj'     )
  										__a    : List[Any]      					=						new_item.replace('skip_connection'		,   'conv_shortcut'     )
  
  										__a    : int      					=						shave_segments(_SCREAMING_SNAKE_CASE		,   n_shave_prefix_segments=_SCREAMING_SNAKE_CASE     )
  
  										if "temopral_conv" not in old_item:
  															mapping.append({'old': old_item, 'new': new_item}     )
  
  					return mapping
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     str		,   _SCREAMING_SNAKE_CASE   :     Union[str, Any]		,   _SCREAMING_SNAKE_CASE   :     Any=None		,   _SCREAMING_SNAKE_CASE   :     Optional[Any]=False     ):
  					__a    : Dict      					=						{}
  					__a    : Tuple      					=						list(checkpoint.keys()     )
  
  					__a    : Optional[int]      					=						'model.diffusion_model.'
  
  					# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
  					if sum(k.startswith('model_ema'     ) for k in keys     ) > 100 and extract_ema:
  										print(F"""Checkpoint {path} has both EMA and non-EMA weights."""     )
  										print(
  										    'In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA'
  										    ' weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.'     )
  										for key in keys:
  															if key.startswith('model.diffusion_model'     ):
  																				__a    : int      					=						'model_ema.' + ''.join(key.split('.'     )[1:]     )
  																				__a    : Optional[int]      					=						checkpoint.pop(_SCREAMING_SNAKE_CASE     )
  					else:
  										if sum(k.startswith('model_ema'     ) for k in keys     ) > 100:
  															print(
  															    'In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA'
  															    ' weights (usually better for inference), please make sure to add the `--extract_ema` flag.'     )
  
  										for key in keys:
  															__a    : Optional[Any]      					=						checkpoint.pop(_SCREAMING_SNAKE_CASE     )
  
  					__a    : Union[str, Any]      					=						{}
  
  					__a    : Optional[Any]      					=						unet_state_dict['time_embed.0.weight']
  					__a    : Union[str, Any]      					=						unet_state_dict['time_embed.0.bias']
  					__a    : Any      					=						unet_state_dict['time_embed.2.weight']
  					__a    : Dict      					=						unet_state_dict['time_embed.2.bias']
  
  					if config["class_embed_type"] is None:
  										# No parameters to port
  										...
  					elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
  										__a    : List[Any]      					=						unet_state_dict['label_emb.0.0.weight']
  										__a    : Union[str, Any]      					=						unet_state_dict['label_emb.0.0.bias']
  										__a    : Optional[Any]      					=						unet_state_dict['label_emb.0.2.weight']
  										__a    : Union[str, Any]      					=						unet_state_dict['label_emb.0.2.bias']
  					else:
  										raise NotImplementedError(F"""Not implemented `class_embed_type`: {config["class_embed_type"]}"""     )
  
  					__a    : str      					=						unet_state_dict['input_blocks.0.0.weight']
  					__a    : Tuple      					=						unet_state_dict['input_blocks.0.0.bias']
  
  					__a    : Optional[int]      					=						[v for v in unet_state_dict if v.startswith('input_blocks.0.1'     )]
  					__a    : List[str]      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
  					__a    : List[str]      					=						{'old': 'input_blocks.0.1', 'new': 'transformer_in'}
  					assign_to_checkpoint(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  					__a    : Any      					=						unet_state_dict['out.0.weight']
  					__a    : Union[str, Any]      					=						unet_state_dict['out.0.bias']
  					__a    : Optional[int]      					=						unet_state_dict['out.2.weight']
  					__a    : Union[str, Any]      					=						unet_state_dict['out.2.bias']
  
  					# Retrieves the keys for the input blocks only
  					__a    : Optional[Any]      					=						len({'.'.join(layer.split('.'     )[:2]     ) for layer in unet_state_dict if 'input_blocks' in layer}     )
  					__a    : Dict      					=						{
  					    layer_id: [key for key in unet_state_dict if F"""input_blocks.{layer_id}""" in key]
  					    for layer_id in range(_SCREAMING_SNAKE_CASE     )
  					}
  
  					# Retrieves the keys for the middle blocks only
  					__a    : Dict      					=						len({'.'.join(layer.split('.'     )[:2]     ) for layer in unet_state_dict if 'middle_block' in layer}     )
  					__a    : Optional[Any]      					=						{
  					    layer_id: [key for key in unet_state_dict if F"""middle_block.{layer_id}""" in key]
  					    for layer_id in range(_SCREAMING_SNAKE_CASE     )
  					}
  
  					# Retrieves the keys for the output blocks only
  					__a    : Optional[Any]      					=						len({'.'.join(layer.split('.'     )[:2]     ) for layer in unet_state_dict if 'output_blocks' in layer}     )
  					__a    : Dict      					=						{
  					    layer_id: [key for key in unet_state_dict if F"""output_blocks.{layer_id}""" in key]
  					    for layer_id in range(_SCREAMING_SNAKE_CASE     )
  					}
  
  					for i in range(1		,   _SCREAMING_SNAKE_CASE     ):
  										__a    : List[str]      					=						(i - 1) // (config['layers_per_block'] + 1)
  										__a    : Tuple      					=						(i - 1) % (config['layers_per_block'] + 1)
  
  										__a    : Optional[Any]      					=						[
  										    key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key and F"""input_blocks.{i}.0.op""" not in key
  										]
  										__a    : List[str]      					=						[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]
  										__a    : Tuple      					=						[key for key in input_blocks[i] if F"""input_blocks.{i}.2""" in key]
  
  										if F"""input_blocks.{i}.op.weight""" in unet_state_dict:
  															__a    : Optional[Any]      					=						unet_state_dict.pop(
  															    F"""input_blocks.{i}.op.weight"""     )
  															__a    : Optional[int]      					=						unet_state_dict.pop(
  															    F"""input_blocks.{i}.op.bias"""     )
  
  										__a    : str      					=						renew_resnet_paths(_SCREAMING_SNAKE_CASE     )
  										__a    : Tuple      					=						{'old': F"""input_blocks.{i}.0""", 'new': F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
  										assign_to_checkpoint(
  										    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  										__a    : Optional[int]      					=						[key for key in resnets if 'temopral_conv' in key]
  										__a    : Tuple      					=						renew_temp_conv_paths(_SCREAMING_SNAKE_CASE     )
  										__a    : List[Any]      					=						{
  										    'old': F"""input_blocks.{i}.0.temopral_conv""",
  										    'new': F"""down_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
  										}
  										assign_to_checkpoint(
  										    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  										if len(_SCREAMING_SNAKE_CASE     ):
  															__a    : List[Any]      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
  															__a    : Optional[Any]      					=						{'old': F"""input_blocks.{i}.1""", 'new': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}"""}
  															assign_to_checkpoint(
  															    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  										if len(_SCREAMING_SNAKE_CASE     ):
  															__a    : List[str]      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
  															__a    : str      					=						{
  															    'old': F"""input_blocks.{i}.2""",
  															    'new': F"""down_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
  															}
  															assign_to_checkpoint(
  															    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  					__a    : Union[str, Any]      					=						middle_blocks[0]
  					__a    : str      					=						[key for key in resnet_a if 'temopral_conv' in key]
  					__a    : Dict      					=						middle_blocks[1]
  					__a    : str      					=						middle_blocks[2]
  					__a    : List[str]      					=						middle_blocks[3]
  					__a    : Any      					=						[key for key in resnet_a if 'temopral_conv' in key]
  
  					__a    : Optional[int]      					=						renew_resnet_paths(_SCREAMING_SNAKE_CASE     )
  					__a    : List[Any]      					=						{'old': 'middle_block.0', 'new': 'mid_block.resnets.0'}
  					assign_to_checkpoint(
  					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   config=_SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]     )
  
  					__a    : Optional[int]      					=						renew_temp_conv_paths(_SCREAMING_SNAKE_CASE     )
  					__a    : List[Any]      					=						{'old': 'middle_block.0.temopral_conv', 'new': 'mid_block.temp_convs.0'}
  					assign_to_checkpoint(
  					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   config=_SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]     )
  
  					__a    : Optional[int]      					=						renew_resnet_paths(_SCREAMING_SNAKE_CASE     )
  					__a    : Dict      					=						{'old': 'middle_block.3', 'new': 'mid_block.resnets.1'}
  					assign_to_checkpoint(
  					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   config=_SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]     )
  
  					__a    : Dict      					=						renew_temp_conv_paths(_SCREAMING_SNAKE_CASE     )
  					__a    : Optional[int]      					=						{'old': 'middle_block.3.temopral_conv', 'new': 'mid_block.temp_convs.1'}
  					assign_to_checkpoint(
  					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   config=_SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]     )
  
  					__a    : List[Any]      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
  					__a    : int      					=						{'old': 'middle_block.1', 'new': 'mid_block.attentions.0'}
  					assign_to_checkpoint(
  					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  					__a    : str      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
  					__a    : Optional[Any]      					=						{'old': 'middle_block.2', 'new': 'mid_block.temp_attentions.0'}
  					assign_to_checkpoint(
  					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  					for i in range(_SCREAMING_SNAKE_CASE     ):
  										__a    : List[str]      					=						i // (config['layers_per_block'] + 1)
  										__a    : Optional[int]      					=						i % (config['layers_per_block'] + 1)
  										__a    : Any      					=						[shave_segments(_SCREAMING_SNAKE_CASE		,   2     ) for name in output_blocks[i]]
  										__a    : Optional[Any]      					=						{}
  
  										for layer in output_block_layers:
  															__a					,       __a    : Optional[int]      					=						layer.split('.'     )[0], shave_segments(_SCREAMING_SNAKE_CASE		,   1     )
  															if layer_id in output_block_list:
  																				output_block_list[layer_id].append(_SCREAMING_SNAKE_CASE     )
  															else:
  																				__a    : int      					=						[layer_name]
  
  										if len(_SCREAMING_SNAKE_CASE     ) > 1:
  															__a    : Any      					=						[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
  															__a    : List[Any]      					=						[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]
  															__a    : Optional[Any]      					=						[key for key in output_blocks[i] if F"""output_blocks.{i}.2""" in key]
  
  															__a    : Optional[int]      					=						renew_resnet_paths(_SCREAMING_SNAKE_CASE     )
  															__a    : List[Any]      					=						renew_resnet_paths(_SCREAMING_SNAKE_CASE     )
  
  															__a    : Dict      					=						{'old': F"""output_blocks.{i}.0""", 'new': F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
  															assign_to_checkpoint(
  															    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  															__a    : Dict      					=						[key for key in resnets if 'temopral_conv' in key]
  															__a    : Tuple      					=						renew_temp_conv_paths(_SCREAMING_SNAKE_CASE     )
  															__a    : List[str]      					=						{
  															    'old': F"""output_blocks.{i}.0.temopral_conv""",
  															    'new': F"""up_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
  															}
  															assign_to_checkpoint(
  															    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  															__a    : List[str]      					=						{k: sorted(_SCREAMING_SNAKE_CASE     ) for k, v in output_block_list.items()}
  															if ["conv.bias", "conv.weight"] in output_block_list.values():
  																				__a    : Tuple      					=						list(output_block_list.values()     ).index(['conv.bias', 'conv.weight']     )
  																				__a    : str      					=						unet_state_dict[
  																				    F"""output_blocks.{i}.{index}.conv.weight"""
  																				]
  																				__a    : Tuple      					=						unet_state_dict[
  																				    F"""output_blocks.{i}.{index}.conv.bias"""
  																				]
  
  																				# Clear attentions as they have been attributed above.
  																				if len(_SCREAMING_SNAKE_CASE     ) == 2:
  																									__a    : Optional[Any]      					=						[]
  
  															if len(_SCREAMING_SNAKE_CASE     ):
  																				__a    : Tuple      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
  																				__a    : int      					=						{
  																				    'old': F"""output_blocks.{i}.1""",
  																				    'new': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
  																				}
  																				assign_to_checkpoint(
  																				    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  															if len(_SCREAMING_SNAKE_CASE     ):
  																				__a    : Optional[Any]      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
  																				__a    : Optional[Any]      					=						{
  																				    'old': F"""output_blocks.{i}.2""",
  																				    'new': F"""up_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
  																				}
  																				assign_to_checkpoint(
  																				    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  										else:
  															__a    : int      					=						renew_resnet_paths(_SCREAMING_SNAKE_CASE		,   n_shave_prefix_segments=1     )
  															for path in resnet_0_paths:
  																				__a    : str      					=						'.'.join(['output_blocks', str(_SCREAMING_SNAKE_CASE     ), path['old']]     )
  																				__a    : Dict      					=						'.'.join(['up_blocks', str(_SCREAMING_SNAKE_CASE     ), 'resnets', str(_SCREAMING_SNAKE_CASE     ), path['new']]     )
  																				__a    : Optional[int]      					=						unet_state_dict[old_path]
  
  															__a    : Union[str, Any]      					=						[l for l in output_block_layers if 'temopral_conv' in l]
  															for path in temopral_conv_paths:
  																				__a    : Any      					=						path.split('temopral_conv.'     )[-1]
  																				__a    : int      					=						'.'.join(['output_blocks', str(_SCREAMING_SNAKE_CASE     ), str(_SCREAMING_SNAKE_CASE     ), 'temopral_conv', pruned_path]     )
  																				__a    : str      					=						'.'.join(['up_blocks', str(_SCREAMING_SNAKE_CASE     ), 'temp_convs', str(_SCREAMING_SNAKE_CASE     ), pruned_path]     )
  																				__a    : Optional[Any]      					=						unet_state_dict[old_path]
  
  					return new_checkpoint
  
  
  if __name__ == "__main__":
  	__lowercase			:		Optional[int] =			argparse.ArgumentParser()
  
  	parser.add_argument(
  	    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
  	)
  	parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
  	__lowercase			:		str =			parser.parse_args()
  
  	__lowercase			:		Dict =			torch.load(args.checkpoint_path, map_location='cpu')
  	__lowercase			:		Union[str, Any] =			UNetaDConditionModel()
  
  	__lowercase			:		Optional[Any] =			convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)
  
  	__lowercase			:		Optional[int] =			set(unet.state_dict().keys()) - set(converted_ckpt.keys())
  	__lowercase			:		List[str] =			set(converted_ckpt.keys()) - set(unet.state_dict().keys())
  
  	assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"
  
  	# load state_dict
  	unet.load_state_dict(converted_ckpt)
  
  	unet.save_pretrained(args.dump_path)
  
  	# -- finish converting the unet --
  
  
  
  