


'''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)







'''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 --



