





import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

lowerCAmelCase							:       Tuple					 =	[
    # (stable-diffusion, HF Diffusers)
    ("""time_embed.0.weight""", """time_embedding.linear_1.weight"""),
    ("""time_embed.0.bias""", """time_embedding.linear_1.bias"""),
    ("""time_embed.2.weight""", """time_embedding.linear_2.weight"""),
    ("""time_embed.2.bias""", """time_embedding.linear_2.bias"""),
    ("""input_blocks.0.0.weight""", """conv_in.weight"""),
    ("""input_blocks.0.0.bias""", """conv_in.bias"""),
    ("""out.0.weight""", """conv_norm_out.weight"""),
    ("""out.0.bias""", """conv_norm_out.bias"""),
    ("""out.2.weight""", """conv_out.weight"""),
    ("""out.2.bias""", """conv_out.bias"""),
]

lowerCAmelCase							:       List[Any]					 =	[
    # (stable-diffusion, HF Diffusers)
    ("""in_layers.0""", """norm1"""),
    ("""in_layers.2""", """conv1"""),
    ("""out_layers.0""", """norm2"""),
    ("""out_layers.3""", """conv2"""),
    ("""emb_layers.1""", """time_emb_proj"""),
    ("""skip_connection""", """conv_shortcut"""),
]

lowerCAmelCase							:       Union[str, Any]					 =	[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
							# loop over downblocks/upblocks

							for j in range(2):
														# loop over resnets/attentions for downblocks
														lowerCAmelCase							:       Tuple					 =	f'''down_blocks.{i}.resnets.{j}.'''
														lowerCAmelCase							:       Any					 =	f'''input_blocks.{3*i + j + 1}.0.'''
														unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

														if i < 3:
																					# no attention layers in down_blocks.3
																					lowerCAmelCase							:       Dict					 =	f'''down_blocks.{i}.attentions.{j}.'''
																					lowerCAmelCase							:       int					 =	f'''input_blocks.{3*i + j + 1}.1.'''
																					unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

							for j in range(3):
														# loop over resnets/attentions for upblocks
														lowerCAmelCase							:       List[Any]					 =	f'''up_blocks.{i}.resnets.{j}.'''
														lowerCAmelCase							:       List[Any]					 =	f'''output_blocks.{3*i + j}.0.'''
														unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

														if i > 0:
																					# no attention layers in up_blocks.0
																					lowerCAmelCase							:       Tuple					 =	f'''up_blocks.{i}.attentions.{j}.'''
																					lowerCAmelCase							:       List[Any]					 =	f'''output_blocks.{3*i + j}.1.'''
																					unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

							if i < 3:
														# no downsample in down_blocks.3
														lowerCAmelCase							:       List[Any]					 =	f'''down_blocks.{i}.downsamplers.0.conv.'''
														lowerCAmelCase							:       int					 =	f'''input_blocks.{3*(i+1)}.0.op.'''
														unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

														# no upsample in up_blocks.3
														lowerCAmelCase							:       Union[str, Any]					 =	f'''up_blocks.{i}.upsamplers.0.'''
														lowerCAmelCase							:       Dict					 =	f'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
														unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

lowerCAmelCase							:       int					 =	"""mid_block.attentions.0."""
lowerCAmelCase							:       List[str]					 =	"""middle_block.1."""
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
							lowerCAmelCase							:       Optional[Any]					 =	f'''mid_block.resnets.{j}.'''
							lowerCAmelCase							:       int					 =	f'''middle_block.{2*j}.'''
							unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))






def 		lowerCAmelCase  (	UpperCamelCase__   :   Dict					)      ->    Tuple:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Dict           =   {k: k for k in unet_state_dict.keys()}
						for sd_name, hf_name in unet_conversion_map:
												__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   sd_name
						for k, v in mapping.items():
												if "resnets" in k:
																		for sd_part, hf_part in unet_conversion_map_resnet:
																								__SCREAMING_SNAKE_CASE:				Optional[int]           =   v.replace(UpperCamelCase__       ,			UpperCamelCase__					)
																		__SCREAMING_SNAKE_CASE:				Optional[int]           =   v
						for k, v in mapping.items():
												for sd_part, hf_part in unet_conversion_map_layer:
																		__SCREAMING_SNAKE_CASE:				Optional[int]           =   v.replace(UpperCamelCase__       ,			UpperCamelCase__					)
												__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   v
						__SCREAMING_SNAKE_CASE:				Tuple           =   {v: unet_state_dict[k] for k, v in mapping.items()}
						return new_state_dict


# ================#
# VAE Conversion #
# ================#

lowerCAmelCase							:       Any					 =	[
    # (stable-diffusion, HF Diffusers)
    ("""nin_shortcut""", """conv_shortcut"""),
    ("""norm_out""", """conv_norm_out"""),
    ("""mid.attn_1.""", """mid_block.attentions.0."""),
]

for i in range(4):
							# down_blocks have two resnets
							for j in range(2):
														lowerCAmelCase							:       Any					 =	f'''encoder.down_blocks.{i}.resnets.{j}.'''
														lowerCAmelCase							:       List[Any]					 =	f'''encoder.down.{i}.block.{j}.'''
														vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

							if i < 3:
														lowerCAmelCase							:       Dict					 =	f'''down_blocks.{i}.downsamplers.0.'''
														lowerCAmelCase							:       Optional[int]					 =	f'''down.{i}.downsample.'''
														vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

														lowerCAmelCase							:       Union[str, Any]					 =	f'''up_blocks.{i}.upsamplers.0.'''
														lowerCAmelCase							:       Optional[Any]					 =	f'''up.{3-i}.upsample.'''
														vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

							# up_blocks have three resnets
							# also, up blocks in hf are numbered in reverse from sd
							for j in range(3):
														lowerCAmelCase							:       Optional[int]					 =	f'''decoder.up_blocks.{i}.resnets.{j}.'''
														lowerCAmelCase							:       int					 =	f'''decoder.up.{3-i}.block.{j}.'''
														vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
							lowerCAmelCase							:       Optional[int]					 =	f'''mid_block.resnets.{i}.'''
							lowerCAmelCase							:       Dict					 =	f'''mid.block_{i+1}.'''
							vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


lowerCAmelCase							:       Union[str, Any]					 =	[
    # (stable-diffusion, HF Diffusers)
    ("""norm.""", """group_norm."""),
    ("""q.""", """query."""),
    ("""k.""", """key."""),
    ("""v.""", """value."""),
    ("""proj_out.""", """proj_attn."""),
]






def 		lowerCAmelCase  (	UpperCamelCase__   :   int					)      ->    Optional[int]:

						"""simple docstring"""





						return w.reshape(*w.shape       ,			1       ,			1					)






def 		lowerCAmelCase  (	UpperCamelCase__   :   List[str]					)      ->    Any:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				List[str]           =   {k: k for k in vae_state_dict.keys()}
						for k, v in mapping.items():
												for sd_part, hf_part in vae_conversion_map:
																		__SCREAMING_SNAKE_CASE:				Optional[int]           =   v.replace(UpperCamelCase__       ,			UpperCamelCase__					)
												__SCREAMING_SNAKE_CASE:				Tuple           =   v
						for k, v in mapping.items():
												if "attentions" in k:
																		for sd_part, hf_part in vae_conversion_map_attn:
																								__SCREAMING_SNAKE_CASE:				Optional[int]           =   v.replace(UpperCamelCase__       ,			UpperCamelCase__					)
																		__SCREAMING_SNAKE_CASE:				str           =   v
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   {v: vae_state_dict[k] for k, v in mapping.items()}
						__SCREAMING_SNAKE_CASE:				int           =   ['''q''', '''k''', '''v''', '''proj_out''']
						for k, v in new_state_dict.items():
												for weight_name in weights_to_convert:
																		if F"""mid.attn_1.{weight_name}.weight""" in k:
																								print(F"""Reshaping {k} for SD format"""					)
																								__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   reshape_weight_for_sd(UpperCamelCase__					)
						return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


lowerCAmelCase							:       int					 =	[
    # (stable-diffusion, HF Diffusers)
    ("""resblocks.""", """text_model.encoder.layers."""),
    ("""ln_1""", """layer_norm1"""),
    ("""ln_2""", """layer_norm2"""),
    (""".c_fc.""", """.fc1."""),
    (""".c_proj.""", """.fc2."""),
    (""".attn""", """.self_attn"""),
    ("""ln_final.""", """transformer.text_model.final_layer_norm."""),
    ("""token_embedding.weight""", """transformer.text_model.embeddings.token_embedding.weight"""),
    ("""positional_embedding""", """transformer.text_model.embeddings.position_embedding.weight"""),
]
lowerCAmelCase							:       Dict					 =	{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowerCAmelCase							:       str					 =	re.compile("""|""".join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
lowerCAmelCase							:       str					 =	{"""q""": 0, """k""": 1, """v""": 2}






def 		lowerCAmelCase  (	UpperCamelCase__   :   Dict					)      ->    Optional[Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Any           =   {}
						__SCREAMING_SNAKE_CASE:				str           =   {}
						__SCREAMING_SNAKE_CASE:				int           =   {}
						for k, v in text_enc_dict.items():
												if (
												    k.endswith('''.self_attn.q_proj.weight'''					)
												    or k.endswith('''.self_attn.k_proj.weight'''					)
												    or k.endswith('''.self_attn.v_proj.weight'''					)
												):
																		__SCREAMING_SNAKE_CASE:				str           =   k[: -len('''.q_proj.weight'''					)]
																		__SCREAMING_SNAKE_CASE:				str           =   k[-len('''q_proj.weight'''					)]
																		if k_pre not in capture_qkv_weight:
																								__SCREAMING_SNAKE_CASE:				Tuple           =   [None, None, None]
																		__SCREAMING_SNAKE_CASE:				Dict           =   v
																		continue

												if (
												    k.endswith('''.self_attn.q_proj.bias'''					)
												    or k.endswith('''.self_attn.k_proj.bias'''					)
												    or k.endswith('''.self_attn.v_proj.bias'''					)
												):
																		__SCREAMING_SNAKE_CASE:				List[Any]           =   k[: -len('''.q_proj.bias'''					)]
																		__SCREAMING_SNAKE_CASE:				Tuple           =   k[-len('''q_proj.bias'''					)]
																		if k_pre not in capture_qkv_bias:
																								__SCREAMING_SNAKE_CASE:				str           =   [None, None, None]
																		__SCREAMING_SNAKE_CASE:				List[Any]           =   v
																		continue

												__SCREAMING_SNAKE_CASE:				List[str]           =   textenc_pattern.sub(lambda UpperCamelCase__					: protected[re.escape(m.group(0					)					)]       ,			UpperCamelCase__					)
												__SCREAMING_SNAKE_CASE:				List[Any]           =   v

						for k_pre, tensors in capture_qkv_weight.items():
												if None in tensors:
																		raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'''					)
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   textenc_pattern.sub(lambda UpperCamelCase__					: protected[re.escape(m.group(0					)					)]       ,			UpperCamelCase__					)
												__SCREAMING_SNAKE_CASE:				int           =   torch.cat(UpperCamelCase__					)

						for k_pre, tensors in capture_qkv_bias.items():
												if None in tensors:
																		raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'''					)
												__SCREAMING_SNAKE_CASE:				Optional[int]           =   textenc_pattern.sub(lambda UpperCamelCase__					: protected[re.escape(m.group(0					)					)]       ,			UpperCamelCase__					)
												__SCREAMING_SNAKE_CASE:				List[Any]           =   torch.cat(UpperCamelCase__					)

						return new_state_dict






def 		lowerCAmelCase  (	UpperCamelCase__   :   List[Any]					)      ->    List[str]:

						"""simple docstring"""





						return text_enc_dict


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

							parser.add_argument("""--model_path""", default=None, type=str, required=True, help="""Path to the model to convert.""")
							parser.add_argument("""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the output model.""")
							parser.add_argument("""--half""", action="""store_true""", help="""Save weights in half precision.""")
							parser.add_argument(
							    """--use_safetensors""", action="""store_true""", help="""Save weights use safetensors, default is ckpt."""
							)

							lowerCAmelCase							:       Union[str, Any]					 =	parser.parse_args()

							assert args.model_path is not None, "Must provide a model path!"

							assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

							# Path for safetensors
							lowerCAmelCase							:       Dict					 =	osp.join(args.model_path, """unet""", """diffusion_pytorch_model.safetensors""")
							lowerCAmelCase							:       Tuple					 =	osp.join(args.model_path, """vae""", """diffusion_pytorch_model.safetensors""")
							lowerCAmelCase							:       str					 =	osp.join(args.model_path, """text_encoder""", """model.safetensors""")

							# Load models from safetensors if it exists, if it doesn't pytorch
							if osp.exists(unet_path):
														lowerCAmelCase							:       List[str]					 =	load_file(unet_path, device="""cpu""")
							else:
														lowerCAmelCase							:       Union[str, Any]					 =	osp.join(args.model_path, """unet""", """diffusion_pytorch_model.bin""")
														lowerCAmelCase							:       Union[str, Any]					 =	torch.load(unet_path, map_location="""cpu""")

							if osp.exists(vae_path):
														lowerCAmelCase							:       List[str]					 =	load_file(vae_path, device="""cpu""")
							else:
														lowerCAmelCase							:       Any					 =	osp.join(args.model_path, """vae""", """diffusion_pytorch_model.bin""")
														lowerCAmelCase							:       List[str]					 =	torch.load(vae_path, map_location="""cpu""")

							if osp.exists(text_enc_path):
														lowerCAmelCase							:       int					 =	load_file(text_enc_path, device="""cpu""")
							else:
														lowerCAmelCase							:       List[str]					 =	osp.join(args.model_path, """text_encoder""", """pytorch_model.bin""")
														lowerCAmelCase							:       Optional[Any]					 =	torch.load(text_enc_path, map_location="""cpu""")

							# Convert the UNet model
							lowerCAmelCase							:       Optional[Any]					 =	convert_unet_state_dict(unet_state_dict)
							lowerCAmelCase							:       List[str]					 =	{"""model.diffusion_model.""" + k: v for k, v in unet_state_dict.items()}

							# Convert the VAE model
							lowerCAmelCase							:       Optional[Any]					 =	convert_vae_state_dict(vae_state_dict)
							lowerCAmelCase							:       Dict					 =	{"""first_stage_model.""" + k: v for k, v in vae_state_dict.items()}

							# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
							lowerCAmelCase							:       Optional[Any]					 =	"""text_model.encoder.layers.22.layer_norm2.bias""" in text_enc_dict

							if is_vaa_model:
														# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
														lowerCAmelCase							:       List[Any]					 =	{"""transformer.""" + k: v for k, v in text_enc_dict.items()}
														lowerCAmelCase							:       Optional[Any]					 =	convert_text_enc_state_dict_vaa(text_enc_dict)
														lowerCAmelCase							:       Optional[Any]					 =	{"""cond_stage_model.model.""" + k: v for k, v in text_enc_dict.items()}
							else:
														lowerCAmelCase							:       str					 =	convert_text_enc_state_dict(text_enc_dict)
														lowerCAmelCase							:       List[Any]					 =	{"""cond_stage_model.transformer.""" + k: v for k, v in text_enc_dict.items()}

							# Put together new checkpoint
							lowerCAmelCase							:       Dict					 =	{**unet_state_dict, **vae_state_dict, **text_enc_dict}
							if args.half:
														lowerCAmelCase							:       Tuple					 =	{k: v.half() for k, v in state_dict.items()}

							if args.use_safetensors:
														save_file(state_dict, args.checkpoint_path)
							else:
														lowerCAmelCase							:       Tuple					 =	{"""state_dict""": state_dict}
														torch.save(state_dict, args.checkpoint_path)










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


lowerCAmelCase							:       Optional[int]					 =	logging.get_logger(__name__)

lowerCAmelCase							:       Tuple					 =	{
    """facebook/data2vec-vision-base-ft""": (
        """https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"""
    ),
}





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   Union[str, Any]		      =     '''data2vec-vision'''






				def __init__(  self			, _lowerCAmelCase=768			, _lowerCAmelCase=12			, _lowerCAmelCase=12			, _lowerCAmelCase=3072			, _lowerCAmelCase="gelu"			, _lowerCAmelCase=0.0			, _lowerCAmelCase=0.0			, _lowerCAmelCase=0.02			, _lowerCAmelCase=1e-12			, _lowerCAmelCase=224			, _lowerCAmelCase=16			, _lowerCAmelCase=3			, _lowerCAmelCase=False			, _lowerCAmelCase=False			, _lowerCAmelCase=False			, _lowerCAmelCase=False			, _lowerCAmelCase=0.1			, _lowerCAmelCase=0.1			, _lowerCAmelCase=True			, _lowerCAmelCase=[3, 5, 7, 11]			, _lowerCAmelCase=[1, 2, 3, 6]			, _lowerCAmelCase=True			, _lowerCAmelCase=0.4			, _lowerCAmelCase=256			, _lowerCAmelCase=1			, _lowerCAmelCase=False			, _lowerCAmelCase=255			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										super().__init__(**_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				str           =   hidden_size
										__SCREAMING_SNAKE_CASE:				Tuple           =   num_hidden_layers
										__SCREAMING_SNAKE_CASE:				Tuple           =   num_attention_heads
										__SCREAMING_SNAKE_CASE:				int           =   intermediate_size
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   hidden_act
										__SCREAMING_SNAKE_CASE:				int           =   hidden_dropout_prob
										__SCREAMING_SNAKE_CASE:				str           =   attention_probs_dropout_prob
										__SCREAMING_SNAKE_CASE:				Any           =   initializer_range
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   layer_norm_eps

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   image_size
										__SCREAMING_SNAKE_CASE:				List[str]           =   patch_size
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   num_channels
										__SCREAMING_SNAKE_CASE:				List[Any]           =   use_mask_token
										__SCREAMING_SNAKE_CASE:				Tuple           =   use_absolute_position_embeddings
										__SCREAMING_SNAKE_CASE:				Any           =   use_relative_position_bias
										__SCREAMING_SNAKE_CASE:				Dict           =   use_shared_relative_position_bias
										__SCREAMING_SNAKE_CASE:				Any           =   layer_scale_init_value
										__SCREAMING_SNAKE_CASE:				List[Any]           =   drop_path_rate
										__SCREAMING_SNAKE_CASE:				int           =   use_mean_pooling
										# decode head attributes (semantic segmentation)
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   out_indices
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   pool_scales
										# auxiliary head attributes (semantic segmentation)
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   use_auxiliary_head
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   auxiliary_loss_weight
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   auxiliary_channels
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   auxiliary_num_convs
										__SCREAMING_SNAKE_CASE:				Any           =   auxiliary_concat_input
										__SCREAMING_SNAKE_CASE:				List[str]           =   semantic_loss_ignore_index





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   List[Any]		      =     version.parse('''1.11'''			)






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										return OrderedDict(
										    [
										        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
										    ]					)






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										return 1e-4



