


'''simple docstring'''



import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

_lowercase    :    List[Any]			     =  [
    # (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"""),
]

_lowercase    :    int			     =  [
    # (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"""),
]

_lowercase    :    List[str]			     =  []
# 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
		_lowercase    :    List[Any]			     =  F"""down_blocks.{i}.resnets.{j}."""
		_lowercase    :    Union[str, 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
			_lowercase    :    int			     =  F"""down_blocks.{i}.attentions.{j}."""
			_lowercase    :    Tuple			     =  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
		_lowercase    :    List[str]			     =  F"""up_blocks.{i}.resnets.{j}."""
		_lowercase    :    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
			_lowercase    :    Optional[Any]			     =  F"""up_blocks.{i}.attentions.{j}."""
			_lowercase    :    Optional[int]			     =  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
		_lowercase    :    Dict			     =  F"""down_blocks.{i}.downsamplers.0.conv."""
		_lowercase    :    Tuple			     =  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
		_lowercase    :    Union[str, Any]			     =  F"""up_blocks.{i}.upsamplers.0."""
		_lowercase    :    Tuple			     =  F"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
		unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

_lowercase    :    Tuple			     =  """mid_block.attentions.0."""
_lowercase    :    Optional[Any]			     =  """middle_block.1."""
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
	_lowercase    :    int			     =  F"""mid_block.resnets.{j}."""
	_lowercase    :    Any			     =  F"""middle_block.{2*j}."""
	unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))


def    lowerCamelCase__   ( A     :    List[Any]  ):




						'''simple docstring'''






						UpperCAmelCase         =			{k: k for k in unet_state_dict.keys()}
						for sd_name, hf_name in unet_conversion_map:
												UpperCAmelCase         =			sd_name
						for k, v in mapping.items():
												if "resnets" in k:
																		for sd_part, hf_part in unet_conversion_map_resnet:
																								UpperCAmelCase         =			v.replace(A   ,     A  )
																		UpperCAmelCase         =			v
						for k, v in mapping.items():
												for sd_part, hf_part in unet_conversion_map_layer:
																		UpperCAmelCase         =			v.replace(A   ,     A  )
												UpperCAmelCase         =			v
						UpperCAmelCase         =			{v: unet_state_dict[k] for k, v in mapping.items()}
						return new_state_dict


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

_lowercase    :    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):
		_lowercase    :    List[str]			     =  F"""encoder.down_blocks.{i}.resnets.{j}."""
		_lowercase    :    Optional[int]			     =  F"""encoder.down.{i}.block.{j}."""
		vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

	if i < 3:
		_lowercase    :    Union[str, Any]			     =  F"""down_blocks.{i}.downsamplers.0."""
		_lowercase    :    str			     =  F"""down.{i}.downsample."""
		vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

		_lowercase    :    Optional[int]			     =  F"""up_blocks.{i}.upsamplers.0."""
		_lowercase    :    Union[str, 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):
		_lowercase    :    Tuple			     =  F"""decoder.up_blocks.{i}.resnets.{j}."""
		_lowercase    :    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):
	_lowercase    :    Any			     =  F"""mid_block.resnets.{i}."""
	_lowercase    :    List[Any]			     =  F"""mid.block_{i+1}."""
	vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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


def    lowerCamelCase__   ( A     :    Dict  ):




						'''simple docstring'''






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


def    lowerCamelCase__   ( A     :    str  ):




						'''simple docstring'''






						UpperCAmelCase         =			{k: k for k in vae_state_dict.keys()}
						for k, v in mapping.items():
												for sd_part, hf_part in vae_conversion_map:
																		UpperCAmelCase         =			v.replace(A   ,     A  )
												UpperCAmelCase         =			v
						for k, v in mapping.items():
												if "attentions" in k:
																		for sd_part, hf_part in vae_conversion_map_attn:
																								UpperCAmelCase         =			v.replace(A   ,     A  )
																		UpperCAmelCase         =			v
						UpperCAmelCase         =			{v: vae_state_dict[k] for k, v in mapping.items()}
						UpperCAmelCase         =			['''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"""  )
																								UpperCAmelCase         =			reshape_weight_for_sd(A  )
						return new_state_dict


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


_lowercase    :    Union[str, Any]			     =  [
    # (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"""),
]
_lowercase    :    int			     =  {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
_lowercase    :    str			     =  re.compile("""|""".join(protected.keys()))

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


def    lowerCamelCase__   ( A     :    Optional[int]  ):




						'''simple docstring'''






						UpperCAmelCase         =			{}
						UpperCAmelCase         =			{}
						UpperCAmelCase         =			{}
						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'''  )
												):
																		UpperCAmelCase         =			k[: -len('''.q_proj.weight'''  )]
																		UpperCAmelCase         =			k[-len('''q_proj.weight'''  )]
																		if k_pre not in capture_qkv_weight:
																								UpperCAmelCase         =			[None, None, None]
																		UpperCAmelCase         =			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'''  )
												):
																		UpperCAmelCase         =			k[: -len('''.q_proj.bias'''  )]
																		UpperCAmelCase         =			k[-len('''q_proj.bias'''  )]
																		if k_pre not in capture_qkv_bias:
																								UpperCAmelCase         =			[None, None, None]
																		UpperCAmelCase         =			v
																		continue

												UpperCAmelCase         =			textenc_pattern.sub(lambda A  : protected[re.escape(m.group(0  )  )]   ,     A  )
												UpperCAmelCase         =			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'''  )
												UpperCAmelCase         =			textenc_pattern.sub(lambda A  : protected[re.escape(m.group(0  )  )]   ,     A  )
												UpperCAmelCase         =			torch.cat(A  )

						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'''  )
												UpperCAmelCase         =			textenc_pattern.sub(lambda A  : protected[re.escape(m.group(0  )  )]   ,     A  )
												UpperCAmelCase         =			torch.cat(A  )

						return new_state_dict


def    lowerCamelCase__   ( A     :    Tuple  ):




						'''simple docstring'''






						return text_enc_dict


if __name__ == "__main__":
	_lowercase    :    Tuple			     =  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."""
	)

	_lowercase    :    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
	_lowercase    :    Optional[Any]			     =  osp.join(args.model_path, """unet""", """diffusion_pytorch_model.safetensors""")
	_lowercase    :    Union[str, Any]			     =  osp.join(args.model_path, """vae""", """diffusion_pytorch_model.safetensors""")
	_lowercase    :    Optional[Any]			     =  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):
		_lowercase    :    List[Any]			     =  load_file(unet_path, device="""cpu""")
	else:
		_lowercase    :    List[Any]			     =  osp.join(args.model_path, """unet""", """diffusion_pytorch_model.bin""")
		_lowercase    :    int			     =  torch.load(unet_path, map_location="""cpu""")

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

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

	# Convert the UNet model
	_lowercase    :    Optional[int]			     =  convert_unet_state_dict(unet_state_dict)
	_lowercase    :    Dict			     =  {"""model.diffusion_model.""" + k: v for k, v in unet_state_dict.items()}

	# Convert the VAE model
	_lowercase    :    Dict			     =  convert_vae_state_dict(vae_state_dict)
	_lowercase    :    Tuple			     =  {"""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
	_lowercase    :    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
		_lowercase    :    Any			     =  {"""transformer.""" + k: v for k, v in text_enc_dict.items()}
		_lowercase    :    Dict			     =  convert_text_enc_state_dict_vaa(text_enc_dict)
		_lowercase    :    Dict			     =  {"""cond_stage_model.model.""" + k: v for k, v in text_enc_dict.items()}
	else:
		_lowercase    :    Optional[Any]			     =  convert_text_enc_state_dict(text_enc_dict)
		_lowercase    :    Any			     =  {"""cond_stage_model.transformer.""" + k: v for k, v in text_enc_dict.items()}

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

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









'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowercase    :    Any			     =  {
    """configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
    """configuration_maskformer_swin""": ["""MaskFormerSwinConfig"""],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    Any			     =  ["""MaskFormerFeatureExtractor"""]
	_lowercase    :    Dict			     =  ["""MaskFormerImageProcessor"""]


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    Any			     =  [
	    """MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """MaskFormerForInstanceSegmentation""",
	    """MaskFormerModel""",
	    """MaskFormerPreTrainedModel""",
	]
	_lowercase    :    List[Any]			     =  [
	    """MaskFormerSwinBackbone""",
	    """MaskFormerSwinModel""",
	    """MaskFormerSwinPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
	from .configuration_maskformer_swin import MaskFormerSwinConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_maskformer import MaskFormerFeatureExtractor
		from .image_processing_maskformer import MaskFormerImageProcessor
	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_maskformer import (
		    MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MaskFormerForInstanceSegmentation,
		    MaskFormerModel,
		    MaskFormerPreTrainedModel,
		)
		from .modeling_maskformer_swin import (
		    MaskFormerSwinBackbone,
		    MaskFormerSwinModel,
		    MaskFormerSwinPreTrainedModel,
		)


else:
	import sys

	_lowercase    :    List[Any]			     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure)





