






import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  int	,			__magic_name__					:  Any					)		->			Any:






	"""simple docstring"""



	UpperCamelCase     :List[str]          =	checkpoint

	UpperCamelCase     :Optional[int]          =	{}

	UpperCamelCase     :Any          =	vae_state_dict["""encoder.conv_in.weight"""]
	UpperCamelCase     :Union[str, Any]          =	vae_state_dict["""encoder.conv_in.bias"""]
	UpperCamelCase     :Optional[int]          =	vae_state_dict["""encoder.conv_out.weight"""]
	UpperCamelCase     :str          =	vae_state_dict["""encoder.conv_out.bias"""]
	UpperCamelCase     :str          =	vae_state_dict["""encoder.norm_out.weight"""]
	UpperCamelCase     :Optional[int]          =	vae_state_dict["""encoder.norm_out.bias"""]

	UpperCamelCase     :Optional[int]          =	vae_state_dict["""decoder.conv_in.weight"""]
	UpperCamelCase     :int          =	vae_state_dict["""decoder.conv_in.bias"""]
	UpperCamelCase     :str          =	vae_state_dict["""decoder.conv_out.weight"""]
	UpperCamelCase     :Union[str, Any]          =	vae_state_dict["""decoder.conv_out.bias"""]
	UpperCamelCase     :Optional[Any]          =	vae_state_dict["""decoder.norm_out.weight"""]
	UpperCamelCase     :List[Any]          =	vae_state_dict["""decoder.norm_out.bias"""]

	UpperCamelCase     :List[str]          =	vae_state_dict["""quant_conv.weight"""]
	UpperCamelCase     :Any          =	vae_state_dict["""quant_conv.bias"""]
	UpperCamelCase     :int          =	vae_state_dict["""post_quant_conv.weight"""]
	UpperCamelCase     :List[Any]          =	vae_state_dict["""post_quant_conv.bias"""]

	# Retrieves the keys for the encoder down blocks only
	UpperCamelCase     :List[str]          =	len({""".""".join(layer.split("""."""					)[:3]					) for layer in vae_state_dict if """encoder.down""" in layer}					)
	UpperCamelCase     :Optional[int]          =	{
	    layer_id: [key for key in vae_state_dict if f"""down.{layer_id}""" in key] for layer_id in range(__magic_name__					)
	}

	# Retrieves the keys for the decoder up blocks only
	UpperCamelCase     :Optional[int]          =	len({""".""".join(layer.split("""."""					)[:3]					) for layer in vae_state_dict if """decoder.up""" in layer}					)
	UpperCamelCase     :Optional[Any]          =	{
	    layer_id: [key for key in vae_state_dict if f"""up.{layer_id}""" in key] for layer_id in range(__magic_name__					)
	}

	for i in range(__magic_name__					):
		UpperCamelCase     :Tuple          =	[key for key in down_blocks[i] if f"""down.{i}""" in key and f"""down.{i}.downsample""" not in key]

		if f"""encoder.down.{i}.downsample.conv.weight""" in vae_state_dict:
			UpperCamelCase     :Any          =	vae_state_dict.pop(
			    f"""encoder.down.{i}.downsample.conv.weight"""					)
			UpperCamelCase     :Union[str, Any]          =	vae_state_dict.pop(
			    f"""encoder.down.{i}.downsample.conv.bias"""					)

		UpperCamelCase     :Any          =	renew_vae_resnet_paths(__magic_name__					)
		UpperCamelCase     :str          =	{"""old""": f"""down.{i}.block""", """new""": f"""down_blocks.{i}.resnets"""}
		assign_to_checkpoint(__magic_name__	,			__magic_name__	,			__magic_name__	,			additional_replacements=[meta_path]	,			config=__magic_name__					)

	UpperCamelCase     :Optional[Any]          =	[key for key in vae_state_dict if """encoder.mid.block""" in key]
	UpperCamelCase     :Dict          =	2
	for i in range(1	,			num_mid_res_blocks + 1					):
		UpperCamelCase     :int          =	[key for key in mid_resnets if f"""encoder.mid.block_{i}""" in key]

		UpperCamelCase     :List[Any]          =	renew_vae_resnet_paths(__magic_name__					)
		UpperCamelCase     :str          =	{"""old""": f"""mid.block_{i}""", """new""": f"""mid_block.resnets.{i - 1}"""}
		assign_to_checkpoint(__magic_name__	,			__magic_name__	,			__magic_name__	,			additional_replacements=[meta_path]	,			config=__magic_name__					)

	UpperCamelCase     :List[Any]          =	[key for key in vae_state_dict if """encoder.mid.attn""" in key]
	UpperCamelCase     :List[str]          =	renew_vae_attention_paths(__magic_name__					)
	UpperCamelCase     :List[str]          =	{"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
	assign_to_checkpoint(__magic_name__	,			__magic_name__	,			__magic_name__	,			additional_replacements=[meta_path]	,			config=__magic_name__					)
	conv_attn_to_linear(__magic_name__					)

	for i in range(__magic_name__					):
		UpperCamelCase     :Optional[int]          =	num_up_blocks - 1 - i
		UpperCamelCase     :List[Any]          =	[
		    key for key in up_blocks[block_id] if f"""up.{block_id}""" in key and f"""up.{block_id}.upsample""" not in key
		]

		if f"""decoder.up.{block_id}.upsample.conv.weight""" in vae_state_dict:
			UpperCamelCase     :str          =	vae_state_dict[
			    f"""decoder.up.{block_id}.upsample.conv.weight"""
			]
			UpperCamelCase     :int          =	vae_state_dict[
			    f"""decoder.up.{block_id}.upsample.conv.bias"""
			]

		UpperCamelCase     :int          =	renew_vae_resnet_paths(__magic_name__					)
		UpperCamelCase     :List[Any]          =	{"""old""": f"""up.{block_id}.block""", """new""": f"""up_blocks.{i}.resnets"""}
		assign_to_checkpoint(__magic_name__	,			__magic_name__	,			__magic_name__	,			additional_replacements=[meta_path]	,			config=__magic_name__					)

	UpperCamelCase     :Optional[int]          =	[key for key in vae_state_dict if """decoder.mid.block""" in key]
	UpperCamelCase     :str          =	2
	for i in range(1	,			num_mid_res_blocks + 1					):
		UpperCamelCase     :Optional[Any]          =	[key for key in mid_resnets if f"""decoder.mid.block_{i}""" in key]

		UpperCamelCase     :List[str]          =	renew_vae_resnet_paths(__magic_name__					)
		UpperCamelCase     :int          =	{"""old""": f"""mid.block_{i}""", """new""": f"""mid_block.resnets.{i - 1}"""}
		assign_to_checkpoint(__magic_name__	,			__magic_name__	,			__magic_name__	,			additional_replacements=[meta_path]	,			config=__magic_name__					)

	UpperCamelCase     :List[Any]          =	[key for key in vae_state_dict if """decoder.mid.attn""" in key]
	UpperCamelCase     :Optional[Any]          =	renew_vae_attention_paths(__magic_name__					)
	UpperCamelCase     :Union[str, Any]          =	{"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
	assign_to_checkpoint(__magic_name__	,			__magic_name__	,			__magic_name__	,			additional_replacements=[meta_path]	,			config=__magic_name__					)
	conv_attn_to_linear(__magic_name__					)
	return new_checkpoint

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  str	,			__magic_name__					:  str	,			)		->			Optional[int]:






	"""simple docstring"""



	UpperCamelCase     :Tuple          =	requests.get(
	    """ https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"""					)
	UpperCamelCase     :int          =	io.BytesIO(r.content					)

	UpperCamelCase     :str          =	OmegaConf.load(__magic_name__					)
	UpperCamelCase     :str          =	512
	UpperCamelCase     :List[Any]          =	"""cuda""" if torch.cuda.is_available() else """cpu"""
	if checkpoint_path.endswith("""safetensors"""					):
		from safetensors import safe_open

		UpperCamelCase     :List[str]          =	{}
		with safe_open(__magic_name__	,			framework="""pt"""	,			device="""cpu"""					) as f:
			for key in f.keys():
				UpperCamelCase     :List[str]          =	f.get_tensor(__magic_name__					)
	else:
		UpperCamelCase     :Optional[int]          =	torch.load(__magic_name__	,			map_location=__magic_name__					)["""state_dict"""]

	# Convert the VAE model.
	UpperCamelCase     :Union[str, Any]          =	create_vae_diffusers_config(__magic_name__	,			image_size=__magic_name__					)
	UpperCamelCase     :Union[str, Any]          =	custom_convert_ldm_vae_checkpoint(__magic_name__	,			__magic_name__					)

	UpperCamelCase     :str          =	AutoencoderKL(**__magic_name__					)
	vae.load_state_dict(__magic_name__					)
	vae.save_pretrained(__magic_name__					)


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

	parser.add_argument('''--vae_pt_path''', default=None, type=str, required=True, help='''Path to the VAE.pt to convert.''')
	parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the VAE.pt to convert.''')

	UpperCAmelCase_ :						Any      =      parser.parse_args()

	vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)


"""simple docstring"""



import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
 UpperCAmelCase__          = argparse.ArgumentParser(
     description=(
         'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
         ' Distillation'
     )
 )
 parser.add_argument('--model_type', default='bert', choices=['bert'])
 parser.add_argument('--model_name', default='bert-base-uncased', type=str)
 parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
 parser.add_argument('--vocab_transform', action='store_true')
 UpperCAmelCase__          = parser.parse_args()

 if args.model_type == "bert":
  UpperCAmelCase__          = BertForMaskedLM.from_pretrained(args.model_name)
  UpperCAmelCase__          = 'bert'
 else:
  raise ValueError('args.model_type should be "bert".')

 UpperCAmelCase__          = model.state_dict()
 UpperCAmelCase__          = {}

 for w in ["word_embeddings", "position_embeddings"]:
  UpperCAmelCase__          = state_dict[F"{prefix}.embeddings.{w}.weight"]
 for w in ["weight", "bias"]:
  UpperCAmelCase__          = state_dict[F"{prefix}.embeddings.LayerNorm.{w}"]

 UpperCAmelCase__          = 0
 for teacher_idx in [0, 2, 4, 7, 9, 11]:
  for w in ["weight", "bias"]:
   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"
   ]
   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"
   ]
   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"
   ]

   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"
   ]
   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"
   ]

   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"
   ]
   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"
   ]
   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"
   ]
  std_idx += 1

 UpperCAmelCase__          = state_dict['cls.predictions.decoder.weight']
 UpperCAmelCase__          = state_dict['cls.predictions.bias']
 if args.vocab_transform:
  for w in ["weight", "bias"]:
   UpperCAmelCase__          = state_dict[F"cls.predictions.transform.dense.{w}"]
   UpperCAmelCase__          = state_dict[F"cls.predictions.transform.LayerNorm.{w}"]

 print(F"N layers selected for distillation: {std_idx}")
 print(F"Number of params transferred for distillation: {len(compressed_sd.keys())}")

 print(F"Save transferred checkpoint to {args.dump_checkpoint}.")
 torch.save(compressed_sd, args.dump_checkpoint)



