






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)








import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  int = 3					)		->			qiskit.result.counts.Counts:






	"""simple docstring"""



	if isinstance(__magic_name__	,			__magic_name__					):
		raise TypeError("""number of qubits must be a integer."""					)
	if number_of_qubits <= 0:
		raise ValueError("""number of qubits must be > 0."""					)
	if math.floor(__magic_name__					) != number_of_qubits:
		raise ValueError("""number of qubits must be exact integer."""					)
	if number_of_qubits > 10:
		raise ValueError("""number of qubits too large to simulate(>10)."""					)

	UpperCamelCase     :int          =	QuantumRegister(__magic_name__	,			"""qr"""					)
	UpperCamelCase     :str          =	ClassicalRegister(__magic_name__	,			"""cr"""					)

	UpperCamelCase     :str          =	QuantumCircuit(__magic_name__	,			__magic_name__					)

	UpperCamelCase     :List[Any]          =	number_of_qubits

	for i in range(__magic_name__					):
		quantum_circuit.h(number_of_qubits - i - 1					)
		counter -= 1
		for j in range(__magic_name__					):
			quantum_circuit.cp(np.pi / 2 ** (counter - j)	,			__magic_name__	,			__magic_name__					)

	for k in range(number_of_qubits // 2					):
		quantum_circuit.swap(__magic_name__	,			number_of_qubits - k - 1					)

	# measure all the qubits
	quantum_circuit.measure(__magic_name__	,			__magic_name__					)
	# simulate with 10000 shots
	UpperCamelCase     :str          =	Aer.get_backend("""qasm_simulator"""					)
	UpperCamelCase     :Dict          =	execute(__magic_name__	,			__magic_name__	,			shots=1_0000					)

	return job.result().get_counts(__magic_name__					)


if __name__ == "__main__":
	print(
	    F'''Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}'''
	)
