


import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


lowerCAmelCase__    :List[Any]       =				{
    '''sample_size''': 3_2,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1_0_0_0,
    '''block_out_channels''': [3_2, 6_4],
    '''attention_head_dim''': 8,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

lowerCAmelCase__    :List[str]       =				{
    '''sample_size''': 6_4,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1_0_0_0,
    '''block_out_channels''': [1_9_2, 1_9_2 * 2, 1_9_2 * 3, 1_9_2 * 4],
    '''attention_head_dim''': 6_4,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

lowerCAmelCase__    :Any       =				{
    '''sample_size''': 2_5_6,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': None,
    '''block_out_channels''': [2_5_6, 2_5_6, 2_5_6 * 2, 2_5_6 * 2, 2_5_6 * 4, 2_5_6 * 4],
    '''attention_head_dim''': 6_4,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''default''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

lowerCAmelCase__    :Optional[int]       =				{
    '''num_train_timesteps''': 4_0,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

lowerCAmelCase__    :Optional[int]       =				{
    '''num_train_timesteps''': 2_0_1,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

lowerCAmelCase__    :Any       =				{
    '''num_train_timesteps''': 1_5_1,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

def 			lowerCAmelCase__					(     a__:						Optional[int]			)    ->						int:


							'''simple docstring'''





							if isinstance(a__     ,    a__			):
														return v
							if v.lower() in ("yes", "true", "t", "y", "1"):
														return True
							elif v.lower() in ("no", "false", "f", "n", "0"):
														return False
							else:
														raise argparse.ArgumentTypeError('boolean value expected'			)

def 			lowerCAmelCase__					(     a__:						Dict     ,    a__:						int     ,    a__:						Union[str, Any]     ,    a__:						Dict     ,    a__:						Optional[int]=False			)    ->						Dict:


							'''simple docstring'''





							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.in_layers.0.weight''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.in_layers.0.bias''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.in_layers.2.weight''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.in_layers.2.bias''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.out_layers.0.weight''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.out_layers.0.bias''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.out_layers.3.weight''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.out_layers.3.bias''']

							if has_skip:
														_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.skip_connection.weight''']
														_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.skip_connection.bias''']

							return new_checkpoint

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						Any     ,    a__:						List[str]     ,    a__:						List[Any]     ,    a__:						List[str]=None			)    ->						Optional[Any]:


							'''simple docstring'''





							_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3     ,    dim=0			)
							_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3     ,    dim=0			)

							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.norm.weight''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.norm.bias''']

							_UpperCAmelCase											=     weight_q.squeeze(-1			).squeeze(-1			)
							_UpperCAmelCase											=     bias_q.squeeze(-1			).squeeze(-1			)
							_UpperCAmelCase											=     weight_k.squeeze(-1			).squeeze(-1			)
							_UpperCAmelCase											=     bias_k.squeeze(-1			).squeeze(-1			)
							_UpperCAmelCase											=     weight_v.squeeze(-1			).squeeze(-1			)
							_UpperCAmelCase											=     bias_v.squeeze(-1			).squeeze(-1			)

							_UpperCAmelCase											=     (
							    checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1			).squeeze(-1			)
							)
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1			).squeeze(-1			)

							return new_checkpoint

def 			lowerCAmelCase__					(     a__:						str     ,    a__:						Any			)    ->						Tuple:


							'''simple docstring'''





							_UpperCAmelCase											=     torch.load(a__     ,    map_location='cpu'			)
							_UpperCAmelCase											=     {}

							_UpperCAmelCase											=     checkpoint['time_embed.0.weight']
							_UpperCAmelCase											=     checkpoint['time_embed.0.bias']
							_UpperCAmelCase											=     checkpoint['time_embed.2.weight']
							_UpperCAmelCase											=     checkpoint['time_embed.2.bias']

							if unet_config["num_class_embeds"] is not None:
														_UpperCAmelCase											=     checkpoint['label_emb.weight']

							_UpperCAmelCase											=     checkpoint['input_blocks.0.0.weight']
							_UpperCAmelCase											=     checkpoint['input_blocks.0.0.bias']

							_UpperCAmelCase											=     unet_config['down_block_types']
							_UpperCAmelCase											=     unet_config['layers_per_block']
							_UpperCAmelCase											=     unet_config['attention_head_dim']
							_UpperCAmelCase											=     unet_config['block_out_channels']
							_UpperCAmelCase											=     1
							_UpperCAmelCase											=     channels_list[0]

							for i, layer_type in enumerate(a__			):
														_UpperCAmelCase											=     channels_list[i]
														_UpperCAmelCase											=     current_channels != prev_channels
														if layer_type == "ResnetDownsampleBlock2D":
																					for j in range(a__			):
																												_UpperCAmelCase											=     F'''down_blocks.{i}.resnets.{j}'''
																												_UpperCAmelCase											=     F'''input_blocks.{current_layer}.0'''
																												_UpperCAmelCase											=     True if j == 0 and downsample_block_has_skip else False
																												_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__     ,    has_skip=a__			)
																												current_layer += 1

														elif layer_type == "AttnDownBlock2D":
																					for j in range(a__			):
																												_UpperCAmelCase											=     F'''down_blocks.{i}.resnets.{j}'''
																												_UpperCAmelCase											=     F'''input_blocks.{current_layer}.0'''
																												_UpperCAmelCase											=     True if j == 0 and downsample_block_has_skip else False
																												_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__     ,    has_skip=a__			)
																												_UpperCAmelCase											=     F'''down_blocks.{i}.attentions.{j}'''
																												_UpperCAmelCase											=     F'''input_blocks.{current_layer}.1'''
																												_UpperCAmelCase											=     convert_attention(
																												    a__     ,    a__     ,    a__     ,    a__     ,    a__			)
																												current_layer += 1

														if i != len(a__			) - 1:
																					_UpperCAmelCase											=     F'''down_blocks.{i}.downsamplers.0'''
																					_UpperCAmelCase											=     F'''input_blocks.{current_layer}.0'''
																					_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__			)
																					current_layer += 1

														_UpperCAmelCase											=     current_channels

							# hardcoded the mid-block for now
							_UpperCAmelCase											=     'mid_block.resnets.0'
							_UpperCAmelCase											=     'middle_block.0'
							_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__			)
							_UpperCAmelCase											=     'mid_block.attentions.0'
							_UpperCAmelCase											=     'middle_block.1'
							_UpperCAmelCase											=     convert_attention(a__     ,    a__     ,    a__     ,    a__     ,    a__			)
							_UpperCAmelCase											=     'mid_block.resnets.1'
							_UpperCAmelCase											=     'middle_block.2'
							_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__			)

							_UpperCAmelCase											=     0
							_UpperCAmelCase											=     unet_config['up_block_types']

							for i, layer_type in enumerate(a__			):
														if layer_type == "ResnetUpsampleBlock2D":
																					for j in range(layers_per_block + 1			):
																												_UpperCAmelCase											=     F'''up_blocks.{i}.resnets.{j}'''
																												_UpperCAmelCase											=     F'''output_blocks.{current_layer}.0'''
																												_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__     ,    has_skip=a__			)
																												current_layer += 1

																					if i != len(a__			) - 1:
																												_UpperCAmelCase											=     F'''up_blocks.{i}.upsamplers.0'''
																												_UpperCAmelCase											=     F'''output_blocks.{current_layer-1}.1'''
																												_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__			)
														elif layer_type == "AttnUpBlock2D":
																					for j in range(layers_per_block + 1			):
																												_UpperCAmelCase											=     F'''up_blocks.{i}.resnets.{j}'''
																												_UpperCAmelCase											=     F'''output_blocks.{current_layer}.0'''
																												_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__     ,    has_skip=a__			)
																												_UpperCAmelCase											=     F'''up_blocks.{i}.attentions.{j}'''
																												_UpperCAmelCase											=     F'''output_blocks.{current_layer}.1'''
																												_UpperCAmelCase											=     convert_attention(
																												    a__     ,    a__     ,    a__     ,    a__     ,    a__			)
																												current_layer += 1

																					if i != len(a__			) - 1:
																												_UpperCAmelCase											=     F'''up_blocks.{i}.upsamplers.0'''
																												_UpperCAmelCase											=     F'''output_blocks.{current_layer-1}.2'''
																												_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__			)

							_UpperCAmelCase											=     checkpoint['out.0.weight']
							_UpperCAmelCase											=     checkpoint['out.0.bias']
							_UpperCAmelCase											=     checkpoint['out.2.weight']
							_UpperCAmelCase											=     checkpoint['out.2.bias']

							return new_checkpoint


if __name__ == "__main__":
		lowerCAmelCase__    :Union[str, Any]       =				argparse.ArgumentParser()

		parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
		parser.add_argument(
		    '''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
		)
		parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')

		lowerCAmelCase__    :Dict       =				parser.parse_args()
		lowerCAmelCase__    :List[str]       =				strabool(args.class_cond)

		lowerCAmelCase__    :Any       =				os.path.basename(args.unet_path)
		print(f'''Checkpoint: {ckpt_name}''')

		# Get U-Net config
		if "imagenet64" in ckpt_name:
				lowerCAmelCase__    :Union[str, Any]       =				IMAGENET_64_UNET_CONFIG
		elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				lowerCAmelCase__    :Tuple       =				LSUN_256_UNET_CONFIG
		elif "test" in ckpt_name:
				lowerCAmelCase__    :Any       =				TEST_UNET_CONFIG
		else:
				raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

		if not args.class_cond:
				lowerCAmelCase__    :str       =				None

		lowerCAmelCase__    :Tuple       =				con_pt_to_diffuser(args.unet_path, unet_config)

		lowerCAmelCase__    :Optional[int]       =				UNetaDModel(**unet_config)
		image_unet.load_state_dict(converted_unet_ckpt)

		# Get scheduler config
		if "cd" in ckpt_name or "test" in ckpt_name:
				lowerCAmelCase__    :int       =				CD_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
				lowerCAmelCase__    :Union[str, Any]       =				CT_IMAGENET_64_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				lowerCAmelCase__    :Tuple       =				CT_LSUN_256_SCHEDULER_CONFIG
		else:
				raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

		lowerCAmelCase__    :Any       =				CMStochasticIterativeScheduler(**scheduler_config)

		lowerCAmelCase__    :Union[str, Any]       =				ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
		consistency_model.save_pretrained(args.dump_path)




import math

import qiskit

def 			lowerCAmelCase__					(     a__:						int = 1     ,    a__:						int = 1     ,    a__:						int = 1			)    ->						qiskit.result.counts.Counts:


							'''simple docstring'''





							if (
							    isinstance(a__     ,    a__			)
							    or isinstance(a__     ,    a__			)
							    or isinstance(a__     ,    a__			)
							):
														raise TypeError('inputs must be integers.'			)

							if (input_a < 0) or (input_a < 0) or (carry_in < 0):
														raise ValueError('inputs must be positive.'			)

							if (
							    (math.floor(a__			) != input_a)
							    or (math.floor(a__			) != input_a)
							    or (math.floor(a__			) != carry_in)
							):
														raise ValueError('inputs must be exact integers.'			)

							if (input_a > 2) or (input_a > 2) or (carry_in > 2):
														raise ValueError('inputs must be less or equal to 2.'			)

							# build registers
							_UpperCAmelCase											=     qiskit.QuantumRegister(4     ,    'qr'			)
							_UpperCAmelCase											=     qiskit.ClassicalRegister(2     ,    'cr'			)
							# list the entries
							_UpperCAmelCase											=     [input_a, input_a, carry_in]

							_UpperCAmelCase											=     qiskit.QuantumCircuit(a__     ,    a__			)

							for i in range(0     ,    3			):
														if entry[i] == 2:
																					quantum_circuit.h(a__			)  # for hadamard entries
														elif entry[i] == 1:
																					quantum_circuit.x(a__			)  # for 1 entries
														elif entry[i] == 0:
																					quantum_circuit.i(a__			)  # for 0 entries

    # build the circuit
							quantum_circuit.ccx(0     ,    1     ,    3			)  # ccx = toffoli gate
							quantum_circuit.cx(0     ,    1			)
							quantum_circuit.ccx(1     ,    2     ,    3			)
							quantum_circuit.cx(1     ,    2			)
							quantum_circuit.cx(0     ,    1			)

							quantum_circuit.measure([2, 3]     ,    a__			)  # measure the last two qbits

							_UpperCAmelCase											=     qiskit.Aer.get_backend('aer_simulator'			)
							_UpperCAmelCase											=     qiskit.execute(a__     ,    a__     ,    shots=1_0_0_0			)

							return job.result().get_counts(a__			)


if __name__ == "__main__":
		print(f'''Total sum count for state is: {quantum_full_adder(1, 1, 1)}''')
