


'''simple docstring'''
import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


lowerCAmelCase   : Dict             =				{
    """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   : 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   : Dict             =				{
    """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   : int             =				{
    """num_train_timesteps""": 4_0,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}

lowerCAmelCase   : List[Any]             =				{
    """num_train_timesteps""": 2_0_1,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}

lowerCAmelCase   : List[Any]             =				{
    """num_train_timesteps""": 1_5_1,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}




def 	_A  (       A							)  ->				str:



	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 	_A  (       A							,A							,A							,A							,A=False							)  ->				Tuple:
	lowercase						:		Dict   					=		checkpoint[F'''{old_prefix}.in_layers.0.weight''']
	lowercase						:		List[Any]   					=		checkpoint[F'''{old_prefix}.in_layers.0.bias''']
	lowercase						:		Dict   					=		checkpoint[F'''{old_prefix}.in_layers.2.weight''']
	lowercase						:		List[Any]   					=		checkpoint[F'''{old_prefix}.in_layers.2.bias''']
	lowercase						:		Optional[int]   					=		checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
	lowercase						:		Dict   					=		checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
	lowercase						:		Any   					=		checkpoint[F'''{old_prefix}.out_layers.0.weight''']
	lowercase						:		List[Any]   					=		checkpoint[F'''{old_prefix}.out_layers.0.bias''']
	lowercase						:		int   					=		checkpoint[F'''{old_prefix}.out_layers.3.weight''']
	lowercase						:		int   					=		checkpoint[F'''{old_prefix}.out_layers.3.bias''']

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

	return new_checkpoint




def 	_A  (       A							,A							,A							,A							,A=None							)  ->				Tuple:
	lowercase						:		Optional[Any]   					=		checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3							,dim=0							)
	lowercase						:		str   					=		checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3							,dim=0							)

	lowercase						:		Dict   					=		checkpoint[F'''{old_prefix}.norm.weight''']
	lowercase						:		Tuple   					=		checkpoint[F'''{old_prefix}.norm.bias''']

	lowercase						:		Tuple   					=		weight_q.squeeze(-1							).squeeze(-1							)
	lowercase						:		int   					=		bias_q.squeeze(-1							).squeeze(-1							)
	lowercase						:		Optional[Any]   					=		weight_k.squeeze(-1							).squeeze(-1							)
	lowercase						:		Optional[int]   					=		bias_k.squeeze(-1							).squeeze(-1							)
	lowercase						:		Tuple   					=		weight_v.squeeze(-1							).squeeze(-1							)
	lowercase						:		Optional[Any]   					=		bias_v.squeeze(-1							).squeeze(-1							)

	lowercase						:		Union[str, Any]   					=		(
	    checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1							).squeeze(-1							)
	)
	lowercase						:		List[str]   					=		checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1							).squeeze(-1							)

	return new_checkpoint




def 	_A  (       A							,A							)  ->				List[str]:
	lowercase						:		Any   					=		torch.load(A							,map_location="cpu"							)
	lowercase						:		Any   					=		{}

	lowercase						:		Any   					=		checkpoint["time_embed.0.weight"]
	lowercase						:		Optional[int]   					=		checkpoint["time_embed.0.bias"]
	lowercase						:		Any   					=		checkpoint["time_embed.2.weight"]
	lowercase						:		Optional[Any]   					=		checkpoint["time_embed.2.bias"]

	if unet_config["num_class_embeds"] is not None:
		lowercase						:		List[Any]   					=		checkpoint["label_emb.weight"]

	lowercase						:		Dict   					=		checkpoint["input_blocks.0.0.weight"]
	lowercase						:		List[Any]   					=		checkpoint["input_blocks.0.0.bias"]

	lowercase						:		Any   					=		unet_config["down_block_types"]
	lowercase						:		Tuple   					=		unet_config["layers_per_block"]
	lowercase						:		str   					=		unet_config["attention_head_dim"]
	lowercase						:		str   					=		unet_config["block_out_channels"]
	lowercase						:		Optional[int]   					=		1
	lowercase						:		Tuple   					=		channels_list[0]

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

		elif layer_type == "AttnDownBlock2D":
			for j in range(A							):
				lowercase						:		List[str]   					=		F'''down_blocks.{i}.resnets.{j}'''
				lowercase						:		Optional[Any]   					=		F'''input_blocks.{current_layer}.0'''
				lowercase						:		Optional[int]   					=		True if j == 0 and downsample_block_has_skip else False
				lowercase						:		Any   					=		convert_resnet(A							,A							,A							,A							,has_skip=A							)
				lowercase						:		Dict   					=		F'''down_blocks.{i}.attentions.{j}'''
				lowercase						:		List[Any]   					=		F'''input_blocks.{current_layer}.1'''
				lowercase						:		Optional[Any]   					=		convert_attention(
				    A							,A							,A							,A							,A							)
				current_layer += 1

		if i != len(A							) - 1:
			lowercase						:		Dict   					=		F'''down_blocks.{i}.downsamplers.0'''
			lowercase						:		Optional[int]   					=		F'''input_blocks.{current_layer}.0'''
			lowercase						:		Tuple   					=		convert_resnet(A							,A							,A							,A							)
			current_layer += 1

		lowercase						:		Dict   					=		current_channels

	# hardcoded the mid-block for now
	lowercase						:		int   					=		"mid_block.resnets.0"
	lowercase						:		Optional[int]   					=		"middle_block.0"
	lowercase						:		Any   					=		convert_resnet(A							,A							,A							,A							)
	lowercase						:		Optional[int]   					=		"mid_block.attentions.0"
	lowercase						:		Tuple   					=		"middle_block.1"
	lowercase						:		Any   					=		convert_attention(A							,A							,A							,A							,A							)
	lowercase						:		Optional[int]   					=		"mid_block.resnets.1"
	lowercase						:		Union[str, Any]   					=		"middle_block.2"
	lowercase						:		Optional[int]   					=		convert_resnet(A							,A							,A							,A							)

	lowercase						:		Optional[Any]   					=		0
	lowercase						:		int   					=		unet_config["up_block_types"]

	for i, layer_type in enumerate(A							):
		if layer_type == "ResnetUpsampleBlock2D":
			for j in range(layers_per_block + 1							):
				lowercase						:		Optional[Any]   					=		F'''up_blocks.{i}.resnets.{j}'''
				lowercase						:		List[str]   					=		F'''output_blocks.{current_layer}.0'''
				lowercase						:		List[str]   					=		convert_resnet(A							,A							,A							,A							,has_skip=A							)
				current_layer += 1

			if i != len(A							) - 1:
				lowercase						:		Dict   					=		F'''up_blocks.{i}.upsamplers.0'''
				lowercase						:		Optional[Any]   					=		F'''output_blocks.{current_layer-1}.1'''
				lowercase						:		Optional[Any]   					=		convert_resnet(A							,A							,A							,A							)
		elif layer_type == "AttnUpBlock2D":
			for j in range(layers_per_block + 1							):
				lowercase						:		Optional[Any]   					=		F'''up_blocks.{i}.resnets.{j}'''
				lowercase						:		Tuple   					=		F'''output_blocks.{current_layer}.0'''
				lowercase						:		Union[str, Any]   					=		convert_resnet(A							,A							,A							,A							,has_skip=A							)
				lowercase						:		Optional[int]   					=		F'''up_blocks.{i}.attentions.{j}'''
				lowercase						:		List[Any]   					=		F'''output_blocks.{current_layer}.1'''
				lowercase						:		Any   					=		convert_attention(
				    A							,A							,A							,A							,A							)
				current_layer += 1

			if i != len(A							) - 1:
				lowercase						:		Dict   					=		F'''up_blocks.{i}.upsamplers.0'''
				lowercase						:		Any   					=		F'''output_blocks.{current_layer-1}.2'''
				lowercase						:		int   					=		convert_resnet(A							,A							,A							,A							)

	lowercase						:		Union[str, Any]   					=		checkpoint["out.0.weight"]
	lowercase						:		Any   					=		checkpoint["out.0.bias"]
	lowercase						:		int   					=		checkpoint["out.2.weight"]
	lowercase						:		str   					=		checkpoint["out.2.bias"]

	return new_checkpoint


if __name__ == "__main__":
					lowerCAmelCase   : str             =				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   : int             =				parser.parse_args()
					lowerCAmelCase   : Optional[int]             =				strabool(args.class_cond)

					lowerCAmelCase   : Optional[Any]             =				os.path.basename(args.unet_path)
					print(F'''Checkpoint: {ckpt_name}''')

					# Get U-Net config
					if "imagenet64" in ckpt_name:
										lowerCAmelCase   : Dict             =				IMAGENET_64_UNET_CONFIG
					elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
										lowerCAmelCase   : Dict             =				LSUN_256_UNET_CONFIG
					elif "test" in ckpt_name:
										lowerCAmelCase   : str             =				TEST_UNET_CONFIG
					else:
										raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

					if not args.class_cond:
										lowerCAmelCase   : int             =				None

					lowerCAmelCase   : Optional[int]             =				con_pt_to_diffuser(args.unet_path, unet_config)

					lowerCAmelCase   : Optional[Any]             =				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   : Optional[int]             =				CD_SCHEDULER_CONFIG
					elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
										lowerCAmelCase   : str             =				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   : Any             =				CT_LSUN_256_SCHEDULER_CONFIG
					else:
										raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

					lowerCAmelCase   : Dict             =				CMStochasticIterativeScheduler(**scheduler_config)

					lowerCAmelCase   : Tuple             =				ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
					consistency_model.save_pretrained(args.dump_path)










"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

_lowerCAmelCase	:	Tuple				=      {
    """uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}




class 			lowerCAmelCase__			(    __magic_name__   ):
   SCREAMING_SNAKE_CASE_    ='''mra'''
   def __init__(		self      :	Any							,			snake_case__      :	List[str]=5_0_2_6_5							,			snake_case__      :	Any=7_6_8							,			snake_case__      :	Union[str, Any]=1_2							,			snake_case__      :	Optional[Any]=1_2							,			snake_case__      :	Tuple=3_0_7_2							,			snake_case__      :	str="gelu"							,			snake_case__      :	Any=0.1							,			snake_case__      :	Tuple=0.1							,			snake_case__      :	Tuple=5_1_2							,			snake_case__      :	Union[str, Any]=1							,			snake_case__      :	List[Any]=0.02							,			snake_case__      :	str=1e-5							,			snake_case__      :	List[Any]="absolute"							,			snake_case__      :	str=4							,			snake_case__      :	List[str]="full"							,			snake_case__      :	Tuple=0							,			snake_case__      :	Any=0							,			snake_case__      :	Union[str, Any]=1							,			snake_case__      :	int=0							,			snake_case__      :	int=2							,			**snake_case__      :	List[Any]							,			):


        '''simple docstring'''
        super().__init__(pad_token_id=snake_case__							,			bos_token_id=snake_case__							,			eos_token_id=snake_case__							,			**snake_case__							)

        UpperCAmelCase__						:    List[Any]							=							vocab_size
        UpperCAmelCase__						:    str							=							max_position_embeddings
        UpperCAmelCase__						:    Any							=							hidden_size
        UpperCAmelCase__						:    Union[str, Any]							=							num_hidden_layers
        UpperCAmelCase__						:    str							=							num_attention_heads
        UpperCAmelCase__						:    int							=							intermediate_size
        UpperCAmelCase__						:    int							=							hidden_act
        UpperCAmelCase__						:    List[str]							=							hidden_dropout_prob
        UpperCAmelCase__						:    List[str]							=							attention_probs_dropout_prob
        UpperCAmelCase__						:    Any							=							initializer_range
        UpperCAmelCase__						:    Any							=							type_vocab_size
        UpperCAmelCase__						:    Dict							=							layer_norm_eps
        UpperCAmelCase__						:    Tuple							=							position_embedding_type
        UpperCAmelCase__						:    List[str]							=							block_per_row
        UpperCAmelCase__						:    Optional[Any]							=							approx_mode
        UpperCAmelCase__						:    Any							=							initial_prior_first_n_blocks
        UpperCAmelCase__						:    List[Any]							=							initial_prior_diagonal_n_blocks


