"""simple docstring"""
import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


SCREAMING_SNAKE_CASE__   =					{
    "sample_size": 32,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 1_000,
    "block_out_channels": [32, 64],
    "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",
}

SCREAMING_SNAKE_CASE__   =					{
    "sample_size": 64,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 1_000,
    "block_out_channels": [192, 192 * 2, 192 * 3, 192 * 4],
    "attention_head_dim": 64,
    "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",
}

SCREAMING_SNAKE_CASE__   =					{
    "sample_size": 256,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": None,
    "block_out_channels": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    "attention_head_dim": 64,
    "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",
}

SCREAMING_SNAKE_CASE__   =					{
    "num_train_timesteps": 40,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

SCREAMING_SNAKE_CASE__   =					{
    "num_train_timesteps": 201,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

SCREAMING_SNAKE_CASE__   =					{
    "num_train_timesteps": 151,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}





def 				lowerCAmelCase__      (     _UpperCamelCase    :			List[Any]   )   ->			Any:





						"""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__      (     _UpperCamelCase    :			Any	,     _UpperCamelCase    :			Union[str, Any]	,     _UpperCamelCase    :			Optional[int]	,     _UpperCamelCase    :			Tuple	,     _UpperCamelCase    :			Tuple=False   )   ->			List[Any]:





						"""simple docstring"""






						snake_case        =     checkpoint[f"""{old_prefix}.in_layers.0.weight"""]
						snake_case        =     checkpoint[f"""{old_prefix}.in_layers.0.bias"""]
						snake_case        =     checkpoint[f"""{old_prefix}.in_layers.2.weight"""]
						snake_case        =     checkpoint[f"""{old_prefix}.in_layers.2.bias"""]
						snake_case        =     checkpoint[f"""{old_prefix}.emb_layers.1.weight"""]
						snake_case        =     checkpoint[f"""{old_prefix}.emb_layers.1.bias"""]
						snake_case        =     checkpoint[f"""{old_prefix}.out_layers.0.weight"""]
						snake_case        =     checkpoint[f"""{old_prefix}.out_layers.0.bias"""]
						snake_case        =     checkpoint[f"""{old_prefix}.out_layers.3.weight"""]
						snake_case        =     checkpoint[f"""{old_prefix}.out_layers.3.bias"""]

						if has_skip:
												snake_case        =     checkpoint[f"""{old_prefix}.skip_connection.weight"""]
												snake_case        =     checkpoint[f"""{old_prefix}.skip_connection.bias"""]

						return new_checkpoint





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Optional[int]	,     _UpperCamelCase    :			Optional[int]	,     _UpperCamelCase    :			Union[str, Any]	,     _UpperCamelCase    :			Dict	,     _UpperCamelCase    :			str=None   )   ->			List[Any]:





						"""simple docstring"""






						snake_case   ,snake_case   ,snake_case        =     checkpoint[f"""{old_prefix}.qkv.weight"""].chunk(3	,     dim=0   )
						snake_case   ,snake_case   ,snake_case        =     checkpoint[f"""{old_prefix}.qkv.bias"""].chunk(3	,     dim=0   )

						snake_case        =     checkpoint[f"""{old_prefix}.norm.weight"""]
						snake_case        =     checkpoint[f"""{old_prefix}.norm.bias"""]

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

						snake_case        =     (
						    checkpoint[f"""{old_prefix}.proj_out.weight"""].squeeze(-1   ).squeeze(-1   )
						)
						snake_case        =     checkpoint[f"""{old_prefix}.proj_out.bias"""].squeeze(-1   ).squeeze(-1   )

						return new_checkpoint





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str	,     _UpperCamelCase    :			int   )   ->			Optional[Any]:





						"""simple docstring"""






						snake_case        =     torch.load(__A	,     map_location='cpu'   )
						snake_case        =     {}

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

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

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

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

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

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

												if i != len(__A   ) - 1:
																		snake_case        =     f"""down_blocks.{i}.downsamplers.0"""
																		snake_case        =     f"""input_blocks.{current_layer}.0"""
																		snake_case        =     convert_resnet(__A	,     __A	,     __A	,     __A   )
																		current_layer += 1

												snake_case        =     current_channels

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

						snake_case        =     0
						snake_case        =     unet_config['up_block_types']

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

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

																		if i != len(__A   ) - 1:
																								snake_case        =     f"""up_blocks.{i}.upsamplers.0"""
																								snake_case        =     f"""output_blocks.{current_layer-1}.2"""
																								snake_case        =     convert_resnet(__A	,     __A	,     __A	,     __A   )

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

						return new_checkpoint


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   =					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.")

					SCREAMING_SNAKE_CASE__   =					parser.parse_args()
					SCREAMING_SNAKE_CASE__   =					strabool(args.class_cond)

					SCREAMING_SNAKE_CASE__   =					os.path.basename(args.unet_path)
					print(f"""Checkpoint: {ckpt_name}""")

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

					if not args.class_cond:
										SCREAMING_SNAKE_CASE__   =					None

					SCREAMING_SNAKE_CASE__   =					con_pt_to_diffuser(args.unet_path, unet_config)

					SCREAMING_SNAKE_CASE__   =					UNetaDModel(**unet_config)
					image_unet.load_state_dict(converted_unet_ckpt)

					# Get scheduler config
					if "cd" in ckpt_name or "test" in ckpt_name:
										SCREAMING_SNAKE_CASE__   =					CD_SCHEDULER_CONFIG
					elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
										SCREAMING_SNAKE_CASE__   =					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)):
										SCREAMING_SNAKE_CASE__   =					CT_LSUN_256_SCHEDULER_CONFIG
					else:
										raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

					SCREAMING_SNAKE_CASE__   =					CMStochasticIterativeScheduler(**scheduler_config)

					SCREAMING_SNAKE_CASE__   =					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


SCREAMING_SNAKE_CASE__   =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =					{
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}



class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Tuple     					=  """swinv2"""

				_lowerCAmelCase       :      Any     					=  {
				    """num_attention_heads""": """num_heads""",
				    """num_hidden_layers""": """num_layers""",
				}

				def __init__(			self						,      lowerCAmelCase=2_24						,      lowerCAmelCase=4						,      lowerCAmelCase=3						,      lowerCAmelCase=96						,      lowerCAmelCase=[2, 2, 6, 2]						,      lowerCAmelCase=[3, 6, 12, 24]						,      lowerCAmelCase=7						,      lowerCAmelCase=4.0						,      lowerCAmelCase=True						,      lowerCAmelCase=0.0						,      lowerCAmelCase=0.0						,      lowerCAmelCase=0.1						,      lowerCAmelCase="gelu"						,      lowerCAmelCase=False						,      lowerCAmelCase=0.02						,      lowerCAmelCase=1E-5						,      lowerCAmelCase=32						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										super().__init__(**lowerCAmelCase      )

										snake_case        =     image_size
										snake_case        =     patch_size
										snake_case        =     num_channels
										snake_case        =     embed_dim
										snake_case        =     depths
										snake_case        =     len(lowerCAmelCase      )
										snake_case        =     num_heads
										snake_case        =     window_size
										snake_case        =     mlp_ratio
										snake_case        =     qkv_bias
										snake_case        =     hidden_dropout_prob
										snake_case        =     attention_probs_dropout_prob
										snake_case        =     drop_path_rate
										snake_case        =     hidden_act
										snake_case        =     use_absolute_embeddings
										snake_case        =     layer_norm_eps
										snake_case        =     initializer_range
										snake_case        =     encoder_stride
										# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
										# this indicates the channel dimension after the last stage of the model
										snake_case        =     int(embed_dim * 2 ** (len(lowerCAmelCase      ) - 1)      )
										snake_case        =     (0, 0, 0, 0)


