

'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


a   :			List[Any]         = logging.get_logger(__name__)







class   UpperCamelCase__    (   lowerCAmelCase_   ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :	List[str]   =							["pixel_values"]







		def __init__( self    ,			snake_case = True    ,			snake_case = None    ,			snake_case = 0.9    ,			snake_case = PILImageResampling.BICUBIC    ,			snake_case = True    ,			snake_case = None    ,			snake_case = 1 / 2_5_5    ,			snake_case = True    ,			snake_case = True    ,			snake_case = None    ,			snake_case = None    ,			**snake_case    ,			):


							'''simple docstring'''

							super().__init__(**__snake_case			)
							UpperCAmelCase		:							Dict   					=   size if size is not None else {"shortest_edge": 2_2_4}
							UpperCAmelCase		:							Tuple   					=   get_size_dict(__snake_case    ,			default_to_square=__snake_case			)
							UpperCAmelCase		:							Optional[int]   					=   crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
							UpperCAmelCase		:							Any   					=   get_size_dict(__snake_case    ,			param_name="crop_size"			)

							UpperCAmelCase		:							int   					=   do_resize
							UpperCAmelCase		:							int   					=   size
							UpperCAmelCase		:							Any   					=   crop_pct
							UpperCAmelCase		:							Union[str, Any]   					=   resample
							UpperCAmelCase		:							int   					=   do_center_crop
							UpperCAmelCase		:							Any   					=   crop_size
							UpperCAmelCase		:							Optional[int]   					=   do_rescale
							UpperCAmelCase		:							Union[str, Any]   					=   rescale_factor
							UpperCAmelCase		:							Optional[int]   					=   do_normalize
							UpperCAmelCase		:							Optional[Any]   					=   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
							UpperCAmelCase		:							Dict   					=   image_std if image_std is not None else IMAGENET_DEFAULT_STD







		def       A_       ( self    ,			snake_case    ,			snake_case    ,			snake_case = None    ,			snake_case = PILImageResampling.BICUBIC    ,			snake_case = None    ,			**snake_case    ,			):


							'''simple docstring'''

							UpperCAmelCase		:							Dict   					=   get_size_dict(__snake_case    ,			default_to_square=__snake_case			)
							if "shortest_edge" not in size and ("height" not in size or "width" not in size):
												raise ValueError(f"size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}"			)
							if crop_pct is not None:
												if "shortest_edge" in size:
																	UpperCAmelCase		:							str   					=   int(size["shortest_edge"] / crop_pct			)
												elif "height" in size and "width" in size:
																	if size["height"] == size["width"]:
																						UpperCAmelCase		:							str   					=   int(size["height"] / crop_pct			)
																	else:
																						UpperCAmelCase		:							List[str]   					=   (int(size["height"] / crop_pct			), int(size["width"] / crop_pct			))
												else:
																	raise ValueError("Invalid size for resize: {}".format(__snake_case			)			)

												UpperCAmelCase		:							List[Any]   					=   get_resize_output_image_size(__snake_case    ,			size=__snake_case    ,			default_to_square=__snake_case			)
							else:
												if "shortest_edge" in size:
																	UpperCAmelCase		:							Any   					=   get_resize_output_image_size(__snake_case    ,			size=size["shortest_edge"]    ,			default_to_square=__snake_case			)
												elif "height" in size and "width" in size:
																	UpperCAmelCase		:							int   					=   (size["height"], size["width"])
												else:
																	raise ValueError("Invalid size for resize: {}".format(__snake_case			)			)

							return resize(__snake_case    ,			size=__snake_case    ,			resample=__snake_case    ,			data_format=__snake_case    ,			**__snake_case			)







		def       A_       ( self    ,			snake_case    ,			snake_case    ,			snake_case = None    ,			**snake_case    ,			):


							'''simple docstring'''

							UpperCAmelCase		:							str   					=   get_size_dict(__snake_case			)
							if "height" not in size or "width" not in size:
												raise ValueError(f"size must contain \'height\' and \'width\' as keys. Got {size.keys()}"			)
							return center_crop(__snake_case    ,			size=(size["height"], size["width"])    ,			data_format=__snake_case    ,			**__snake_case			)







		def       A_       ( self    ,			snake_case    ,			snake_case    ,			snake_case = None    ,			**snake_case    ,			):


							'''simple docstring'''

							return rescale(__snake_case    ,			scale=__snake_case    ,			data_format=__snake_case    ,			**__snake_case			)







		def       A_       ( self    ,			snake_case    ,			snake_case    ,			snake_case    ,			snake_case = None    ,			**snake_case    ,			):


							'''simple docstring'''

							return normalize(__snake_case    ,			mean=__snake_case    ,			std=__snake_case    ,			data_format=__snake_case    ,			**__snake_case			)







		def       A_       ( self    ,			snake_case    ,			snake_case = None    ,			snake_case = None    ,			snake_case = None    ,			snake_case = None    ,			snake_case = None    ,			snake_case = None    ,			snake_case = None    ,			snake_case = None    ,			snake_case = None    ,			snake_case = None    ,			snake_case = None    ,			snake_case = None    ,			snake_case = ChannelDimension.FIRST    ,			**snake_case    ,			):


							'''simple docstring'''

							UpperCAmelCase		:							Optional[int]   					=   do_resize if do_resize is not None else self.do_resize
							UpperCAmelCase		:							str   					=   crop_pct if crop_pct is not None else self.crop_pct
							UpperCAmelCase		:							Union[str, Any]   					=   resample if resample is not None else self.resample
							UpperCAmelCase		:							int   					=   do_center_crop if do_center_crop is not None else self.do_center_crop
							UpperCAmelCase		:							Union[str, Any]   					=   do_rescale if do_rescale is not None else self.do_rescale
							UpperCAmelCase		:							Optional[int]   					=   rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCAmelCase		:							Any   					=   do_normalize if do_normalize is not None else self.do_normalize
							UpperCAmelCase		:							str   					=   image_mean if image_mean is not None else self.image_mean
							UpperCAmelCase		:							Optional[int]   					=   image_std if image_std is not None else self.image_std

							UpperCAmelCase		:							Any   					=   size if size is not None else self.size
							UpperCAmelCase		:							Any   					=   get_size_dict(__snake_case    ,			default_to_square=__snake_case			)
							UpperCAmelCase		:							Union[str, Any]   					=   crop_size if crop_size is not None else self.crop_size
							UpperCAmelCase		:							Union[str, Any]   					=   get_size_dict(__snake_case    ,			param_name="crop_size"			)

							UpperCAmelCase		:							Optional[Any]   					=   make_list_of_images(__snake_case			)

							if not valid_images(__snake_case			):
												raise ValueError(
												    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
												    "torch.Tensor, tf.Tensor or jax.ndarray."			)

							if do_resize and size is None or resample is None:
												raise ValueError("Size and resample must be specified if do_resize is True."			)

							if do_center_crop and crop_pct is None:
												raise ValueError("Crop_pct must be specified if do_center_crop is True."			)

							if do_rescale and rescale_factor is None:
												raise ValueError("Rescale factor must be specified if do_rescale is True."			)

							if do_normalize and (image_mean is None or image_std is None):
												raise ValueError("Image mean and std must be specified if do_normalize is True."			)

							# All transformations expect numpy arrays.
							UpperCAmelCase		:							Dict   					=   [to_numpy_array(__snake_case			) for image in images]

							if do_resize:
												UpperCAmelCase		:							Any   					=   [self.resize(image=__snake_case    ,			size=__snake_case    ,			crop_pct=__snake_case    ,			resample=__snake_case			) for image in images]

							if do_center_crop:
												UpperCAmelCase		:							Optional[int]   					=   [self.center_crop(image=__snake_case    ,			size=__snake_case			) for image in images]

							if do_rescale:
												UpperCAmelCase		:							Optional[int]   					=   [self.rescale(image=__snake_case    ,			scale=__snake_case			) for image in images]

							if do_normalize:
												UpperCAmelCase		:							List[str]   					=   [self.normalize(image=__snake_case    ,			mean=__snake_case    ,			std=__snake_case			) for image in images]

							UpperCAmelCase		:							Optional[int]   					=   [to_channel_dimension_format(__snake_case    ,			__snake_case			) for image in images]

							UpperCAmelCase		:							str   					=   {"pixel_values": images}
							return BatchFeature(data=__snake_case    ,			tensor_type=__snake_case			)



import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_lowerCAmelCase					:			str  				=			{
    "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",
}

_lowerCAmelCase					:			List[Any]  				=			{
    "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",
}

_lowerCAmelCase					:			str  				=			{
    "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",
}

_lowerCAmelCase					:			List[str]  				=			{
    "num_train_timesteps": 40,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

_lowerCAmelCase					:			Optional[Any]  				=			{
    "num_train_timesteps": 201,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

_lowerCAmelCase					:			List[str]  				=			{
    "num_train_timesteps": 151,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}






def        UpperCamelCase_(							_snake_case   :  str	):




			"""simple docstring"""






			if isinstance(_snake_case       ,     _snake_case	):
						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        UpperCamelCase_(							_snake_case   :  Tuple       ,     _snake_case   :  List[str]       ,     _snake_case   :  Any       ,     _snake_case   :  str       ,     _snake_case   :  Union[str, Any]=False	):




			"""simple docstring"""






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

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

			return new_checkpoint






def        UpperCamelCase_(							_snake_case   :  Dict       ,     _snake_case   :  Union[str, Any]       ,     _snake_case   :  Union[str, Any]       ,     _snake_case   :  Optional[Any]       ,     _snake_case   :  Optional[int]=None	):




			"""simple docstring"""






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

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

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

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

			return new_checkpoint






def        UpperCamelCase_(							_snake_case   :  str       ,     _snake_case   :  Tuple	):




			"""simple docstring"""






			__a        =torch.load(_snake_case       ,     map_location='cpu'	)
			__a        ={}

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

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

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

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

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

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

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

						__a        =current_channels

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

			__a        =0
			__a        =unet_config['up_block_types']

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

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

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

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

			return new_checkpoint


if __name__ == "__main__":
						_lowerCAmelCase					:			Optional[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					:			Optional[Any]  				=			parser.parse_args()
						_lowerCAmelCase					:			Optional[Any]  				=			strabool(args.class_cond)

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

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

						if not args.class_cond:
												_lowerCAmelCase					:			List[Any]  				=			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					:			Optional[int]  				=			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					:			List[str]  				=			CT_LSUN_256_SCHEDULER_CONFIG
						else:
												raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

						_lowerCAmelCase					:			Any  				=			CMStochasticIterativeScheduler(**scheduler_config)

						_lowerCAmelCase					:			str  				=			ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
						consistency_model.save_pretrained(args.dump_path)






