
from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


_snake_case      :							Optional[Any]     	=    logging.get_logger(__name__)





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						UpperCamelCase      :  Union[str, Any]				   =					['''pixel_values''']





						def __init__(			self							:Optional[Any]		,    lowerCAmelCase__							:bool = True		,    lowerCAmelCase__							:Union[int, float] = 1 / 255		,    lowerCAmelCase__							:bool = True		,    lowerCAmelCase__							:int = 8		,    **lowerCAmelCase__							:Optional[Any]		,    )  ->None:
										super().__init__(**lowerCAmelCase__     )

										lowercase         =      do_rescale
										lowercase         =      rescale_factor
										lowercase         =      do_pad
										lowercase         =      pad_size





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]		,    lowerCAmelCase__							:np.ndarray		,    lowerCAmelCase__							:float		,    lowerCAmelCase__							:Optional[Union[str, ChannelDimension]] = None		,    **lowerCAmelCase__							:str     )  ->np.ndarray:
										return rescale(lowerCAmelCase__		,    scale=lowerCAmelCase__		,    data_format=lowerCAmelCase__		,    **lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:int		,    lowerCAmelCase__							:np.ndarray		,    lowerCAmelCase__							:int		,    lowerCAmelCase__							:Optional[Union[str, ChannelDimension]] = None     )  ->Any:
										lowercase     ,	lowercase         =      get_image_size(lowerCAmelCase__     )
										lowercase         =      (old_height // size + 1) * size - old_height
										lowercase         =      (old_width // size + 1) * size - old_width

										return pad(lowerCAmelCase__		,    ((0, pad_height), (0, pad_width))		,    mode="symmetric"		,    data_format=lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Any		,    lowerCAmelCase__							:ImageInput		,    lowerCAmelCase__							:Optional[bool] = None		,    lowerCAmelCase__							:Optional[float] = None		,    lowerCAmelCase__							:Optional[bool] = None		,    lowerCAmelCase__							:Optional[int] = None		,    lowerCAmelCase__							:Optional[Union[str, TensorType]] = None		,    lowerCAmelCase__							:Union[str, ChannelDimension] = ChannelDimension.FIRST		,    **lowerCAmelCase__							:int		,    )  ->Any:
										lowercase         =      do_rescale if do_rescale is not None else self.do_rescale
										lowercase         =      rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase         =      do_pad if do_pad is not None else self.do_pad
										lowercase         =      pad_size if pad_size is not None else self.pad_size

										lowercase         =      make_list_of_images(lowerCAmelCase__     )

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

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

										# All transformations expect numpy arrays.
										lowercase         =      [to_numpy_array(lowerCAmelCase__     ) for image in images]

										if do_rescale:
														lowercase         =      [self.rescale(image=lowerCAmelCase__		,    scale=lowerCAmelCase__     ) for image in images]

										if do_pad:
														lowercase         =      [self.pad(lowerCAmelCase__		,    size=lowerCAmelCase__     ) for image in images]

										lowercase         =      [to_channel_dimension_format(lowerCAmelCase__		,    lowerCAmelCase__     ) for image in images]

										lowercase         =      {"pixel_values": images}
										return BatchFeature(data=lowerCAmelCase__		,    tensor_type=lowerCAmelCase__     )


import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


_snake_case      :							Tuple     	=    False
_snake_case      :							Optional[int]     	=    True
_snake_case      :							Any     	=    False


if __name__ == "__main__":
						_snake_case      :							Any     	=    argparse.ArgumentParser()

						parser.add_argument(
						    "--repo_path",
						    default=None,
						    type=str,
						    required=True,
						    help="The config json file corresponding to the architecture.",
						)

						parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

						_snake_case      :							Tuple     	=    parser.parse_args()

						_snake_case      :							Optional[Any]     	=    {
						    "image_size": "sample_size",
						    "num_res_blocks": "layers_per_block",
						    "block_channels": "block_out_channels",
						    "down_blocks": "down_block_types",
						    "up_blocks": "up_block_types",
						    "downscale_freq_shift": "freq_shift",
						    "resnet_num_groups": "norm_num_groups",
						    "resnet_act_fn": "act_fn",
						    "resnet_eps": "norm_eps",
						    "num_head_channels": "attention_head_dim",
						}

						_snake_case      :							Tuple     	=    {
						    "time_steps": "time_proj",
						    "mid": "mid_block",
						    "downsample_blocks": "down_blocks",
						    "upsample_blocks": "up_blocks",
						}

						_snake_case      :							Any     	=    "" if has_file(args.repo_path, "config.json") else "unet"

						with open(os.path.join(args.repo_path, subfolder, "config.json"), "r", encoding="utf-8") as reader:
												_snake_case      :							List[Any]     	=    reader.read()
												_snake_case      :							List[Any]     	=    json.loads(text)

						if do_only_config:
												for key in config_parameters_to_change.keys():
																		config.pop(key, None)

						if has_file(args.repo_path, "config.json"):
												_snake_case      :							List[Any]     	=    UNetaDModel(**config)
						else:
												_snake_case      :							int     	=    UNetaDConditionModel if "ldm-text2im-large-256" in args.repo_path else UNetaDModel
												_snake_case      :							Optional[Any]     	=    class_name(**config)

						if do_only_config:
												model.save_config(os.path.join(args.repo_path, subfolder))

						_snake_case      :							Any     	=    dict(model.config)

						if do_only_renaming:
												for key, value in config_parameters_to_change.items():
																		if key in config:
																								_snake_case      :							Any     	=    config[key]
																								del config[key]

												_snake_case      :							List[str]     	=    [k.replace("UNetRes", "") for k in config["down_block_types"]]
												_snake_case      :							Any     	=    [k.replace("UNetRes", "") for k in config["up_block_types"]]

						if do_only_weights:
												_snake_case      :							Optional[int]     	=    torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))

												_snake_case      :							Any     	=    {}
												for param_key, param_value in state_dict.items():
																		if param_key.endswith(".op.bias") or param_key.endswith(".op.weight"):
																								continue
																		_snake_case      :							List[Any]     	=    False
																		for key, new_key in key_parameters_to_change.items():
																								if not has_changed and param_key.split(".")[0] == key:
																														_snake_case      :							Dict     	=    param_value
																														_snake_case      :							Any     	=    True
																		if not has_changed:
																								_snake_case      :							int     	=    param_value

												model.load_state_dict(new_state_dict)
												model.save_pretrained(os.path.join(args.repo_path, subfolder))
