



"""simple docstring"""






from typing import Dict, Iterable, 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, logging


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



class     A__     (						UpperCAmelCase_							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			["pixel_values"]

	def __init__(  self:	List[str] ,    _SCREAMING_SNAKE_CASE:	bool = True ,    _SCREAMING_SNAKE_CASE:	Dict[str, int] = None ,    _SCREAMING_SNAKE_CASE:	PILImageResampling = PILImageResampling.BICUBIC ,    _SCREAMING_SNAKE_CASE:	bool = True ,    _SCREAMING_SNAKE_CASE:	Dict[str, int] = None ,    _SCREAMING_SNAKE_CASE:	bool = True ,    _SCREAMING_SNAKE_CASE:	Union[int, float] = 1 / 255 ,    _SCREAMING_SNAKE_CASE:	bool = True ,    _SCREAMING_SNAKE_CASE:	Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN ,    _SCREAMING_SNAKE_CASE:	Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD ,    **_SCREAMING_SNAKE_CASE:	Any ,    )       -> List[Any]:

				"""simple docstring"""

				super().__init__(**_snake_case)
				__lowerCAmelCase   :						Dict				= size if size is not None else {"shortest_edge": 224}
				__lowerCAmelCase   :						Union[str, Any]				= get_size_dict(_snake_case ,    default_to_square=_snake_case)
				__lowerCAmelCase   :						Union[str, Any]				= crop_size if crop_size is not None else {"height": 224, "width": 224}
				__lowerCAmelCase   :						Any				= get_size_dict(_snake_case ,    param_name="crop_size")

				__lowerCAmelCase   :						List[Any]				= do_resize
				__lowerCAmelCase   :						str				= size
				__lowerCAmelCase   :						Tuple				= resample
				__lowerCAmelCase   :						Dict				= do_center_crop
				__lowerCAmelCase   :						List[Any]				= crop_size
				__lowerCAmelCase   :						str				= do_rescale
				__lowerCAmelCase   :						Optional[Any]				= rescale_factor
				__lowerCAmelCase   :						Optional[Any]				= do_normalize
				__lowerCAmelCase   :						Tuple				= image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
				__lowerCAmelCase   :						Optional[int]				= image_std if image_std is not None else IMAGENET_DEFAULT_STD

	def 							_SCREAMING_SNAKE_CASE     (  self:	str ,    _SCREAMING_SNAKE_CASE:	np.ndarray ,    _SCREAMING_SNAKE_CASE:	Dict[str, int] ,    _SCREAMING_SNAKE_CASE:	PILImageResampling = PILImageResampling.BICUBIC ,    _SCREAMING_SNAKE_CASE:	Optional[Union[str, ChannelDimension]] = None ,    **_SCREAMING_SNAKE_CASE:	List[Any] ,    )       -> List[Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						Any				= get_size_dict(_snake_case ,    default_to_square=_snake_case)
				# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
				if "shortest_edge" in size:
							__lowerCAmelCase   :						List[Any]				= int((256 / 224) * size["shortest_edge"])
							__lowerCAmelCase   :						List[str]				= get_resize_output_image_size(_snake_case ,    size=_snake_case ,    default_to_square=_snake_case)
							__lowerCAmelCase   :						Dict				= {"height": output_size[0], "width": output_size[1]}
				if "height" not in size_dict or "width" not in size_dict:
							raise ValueError(
							    F"""Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}""")
				return resize(
				    _snake_case ,    size=(size_dict["height"], size_dict["width"]) ,    resample=_snake_case ,    data_format=_snake_case ,    **_snake_case)

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str] ,    _SCREAMING_SNAKE_CASE:	np.ndarray ,    _SCREAMING_SNAKE_CASE:	Dict[str, int] ,    _SCREAMING_SNAKE_CASE:	Optional[Union[str, ChannelDimension]] = None ,    **_SCREAMING_SNAKE_CASE:	Optional[int] ,    )       -> Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[Any]				= get_size_dict(_snake_case)
				if "height" not in size or "width" not in size:
							raise ValueError(F"""Size dict must have keys \'height\' and \'width\'. Got {size.keys()}""")
				return center_crop(_snake_case ,    size=(size["height"], size["width"]) ,    data_format=_snake_case ,    **_snake_case)

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any] ,    _SCREAMING_SNAKE_CASE:	np.ndarray ,    _SCREAMING_SNAKE_CASE:	Union[int, float] ,    _SCREAMING_SNAKE_CASE:	Optional[Union[str, ChannelDimension]] = None ,    **_SCREAMING_SNAKE_CASE:	Any ,    )       -> int:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	np.ndarray ,    _SCREAMING_SNAKE_CASE:	Union[float, List[float]] ,    _SCREAMING_SNAKE_CASE:	Union[float, List[float]] ,    _SCREAMING_SNAKE_CASE:	Optional[Union[str, ChannelDimension]] = None ,    **_SCREAMING_SNAKE_CASE:	Union[str, Any] ,    )       -> Union[str, Any]:

				"""simple docstring"""

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







	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple ,    _SCREAMING_SNAKE_CASE:	ImageInput ,    _SCREAMING_SNAKE_CASE:	Optional[bool] = None ,    _SCREAMING_SNAKE_CASE:	Optional[Dict[str, int]] = None ,    _SCREAMING_SNAKE_CASE:	PILImageResampling = None ,    _SCREAMING_SNAKE_CASE:	Optional[bool] = None ,    _SCREAMING_SNAKE_CASE:	Optional[Dict[str, int]] = None ,    _SCREAMING_SNAKE_CASE:	Optional[bool] = None ,    _SCREAMING_SNAKE_CASE:	Optional[float] = None ,    _SCREAMING_SNAKE_CASE:	Optional[bool] = None ,    _SCREAMING_SNAKE_CASE:	Optional[Union[float, Iterable[float]]] = None ,    _SCREAMING_SNAKE_CASE:	Optional[Union[float, Iterable[float]]] = None ,    _SCREAMING_SNAKE_CASE:	Optional[TensorType] = None ,    _SCREAMING_SNAKE_CASE:	ChannelDimension = ChannelDimension.FIRST ,    **_SCREAMING_SNAKE_CASE:	Any ,    )       -> Dict:

				"""simple docstring"""

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

				__lowerCAmelCase   :						Optional[Any]				= size if size is not None else self.size
				__lowerCAmelCase   :						Tuple				= get_size_dict(_snake_case ,    default_to_square=_snake_case)
				__lowerCAmelCase   :						List[str]				= crop_size if crop_size is not None else self.crop_size
				__lowerCAmelCase   :						Optional[int]				= get_size_dict(_snake_case ,    param_name="crop_size")

				__lowerCAmelCase   :						List[str]				= 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:
							raise ValueError("Size must be specified if do_resize is True.")

				if do_center_crop and crop_size is None:
							raise ValueError("Crop size 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.
				__lowerCAmelCase   :						List[str]				= [to_numpy_array(_snake_case) for image in images]

				if do_resize:
							__lowerCAmelCase   :						List[Any]				= [self.resize(_snake_case ,    _snake_case ,    _snake_case) for image in images]

				if do_center_crop:
							__lowerCAmelCase   :						int				= [self.center_crop(_snake_case ,    _snake_case) for image in images]

				if do_rescale:
							__lowerCAmelCase   :						List[Any]				= [self.rescale(_snake_case ,    _snake_case) for image in images]

				if do_normalize:
							__lowerCAmelCase   :						List[Any]				= [self.normalize(_snake_case ,    _snake_case ,    _snake_case) for image in images]

				__lowerCAmelCase   :						Optional[int]				= [to_channel_dimension_format(_snake_case ,    _snake_case) for image in images]

				__lowerCAmelCase   :						Optional[int]				= {"pixel_values": images}
				return BatchFeature(data=_snake_case ,    tensor_type=_snake_case)




"""simple docstring"""






import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)

os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)


def     _lowercase       (						__snake_case			)		->     int:
			if hor == 128:
						__lowerCAmelCase   :						str				= ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
						__lowerCAmelCase   :						int				= (32, 128, 256)
						__lowerCAmelCase   :						Optional[Any]				= ("UpResnetBlock1D", "UpResnetBlock1D")

			elif hor == 32:
						__lowerCAmelCase   :						List[str]				= ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
						__lowerCAmelCase   :						Optional[Any]				= (32, 64, 128, 256)
						__lowerCAmelCase   :						Any				= ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
			__lowerCAmelCase   :						Union[str, Any]				= torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""			)
			__lowerCAmelCase   :						List[Any]				= model.state_dict()
			__lowerCAmelCase   :						Optional[Any]				= {
			    "down_block_types": down_block_types,
			    "block_out_channels": block_out_channels,
			    "up_block_types": up_block_types,
			    "layers_per_block": 1,
			    "use_timestep_embedding": True,
			    "out_block_type": "OutConv1DBlock",
			    "norm_num_groups": 8,
			    "downsample_each_block": False,
			    "in_channels": 14,
			    "out_channels": 14,
			    "extra_in_channels": 0,
			    "time_embedding_type": "positional",
			    "flip_sin_to_cos": False,
			    "freq_shift": 1,
			    "sample_size": 65_536,
			    "mid_block_type": "MidResTemporalBlock1D",
			    "act_fn": "mish",
			}
			__lowerCAmelCase   :						Dict				= UNetaDModel(**__snake_case			)
			print(F"""length of state dict: {len(state_dict.keys()			)}"""			)
			print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()			)}"""			)
			__lowerCAmelCase   :						Dict				= dict(zip(model.state_dict().keys()      ,hf_value_function.state_dict().keys()			)			)
			for k, v in mapping.items():
						__lowerCAmelCase   :						int				= state_dict.pop(__snake_case			)
			hf_value_function.load_state_dict(__snake_case			)

			torch.save(hf_value_function.state_dict()      ,F"""hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin"""			)
			with open(F"""hub/hopper-medium-v2/unet/hor{hor}/config.json"""      ,"w"			) as f:
						json.dump(__snake_case      ,__snake_case			)


def     _lowercase       (						)		->     List[str]:
			__lowerCAmelCase   :						Union[str, Any]				= {
			    "in_channels": 14,
			    "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
			    "up_block_types": (),
			    "out_block_type": "ValueFunction",
			    "mid_block_type": "ValueFunctionMidBlock1D",
			    "block_out_channels": (32, 64, 128, 256),
			    "layers_per_block": 1,
			    "downsample_each_block": True,
			    "sample_size": 65_536,
			    "out_channels": 14,
			    "extra_in_channels": 0,
			    "time_embedding_type": "positional",
			    "use_timestep_embedding": True,
			    "flip_sin_to_cos": False,
			    "freq_shift": 1,
			    "norm_num_groups": 8,
			    "act_fn": "mish",
			}

			__lowerCAmelCase   :						int				= torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"			)
			__lowerCAmelCase   :						Any				= model
			__lowerCAmelCase   :						Optional[int]				= UNetaDModel(**__snake_case			)
			print(F"""length of state dict: {len(state_dict.keys()			)}"""			)
			print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()			)}"""			)

			__lowerCAmelCase   :						Any				= dict(zip(state_dict.keys()      ,hf_value_function.state_dict().keys()			)			)
			for k, v in mapping.items():
						__lowerCAmelCase   :						Union[str, Any]				= state_dict.pop(__snake_case			)

			hf_value_function.load_state_dict(__snake_case			)

			torch.save(hf_value_function.state_dict()      ,"hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin"			)
			with open("hub/hopper-medium-v2/value_function/config.json"      ,"w"			) as f:
						json.dump(__snake_case      ,__snake_case			)


if __name__ == "__main__":
	unet(32)
	# unet(128)
	value_function()