
from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
			from PIL import Image

			from ..image_utils import load_image

if is_torch_available():
			import torch

			from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

a_						     =    logging.get_logger(__name__)






@add_end_docstrings(__A   )
class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			def __init__(			self							: List[str]      ,      *a							: List[str]      ,      **a							: str	)							->       int:

				"""simple docstring"""

				super().__init__(*a      ,      **a	)
				requires_backends(self      ,      "vision"	)
				self.check_model_type(a	)
			def __call__(			self							: List[str]      ,      a							: Union[str, List[str], "Image.Image", List["Image.Image"]]      ,      **a							: int	)							->       int:

				"""simple docstring"""

				return super().__call__(a      ,      **a	)
			def 							__UpperCamelCase (			self							: Optional[int]      ,      **a							: Dict	)							->       Any:

				"""simple docstring"""

				return {}, {}, {}
			def 							__UpperCamelCase (			self							: Any      ,      a							: List[str]	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       load_image(a	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       image.size
				SCREAMING_SNAKE_CASE   :      Any				     =       self.image_processor(images=a      ,      return_tensors=self.framework	)
				return model_inputs
			def 							__UpperCamelCase (			self							: Any      ,      a							: Tuple	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.model(**a	)
				return model_outputs






			def 							__UpperCamelCase (			self							: str      ,      a							: Any	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       model_outputs.predicted_depth
				SCREAMING_SNAKE_CASE   :      int				     =       torch.nn.functional.interpolate(
				    predicted_depth.unsqueeze(1	)      ,      size=self.image_size[::-1]      ,      mode="bicubic"      ,      align_corners=a	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       prediction.squeeze().cpu().numpy()
				SCREAMING_SNAKE_CASE   :      Any				     =       (output * 255 / np.max(a	)).astype("uint8"	)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       Image.fromarray(a	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       {}
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       predicted_depth
				SCREAMING_SNAKE_CASE   :      int				     =       depth
				return output_dict


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  lowerCamelCase__ (					_a):
	if hor == 128:
		SCREAMING_SNAKE_CASE   :      str				     =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
		SCREAMING_SNAKE_CASE   :      List[str]				     =       (32, 128, 256)
		SCREAMING_SNAKE_CASE   :      List[str]				     =       ("UpResnetBlock1D", "UpResnetBlock1D")

	elif hor == 32:
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       (32, 64, 128, 256)
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
	SCREAMING_SNAKE_CASE   :      Any				     =       torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch")
	SCREAMING_SNAKE_CASE   :      int				     =       model.state_dict()
	SCREAMING_SNAKE_CASE   :      Dict				     =       {
	    "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": 65536,
	    "mid_block_type": "MidResTemporalBlock1D",
	    "act_fn": "mish",
	}
	SCREAMING_SNAKE_CASE   :      str				     =       UNetaDModel(**_a)
	print(f"length of state dict: {len(state_dict.keys())}")
	print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}")
	SCREAMING_SNAKE_CASE   :      List[str]				     =       dict(zip(model.state_dict().keys()							,			hf_value_function.state_dict().keys()))
	for k, v in mapping.items():
		SCREAMING_SNAKE_CASE   :      int				     =       state_dict.pop(_a)
	hf_value_function.load_state_dict(_a)

	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(_a							,			_a)

def  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      int				     =       {
	    "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": 65536,
	    "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",
	}

	SCREAMING_SNAKE_CASE   :      Dict				     =       torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch")
	SCREAMING_SNAKE_CASE   :      Dict				     =       model
	SCREAMING_SNAKE_CASE   :      List[str]				     =       UNetaDModel(**_a)
	print(f"length of state dict: {len(state_dict.keys())}")
	print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}")

	SCREAMING_SNAKE_CASE   :      int				     =       dict(zip(state_dict.keys()							,			hf_value_function.state_dict().keys()))
	for k, v in mapping.items():
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       state_dict.pop(_a)

	hf_value_function.load_state_dict(_a)

	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(_a							,			_a)


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