





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       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Union[str, Any]:
  if hor == 128:
    lowercase__           =  ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
    lowercase__           =  (32, 128, 256)
    lowercase__           =  ('UpResnetBlock1D', 'UpResnetBlock1D')

  elif hor == 32:
    lowercase__           =  ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
    lowercase__           =  (32, 64, 128, 256)
    lowercase__           =  ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
  lowercase__           =  torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""		)
  lowercase__           =  model.state_dict()
  lowercase__           =  {
      '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',
  }
  lowercase__           =  UNetaDModel(**_SCREAMING_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()		)}"""		)
  lowercase__           =  dict(zip(model.state_dict().keys()		,						hf_value_function.state_dict().keys()		)		)
  for k, v in mapping.items():
    lowercase__           =  state_dict.pop(_SCREAMING_SNAKE_CASE		)
  hf_value_function.load_state_dict(_SCREAMING_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(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)




def       __UpperCamelCase  ()    ->       List[str]:
  lowercase__           =  {
      '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',
  }

  lowercase__           =  torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'		)
  lowercase__           =  model
  lowercase__           =  UNetaDModel(**_SCREAMING_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()		)}"""		)

  lowercase__           =  dict(zip(state_dict.keys()		,						hf_value_function.state_dict().keys()		)		)
  for k, v in mapping.items():
    lowercase__           =  state_dict.pop(_SCREAMING_SNAKE_CASE		)

  hf_value_function.load_state_dict(_SCREAMING_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(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)


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









import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
      import torch

if is_vision_available():
      import PIL


lowercase_          =		logging.get_logger(__name__)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Tuple[int, int]:
  def constraint_to_multiple_of(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE=0		,						_SCREAMING_SNAKE_CASE=None		):
    lowercase__           =  round(val / multiple		) * multiple

    if max_val is not None and x > max_val:
      lowercase__           =  math.floor(val / multiple		) * multiple

    if x < min_val:
      lowercase__           =  math.ceil(val / multiple		) * multiple

    return x

  lowercase__           =  (output_size, output_size) if isinstance(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		) else output_size

  lowercase__      ,   lowercase__           =  get_image_size(_SCREAMING_SNAKE_CASE		)
  lowercase__      ,   lowercase__           =  output_size

  # determine new height and width
  lowercase__           =  output_height / input_height
  lowercase__           =  output_width / input_width

  if keep_aspect_ratio:
    # scale as little as possible
    if abs(1 - scale_width		) < abs(1 - scale_height		):
      # fit width
      lowercase__           =  scale_width
    else:
      # fit height
      lowercase__           =  scale_height

  lowercase__           =  constraint_to_multiple_of(scale_height * input_height		,						multiple=_SCREAMING_SNAKE_CASE		)
  lowercase__           =  constraint_to_multiple_of(scale_width * input_width		,						multiple=_SCREAMING_SNAKE_CASE		)

  return (new_height, new_width)


class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		Dict			 =	['pixel_values']


   def __init__(   self      :    Any      ,					a      :    bool = True      ,					a      :    Dict[str, int] = None      ,					a      :    PILImageResampling = PILImageResampling.BILINEAR      ,					a      :    bool = False      ,					a      :    int = 1      ,					a      :    bool = True      ,					a      :    Union[int, float] = 1 / 255      ,					a      :    bool = True      ,					a      :    Optional[Union[float, List[float]]] = None      ,					a      :    Optional[Union[float, List[float]]] = None      ,					**a      :    Tuple      ,					)->       None:





     """simple docstring"""







     super().__init__(**a	)
     lowercase__           =  size if size is not None else {'height': 384, 'width': 384}
     lowercase__           =  get_size_dict(a	)
     lowercase__           =  do_resize
     lowercase__           =  size
     lowercase__           =  keep_aspect_ratio
     lowercase__           =  ensure_multiple_of
     lowercase__           =  resample
     lowercase__           =  do_rescale
     lowercase__           =  rescale_factor
     lowercase__           =  do_normalize
     lowercase__           =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     lowercase__           =  image_std if image_std is not None else IMAGENET_STANDARD_STD


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict      ,					a      :    np.ndarray      ,					a      :    Dict[str, int]      ,					a      :    bool = False      ,					a      :    int = 1      ,					a      :    PILImageResampling = PILImageResampling.BICUBIC      ,					a      :    Optional[Union[str, ChannelDimension]] = None      ,					**a      :    Optional[Any]      ,					)->       np.ndarray:





     """simple docstring"""







     lowercase__           =  get_size_dict(a	)
     if "height" not in size or "width" not in size:
       raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""	)
     lowercase__           =  get_resize_output_image_size(
         a      ,					output_size=(size['height'], size['width'])      ,					keep_aspect_ratio=a      ,					multiple=a      ,					)
     return resize(a      ,					size=a      ,					resample=a      ,					data_format=a      ,					**a	)


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





     """simple docstring"""







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


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





     """simple docstring"""







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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    int      ,					a      :    ImageInput      ,					a      :    bool = None      ,					a      :    int = None      ,					a      :    bool = None      ,					a      :    int = None      ,					a      :    PILImageResampling = None      ,					a      :    bool = None      ,					a      :    float = None      ,					a      :    bool = None      ,					a      :    Optional[Union[float, List[float]]] = None      ,					a      :    Optional[Union[float, List[float]]] = None      ,					a      :    Optional[Union[str, TensorType]] = None      ,					a      :    ChannelDimension = ChannelDimension.FIRST      ,					**a      :    str      ,					)->       PIL.Image.Image:





     """simple docstring"""







     lowercase__           =  do_resize if do_resize is not None else self.do_resize
     lowercase__           =  size if size is not None else self.size
     lowercase__           =  get_size_dict(a	)
     lowercase__           =  keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
     lowercase__           =  ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
     lowercase__           =  resample if resample is not None else self.resample
     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_normalize if do_normalize is not None else self.do_normalize
     lowercase__           =  image_mean if image_mean is not None else self.image_mean
     lowercase__           =  image_std if image_std is not None else self.image_std

     lowercase__           =  make_list_of_images(a	)

     if not valid_images(a	):
       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_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.
     lowercase__           =  [to_numpy_array(a	) for image in images]

     if do_resize:
       lowercase__           =  [self.resize(image=a      ,					size=a      ,					resample=a	) for image in images]

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

     if do_normalize:
       lowercase__           =  [self.normalize(image=a      ,					mean=a      ,					std=a	) for image in images]

     lowercase__           =  [to_channel_dimension_format(a      ,					a	) for image in images]

     lowercase__           =  {'pixel_values': images}
     return BatchFeature(data=a      ,					tensor_type=a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict      ,					a      :    str      ,					a      :    List[Tuple] = None	)->       Optional[int]:





     """simple docstring"""







     lowercase__           =  outputs.logits

     # Resize logits and compute semantic segmentation maps
     if target_sizes is not None:
       if len(a	) != len(a	):
         raise ValueError(
             'Make sure that you pass in as many target sizes as the batch dimension of the logits'	)

       if is_torch_tensor(a	):
         lowercase__           =  target_sizes.numpy()

       lowercase__           =  []

       for idx in range(len(a	)	):
         lowercase__           =  torch.nn.functional.interpolate(
             logits[idx].unsqueeze(dim=0	)      ,					size=target_sizes[idx]      ,					mode='bilinear'      ,					align_corners=a	)
         lowercase__           =  resized_logits[0].argmax(dim=0	)
         semantic_segmentation.append(a	)
     else:
       lowercase__           =  logits.argmax(dim=1	)
       lowercase__           =  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]	)]

     return semantic_segmentation


