




import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
__lowerCAmelCase :Any     =    (7_20, 12_80)  # Height, Width
__lowerCAmelCase :Optional[int]     =    (0.4, 0.6)  # if height or width lower than this scale, drop it.
__lowerCAmelCase :int     =    1 / 1_00
__lowerCAmelCase :int     =    ''''''
__lowerCAmelCase :str     =    ''''''
__lowerCAmelCase :int     =    ''''''
__lowerCAmelCase :List[str]     =    2_50







def    A				(							):
					_snake_case       , _snake_case       :					Tuple				       =   get_dataset(_UpperCAmelCase  ,  _UpperCAmelCase				)
					for index in range(_UpperCAmelCase				):
										_snake_case       :					Optional[int]				       =   random.sample(range(len(_UpperCAmelCase				)				)  ,  4				)
										_snake_case       , _snake_case       , _snake_case       :					int				       =   update_image_and_anno(
										    _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  filter_scale=_UpperCAmelCase  ,  )

										# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
										_snake_case       :					Tuple				       =   random_chars(32				)
										_snake_case       :					Any				       =   path.split(os.sep				)[-1].rsplit("."  ,  1				)[0]
										_snake_case       :					int				       =   F"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
										cva.imwrite(F"""{file_root}.jpg"""  ,  _UpperCAmelCase  ,  [cva.IMWRITE_JPEG_QUALITY, 85]				)
										print(F"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"""				)
										_snake_case       :					Optional[Any]				       =   []
										for anno in new_annos:
															_snake_case       :					Any				       =   anno[3] - anno[1]
															_snake_case       :					int				       =   anno[4] - anno[2]
															_snake_case       :					str				       =   anno[1] + width / 2
															_snake_case       :					Dict				       =   anno[2] + height / 2
															_snake_case       :					Dict				       =   F"""{anno[0]} {x_center} {y_center} {width} {height}"""
															annos_list.append(_UpperCAmelCase				)
										with open(F"""{file_root}.txt"""  ,  "w"				) as outfile:
															outfile.write("\n".join(line for line in annos_list				)				)







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					int				       =   []
					_snake_case       :					Tuple				       =   []
					for label_file in glob.glob(os.path.join(_UpperCAmelCase  ,  "*.txt"				)				):
										_snake_case       :					List[str]				       =   label_file.split(os.sep				)[-1].rsplit("."  ,  1				)[0]
										with open(_UpperCAmelCase				) as in_file:
															_snake_case       :					Union[str, Any]				       =   in_file.readlines()
										_snake_case       :					int				       =   os.path.join(_UpperCAmelCase  ,  F"""{label_name}.jpg"""				)

										_snake_case       :					str				       =   []
										for obj_list in obj_lists:
															_snake_case       :					List[str]				       =   obj_list.rstrip("\n"				).split(" "				)
															_snake_case       :					List[Any]				       =   float(obj[1]				) - float(obj[3]				) / 2
															_snake_case       :					List[Any]				       =   float(obj[2]				) - float(obj[4]				) / 2
															_snake_case       :					Union[str, Any]				       =   float(obj[1]				) + float(obj[3]				) / 2
															_snake_case       :					Optional[Any]				       =   float(obj[2]				) + float(obj[4]				) / 2

															boxes.append([int(obj[0]				), xmin, ymin, xmax, ymax]				)
										if not boxes:
															continue
										img_paths.append(_UpperCAmelCase				)
										labels.append(_UpperCAmelCase				)
					return img_paths, labels







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = 0.0  ,  ):
					_snake_case       :					Optional[int]				       =   np.zeros([output_size[0], output_size[1], 3]  ,  dtype=np.uinta				)
					_snake_case       :					Dict				       =   scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
					_snake_case       :					str				       =   scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
					_snake_case       :					List[Any]				       =   int(scale_x * output_size[1]				)
					_snake_case       :					Union[str, Any]				       =   int(scale_y * output_size[0]				)

					_snake_case       :					List[str]				       =   []
					_snake_case       :					int				       =   []
					for i, index in enumerate(_UpperCAmelCase				):
										_snake_case       :					Optional[int]				       =   all_img_list[index]
										path_list.append(_UpperCAmelCase				)
										_snake_case       :					Optional[int]				       =   all_annos[index]
										_snake_case       :					List[str]				       =   cva.imread(_UpperCAmelCase				)
										if i == 0:  # top-left
															_snake_case       :					List[Any]				       =   cva.resize(_UpperCAmelCase  ,  (divid_point_x, divid_point_y)				)
															_snake_case       :					List[Any]				       =   img
															for bbox in img_annos:
																				_snake_case       :					Any				       =   bbox[1] * scale_x
																				_snake_case       :					Optional[Any]				       =   bbox[2] * scale_y
																				_snake_case       :					Tuple				       =   bbox[3] * scale_x
																				_snake_case       :					int				       =   bbox[4] * scale_y
																				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)
										elif i == 1:  # top-right
															_snake_case       :					Tuple				       =   cva.resize(_UpperCAmelCase  ,  (output_size[1] - divid_point_x, divid_point_y)				)
															_snake_case       :					Union[str, Any]				       =   img
															for bbox in img_annos:
																				_snake_case       :					List[Any]				       =   scale_x + bbox[1] * (1 - scale_x)
																				_snake_case       :					Any				       =   bbox[2] * scale_y
																				_snake_case       :					List[str]				       =   scale_x + bbox[3] * (1 - scale_x)
																				_snake_case       :					List[str]				       =   bbox[4] * scale_y
																				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)
										elif i == 2:  # bottom-left
															_snake_case       :					int				       =   cva.resize(_UpperCAmelCase  ,  (divid_point_x, output_size[0] - divid_point_y)				)
															_snake_case       :					str				       =   img
															for bbox in img_annos:
																				_snake_case       :					str				       =   bbox[1] * scale_x
																				_snake_case       :					Optional[int]				       =   scale_y + bbox[2] * (1 - scale_y)
																				_snake_case       :					Optional[int]				       =   bbox[3] * scale_x
																				_snake_case       :					Optional[Any]				       =   scale_y + bbox[4] * (1 - scale_y)
																				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)
										else:  # bottom-right
															_snake_case       :					Any				       =   cva.resize(
															    _UpperCAmelCase  ,  (output_size[1] - divid_point_x, output_size[0] - divid_point_y)				)
															_snake_case       :					List[Any]				       =   img
															for bbox in img_annos:
																				_snake_case       :					int				       =   scale_x + bbox[1] * (1 - scale_x)
																				_snake_case       :					List[str]				       =   scale_y + bbox[2] * (1 - scale_y)
																				_snake_case       :					Union[str, Any]				       =   scale_x + bbox[3] * (1 - scale_x)
																				_snake_case       :					Optional[Any]				       =   scale_y + bbox[4] * (1 - scale_y)
																				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)

    # Remove bounding box small than scale of filter
					if filter_scale > 0:
										_snake_case       :					str				       =   [
										    anno
										    for anno in new_anno
										    if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
										]

					return output_img, new_anno, path_list[0]







def    A				(							UpperCAmelCase				):
					assert number_char > 1, "The number of character should greater than 1"
					_snake_case       :					List[Any]				       =   ascii_lowercase + digits
					return "".join(random.choice(_UpperCAmelCase				) for _ in range(_UpperCAmelCase				)				)


if __name__ == "__main__":
							main()
							print('DONE ✅')






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    A				(							UpperCAmelCase				):
					if hor == 128:
										_snake_case       :					int				       =   ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										_snake_case       :					Tuple				       =   (32, 128, 256)
										_snake_case       :					str				       =   ("UpResnetBlock1D", "UpResnetBlock1D")

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

					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(UpperCAmelCase  ,  UpperCAmelCase				)







def    A				(							):
					_snake_case       :					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",
					}

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

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

					hf_value_function.load_state_dict(UpperCAmelCase				)

					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(UpperCAmelCase  ,  UpperCAmelCase				)


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