


'''simple docstring'''



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

import cva
import numpy as np

# Parrameters
__UpperCAmelCase    :List[str]				       =     (7_2_0, 1_2_8_0)  # Height, Width
__UpperCAmelCase    :int				       =     (0.4, 0.6)  # if height or width lower than this scale, drop it.
__UpperCAmelCase    :str				       =     1 / 1_0_0
__UpperCAmelCase    :Any				       =     ""
__UpperCAmelCase    :List[str]				       =     ""
__UpperCAmelCase    :Tuple				       =     ""
__UpperCAmelCase    :Optional[int]				       =     2_5_0




def 			_a			(		):





					'''simple docstring'''






					__UpperCAmelCase							,     __UpperCAmelCase			:				str													=    get_dataset(_lowercase    ,      _lowercase						)
					for index in range(_lowercase						):
										__UpperCAmelCase			:				Tuple													=    random.sample(range(len(_lowercase						)						)    ,      4						)
										__UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase			:				Union[str, Any]													=    update_image_and_anno(
										    _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase    ,      filter_scale=_lowercase    ,      )

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




def 			_a			(		_lowercase      :			str    ,      _lowercase      :			str						):





					'''simple docstring'''






					__UpperCAmelCase			:				Dict													=    []
					__UpperCAmelCase			:				List[str]													=    []
					for label_file in glob.glob(os.path.join(_lowercase    ,      '''*.txt'''						)						):
										__UpperCAmelCase			:				Optional[Any]													=    label_file.split(os.sep						)[-1].rsplit('''.'''    ,      1						)[0]
										with open(_lowercase						) as in_file:
															__UpperCAmelCase			:				List[str]													=    in_file.readlines()
										__UpperCAmelCase			:				Any													=    os.path.join(_lowercase    ,      F'{label_name}.jpg'						)

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

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




def 			_a			(		_lowercase      :			list    ,      _lowercase      :			list    ,      _lowercase      :			list[int]    ,      _lowercase      :			tuple[int, int]    ,      _lowercase      :			tuple[float, float]    ,      _lowercase      :			float = 0.0    ,      ):





					'''simple docstring'''






					__UpperCAmelCase			:				List[str]													=    np.zeros([output_size[0], output_size[1], 3]    ,      dtype=np.uinta						)
					__UpperCAmelCase			:				int													=    scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
					__UpperCAmelCase			:				Optional[int]													=    scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
					__UpperCAmelCase			:				str													=    int(scale_x * output_size[1]						)
					__UpperCAmelCase			:				List[Any]													=    int(scale_y * output_size[0]						)

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





					'''simple docstring'''






					assert number_char > 1, "The number of character should greater than 1"
					__UpperCAmelCase			:				Optional[Any]													=    ascii_lowercase + digits
					return "".join(random.choice(_lowercase						) for _ in range(_lowercase						)						)


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



'''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 			_a			(		_lowercase      :			Any						):





     '''simple docstring'''






     if hor == 128:
          __UpperCAmelCase			:				Optional[Any]													=    ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
          __UpperCAmelCase			:				Union[str, Any]													=    (32, 128, 256)
          __UpperCAmelCase			:				str													=    ('''UpResnetBlock1D''', '''UpResnetBlock1D''')

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

     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(_lowercase    ,      _lowercase						)




def 			_a			(		):





     '''simple docstring'''






     __UpperCAmelCase			:				str													=    {
         '''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''',
     }

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

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

     hf_value_function.load_state_dict(_lowercase						)

     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(_lowercase    ,      _lowercase						)


if __name__ == "__main__":
      unet(3_2)
      # unet(128)
      value_function()