





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

import cva
import numpy as np

# Parrameters
__lowerCamelCase       :     Tuple   =     (720, 1280)  # Height, Width
__lowerCamelCase       :     int   =     (0.4, 0.6)  # if height or width lower than this scale, drop it.
__lowerCamelCase       :     int   =     1 / 100
__lowerCamelCase       :     Any   =     """"""
__lowerCamelCase       :     List[str]   =     """"""
__lowerCamelCase       :     List[Any]   =     """"""
__lowerCamelCase       :     Tuple   =     250



def 		A_				(     )						-> None:
 UpperCamelCase	,    UpperCamelCase	:					Tuple       	=   get_dataset(_lowerCAmelCase							,   _lowerCAmelCase     )
 for index in range(_lowerCAmelCase     ):
  UpperCamelCase	:					Union[str, Any]       	=   random.sample(range(len(_lowerCAmelCase     )     )							,   4     )
  UpperCamelCase	,    UpperCamelCase	,    UpperCamelCase	:					str       	=   update_image_and_anno(
      _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   filter_scale=_lowerCAmelCase							,   )

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



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> tuple[list, list]:
 UpperCamelCase	:					int       	=   []
 UpperCamelCase	:					Tuple       	=   []
 for label_file in glob.glob(os.path.join(_lowerCAmelCase							,   "*.txt"     )     ):
  UpperCamelCase	:					Union[str, Any]       	=   label_file.split(os.sep     )[-1].rsplit("."							,   1     )[0]
  with open(_lowerCAmelCase     ) as in_file:
   UpperCamelCase	:					int       	=   in_file.readlines()
  UpperCamelCase	:					Optional[Any]       	=   os.path.join(_lowerCAmelCase							,   F"""{label_name}.jpg"""     )

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

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



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase = 0.0							,   )						-> tuple[list, list, str]:
 UpperCamelCase	:					str       	=   np.zeros([output_size[0], output_size[1], 3]							,   dtype=np.uinta     )
 UpperCamelCase	:					List[Any]       	=   scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
 UpperCamelCase	:					Dict       	=   scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
 UpperCamelCase	:					Dict       	=   int(scale_x * output_size[1]     )
 UpperCamelCase	:					Tuple       	=   int(scale_y * output_size[0]     )

 UpperCamelCase	:					Union[str, Any]       	=   []
 UpperCamelCase	:					int       	=   []
 for i, index in enumerate(_lowerCAmelCase     ):
  UpperCamelCase	:					str       	=   all_img_list[index]
  path_list.append(_lowerCAmelCase     )
  UpperCamelCase	:					Tuple       	=   all_annos[index]
  UpperCamelCase	:					Union[str, Any]       	=   cva.imread(_lowerCAmelCase     )
  if i == 0:  # top-left
   UpperCamelCase	:					int       	=   cva.resize(_lowerCAmelCase							,   (divid_point_x, divid_point_y)     )
   UpperCamelCase	:					int       	=   img
   for bbox in img_annos:
    UpperCamelCase	:					Any       	=   bbox[1] * scale_x
    UpperCamelCase	:					Optional[Any]       	=   bbox[2] * scale_y
    UpperCamelCase	:					str       	=   bbox[3] * scale_x
    UpperCamelCase	:					Any       	=   bbox[4] * scale_y
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]     )
  elif i == 1:  # top-right
   UpperCamelCase	:					Any       	=   cva.resize(_lowerCAmelCase							,   (output_size[1] - divid_point_x, divid_point_y)     )
   UpperCamelCase	:					str       	=   img
   for bbox in img_annos:
    UpperCamelCase	:					str       	=   scale_x + bbox[1] * (1 - scale_x)
    UpperCamelCase	:					Tuple       	=   bbox[2] * scale_y
    UpperCamelCase	:					int       	=   scale_x + bbox[3] * (1 - scale_x)
    UpperCamelCase	:					str       	=   bbox[4] * scale_y
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]     )
  elif i == 2:  # bottom-left
   UpperCamelCase	:					Dict       	=   cva.resize(_lowerCAmelCase							,   (divid_point_x, output_size[0] - divid_point_y)     )
   UpperCamelCase	:					Dict       	=   img
   for bbox in img_annos:
    UpperCamelCase	:					str       	=   bbox[1] * scale_x
    UpperCamelCase	:					List[str]       	=   scale_y + bbox[2] * (1 - scale_y)
    UpperCamelCase	:					Dict       	=   bbox[3] * scale_x
    UpperCamelCase	:					Optional[int]       	=   scale_y + bbox[4] * (1 - scale_y)
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]     )
  else:  # bottom-right
   UpperCamelCase	:					str       	=   cva.resize(
       _lowerCAmelCase							,   (output_size[1] - divid_point_x, output_size[0] - divid_point_y)     )
   UpperCamelCase	:					Optional[int]       	=   img
   for bbox in img_annos:
    UpperCamelCase	:					Dict       	=   scale_x + bbox[1] * (1 - scale_x)
    UpperCamelCase	:					Any       	=   scale_y + bbox[2] * (1 - scale_y)
    UpperCamelCase	:					int       	=   scale_x + bbox[3] * (1 - scale_x)
    UpperCamelCase	:					Union[str, 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:
  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_				(     _lowerCAmelCase     )						-> str:
 assert number_char > 1, "The number of character should greater than 1"
 UpperCamelCase	:					Optional[Any]       	=   ascii_lowercase + digits
 return "".join(random.choice(_lowerCAmelCase     ) for _ in range(_lowerCAmelCase     )     )


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







from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block

@dataclass
class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		42


class    lowerCAmelCase							(					__UpperCamelCase,  __UpperCamelCase			):
 @register_to_config
 def __init__(      self  :      List[str]			,     UpperCAmelCase  :      int = 65536			,     UpperCAmelCase  :      Optional[int] = None			,     UpperCAmelCase  :      int = 2			,     UpperCAmelCase  :      int = 2			,     UpperCAmelCase  :      int = 0			,     UpperCAmelCase  :      str = "fourier"			,     UpperCAmelCase  :      bool = True			,     UpperCAmelCase  :      bool = False			,     UpperCAmelCase  :      float = 0.0			,     UpperCAmelCase  :      Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")			,     UpperCAmelCase  :      Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")			,     UpperCAmelCase  :      Tuple[str] = "UNetMidBlock1D"			,     UpperCAmelCase  :      str = None			,     UpperCAmelCase  :      Tuple[int] = (32, 32, 64)			,     UpperCAmelCase  :      str = None			,     UpperCAmelCase  :      int = 8			,     UpperCAmelCase  :      int = 1			,     UpperCAmelCase  :      bool = False			,     )			->	List[Any]:
     super().__init__()
     lowerCamelCase__    :      Optional[int]					 =		sample_size

     # time
     if time_embedding_type == "fourier":
         lowerCamelCase__    :      Optional[Any]					 =		GaussianFourierProjection(
             embedding_size=8			,     set_W_to_weight=UpperCAmelCase			,     log=UpperCAmelCase			,     flip_sin_to_cos=UpperCAmelCase	)
         lowerCamelCase__    :      Any					 =		2 * block_out_channels[0]
     elif time_embedding_type == "positional":
         lowerCamelCase__    :      List[Any]					 =		Timesteps(
             block_out_channels[0]			,     flip_sin_to_cos=UpperCAmelCase			,     downscale_freq_shift=UpperCAmelCase	)
         lowerCamelCase__    :      Dict					 =		block_out_channels[0]

     if use_timestep_embedding:
         lowerCamelCase__    :      str					 =		block_out_channels[0] * 4
         lowerCamelCase__    :      List[Any]					 =		TimestepEmbedding(
             in_channels=UpperCAmelCase			,     time_embed_dim=UpperCAmelCase			,     act_fn=UpperCAmelCase			,     out_dim=block_out_channels[0]			,     )

     lowerCamelCase__    :      Any					 =		nn.ModuleList([]	)
     lowerCamelCase__    :      Tuple					 =		None
     lowerCamelCase__    :      List[str]					 =		nn.ModuleList([]	)
     lowerCamelCase__    :      Optional[int]					 =		None

     # down
     lowerCamelCase__    :      Optional[int]					 =		in_channels
     for i, down_block_type in enumerate(UpperCAmelCase	):
         lowerCamelCase__    :      Union[str, Any]					 =		output_channel
         lowerCamelCase__    :      Tuple					 =		block_out_channels[i]

         if i == 0:
             input_channel += extra_in_channels

         lowerCamelCase__    :      Union[str, Any]					 =		i == len(UpperCAmelCase	) - 1

         lowerCamelCase__    :      Optional[int]					 =		get_down_block(
             UpperCAmelCase			,     num_layers=UpperCAmelCase			,     in_channels=UpperCAmelCase			,     out_channels=UpperCAmelCase			,     temb_channels=block_out_channels[0]			,     add_downsample=not is_final_block or downsample_each_block			,     )
         self.down_blocks.append(UpperCAmelCase	)

     # mid
     lowerCamelCase__    :      Optional[int]					 =		get_mid_block(
         UpperCAmelCase			,     in_channels=block_out_channels[-1]			,     mid_channels=block_out_channels[-1]			,     out_channels=block_out_channels[-1]			,     embed_dim=block_out_channels[0]			,     num_layers=UpperCAmelCase			,     add_downsample=UpperCAmelCase			,     )

     # up
     lowerCamelCase__    :      Optional[int]					 =		list(reversed(UpperCAmelCase	)	)
     lowerCamelCase__    :      Optional[int]					 =		reversed_block_out_channels[0]
     if out_block_type is None:
         lowerCamelCase__    :      List[str]					 =		out_channels
     else:
         lowerCamelCase__    :      Any					 =		block_out_channels[0]

     for i, up_block_type in enumerate(UpperCAmelCase	):
         lowerCamelCase__    :      Tuple					 =		output_channel
         lowerCamelCase__    :      Union[str, Any]					 =		(
             reversed_block_out_channels[i + 1] if i < len(UpperCAmelCase	) - 1 else final_upsample_channels
         )

         lowerCamelCase__    :      List[str]					 =		i == len(UpperCAmelCase	) - 1

         lowerCamelCase__    :      Dict					 =		get_up_block(
             UpperCAmelCase			,     num_layers=UpperCAmelCase			,     in_channels=UpperCAmelCase			,     out_channels=UpperCAmelCase			,     temb_channels=block_out_channels[0]			,     add_upsample=not is_final_block			,     )
         self.up_blocks.append(UpperCAmelCase	)
         lowerCamelCase__    :      int					 =		output_channel

     # out
     lowerCamelCase__    :      int					 =		norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4			,     32	)
     lowerCamelCase__    :      List[Any]					 =		get_out_block(
         out_block_type=UpperCAmelCase			,     num_groups_out=UpperCAmelCase			,     embed_dim=block_out_channels[0]			,     out_channels=UpperCAmelCase			,     act_fn=UpperCAmelCase			,     fc_dim=block_out_channels[-1] // 4			,     )







 def      A_	(      self  :      List[Any]			,     UpperCAmelCase  :      torch.FloatTensor			,     UpperCAmelCase  :      Union[torch.Tensor, float, int]			,     UpperCAmelCase  :      bool = True			,     )			->	Union[UNetaDOutput, Tuple]:
     lowerCamelCase__    :      Optional[Any]					 =		timestep
     if not torch.is_tensor(UpperCAmelCase	):
         lowerCamelCase__    :      Optional[int]					 =		torch.tensor([timesteps]			,     dtype=torch.long			,     device=sample.device	)
     elif torch.is_tensor(UpperCAmelCase	) and len(timesteps.shape	) == 0:
         lowerCamelCase__    :      List[str]					 =		timesteps[None].to(sample.device	)

     lowerCamelCase__    :      Optional[int]					 =		self.time_proj(UpperCAmelCase	)
     if self.config.use_timestep_embedding:
         lowerCamelCase__    :      str					 =		self.time_mlp(UpperCAmelCase	)
     else:
         lowerCamelCase__    :      List[str]					 =		timestep_embed[..., None]
         lowerCamelCase__    :      str					 =		timestep_embed.repeat([1, 1, sample.shape[2]]	).to(sample.dtype	)
         lowerCamelCase__    :      str					 =		timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])	)

     # 2. down
     lowerCamelCase__    :      str					 =		()
     for downsample_block in self.down_blocks:
         lowerCamelCase__ ,							lowerCamelCase__    :      Union[str, Any]					 =		downsample_block(hidden_states=UpperCAmelCase			,     temb=UpperCAmelCase	)
         down_block_res_samples += res_samples

     # 3. mid
     if self.mid_block:
         lowerCamelCase__    :      Optional[Any]					 =		self.mid_block(UpperCAmelCase			,     UpperCAmelCase	)

     # 4. up
     for i, upsample_block in enumerate(self.up_blocks	):
         lowerCamelCase__    :      Dict					 =		down_block_res_samples[-1:]
         lowerCamelCase__    :      Optional[Any]					 =		down_block_res_samples[:-1]
         lowerCamelCase__    :      Any					 =		upsample_block(UpperCAmelCase			,     res_hidden_states_tuple=UpperCAmelCase			,     temb=UpperCAmelCase	)

     # 5. post-process
     if self.out_block:
         lowerCamelCase__    :      Any					 =		self.out_block(UpperCAmelCase			,     UpperCAmelCase	)

     if not return_dict:
         return (sample,)

     return UNetaDOutput(sample=UpperCAmelCase	)





