



"""simple docstring"""





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

import cva
lowercase  							=					''''''
lowercase  							=					''''''
lowercase  							=					''''''
lowercase  							=					1  # (0 is vertical, 1 is horizontal)




def 	UpperCAmelCase     (		):

  '''simple docstring'''


  _UpperCAmelCase ,					_UpperCAmelCase							 =		get_dataset(A     ,				A       )
  print('Processing...'       )
  _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase							 =		update_image_and_anno(A     ,				A     ,				A       )

  for index, image in enumerate(A       ):
    # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
    _UpperCAmelCase							 =		random_chars(32       )
    _UpperCAmelCase							 =		paths[index].split(os.sep       )[-1].rsplit('.'     ,				1       )[0]
    _UpperCAmelCase							 =		f'{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'
    cva.imwrite(f'/{file_root}.jpg'     ,				A     ,				[cva.IMWRITE_JPEG_QUALITY, 85]       )
    print(f'Success {index+1}/{len(A       )} with {file_name}'       )
    _UpperCAmelCase							 =		[]
    for anno in new_annos[index]:
      _UpperCAmelCase							 =		f'{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'
      annos_list.append(A       )
    with open(f'/{file_root}.txt'     ,				'w'       ) as outfile:
      outfile.write('\n'.join(line for line in annos_list       )       )




def 	UpperCAmelCase     (		A	:						str     ,				A	:						str       ):

  '''simple docstring'''


  _UpperCAmelCase							 =		[]
  _UpperCAmelCase							 =		[]
  for label_file in glob.glob(os.path.join(A     ,				'*.txt'       )       ):
    _UpperCAmelCase							 =		label_file.split(os.sep       )[-1].rsplit('.'     ,				1       )[0]
    with open(A       ) as in_file:
      _UpperCAmelCase							 =		in_file.readlines()
    _UpperCAmelCase							 =		os.path.join(A     ,				f'{label_name}.jpg'       )

    _UpperCAmelCase							 =		[]
    for obj_list in obj_lists:
      _UpperCAmelCase							 =		obj_list.rstrip('\n'       ).split(' '       )
      boxes.append(
          [
              int(obj[0]       ),
              float(obj[1]       ),
              float(obj[2]       ),
              float(obj[3]       ),
              float(obj[4]       ),
          ]       )
    if not boxes:
      continue
    img_paths.append(A       )
    labels.append(A       )
  return img_paths, labels




def 	UpperCAmelCase     (		A	:						list     ,				A	:						list     ,				A	:						int = 1       ):

  '''simple docstring'''


  _UpperCAmelCase							 =		[]
  _UpperCAmelCase							 =		[]
  _UpperCAmelCase							 =		[]
  for idx in range(len(A       )       ):
    _UpperCAmelCase							 =		[]
    _UpperCAmelCase							 =		img_list[idx]
    path_list.append(A       )
    _UpperCAmelCase							 =		anno_list[idx]
    _UpperCAmelCase							 =		cva.imread(A       )
    if flip_type == 1:
      _UpperCAmelCase							 =		cva.flip(A     ,				A       )
      for bbox in img_annos:
        _UpperCAmelCase							 =		1 - bbox[1]
        new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]       )
    elif flip_type == 0:
      _UpperCAmelCase							 =		cva.flip(A     ,				A       )
      for bbox in img_annos:
        _UpperCAmelCase							 =		1 - bbox[2]
        new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]       )
    new_annos_lists.append(A       )
    new_imgs_list.append(A       )
  return new_imgs_list, new_annos_lists, path_list




def 	UpperCAmelCase     (		A	:						int = 32       ):

  '''simple docstring'''


  assert number_char > 1, "The number of character should greater than 1"
  _UpperCAmelCase							 =		ascii_lowercase + digits
  return "".join(random.choice(A       ) for _ in range(A       )       )


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







"""simple docstring"""





from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class 			lowercase__			(      nn.Module    ):
							'''simple docstring'''







							def __init__(  self	,       snake_case = 16	,       snake_case = 88	,       snake_case = None	,       snake_case = 1	,       snake_case = 0.0	,       snake_case = 32	,       snake_case = None	,       snake_case = False	,       snake_case = None	,       snake_case = None	,       snake_case = "geglu"	,       snake_case = None	,       )			->							str:
									super().__init__()
									_UpperCAmelCase							 =		nn.ModuleList(
									    [
									        TransformeraDModel(
									            num_attention_heads=snake_case	,       attention_head_dim=snake_case	,       in_channels=snake_case	,       num_layers=snake_case	,       dropout=snake_case	,       norm_num_groups=snake_case	,       cross_attention_dim=snake_case	,       attention_bias=snake_case	,       sample_size=snake_case	,       num_vector_embeds=snake_case	,       activation_fn=snake_case	,       num_embeds_ada_norm=snake_case	,       )
									        for _ in range(2						)
									    ]						)

									# Variables that can be set by a pipeline:

									# The ratio of transformer1 to transformer2's output states to be combined during inference
									_UpperCAmelCase							 =		0.5

									# The shape of `encoder_hidden_states` is expected to be
									# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
									_UpperCAmelCase							 =		[77, 257]

									# Which transformer to use to encode which condition.
									# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
									_UpperCAmelCase							 =		[1, 0]






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case=None	,       snake_case=None	,       snake_case=None	,       snake_case = True	,       )			->							Any:
									_UpperCAmelCase							 =		hidden_states

									_UpperCAmelCase							 =		[]
									_UpperCAmelCase							 =		0
									# attention_mask is not used yet
									for i in range(2						):
											# for each of the two transformers, pass the corresponding condition tokens
											_UpperCAmelCase							 =		encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
											_UpperCAmelCase							 =		self.transformer_index_for_condition[i]
											_UpperCAmelCase							 =		self.transformers[transformer_index](
											    snake_case	,       encoder_hidden_states=snake_case	,       timestep=snake_case	,       cross_attention_kwargs=snake_case	,       return_dict=snake_case	,       )[0]
											encoded_states.append(encoded_state - input_states						)
											tokens_start += self.condition_lengths[i]

									_UpperCAmelCase							 =		encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
									_UpperCAmelCase							 =		output_states + input_states

									if not return_dict:
											return (output_states,)

									return TransformeraDModelOutput(sample=snake_case						)


