





import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class 			A__   (							SCREAMING_SNAKE_CASE__			,  SCREAMING_SNAKE_CASE__     ):




       """simple docstring"""



       @register_to_config
       def __init__( self ,  *,
           lowercase = 4 ,  lowercase = 768 ,  lowercase ,  lowercase ,  )      ->						List[Any]:







              '''simple docstring'''

              super().__init__()

              a__						:     Any			=    nn.Parameter(torch.zeros(_SCREAMING_SNAKE_CASE))

              # parameters for additional clip time embeddings
              a__						:     Union[str, Any]			=    nn.Linear(_SCREAMING_SNAKE_CASE ,  _SCREAMING_SNAKE_CASE)
              a__						:     Tuple			=    nn.Linear(_SCREAMING_SNAKE_CASE ,  _SCREAMING_SNAKE_CASE)

              # parameters for encoder hidden states
              a__						:     int			=    clip_extra_context_tokens
              a__						:     str			=    nn.Linear(
                  _SCREAMING_SNAKE_CASE ,  self.clip_extra_context_tokens * cross_attention_dim)
              a__						:     Dict			=    nn.Linear(_SCREAMING_SNAKE_CASE ,  _SCREAMING_SNAKE_CASE)
              a__						:     Optional[int]			=    nn.LayerNorm(_SCREAMING_SNAKE_CASE)

       def 							__lowercase		( self ,  *, lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						Optional[int]:







              '''simple docstring'''

              if do_classifier_free_guidance:
                     # Add the classifier free guidance embeddings to the image embeddings
                     a__						:     List[str]			=    image_embeddings.shape[0]
                     a__						:     Tuple			=    self.learned_classifier_free_guidance_embeddings.unsqueeze(0)
                     a__						:     Optional[int]			=    classifier_free_guidance_embeddings.expand(
                         _SCREAMING_SNAKE_CASE ,  -1)
                     a__						:     Any			=    torch.cat([classifier_free_guidance_embeddings, image_embeddings] ,  dim=0)

              # The image embeddings batch size and the text embeddings batch size are equal
              assert image_embeddings.shape[0] == prompt_embeds.shape[0]

              a__						:     List[str]			=    prompt_embeds.shape[0]

              # "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
              # adding CLIP embeddings to the existing timestep embedding, ...
              a__						:     int			=    self.embedding_proj(_SCREAMING_SNAKE_CASE)
              a__						:     Tuple			=    self.clip_image_embeddings_project_to_time_embeddings(_SCREAMING_SNAKE_CASE)
              a__						:     Dict			=    time_projected_image_embeddings + time_projected_prompt_embeds

              # ... and by projecting CLIP embeddings into four
              # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
              a__						:     Tuple			=    self.clip_extra_context_tokens_proj(_SCREAMING_SNAKE_CASE)
              a__						:     int			=    clip_extra_context_tokens.reshape(_SCREAMING_SNAKE_CASE ,  -1 ,  self.clip_extra_context_tokens)
              a__						:     int			=    clip_extra_context_tokens.permute(0 ,  2 ,  1)

              a__						:     Union[str, Any]			=    self.encoder_hidden_states_proj(_SCREAMING_SNAKE_CASE)
              a__						:     Tuple			=    self.text_encoder_hidden_states_norm(_SCREAMING_SNAKE_CASE)
              a__						:     str			=    torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] ,  dim=1)

              return text_encoder_hidden_states, additive_clip_time_embeddings









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

import cva
lowercase				:	Optional[Any]      						=  """"""
lowercase				:	int      						=  """"""
lowercase				:	List[Any]      						=  """"""
lowercase				:	Optional[int]      						=  1  # (0 is vertical, 1 is horizontal)



def A_     (		)  ->		None:
       a__			,							a__						:     str			=    get_dataset(A__		,				A__    )
       print('Processing...'    )
       a__			,							a__			,							a__						:     Tuple			=    update_image_and_anno(A__		,				A__		,				A__    )

       for index, image in enumerate(A__    ):
              # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
              a__						:     int			=    random_chars(32    )
              a__						:     Optional[Any]			=    paths[index].split(os.sep    )[-1].rsplit('.'		,				1    )[0]
              a__						:     Optional[int]			=    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}'    )
              a__						:     List[str]			=    []
              for anno in new_annos[index]:
                     a__						:     Union[str, Any]			=    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 A_     (		A__		,				A__    )  ->		tuple[list, list]:
       a__						:     int			=    []
       a__						:     int			=    []
       for label_file in glob.glob(os.path.join(A__		,				'*.txt'    )    ):
              a__						:     Optional[Any]			=    label_file.split(os.sep    )[-1].rsplit('.'		,				1    )[0]
              with open(A__    ) as in_file:
                     a__						:     Tuple			=    in_file.readlines()
              a__						:     Dict			=    os.path.join(A__		,				F'{label_name}.jpg'    )

              a__						:     int			=    []
              for obj_list in obj_lists:
                     a__						:     Union[str, Any]			=    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 A_     (		A__		,				A__		,				A__ = 1    )  ->		tuple[list, list, list]:
       a__						:     Optional[int]			=    []
       a__						:     Any			=    []
       a__						:     Dict			=    []
       for idx in range(len(A__    )    ):
              a__						:     Optional[int]			=    []
              a__						:     Optional[Any]			=    img_list[idx]
              path_list.append(A__    )
              a__						:     Union[str, Any]			=    anno_list[idx]
              a__						:     List[str]			=    cva.imread(A__    )
              if flip_type == 1:
                     a__						:     List[str]			=    cva.flip(A__		,				A__    )
                     for bbox in img_annos:
                            a__						:     Optional[Any]			=    1 - bbox[1]
                            new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]    )
              elif flip_type == 0:
                     a__						:     Optional[Any]			=    cva.flip(A__		,				A__    )
                     for bbox in img_annos:
                            a__						:     Optional[int]			=    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 A_     (		A__ = 32    )  ->		str:
       assert number_char > 1, "The number of character should greater than 1"
       a__						:     Optional[int]			=    ascii_lowercase + digits
       return "".join(random.choice(A__    ) for _ in range(A__    )    )


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


