






from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

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




class 			snake_case__   (							lowercase_	,    lowercase_	,    lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				Dict 				= [r"h\.\d+\.attn\.bias", r"h\.\d+\.attn\.masked_bias"]



       @register_to_config
       def __init__(							self			,						a__			,						a__			,						a__ = None			,						a__ = 5_02_57			,						a__ = 10_24			,						a__ = 7_68			,						a__ = 12			,						a__ = 12			,						a__ = None			,						a__ = "gelu_new"			,						a__ = 0.1			,						a__ = 0.1			,						a__ = 0.1			,						a__ = 1e-5			,						a__ = 0.02			,						a__ = True			,						a__ = True			,						a__ = False			,						a__ = False			,						) ->      str:






            '''simple docstring'''





            super().__init__()

            __snake_case			:Any          =						prefix_length

            if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
                 raise ValueError(
                     F'''`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'''
                     F''' `n_embd`: {n_embd} are not equal.'''    )

            __snake_case			:int          =						prefix_inner_dim
            __snake_case			:Any          =						prefix_hidden_dim

            __snake_case			:List[str]          =						(
                nn.Linear(self.prefix_inner_dim			,						self.prefix_hidden_dim    )
                if self.prefix_hidden_dim is not None
                else nn.Identity()
            )
            __snake_case			:List[Any]          =						(
                nn.Linear(self.prefix_hidden_dim			,						a__    ) if self.prefix_hidden_dim is not None else nn.Identity()
            )

            __snake_case			:Union[str, Any]          =						GPTaConfig(
                vocab_size=a__			,						n_positions=a__			,						n_embd=a__			,						n_layer=a__			,						n_head=a__			,						n_inner=a__			,						activation_function=a__			,						resid_pdrop=a__			,						embd_pdrop=a__			,						attn_pdrop=a__			,						layer_norm_epsilon=a__			,						initializer_range=a__			,						scale_attn_weights=a__			,						use_cache=a__			,						scale_attn_by_inverse_layer_idx=a__			,						reorder_and_upcast_attn=a__			,						)
            __snake_case			:Optional[Any]          =						GPTaLMHeadModel(a__    )



       def 					__lowercase						(							self			,						a__			,						a__			,						a__ = None			,						a__ = None			,						) ->      Dict:






            '''simple docstring'''





            __snake_case			:Any          =						self.transformer.transformer.wte(a__    )
            __snake_case			:Tuple          =						self.encode_prefix(a__    )
            __snake_case			:Dict          =						self.decode_prefix(a__    )
            __snake_case			:Dict          =						torch.cat((prefix_embeds, embedding_text)			,						dim=1    )

            if labels is not None:
                 __snake_case			:Optional[Any]          =						self.get_dummy_token(input_ids.shape[0]			,						input_ids.device    )
                 __snake_case			:int          =						torch.cat((dummy_token, input_ids)			,						dim=1    )
            __snake_case			:int          =						self.transformer(inputs_embeds=a__			,						labels=a__			,						attention_mask=a__    )
            if self.prefix_hidden_dim is not None:
                 return out, hidden
            else:
                 return out



       def 					__lowercase						(							self			,						a__			,						a__    ) ->      torch.Tensor:






            '''simple docstring'''





            return torch.zeros(a__			,						self.prefix_length			,						dtype=torch.intaa			,						device=a__    )



       def 					__lowercase						(							self			,						a__    ) ->      List[str]:






            '''simple docstring'''





            return self.encode_prefix(a__    )



       @torch.no_grad()
       def 					__lowercase						(							self			,						a__			,						a__			,						a__    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:Union[str, Any]          =						torch.split(a__			,						1			,						dim=0    )
            __snake_case			:Optional[int]          =						[]
            __snake_case			:List[Any]          =						[]
            for feature in features:
                 __snake_case			:Optional[Any]          =						self.decode_prefix(feature.to(a__    )    )  # back to the clip feature
                 # Only support beam search for now
                 __snake_case       , __snake_case			:Optional[int]          =						self.generate_beam(
                     input_embeds=a__			,						device=a__			,						eos_token_id=a__    )
                 generated_tokens.append(output_tokens[0]    )
                 generated_seq_lengths.append(seq_lengths[0]    )
            __snake_case			:List[Any]          =						torch.stack(a__    )
            __snake_case			:Dict          =						torch.stack(a__    )
            return generated_tokens, generated_seq_lengths



       @torch.no_grad()
       def 					__lowercase						(							self			,						a__=None			,						a__=None			,						a__=None			,						a__ = 5			,						a__ = 67			,						a__ = 1.0			,						a__ = None			,						) ->      Optional[Any]:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						eos_token_id
            __snake_case			:Any          =						None
            __snake_case			:List[Any]          =						None
            __snake_case			:Optional[int]          =						torch.ones(a__			,						device=a__			,						dtype=torch.int    )
            __snake_case			:int          =						torch.zeros(a__			,						device=a__			,						dtype=torch.bool    )

            if input_embeds is not None:
                 __snake_case			:int          =						input_embeds
            else:
                 __snake_case			:int          =						self.transformer.transformer.wte(a__    )

            for i in range(a__    ):
                 __snake_case			:List[str]          =						self.transformer(inputs_embeds=a__    )
                 __snake_case			:List[Any]          =						outputs.logits
                 __snake_case			:Tuple          =						logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
                 __snake_case			:Dict          =						logits.softmax(-1    ).log()

                 if scores is None:
                      __snake_case       , __snake_case			:Tuple          =						logits.topk(a__			,						-1    )
                      __snake_case			:Union[str, Any]          =						generated.expand(a__			,						*generated.shape[1:]    )
                      __snake_case       , __snake_case			:Dict          =						next_tokens.permute(1			,						0    ), scores.squeeze(0    )
                      if tokens is None:
                           __snake_case			:Optional[Any]          =						next_tokens
                      else:
                           __snake_case			:Union[str, Any]          =						tokens.expand(a__			,						*tokens.shape[1:]    )
                           __snake_case			:Dict          =						torch.cat((tokens, next_tokens)			,						dim=1    )
                 else:
                      __snake_case			:int          =						-float(np.inf    )
                      __snake_case			:str          =						0
                      __snake_case			:Optional[int]          =						scores[:, None] + logits
                      seq_lengths[~is_stopped] += 1
                      __snake_case			:List[Any]          =						scores_sum / seq_lengths[:, None]
                      __snake_case       , __snake_case			:Any          =						scores_sum_average.view(-1    ).topk(a__			,						-1    )
                      __snake_case			:Union[str, Any]          =						next_tokens // scores_sum.shape[1]
                      __snake_case			:Dict          =						seq_lengths[next_tokens_source]
                      __snake_case			:List[Any]          =						next_tokens % scores_sum.shape[1]
                      __snake_case			:Union[str, Any]          =						next_tokens.unsqueeze(1    )
                      __snake_case			:Any          =						tokens[next_tokens_source]
                      __snake_case			:Any          =						torch.cat((tokens, next_tokens)			,						dim=1    )
                      __snake_case			:Union[str, Any]          =						generated[next_tokens_source]
                      __snake_case			:List[str]          =						scores_sum_average * seq_lengths
                      __snake_case			:Tuple          =						is_stopped[next_tokens_source]

                 __snake_case			:Dict          =						self.transformer.transformer.wte(next_tokens.squeeze()    ).view(generated.shape[0]			,						1			,						-1    )
                 __snake_case			:Dict          =						torch.cat((generated, next_token_embed)			,						dim=1    )
                 __snake_case			:List[Any]          =						is_stopped + next_tokens.eq(a__    ).squeeze()
                 if is_stopped.all():
                      break

            __snake_case			:List[str]          =						scores / seq_lengths
            __snake_case			:Optional[Any]          =						scores.argsort(descending=a__    )
            # tokens tensors are already padded to max_seq_length
            __snake_case			:List[Any]          =						[tokens[i] for i in order]
            __snake_case			:Tuple          =						torch.stack(a__			,						dim=0    )
            __snake_case			:List[Any]          =						torch.tensor([seq_lengths[i] for i in order]			,						dtype=seq_lengths.dtype    )
            return output_texts, seq_lengths










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

import cva
lowerCamelCase__							=     """"""
lowerCamelCase__							=     """"""
lowerCamelCase__							=     """"""
lowerCamelCase__							=     1  # (0 is vertical, 1 is horizontal)






def   UpperCamelCase  (       ):




     '''simple docstring'''






     __snake_case       , __snake_case			:List[Any]          =						get_dataset(snake_case__	,snake_case__  )
     print("""Processing..."""  )
     __snake_case       , __snake_case       , __snake_case			:Tuple          =						update_image_and_anno(snake_case__	,snake_case__	,snake_case__  )

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






def   UpperCamelCase  (       snake_case__     :   str	,snake_case__     :   str  ):




     '''simple docstring'''






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

          __snake_case			:List[str]          =						[]
          for obj_list in obj_lists:
               __snake_case			:int          =						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(snake_case__  )
          labels.append(snake_case__  )
     return img_paths, labels






def   UpperCamelCase  (       snake_case__     :   list	,snake_case__     :   list	,snake_case__     :   int = 1  ):




     '''simple docstring'''






     __snake_case			:Dict          =						[]
     __snake_case			:Any          =						[]
     __snake_case			:int          =						[]
     for idx in range(len(snake_case__  )  ):
          __snake_case			:Optional[int]          =						[]
          __snake_case			:List[str]          =						img_list[idx]
          path_list.append(snake_case__  )
          __snake_case			:Union[str, Any]          =						anno_list[idx]
          __snake_case			:List[Any]          =						cva.imread(snake_case__  )
          if flip_type == 1:
               __snake_case			:Optional[Any]          =						cva.flip(snake_case__	,snake_case__  )
               for bbox in img_annos:
                    __snake_case			:Union[str, Any]          =						1 - bbox[1]
                    new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]  )
          elif flip_type == 0:
               __snake_case			:Union[str, Any]          =						cva.flip(snake_case__	,snake_case__  )
               for bbox in img_annos:
                    __snake_case			:Optional[Any]          =						1 - bbox[2]
                    new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]  )
          new_annos_lists.append(snake_case__  )
          new_imgs_list.append(snake_case__  )
     return new_imgs_list, new_annos_lists, path_list






def   UpperCamelCase  (       snake_case__     :   int = 32  ):




     '''simple docstring'''






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


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


