"""simple docstring"""







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

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



def 			lowercase__(       ):
     lowercase_	:				Union[str, Any]													=	get_dataset(lowercase_     ,			lowercase_      )
     print('Processing...'      )
     lowercase_	:				Union[str, Any]													=	update_image_and_anno(lowercase_     ,			lowercase_     ,			lowercase_      )

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



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     str      ):
     lowercase_	:				Tuple													=	[]
     lowercase_	:				Optional[int]													=	[]
     for label_file in glob.glob(os.path.join(lowercase_     ,			'*.txt'      )      ):
          lowercase_	:				str													=	label_file.split(os.sep      )[-1].rsplit('.'     ,			1      )[0]
          with open(lowercase_      ) as in_file:
               lowercase_	:				Tuple													=	in_file.readlines()
          lowercase_	:				int													=	os.path.join(lowercase_     ,			F'''{label_name}.jpg'''      )

          lowercase_	:				int													=	[]
          for obj_list in obj_lists:
               lowercase_	:				Optional[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(lowercase_      )
          labels.append(lowercase_      )
     return img_paths, labels



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



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     int = 32      ):
     assert number_char > 1, "The number of character should greater than 1"
     lowercase_	:				Optional[int]													=	ascii_lowercase + digits
     return "".join(random.choice(lowercase_      ) for _ in range(lowercase_      )      )


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




import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

def 						UpperCamelCase					(lowercase_: int    ,      lowercase_: Dict    ,      lowercase_: Tuple					)     ->      Any:
	# Construct model
	if gpta_config_file == "":
		A__							:    Dict        =		GPTaConfig()
	else:
		A__							:    List[Any]        =		GPTaConfig.from_json_file(lowercase_					)
	A__							:    Tuple        =		GPTaModel(lowercase_					)

	# Load weights from numpy
	load_tf_weights_in_gpta(lowercase_    ,      lowercase_    ,      lowercase_					)

	# Save pytorch-model
	A__							:    Optional[Any]        =		pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
	A__							:    Optional[Any]        =		pytorch_dump_folder_path + """/""" + CONFIG_NAME
	print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""					)
	torch.save(model.state_dict()    ,      lowercase_					)
	print(f"""Save configuration file to {pytorch_config_dump_path}"""					)
	with open(lowercase_    ,      """w"""    ,      encoding="""utf-8"""					) as f:
		f.write(config.to_json_string()					)


if __name__ == "__main__":
	A_       : Union[str, Any]         =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	parser.add_argument(
	    '--gpt2_config_file',
	    default='',
	    type=str,
	    help=(
	        'An optional config json file corresponding to the pre-trained OpenAI model. \n'
	        'This specifies the model architecture.'
	    ),
	)
	A_       : str         =			parser.parse_args()
	convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)



