






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

import cva
UpperCAmelCase_ :						int      =      ''''''
UpperCAmelCase_ :						Tuple      =      ''''''
UpperCAmelCase_ :						str      =      ''''''
UpperCAmelCase_ :						List[str]      =      1  # (0 is vertical, 1 is horizontal)

def    SCREAMING_SNAKE_CASE_							(							)		->			None:






	"""simple docstring"""



	UpperCamelCase     ,      UpperCamelCase     :Optional[Any]          =	get_dataset(__magic_name__	,			__magic_name__					)
	print("""Processing..."""					)
	UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     :Dict          =	update_image_and_anno(__magic_name__	,			__magic_name__	,			__magic_name__					)

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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  str	,			__magic_name__					:  str					)		->			tuple[list, list]:






	"""simple docstring"""



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

		UpperCamelCase     :Any          =	[]
		for obj_list in obj_lists:
			UpperCamelCase     :str          =	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(__magic_name__					)
		labels.append(__magic_name__					)
	return img_paths, labels

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  list	,			__magic_name__					:  list	,			__magic_name__					:  int = 1					)		->			tuple[list, list, list]:






	"""simple docstring"""



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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  int = 32					)		->			str:






	"""simple docstring"""



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


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








from ....configuration_utils import PretrainedConfig
from ....utils import logging


UpperCAmelCase_ :						List[str]      =      logging.get_logger(__name__)

UpperCAmelCase_ :						int      =      {
    '''CarlCochet/trajectory-transformer-halfcheetah-medium-v2''': (
        '''https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json'''
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}

class     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     Any     	=  """trajectory_transformer"""
	snake_case__							:     Optional[Any]     	=  ["""past_key_values"""]
	snake_case__							:     Tuple     	=  {
	    """hidden_size""": """n_embd""",
	    """num_attention_heads""": """n_head""",
	    """num_hidden_layers""": """n_layer""",
	}





	def __init__(		self				:					Union[str, Any] ,  __lowerCamelCase				:					Any=100 ,  __lowerCamelCase				:					str=5 ,  __lowerCamelCase				:					str=1 ,  __lowerCamelCase				:					Optional[int]=1 ,  __lowerCamelCase				:					int=249 ,  __lowerCamelCase				:					str=6 ,  __lowerCamelCase				:					Dict=17 ,  __lowerCamelCase				:					Optional[Any]=25 ,  __lowerCamelCase				:					List[str]=4 ,  __lowerCamelCase				:					str=4 ,  __lowerCamelCase				:					Tuple=128 ,  __lowerCamelCase				:					Dict=0.1 ,  __lowerCamelCase				:					str=0.1 ,  __lowerCamelCase				:					Any=0.1 ,  __lowerCamelCase				:					int=0.0006 ,  __lowerCamelCase				:					List[str]=512 ,  __lowerCamelCase				:					str=0.02 ,  __lowerCamelCase				:					Any=1E-12 ,  __lowerCamelCase				:					int=1 ,  __lowerCamelCase				:					Optional[Any]=True ,  __lowerCamelCase				:					Tuple=1 ,  __lowerCamelCase				:					int=50_256 ,  __lowerCamelCase				:					Union[str, Any]=50_256 ,  **__lowerCamelCase				:					Dict ,  ):
		UpperCamelCase     :Dict          =	vocab_size
		UpperCamelCase     :int          =	action_weight
		UpperCamelCase     :Tuple          =	reward_weight
		UpperCamelCase     :str          =	value_weight
		UpperCamelCase     :Tuple          =	max_position_embeddings
		UpperCamelCase     :Tuple          =	block_size
		UpperCamelCase     :Optional[int]          =	action_dim
		UpperCamelCase     :int          =	observation_dim
		UpperCamelCase     :List[str]          =	transition_dim
		UpperCamelCase     :List[Any]          =	learning_rate
		UpperCamelCase     :Optional[Any]          =	n_layer
		UpperCamelCase     :Any          =	n_head
		UpperCamelCase     :List[str]          =	n_embd
		UpperCamelCase     :Any          =	embd_pdrop
		UpperCamelCase     :str          =	attn_pdrop
		UpperCamelCase     :Union[str, Any]          =	resid_pdrop
		UpperCamelCase     :Optional[Any]          =	initializer_range
		UpperCamelCase     :List[Any]          =	layer_norm_eps
		UpperCamelCase     :Optional[int]          =	kaiming_initializer_range
		UpperCamelCase     :Tuple          =	use_cache
		super().__init__(pad_token_id=__lowerCamelCase ,  bos_token_id=__lowerCamelCase ,  eos_token_id=__lowerCamelCase ,  **__lowerCamelCase	)
