





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

import cva
A   : int			=				''''''
A   : Union[str, Any]			=				''''''
A   : Union[str, Any]			=				''''''
A   : List[str]			=				1  # (0 is vertical, 1 is horizontal)







def        __lowerCamelCase (							):






		"""simple docstring"""

		A__  				=       get_dataset(__a   ,					__a   )
		print("""Processing..."""   )
		A__  				=       update_image_and_anno(__a   ,					__a   ,					__a   )

		for index, image in enumerate(__a   ):
				# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
				A__  				=       random_chars(3_2   )
				A__  				=       paths[index].split(os.sep   )[-1].rsplit("""."""   ,					1   )[0]
				A__  				=       F'{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'
				cva.imwrite(F'/{file_root}.jpg'   ,					__a   ,					[cva.IMWRITE_JPEG_QUALITY, 8_5]   )
				print(F'Success {index+1}/{len(__a   )} with {file_name}'   )
				A__  				=       []
				for anno in new_annos[index]:
						A__  				=       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        __lowerCamelCase (							__a       :str   ,					__a       :str   ):






		"""simple docstring"""

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

				A__  				=       []
				for obj_list in obj_lists:
						A__  				=       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        __lowerCamelCase (							__a       :list   ,					__a       :list   ,					__a       :int = 1   ):






		"""simple docstring"""

		A__  				=       []
		A__  				=       []
		A__  				=       []
		for idx in range(len(__a   )   ):
				A__  				=       []
				A__  				=       img_list[idx]
				path_list.append(__a   )
				A__  				=       anno_list[idx]
				A__  				=       cva.imread(__a   )
				if flip_type == 1:
						A__  				=       cva.flip(__a   ,					__a   )
						for bbox in img_annos:
								A__  				=       1 - bbox[1]
								new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]   )
				elif flip_type == 0:
						A__  				=       cva.flip(__a   ,					__a   )
						for bbox in img_annos:
								A__  				=       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        __lowerCamelCase (							__a       :int = 3_2   ):






		"""simple docstring"""

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


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










from typing import List, Optional, Union

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


A   : Optional[Any]			=				logging.get_logger(__name__)

A   : List[str]			=				{
    '''huggingface/time-series-transformer-tourism-monthly''': (
        '''https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'''
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			__lowerCamelCase     :	Any          =   '''time_series_transformer'''
			__lowerCamelCase     :	Dict          =   {
			    '''hidden_size''': '''d_model''',
			    '''num_attention_heads''': '''encoder_attention_heads''',
			    '''num_hidden_layers''': '''encoder_layers''',
			}






			def __init__(      self       :   Union[str, Any]							,     __lowerCAmelCase       :   Optional[int] = None							,     __lowerCAmelCase       :   Optional[int] = None							,     __lowerCAmelCase       :   str = "student_t"							,     __lowerCAmelCase       :   str = "nll"							,     __lowerCAmelCase       :   int = 1							,     __lowerCAmelCase       :   List[int] = [1, 2, 3, 4, 5, 6, 7]							,     __lowerCAmelCase       :   Optional[Union[str, bool]] = "mean"							,     __lowerCAmelCase       :   int = 0							,     __lowerCAmelCase       :   int = 0							,     __lowerCAmelCase       :   int = 0							,     __lowerCAmelCase       :   int = 0							,     __lowerCAmelCase       :   Optional[List[int]] = None							,     __lowerCAmelCase       :   Optional[List[int]] = None							,     __lowerCAmelCase       :   int = 32							,     __lowerCAmelCase       :   int = 32							,     __lowerCAmelCase       :   int = 2							,     __lowerCAmelCase       :   int = 2							,     __lowerCAmelCase       :   int = 2							,     __lowerCAmelCase       :   int = 2							,     __lowerCAmelCase       :   bool = True							,     __lowerCAmelCase       :   str = "gelu"							,     __lowerCAmelCase       :   int = 64							,     __lowerCAmelCase       :   float = 0.1							,     __lowerCAmelCase       :   float = 0.1							,     __lowerCAmelCase       :   float = 0.1							,     __lowerCAmelCase       :   float = 0.1							,     __lowerCAmelCase       :   float = 0.1							,     __lowerCAmelCase       :   int = 1_00							,     __lowerCAmelCase       :   float = 0.0_2							,     __lowerCAmelCase       :   Optional[Any]=True							,     **__lowerCAmelCase       :   Union[str, Any]							,     )     ->     Optional[int]:







					"""simple docstring"""






					A__  				=       prediction_length
					A__  				=       context_length or prediction_length
					A__  				=       distribution_output
					A__  				=       loss
					A__  				=       input_size
					A__  				=       num_time_features
					A__  				=       lags_sequence
					A__  				=       scaling
					A__  				=       num_dynamic_real_features
					A__  				=       num_static_real_features
					A__  				=       num_static_categorical_features
					if cardinality and num_static_categorical_features > 0:
							if len(__lowerCAmelCase       ) != num_static_categorical_features:
									raise ValueError(
									    """The cardinality should be a list of the same length as `num_static_categorical_features`"""       )
							A__  				=       cardinality
					else:
							A__  				=       [0]
					if embedding_dimension and num_static_categorical_features > 0:
							if len(__lowerCAmelCase       ) != num_static_categorical_features:
									raise ValueError(
									    """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""       )
							A__  				=       embedding_dimension
					else:
							A__  				=       [min(50							,     (cat + 1) // 2       ) for cat in self.cardinality]
					A__  				=       num_parallel_samples

					# Transformer architecture configuration
					A__  				=       input_size * len(__lowerCAmelCase       ) + self._number_of_features
					A__  				=       d_model
					A__  				=       encoder_attention_heads
					A__  				=       decoder_attention_heads
					A__  				=       encoder_ffn_dim
					A__  				=       decoder_ffn_dim
					A__  				=       encoder_layers
					A__  				=       decoder_layers

					A__  				=       dropout
					A__  				=       attention_dropout
					A__  				=       activation_dropout
					A__  				=       encoder_layerdrop
					A__  				=       decoder_layerdrop

					A__  				=       activation_function
					A__  				=       init_std

					A__  				=       use_cache

					super().__init__(is_encoder_decoder=__lowerCAmelCase							,     **__lowerCAmelCase       )







			@property
			def        a_	(      self       :   List[Any]       )     ->     int:







					"""simple docstring"""






					return (
					    sum(self.embedding_dimension       )
					    + self.num_dynamic_real_features
					    + self.num_time_features
					    + self.num_static_real_features
					    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
					)



