"""simple docstring"""





from typing import List, Optional, Union

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


__A  =				logging.get_logger(__name__)

__A  =				{
    '''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  _snake_case     (					a__		):
						snake_case__			  =			"time_series_transformer"
						snake_case__			  =			{
						    "hidden_size": "d_model",
						    "num_attention_heads": "encoder_attention_heads",
						    "num_hidden_layers": "encoder_layers",
						}






						def __init__(       self      :			List[Any]							,					UpperCAmelCase      :			Optional[int] = None							,					UpperCAmelCase      :			Optional[int] = None							,					UpperCAmelCase      :			str = "student_t"							,					UpperCAmelCase      :			str = "nll"							,					UpperCAmelCase      :			int = 1							,					UpperCAmelCase      :			List[int] = [1, 2, 3, 4, 5, 6, 7]							,					UpperCAmelCase      :			Optional[Union[str, bool]] = "mean"							,					UpperCAmelCase      :			int = 0							,					UpperCAmelCase      :			int = 0							,					UpperCAmelCase      :			int = 0							,					UpperCAmelCase      :			int = 0							,					UpperCAmelCase      :			Optional[List[int]] = None							,					UpperCAmelCase      :			Optional[List[int]] = None							,					UpperCAmelCase      :			int = 32							,					UpperCAmelCase      :			int = 32							,					UpperCAmelCase      :			int = 2							,					UpperCAmelCase      :			int = 2							,					UpperCAmelCase      :			int = 2							,					UpperCAmelCase      :			int = 2							,					UpperCAmelCase      :			bool = True							,					UpperCAmelCase      :			str = "gelu"							,					UpperCAmelCase      :			int = 64							,					UpperCAmelCase      :			float = 0.1							,					UpperCAmelCase      :			float = 0.1							,					UpperCAmelCase      :			float = 0.1							,					UpperCAmelCase      :			float = 0.1							,					UpperCAmelCase      :			float = 0.1							,					UpperCAmelCase      :			int = 100							,					UpperCAmelCase      :			float = 0.0_2							,					UpperCAmelCase      :			Tuple=True							,					**UpperCAmelCase      :			Any							,					):
													# time series specific configuration
													__lowerCamelCase					:		Tuple		   =     prediction_length
													__lowerCamelCase					:		Optional[Any]		   =     context_length or prediction_length
													__lowerCamelCase					:		Optional[Any]		   =     distribution_output
													__lowerCamelCase					:		int		   =     loss
													__lowerCamelCase					:		Tuple		   =     input_size
													__lowerCamelCase					:		Any		   =     num_time_features
													__lowerCamelCase					:		List[str]		   =     lags_sequence
													__lowerCamelCase					:		List[str]		   =     scaling
													__lowerCamelCase					:		List[str]		   =     num_dynamic_real_features
													__lowerCamelCase					:		Union[str, Any]		   =     num_static_real_features
													__lowerCamelCase					:		Tuple		   =     num_static_categorical_features
													if cardinality and num_static_categorical_features > 0:
																				if len(UpperCAmelCase    ) != num_static_categorical_features:
																											raise ValueError(
																											    "The cardinality should be a list of the same length as `num_static_categorical_features`"    )
																				__lowerCamelCase					:		Tuple		   =     cardinality
													else:
																				__lowerCamelCase					:		Dict		   =     [0]
													if embedding_dimension and num_static_categorical_features > 0:
																				if len(UpperCAmelCase    ) != num_static_categorical_features:
																											raise ValueError(
																											    "The embedding dimension should be a list of the same length as `num_static_categorical_features`"    )
																				__lowerCamelCase					:		int		   =     embedding_dimension
													else:
																				__lowerCamelCase					:		List[str]		   =     [min(50							,					(cat + 1) // 2    ) for cat in self.cardinality]
													__lowerCamelCase					:		List[str]		   =     num_parallel_samples

													# Transformer architecture configuration
													__lowerCamelCase					:		List[Any]		   =     input_size * len(UpperCAmelCase    ) + self._number_of_features
													__lowerCamelCase					:		List[str]		   =     d_model
													__lowerCamelCase					:		Union[str, Any]		   =     encoder_attention_heads
													__lowerCamelCase					:		List[str]		   =     decoder_attention_heads
													__lowerCamelCase					:		Tuple		   =     encoder_ffn_dim
													__lowerCamelCase					:		Union[str, Any]		   =     decoder_ffn_dim
													__lowerCamelCase					:		Any		   =     encoder_layers
													__lowerCamelCase					:		Dict		   =     decoder_layers

													__lowerCamelCase					:		Tuple		   =     dropout
													__lowerCamelCase					:		Tuple		   =     attention_dropout
													__lowerCamelCase					:		Any		   =     activation_dropout
													__lowerCamelCase					:		List[str]		   =     encoder_layerdrop
													__lowerCamelCase					:		int		   =     decoder_layerdrop

													__lowerCamelCase					:		Optional[int]		   =     activation_function
													__lowerCamelCase					:		Optional[int]		   =     init_std

													__lowerCamelCase					:		int		   =     use_cache

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







						@property
						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													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
													)
"""simple docstring"""





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

import cva
import numpy as np

# Parrameters
__A  =				(720, 1280)  # Height, Width
__A  =				(0.4, 0.6)  # if height or width lower than this scale, drop it.
__A  =				1 / 100
__A  =				''''''
__A  =				''''''
__A  =				''''''
__A  =				250


def    lowercase_   (   ) ->      None:



							'''simple docstring'''

							__lowerCamelCase   ,  __lowerCamelCase					:		List[Any]		   =     get_dataset(_lowerCamelCase						,   _lowerCamelCase      )
							for index in range(_lowerCamelCase      ):
														__lowerCamelCase					:		Optional[Any]		   =     random.sample(range(len(_lowerCamelCase      )      )						,   4      )
														__lowerCamelCase   ,  __lowerCamelCase   ,  __lowerCamelCase					:		Optional[Any]		   =     update_image_and_anno(
														    _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   filter_scale=_lowerCamelCase						,   )

														# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
														__lowerCamelCase					:		Tuple		   =     random_chars(32      )
														__lowerCamelCase					:		Dict		   =     path.split(os.sep      )[-1].rsplit("."						,   1      )[0]
														__lowerCamelCase					:		List[str]		   =     F"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
														cva.imwrite(F"""{file_root}.jpg"""						,   _lowerCamelCase						,   [cva.IMWRITE_JPEG_QUALITY, 85]      )
														print(F"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"""      )
														__lowerCamelCase					:		List[Any]		   =     []
														for anno in new_annos:
																					__lowerCamelCase					:		Any		   =     anno[3] - anno[1]
																					__lowerCamelCase					:		Optional[int]		   =     anno[4] - anno[2]
																					__lowerCamelCase					:		Optional[int]		   =     anno[1] + width / 2
																					__lowerCamelCase					:		Union[str, Any]		   =     anno[2] + height / 2
																					__lowerCamelCase					:		int		   =     F"""{anno[0]} {x_center} {y_center} {width} {height}"""
																					annos_list.append(_lowerCamelCase      )
														with open(F"""{file_root}.txt"""						,   "w"      ) as outfile:
																					outfile.write("\n".join(line for line in annos_list      )      )


def    lowercase_   (   _lowerCamelCase:	str						,   _lowerCamelCase:	str      ) ->      tuple[list, list]:



							'''simple docstring'''

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

														__lowerCamelCase					:		Union[str, Any]		   =     []
														for obj_list in obj_lists:
																					__lowerCamelCase					:		str		   =     obj_list.rstrip("\n"      ).split(" "      )
																					__lowerCamelCase					:		Union[str, Any]		   =     float(obj[1]      ) - float(obj[3]      ) / 2
																					__lowerCamelCase					:		Tuple		   =     float(obj[2]      ) - float(obj[4]      ) / 2
																					__lowerCamelCase					:		Union[str, Any]		   =     float(obj[1]      ) + float(obj[3]      ) / 2
																					__lowerCamelCase					:		Any		   =     float(obj[2]      ) + float(obj[4]      ) / 2

																					boxes.append([int(obj[0]      ), xmin, ymin, xmax, ymax]      )
														if not boxes:
																					continue
														img_paths.append(_lowerCamelCase      )
														labels.append(_lowerCamelCase      )
							return img_paths, labels


def    lowercase_   (   _lowerCamelCase:	list						,   _lowerCamelCase:	list						,   _lowerCamelCase:	list[int]						,   _lowerCamelCase:	tuple[int, int]						,   _lowerCamelCase:	tuple[float, float]						,   _lowerCamelCase:	float = 0.0						,   ) ->      tuple[list, list, str]:



							'''simple docstring'''

							__lowerCamelCase					:		Union[str, Any]		   =     np.zeros([output_size[0], output_size[1], 3]						,   dtype=np.uinta      )
							__lowerCamelCase					:		Optional[Any]		   =     scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
							__lowerCamelCase					:		List[str]		   =     scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
							__lowerCamelCase					:		int		   =     int(scale_x * output_size[1]      )
							__lowerCamelCase					:		Optional[Any]		   =     int(scale_y * output_size[0]      )

							__lowerCamelCase					:		List[Any]		   =     []
							__lowerCamelCase					:		Optional[Any]		   =     []
							for i, index in enumerate(_lowerCamelCase      ):
														__lowerCamelCase					:		List[str]		   =     all_img_list[index]
														path_list.append(_lowerCamelCase      )
														__lowerCamelCase					:		Optional[Any]		   =     all_annos[index]
														__lowerCamelCase					:		List[str]		   =     cva.imread(_lowerCamelCase      )
														if i == 0:  # top-left
																					__lowerCamelCase					:		List[str]		   =     cva.resize(_lowerCamelCase						,   (divid_point_x, divid_point_y)      )
																					__lowerCamelCase					:		Any		   =     img
																					for bbox in img_annos:
																												__lowerCamelCase					:		str		   =     bbox[1] * scale_x
																												__lowerCamelCase					:		Union[str, Any]		   =     bbox[2] * scale_y
																												__lowerCamelCase					:		Optional[int]		   =     bbox[3] * scale_x
																												__lowerCamelCase					:		Union[str, Any]		   =     bbox[4] * scale_y
																												new_anno.append([bbox[0], xmin, ymin, xmax, ymax]      )
														elif i == 1:  # top-right
																					__lowerCamelCase					:		str		   =     cva.resize(_lowerCamelCase						,   (output_size[1] - divid_point_x, divid_point_y)      )
																					__lowerCamelCase					:		Any		   =     img
																					for bbox in img_annos:
																												__lowerCamelCase					:		List[Any]		   =     scale_x + bbox[1] * (1 - scale_x)
																												__lowerCamelCase					:		List[Any]		   =     bbox[2] * scale_y
																												__lowerCamelCase					:		Tuple		   =     scale_x + bbox[3] * (1 - scale_x)
																												__lowerCamelCase					:		Union[str, Any]		   =     bbox[4] * scale_y
																												new_anno.append([bbox[0], xmin, ymin, xmax, ymax]      )
														elif i == 2:  # bottom-left
																					__lowerCamelCase					:		Any		   =     cva.resize(_lowerCamelCase						,   (divid_point_x, output_size[0] - divid_point_y)      )
																					__lowerCamelCase					:		List[str]		   =     img
																					for bbox in img_annos:
																												__lowerCamelCase					:		Any		   =     bbox[1] * scale_x
																												__lowerCamelCase					:		Optional[int]		   =     scale_y + bbox[2] * (1 - scale_y)
																												__lowerCamelCase					:		Dict		   =     bbox[3] * scale_x
																												__lowerCamelCase					:		Tuple		   =     scale_y + bbox[4] * (1 - scale_y)
																												new_anno.append([bbox[0], xmin, ymin, xmax, ymax]      )
														else:  # bottom-right
																					__lowerCamelCase					:		int		   =     cva.resize(
																					    _lowerCamelCase						,   (output_size[1] - divid_point_x, output_size[0] - divid_point_y)      )
																					__lowerCamelCase					:		Optional[Any]		   =     img
																					for bbox in img_annos:
																												__lowerCamelCase					:		Union[str, Any]		   =     scale_x + bbox[1] * (1 - scale_x)
																												__lowerCamelCase					:		Union[str, Any]		   =     scale_y + bbox[2] * (1 - scale_y)
																												__lowerCamelCase					:		int		   =     scale_x + bbox[3] * (1 - scale_x)
																												__lowerCamelCase					:		int		   =     scale_y + bbox[4] * (1 - scale_y)
																												new_anno.append([bbox[0], xmin, ymin, xmax, ymax]      )

    # Remove bounding box small than scale of filter
							if filter_scale > 0:
														__lowerCamelCase					:		str		   =     [
														    anno
														    for anno in new_anno
														    if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
														]

							return output_img, new_anno, path_list[0]


def    lowercase_   (   _lowerCamelCase:	int      ) ->      str:



							'''simple docstring'''

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


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