




'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
_SCREAMING_SNAKE_CASE			=						(7_20, 12_80)  # Height, Width
_SCREAMING_SNAKE_CASE			=						(0.4, 0.6)  # if height or width lower than this scale, drop it.
_SCREAMING_SNAKE_CASE			=						1 / 1_00
_SCREAMING_SNAKE_CASE			=						""
_SCREAMING_SNAKE_CASE			=						""
_SCREAMING_SNAKE_CASE			=						""
_SCREAMING_SNAKE_CASE			=						2_50





def 			lowerCamelCase(							)		->		None:
			A_		, A_									= get_dataset(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )
			for index in range(SCREAMING_SNAKE_CASE_     ):
						A_									= random.sample(range(len(SCREAMING_SNAKE_CASE_     )     )       ,4     )
						A_		, A_		, A_									= update_image_and_anno(
						    SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,filter_scale=SCREAMING_SNAKE_CASE_       ,)

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





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		tuple[list, list]:
			A_									= []
			A_									= []
			for label_file in glob.glob(os.path.join(SCREAMING_SNAKE_CASE_       ,'*.txt'     )     ):
						A_									= label_file.split(os.sep     )[-1].rsplit('.'       ,1     )[0]
						with open(SCREAMING_SNAKE_CASE_     ) as in_file:
									A_									= in_file.readlines()
						A_									= os.path.join(SCREAMING_SNAKE_CASE_       ,F'{label_name}.jpg'     )

						A_									= []
						for obj_list in obj_lists:
									A_									= obj_list.rstrip('\n'     ).split(' '     )
									A_									= float(obj[1]     ) - float(obj[3]     ) / 2
									A_									= float(obj[2]     ) - float(obj[4]     ) / 2
									A_									= float(obj[1]     ) + float(obj[3]     ) / 2
									A_									= float(obj[2]     ) + float(obj[4]     ) / 2

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





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_ = 0.0       ,)		->		tuple[list, list, str]:
			A_									= np.zeros([output_size[0], output_size[1], 3]       ,dtype=np.uinta     )
			A_									= scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
			A_									= scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
			A_									= int(scale_x * output_size[1]     )
			A_									= int(scale_y * output_size[0]     )

			A_									= []
			A_									= []
			for i, index in enumerate(SCREAMING_SNAKE_CASE_     ):
						A_									= all_img_list[index]
						path_list.append(SCREAMING_SNAKE_CASE_     )
						A_									= all_annos[index]
						A_									= cva.imread(SCREAMING_SNAKE_CASE_     )
						if i == 0:  # top-left
									A_									= cva.resize(SCREAMING_SNAKE_CASE_       ,(divid_point_x, divid_point_y)     )
									A_									= img
									for bbox in img_annos:
												A_									= bbox[1] * scale_x
												A_									= bbox[2] * scale_y
												A_									= bbox[3] * scale_x
												A_									= bbox[4] * scale_y
												new_anno.append([bbox[0], xmin, ymin, xmax, ymax]     )
						elif i == 1:  # top-right
									A_									= cva.resize(SCREAMING_SNAKE_CASE_       ,(output_size[1] - divid_point_x, divid_point_y)     )
									A_									= img
									for bbox in img_annos:
												A_									= scale_x + bbox[1] * (1 - scale_x)
												A_									= bbox[2] * scale_y
												A_									= scale_x + bbox[3] * (1 - scale_x)
												A_									= bbox[4] * scale_y
												new_anno.append([bbox[0], xmin, ymin, xmax, ymax]     )
						elif i == 2:  # bottom-left
									A_									= cva.resize(SCREAMING_SNAKE_CASE_       ,(divid_point_x, output_size[0] - divid_point_y)     )
									A_									= img
									for bbox in img_annos:
												A_									= bbox[1] * scale_x
												A_									= scale_y + bbox[2] * (1 - scale_y)
												A_									= bbox[3] * scale_x
												A_									= scale_y + bbox[4] * (1 - scale_y)
												new_anno.append([bbox[0], xmin, ymin, xmax, ymax]     )
						else:  # bottom-right
									A_									= cva.resize(
									    SCREAMING_SNAKE_CASE_       ,(output_size[1] - divid_point_x, output_size[0] - divid_point_y)     )
									A_									= img
									for bbox in img_annos:
												A_									= scale_x + bbox[1] * (1 - scale_x)
												A_									= scale_y + bbox[2] * (1 - scale_y)
												A_									= scale_x + bbox[3] * (1 - scale_x)
												A_									= 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:
						A_									= [
						    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 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		str:
			assert number_char > 1, "The number of character should greater than 1"
			A_									= ascii_lowercase + digits
			return "".join(random.choice(SCREAMING_SNAKE_CASE_     ) for _ in range(SCREAMING_SNAKE_CASE_     )     )


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










'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE			=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE			=						{"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		"""openai-gpt"""
					_UpperCamelCase							=		{
					    """max_position_embeddings""": """n_positions""",
					    """hidden_size""": """n_embd""",
					    """num_attention_heads""": """n_head""",
					    """num_hidden_layers""": """n_layer""",
					}



					def __init__(	self     :						Any      ,       _lowercase     :						List[Any]=40_478      ,       _lowercase     :						Tuple=512      ,       _lowercase     :						Dict=768      ,       _lowercase     :						List[str]=12      ,       _lowercase     :						Optional[int]=12      ,       _lowercase     :						Tuple="gelu"      ,       _lowercase     :						int=0.1      ,       _lowercase     :						Tuple=0.1      ,       _lowercase     :						Any=0.1      ,       _lowercase     :						List[str]=1E-5      ,       _lowercase     :						str=0.02      ,       _lowercase     :						Optional[Any]="cls_index"      ,       _lowercase     :						int=True      ,       _lowercase     :						str=None      ,       _lowercase     :						Tuple=True      ,       _lowercase     :						List[Any]=0.1      ,       **_lowercase     :						int      ,       )    ->							Optional[int]:
								A_									= vocab_size
								A_									= n_positions
								A_									= n_embd
								A_									= n_layer
								A_									= n_head
								A_									= afn
								A_									= resid_pdrop
								A_									= embd_pdrop
								A_									= attn_pdrop
								A_									= layer_norm_epsilon
								A_									= initializer_range
								A_									= summary_type
								A_									= summary_use_proj
								A_									= summary_activation
								A_									= summary_first_dropout
								A_									= summary_proj_to_labels
								super().__init__(**_lowercase)




