



"""simple docstring"""


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

import cva
import numpy as np

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

def 			_lowerCAmelCase				(       ):
						UpperCAmelCase				,   UpperCAmelCase					=			get_dataset(lowercase_		,     lowercase_						)
						for index in range(lowercase_						):
												UpperCAmelCase					=			random.sample(range(len(lowercase_						)						)		,     4						)
												UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase					=			update_image_and_anno(
												    lowercase_		,     lowercase_		,     lowercase_		,     lowercase_		,     lowercase_		,     filter_scale=lowercase_		,     )

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

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

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

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

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_		,     lowercase_		,     lowercase_		,     lowercase_ = 0.0		,     ):
						UpperCAmelCase					=			np.zeros([output_size[0], output_size[1], 3]		,     dtype=np.uinta						)
						UpperCAmelCase					=			scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
						UpperCAmelCase					=			scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
						UpperCAmelCase					=			int(scale_x * output_size[1]						)
						UpperCAmelCase					=			int(scale_y * output_size[0]						)

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


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










"""simple docstring"""


import operator as op


snake_case_    					=	"""scaler.pt"""
snake_case_    					=	"""pytorch_model"""
snake_case_    					=	"""random_states"""
snake_case_    					=	"""optimizer"""
snake_case_    					=	"""scheduler"""
snake_case_    					=	"""pytorch_model.bin"""
snake_case_    					=	"""pytorch_model.bin.index.json"""
snake_case_    					=	"""model.safetensors"""
snake_case_    					=	"""model.safetensors.index.json"""
snake_case_    					=	"""1.10.2"""
snake_case_    					=	"""py38"""
snake_case_    					=	"""4.17.0"""
snake_case_    					=	["""ml.p3.16xlarge""", """ml.p3dn.24xlarge""", """ml.p4dn.24xlarge"""]
snake_case_    					=	["""FULL_SHARD""", """SHARD_GRAD_OP""", """NO_SHARD""", """HYBRID_SHARD""", """HYBRID_SHARD_ZERO2"""]
snake_case_    					=	["""TRANSFORMER_BASED_WRAP""", """SIZE_BASED_WRAP""", """NO_WRAP"""]
snake_case_    					=	["""BACKWARD_PRE""", """BACKWARD_POST""", """NO_PREFETCH"""]
snake_case_    					=	["""FULL_STATE_DICT""", """LOCAL_STATE_DICT""", """SHARDED_STATE_DICT"""]
snake_case_    					=	"""2.0.1"""
snake_case_    					=	["""pdsh""", """standard""", """openmpi""", """mvapich"""]
snake_case_    					=	["""default""", """reduce-overhead""", """max-autotune"""]

snake_case_    					=	{""">""": op.gt, """>=""": op.ge, """==""": op.eq, """!=""": op.ne, """<=""": op.le, """<""": op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
snake_case_    					=	[
    """nnodes""",
    """nproc_per_node""",
    """rdzv_backend""",
    """rdzv_endpoint""",
    """rdzv_id""",
    """rdzv_conf""",
    """standalone""",
    """max_restarts""",
    """monitor_interval""",
    """start_method""",
    """role""",
    """module""",
    """m""",
    """no_python""",
    """run_path""",
    """log_dir""",
    """r""",
    """redirects""",
    """t""",
    """tee""",
    """node_rank""",
    """master_addr""",
    """master_port""",
]

snake_case_    					=	["""DEEPSPEED""", """MULTI_GPU""", """FSDP""", """MEGATRON_LM"""]
snake_case_    					=	["""DEEPSPEED""", """MULTI_XPU""", """FSDP"""]





