


'''simple docstring'''



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

import cva
import numpy as np

# Parrameters
_lowercase    :    Any			     =  (720, 1280)  # Height, Width
_lowercase    :    Dict			     =  (0.4, 0.6)  # if height or width lower than this scale, drop it.
_lowercase    :    Dict			     =  1 / 100
_lowercase    :    List[Any]			     =  """"""
_lowercase    :    Optional[int]			     =  """"""
_lowercase    :    Optional[int]			     =  """"""
_lowercase    :    List[str]			     =  250


def    lowerCamelCase__   ( ):




						'''simple docstring'''






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

												# 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"""   ,     __UpperCAmelCase   ,     [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(__UpperCAmelCase  )
												with open(f"""{file_root}.txt"""   ,     '''w'''  ) as outfile:
																		outfile.write('''\n'''.join(line for line in annos_list  )  )


def    lowerCamelCase__   ( A     :    Optional[int]   ,     A     :    List[str]  ):




						'''simple docstring'''






						UpperCAmelCase         =			[]
						UpperCAmelCase         =			[]
						for label_file in glob.glob(os.path.join(__UpperCAmelCase   ,     '''*.txt'''  )  ):
												UpperCAmelCase         =			label_file.split(os.sep  )[-1].rsplit('''.'''   ,     1  )[0]
												with open(__UpperCAmelCase  ) as in_file:
																		UpperCAmelCase         =			in_file.readlines()
												UpperCAmelCase         =			os.path.join(__UpperCAmelCase   ,     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(__UpperCAmelCase  )
												labels.append(__UpperCAmelCase  )
						return img_paths, labels


def    lowerCamelCase__   ( A     :    Optional[Any]   ,     A     :    Union[str, Any]   ,     A     :    List[str]   ,     A     :    Dict   ,     A     :    Optional[Any]   ,     A     :    List[Any] = 0.0   ,     ):




						'''simple docstring'''






						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(__UpperCAmelCase  ):
												UpperCAmelCase         =			all_img_list[index]
												path_list.append(__UpperCAmelCase  )
												UpperCAmelCase         =			all_annos[index]
												UpperCAmelCase         =			cva.imread(__UpperCAmelCase  )
												if i == 0:  # top-left
																		UpperCAmelCase         =			cva.resize(__UpperCAmelCase   ,     (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(__UpperCAmelCase   ,     (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(__UpperCAmelCase   ,     (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(
																		    __UpperCAmelCase   ,     (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__   ( A     :    Union[str, Any]  ):




						'''simple docstring'''






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


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









'''simple docstring'''



# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowercase    :    Any			     =  {
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    int			     =  [
	    """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """CpmAntForCausalLM""",
	    """CpmAntModel""",
	    """CpmAntPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
	from .tokenization_cpmant import CpmAntTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_cpmant import (
		    CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CpmAntForCausalLM,
		    CpmAntModel,
		    CpmAntPreTrainedModel,
		)


else:
	import sys

	_lowercase    :    Dict			     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





