



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

import cva
SCREAMING_SNAKE_CASE__   			=   ''''''
SCREAMING_SNAKE_CASE__   			=   ''''''
SCREAMING_SNAKE_CASE__   			=   ''''''
SCREAMING_SNAKE_CASE__   			=   1  # (0 is vertical, 1 is horizontal)


def A    (							)    ->   None:
			A__  ,			A__	  =     get_dataset(__UpperCamelCase			,						__UpperCamelCase       )
			print('Processing...'       )
			A__  ,			A__  ,			A__	  =     update_image_and_anno(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )

			for index, image in enumerate(__UpperCamelCase       ):
						# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
						A__	  =     random_chars(32       )
						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'''			,						__UpperCamelCase			,						[cva.IMWRITE_JPEG_QUALITY, 85]       )
						print(f'''Success {index+1}/{len(__UpperCamelCase       )} 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(__UpperCamelCase       )
						with open(f'''/{file_root}.txt'''			,						'w'       ) as outfile:
									outfile.write('\n'.join(line for line in annos_list       )       )


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   tuple[list, list]:
			A__	  =     []
			A__	  =     []
			for label_file in glob.glob(os.path.join(__UpperCamelCase			,						'*.txt'       )       ):
						A__	  =     label_file.split(os.sep       )[-1].rsplit('.'			,						1       )[0]
						with open(__UpperCamelCase       ) as in_file:
									A__	  =     in_file.readlines()
						A__	  =     os.path.join(__UpperCamelCase			,						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(__UpperCamelCase       )
						labels.append(__UpperCamelCase       )
			return img_paths, labels


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase = 1       )    ->   tuple[list, list, list]:
			A__	  =     []
			A__	  =     []
			A__	  =     []
			for idx in range(len(__UpperCamelCase       )       ):
						A__	  =     []
						A__	  =     img_list[idx]
						path_list.append(__UpperCamelCase       )
						A__	  =     anno_list[idx]
						A__	  =     cva.imread(__UpperCamelCase       )
						if flip_type == 1:
									A__	  =     cva.flip(__UpperCamelCase			,						__UpperCamelCase       )
									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(__UpperCamelCase			,						__UpperCamelCase       )
									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(__UpperCamelCase       )
						new_imgs_list.append(__UpperCamelCase       )
			return new_imgs_list, new_annos_lists, path_list


def A    (							__UpperCamelCase = 32       )    ->   str:
			assert number_char > 1, "The number of character should greater than 1"
			A__	  =     ascii_lowercase + digits
			return "".join(random.choice(__UpperCamelCase       ) for _ in range(__UpperCamelCase       )       )


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









# Copyright 2023 The HuggingFace 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


SCREAMING_SNAKE_CASE__   			=   {'''configuration_mra''': ['''MRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MraConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__   			=   [
							    '''MRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''MraForMaskedLM''',
							    '''MraForMultipleChoice''',
							    '''MraForQuestionAnswering''',
							    '''MraForSequenceClassification''',
							    '''MraForTokenClassification''',
							    '''MraLayer''',
							    '''MraModel''',
							    '''MraPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mra import (
														    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MraForMaskedLM,
														    MraForMultipleChoice,
														    MraForQuestionAnswering,
														    MraForSequenceClassification,
														    MraForTokenClassification,
														    MraLayer,
														    MraModel,
														    MraPreTrainedModel,
														)
else:
							import sys

							SCREAMING_SNAKE_CASE__   			=   _LazyModule(__name__, globals()['''__file__'''], _import_structure)




