"""simple docstring"""





import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin
class  _snake_case     (					__lowerCAmelCase				, unittest.TestCase		):
						snake_case__			  =			CanineTokenizer
						snake_case__			  =			False






						def       lowerCamelCase__     (       self      :			int    ):
													super().setUp()
													__lowerCamelCase					:		Union[str, Any]		   =     CanineTokenizer()
													tokenizer.save_pretrained(self.tmpdirname    )






						@cached_property
						def       lowerCamelCase__     (       self      :			Optional[Any]    ):
													return CanineTokenizer.from_pretrained("google/canine-s"    )






						def       lowerCamelCase__     (       self      :			Optional[int]							,					**UpperCAmelCase      :			List[str]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     self.tokenizer_class.from_pretrained(self.tmpdirname							,					**lowerCAmelCase_    )
													__lowerCamelCase					:		Optional[Any]		   =     1024
													return tokenizer






						@require_torch
						def       lowerCamelCase__     (       self      :			str    ):
													__lowerCamelCase					:		Optional[int]		   =     self.canine_tokenizer
													__lowerCamelCase					:		List[Any]		   =     ["Life is like a box of chocolates.", "You never know what you're gonna get."]
													# fmt: off
													__lowerCamelCase					:		Dict		   =     [57344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 57345, 0, 0, 0, 0]
													# fmt: on
													__lowerCamelCase					:		Any		   =     tokenizer(lowerCAmelCase_							,					padding=lowerCAmelCase_							,					return_tensors="pt"    )
													self.assertIsInstance(lowerCAmelCase_							,					lowerCAmelCase_    )

													__lowerCamelCase					:		List[Any]		   =     list(batch.input_ids.numpy()[0]    )

													self.assertListEqual(lowerCAmelCase_							,					lowerCAmelCase_    )

													self.assertEqual((2, 39)							,					batch.input_ids.shape    )
													self.assertEqual((2, 39)							,					batch.attention_mask.shape    )






						@require_torch
						def       lowerCamelCase__     (       self      :			int    ):
													__lowerCamelCase					:		List[str]		   =     self.canine_tokenizer
													__lowerCamelCase					:		Union[str, Any]		   =     ["Once there was a man.", "He wrote a test in HuggingFace Tranformers."]
													__lowerCamelCase					:		List[str]		   =     tokenizer(lowerCAmelCase_							,					padding=lowerCAmelCase_							,					return_tensors="pt"    )
													# check if input_ids, attention_mask and token_type_ids are returned
													self.assertIn("input_ids"							,					lowerCAmelCase_    )
													self.assertIn("attention_mask"							,					lowerCAmelCase_    )
													self.assertIn("token_type_ids"							,					lowerCAmelCase_    )






						@require_torch
						def       lowerCamelCase__     (       self      :			Dict    ):
													__lowerCamelCase					:		Tuple		   =     self.canine_tokenizer
													__lowerCamelCase					:		Optional[int]		   =     [
													    "What's the weater?",
													    "It's about 25 degrees.",
													]
													__lowerCamelCase					:		Optional[Any]		   =     tokenizer(
													    text_target=lowerCAmelCase_							,					max_length=32							,					padding="max_length"							,					truncation=lowerCAmelCase_							,					return_tensors="pt"    )
													self.assertEqual(32							,					targets["input_ids"].shape[1]    )






						def       lowerCamelCase__     (       self      :			str    ):
													# safety check on max_len default value so we are sure the test works
													__lowerCamelCase					:		Optional[Any]		   =     self.get_tokenizers()
													for tokenizer in tokenizers:
																				with self.subTest(F"""{tokenizer.__class__.__name__}"""    ):
																											self.assertNotEqual(tokenizer.model_max_length							,					42    )

        # Now let's start the test
													__lowerCamelCase					:		Dict		   =     self.get_tokenizers()
													for tokenizer in tokenizers:
																				with self.subTest(F"""{tokenizer.__class__.__name__}"""    ):
																											# Isolate this from the other tests because we save additional tokens/etc
																											__lowerCamelCase					:		Dict		   =     tempfile.mkdtemp()

																											__lowerCamelCase					:		Any		   =     " He is very happy, UNwant\u00E9d,running"
																											__lowerCamelCase					:		Optional[int]		   =     tokenizer.encode(lowerCAmelCase_							,					add_special_tokens=lowerCAmelCase_    )
																											tokenizer.save_pretrained(lowerCAmelCase_    )

																											__lowerCamelCase					:		int		   =     tokenizer.__class__.from_pretrained(lowerCAmelCase_    )
																											__lowerCamelCase					:		Union[str, Any]		   =     after_tokenizer.encode(lowerCAmelCase_							,					add_special_tokens=lowerCAmelCase_    )
																											self.assertListEqual(lowerCAmelCase_							,					lowerCAmelCase_    )

																											shutil.rmtree(lowerCAmelCase_    )

													__lowerCamelCase					:		int		   =     self.get_tokenizers(model_max_length=42    )
													for tokenizer in tokenizers:
																				with self.subTest(F"""{tokenizer.__class__.__name__}"""    ):
																											# Isolate this from the other tests because we save additional tokens/etc
																											__lowerCamelCase					:		str		   =     tempfile.mkdtemp()

																											__lowerCamelCase					:		Union[str, Any]		   =     " He is very happy, UNwant\u00E9d,running"

																											__lowerCamelCase					:		Any		   =     tokenizer.additional_special_tokens

																											# We can add a new special token for Canine as follows:
																											__lowerCamelCase					:		Union[str, Any]		   =     chr(0XE_007    )
																											additional_special_tokens.append(lowerCAmelCase_    )
																											tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}    )
																											__lowerCamelCase					:		Union[str, Any]		   =     tokenizer.encode(lowerCAmelCase_							,					add_special_tokens=lowerCAmelCase_    )
																											tokenizer.save_pretrained(lowerCAmelCase_    )

																											__lowerCamelCase					:		Optional[Any]		   =     tokenizer.__class__.from_pretrained(lowerCAmelCase_    )
																											__lowerCamelCase					:		List[str]		   =     after_tokenizer.encode(lowerCAmelCase_							,					add_special_tokens=lowerCAmelCase_    )
																											self.assertListEqual(lowerCAmelCase_							,					lowerCAmelCase_    )
																											self.assertIn(lowerCAmelCase_							,					after_tokenizer.additional_special_tokens    )
																											self.assertEqual(after_tokenizer.model_max_length							,					42    )

																											__lowerCamelCase					:		Optional[int]		   =     tokenizer.__class__.from_pretrained(lowerCAmelCase_							,					model_max_length=43    )
																											self.assertEqual(tokenizer.model_max_length							,					43    )

																											shutil.rmtree(lowerCAmelCase_    )






						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													__lowerCamelCase					:		Dict		   =     self.get_tokenizers(do_lower_case=lowerCAmelCase_    )
													for tokenizer in tokenizers:
																				with self.subTest(F"""{tokenizer.__class__.__name__}"""    ):
																											__lowerCamelCase   ,  __lowerCamelCase					:		str		   =     self.get_clean_sequence(lowerCAmelCase_    )

																											# a special token for Canine can be defined as follows:
																											__lowerCamelCase					:		Union[str, Any]		   =     0XE_005
																											__lowerCamelCase					:		Tuple		   =     chr(lowerCAmelCase_    )

																											tokenizer.add_special_tokens({"cls_token": special_token}    )
																											__lowerCamelCase					:		Any		   =     tokenizer.encode(lowerCAmelCase_							,					add_special_tokens=lowerCAmelCase_    )
																											self.assertEqual(len(lowerCAmelCase_    )							,					1    )

																											__lowerCamelCase					:		List[Any]		   =     tokenizer.decode(ids + encoded_special_token							,					clean_up_tokenization_spaces=lowerCAmelCase_    )
																											__lowerCamelCase					:		Optional[int]		   =     tokenizer.encode(lowerCAmelCase_							,					add_special_tokens=lowerCAmelCase_    )

																											__lowerCamelCase					:		List[Any]		   =     tokenizer.encode(lowerCAmelCase_							,					add_special_tokens=lowerCAmelCase_    )
																											__lowerCamelCase					:		Union[str, Any]		   =     tokenizer.encode(lowerCAmelCase_							,					add_special_tokens=lowerCAmelCase_    )
																											self.assertEqual(lowerCAmelCase_							,					input_encoded + special_token_id    )

																											__lowerCamelCase					:		str		   =     tokenizer.decode(lowerCAmelCase_							,					skip_special_tokens=lowerCAmelCase_    )
																											self.assertTrue(special_token not in decoded    )






						def       lowerCamelCase__     (       self      :			Dict    ):
													__lowerCamelCase					:		Union[str, Any]		   =     self.get_tokenizers(do_lower_case=lowerCAmelCase_    )
													for tokenizer in tokenizers:
																				with self.subTest(F"""{tokenizer.__class__.__name__}"""    ):
																											__lowerCamelCase					:		Dict		   =     chr(0XE_005    )
																											__lowerCamelCase					:		Dict		   =     chr(0XE_006    )

																											# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
																											tokenizer.add_tokens([SPECIAL_TOKEN_1]							,					special_tokens=lowerCAmelCase_    )
																											# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
																											# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
																											tokenizer.add_special_tokens({"additional_special_tokens": [SPECIAL_TOKEN_2]}    )

																											__lowerCamelCase					:		Dict		   =     tokenizer.tokenize(lowerCAmelCase_    )
																											__lowerCamelCase					:		Tuple		   =     tokenizer.tokenize(lowerCAmelCase_    )

																											self.assertEqual(len(lowerCAmelCase_    )							,					1    )
																											self.assertEqual(len(lowerCAmelCase_    )							,					1    )
																											self.assertEqual(token_a[0]							,					lowerCAmelCase_    )
																											self.assertEqual(token_a[0]							,					lowerCAmelCase_    )






						@require_tokenizers
						def       lowerCamelCase__     (       self      :			int    ):
													__lowerCamelCase					:		Optional[Any]		   =     self.get_tokenizers(do_lower_case=lowerCAmelCase_    )
													for tokenizer in tokenizers:
																				with self.subTest(F"""{tokenizer.__class__.__name__}"""    ):
																											# a special token for Canine can be defined as follows:
																											__lowerCamelCase					:		List[Any]		   =     0XE_006
																											__lowerCamelCase					:		Tuple		   =     chr(lowerCAmelCase_    )

																											__lowerCamelCase					:		str		   =     AddedToken(lowerCAmelCase_							,					lstrip=lowerCAmelCase_    )
																											tokenizer.add_special_tokens({"additional_special_tokens": [new_token]}    )

																											with tempfile.TemporaryDirectory() as tmp_dir_name:
																																		tokenizer.save_pretrained(lowerCAmelCase_    )
																																		tokenizer.from_pretrained(lowerCAmelCase_    )






						def       lowerCamelCase__     (       self      :			int    ):
													__lowerCamelCase					:		int		   =     []
													if self.test_slow_tokenizer:
																				tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())    )

													if self.test_rust_tokenizer:
																				tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())    )

													for tokenizer_class, tokenizer_utils in tokenizer_list:
																				with tempfile.TemporaryDirectory() as tmp_dir:
																											tokenizer_utils.save_pretrained(lowerCAmelCase_    )

																											with open(os.path.join(lowerCAmelCase_							,					"special_tokens_map.json"    )							,					encoding="utf-8"    ) as json_file:
																																		__lowerCamelCase					:		Tuple		   =     json.load(lowerCAmelCase_    )

																											with open(os.path.join(lowerCAmelCase_							,					"tokenizer_config.json"    )							,					encoding="utf-8"    ) as json_file:
																																		__lowerCamelCase					:		Tuple		   =     json.load(lowerCAmelCase_    )

																											# a special token for Canine can be defined as follows:
																											__lowerCamelCase					:		List[Any]		   =     0XE_006
																											__lowerCamelCase					:		Any		   =     chr(lowerCAmelCase_    )

																											__lowerCamelCase					:		int		   =     [new_token_a]
																											__lowerCamelCase					:		int		   =     [new_token_a]

																											with open(os.path.join(lowerCAmelCase_							,					"special_tokens_map.json"    )							,					"w"							,					encoding="utf-8"    ) as outfile:
																																		json.dump(lowerCAmelCase_							,					lowerCAmelCase_    )
																											with open(os.path.join(lowerCAmelCase_							,					"tokenizer_config.json"    )							,					"w"							,					encoding="utf-8"    ) as outfile:
																																		json.dump(lowerCAmelCase_							,					lowerCAmelCase_    )

																											# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
																											# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
																											# "special_tokens_map.json" files
																											__lowerCamelCase					:		int		   =     tokenizer_class.from_pretrained(lowerCAmelCase_							,					extra_ids=0    )
																											self.assertIn(lowerCAmelCase_							,					tokenizer_without_change_in_init.additional_special_tokens    )
																											# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
																											self.assertEqual(
																											    [new_token_a]							,					tokenizer_without_change_in_init.convert_ids_to_tokens(
																											        tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]    )    )							,					)

																											__lowerCamelCase					:		Optional[int]		   =     0XE_007
																											__lowerCamelCase					:		Tuple		   =     chr(lowerCAmelCase_    )
																											# Now we test that we can change the value of additional_special_tokens in the from_pretrained
																											__lowerCamelCase					:		Optional[int]		   =     [AddedToken(lowerCAmelCase_							,					lstrip=lowerCAmelCase_    )]
																											__lowerCamelCase					:		Optional[int]		   =     tokenizer_class.from_pretrained(
																											    lowerCAmelCase_							,					additional_special_tokens=lowerCAmelCase_							,					extra_ids=0    )

																											self.assertIn(lowerCAmelCase_							,					tokenizer.additional_special_tokens    )
																											# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
																											self.assertEqual(
																											    [new_token_a]							,					tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]    )    )    )






						@require_tokenizers
						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													__lowerCamelCase					:		str		   =     self.get_tokenizers(do_lower_case=lowerCAmelCase_    )
													for tokenizer in tokenizers:
																				with self.subTest(F"""{tokenizer.__class__.__name__}"""    ):
																											__lowerCamelCase					:		Dict		   =     "hello world"
																											if self.space_between_special_tokens:
																																		__lowerCamelCase					:		Union[str, Any]		   =     "[CLS] hello world [SEP]"
																											else:
																																		__lowerCamelCase					:		Dict		   =     input
																											__lowerCamelCase					:		Any		   =     tokenizer.encode(lowerCAmelCase_							,					add_special_tokens=lowerCAmelCase_    )
																											__lowerCamelCase					:		Dict		   =     tokenizer.decode(lowerCAmelCase_							,					spaces_between_special_tokens=self.space_between_special_tokens    )
																											self.assertIn(lowerCAmelCase_							,					[output, output.lower()]    )






						def       lowerCamelCase__     (       self      :			Dict    ):
													__lowerCamelCase					:		List[str]		   =     self.get_tokenizers()
													for tokenizer in tokenizers:
																				with self.subTest(F"""{tokenizer.__class__.__name__}"""    ):
																											__lowerCamelCase					:		Any		   =     [
																											    "bos_token",
																											    "eos_token",
																											    "unk_token",
																											    "sep_token",
																											    "pad_token",
																											    "cls_token",
																											    "mask_token",
																											]

													__lowerCamelCase					:		Optional[Any]		   =     "a"
													__lowerCamelCase					:		str		   =     ord(lowerCAmelCase_    )

													for attr in attributes_list:
																				setattr(lowerCAmelCase_							,					attr + "_id"							,					lowerCAmelCase_    )
																				self.assertEqual(getattr(lowerCAmelCase_							,					lowerCAmelCase_    )							,					lowerCAmelCase_    )
																				self.assertEqual(getattr(lowerCAmelCase_							,					attr + "_id"    )							,					lowerCAmelCase_    )

																				setattr(lowerCAmelCase_							,					attr + "_id"							,					lowerCAmelCase_    )
																				self.assertEqual(getattr(lowerCAmelCase_							,					lowerCAmelCase_    )							,					lowerCAmelCase_    )
																				self.assertEqual(getattr(lowerCAmelCase_							,					attr + "_id"    )							,					lowerCAmelCase_    )

													setattr(lowerCAmelCase_							,					"additional_special_tokens_ids"							,					[]    )
													self.assertListEqual(getattr(lowerCAmelCase_							,					"additional_special_tokens"    )							,					[]    )
													self.assertListEqual(getattr(lowerCAmelCase_							,					"additional_special_tokens_ids"    )							,					[]    )

													__lowerCamelCase					:		int		   =     0XE_006
													__lowerCamelCase					:		Dict		   =     chr(lowerCAmelCase_    )
													setattr(lowerCAmelCase_							,					"additional_special_tokens_ids"							,					[additional_special_token_id]    )
													self.assertListEqual(getattr(lowerCAmelCase_							,					"additional_special_tokens"    )							,					[additional_special_token]    )
													self.assertListEqual(getattr(lowerCAmelCase_							,					"additional_special_tokens_ids"    )							,					[additional_special_token_id]    )






						def       lowerCamelCase__     (       self      :			int    ):
													pass






						def       lowerCamelCase__     (       self      :			List[str]    ):
													pass






						def       lowerCamelCase__     (       self      :			str    ):
													pass






						def       lowerCamelCase__     (       self      :			str    ):
													pass






						def       lowerCamelCase__     (       self      :			str    ):
													pass






						def       lowerCamelCase__     (       self      :			List[str]    ):
													pass






						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													pass







						def       lowerCamelCase__     (       self      :			Dict    ):
													pass
"""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 ✅''')