


'''simple docstring'''






import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
		from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor




class 						a__							:





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_   ,       lowerCamelCase_=13   ,       lowerCamelCase_=32   ,       lowerCamelCase_=3   ,       lowerCamelCase_=4   ,       lowerCamelCase_=[10, 20, 30, 40]   ,       lowerCamelCase_=[2, 2, 3, 2]   ,       lowerCamelCase_=True   ,       lowerCamelCase_=True   ,       lowerCamelCase_=37   ,       lowerCamelCase_="gelu"   ,       lowerCamelCase_=10   ,       lowerCamelCase_=0.02   ,       lowerCamelCase_=["stage2", "stage3", "stage4"]   ,       lowerCamelCase_=[2, 3, 4]   ,       lowerCamelCase_=None   ,       )			->				Dict:
														lowerCAmelCase__			  =      parent
														lowerCAmelCase__			  =      batch_size
														lowerCAmelCase__			  =      image_size
														lowerCAmelCase__			  =      num_channels
														lowerCAmelCase__			  =      num_stages
														lowerCAmelCase__			  =      hidden_sizes
														lowerCAmelCase__			  =      depths
														lowerCAmelCase__			  =      is_training
														lowerCAmelCase__			  =      use_labels
														lowerCAmelCase__			  =      intermediate_size
														lowerCAmelCase__			  =      hidden_act
														lowerCAmelCase__			  =      num_labels
														lowerCAmelCase__			  =      initializer_range
														lowerCAmelCase__			  =      out_features
														lowerCAmelCase__			  =      out_indices
														lowerCAmelCase__			  =      scope







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														lowerCAmelCase__			  =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

														lowerCAmelCase__			  =      None
														if self.use_labels:
																					lowerCAmelCase__			  =      ids_tensor([self.batch_size]   ,       self.num_labels  )

														lowerCAmelCase__			  =      self.get_config()

														return config, pixel_values, labels







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														return ConvNextVaConfig(
														    num_channels=self.num_channels   ,       hidden_sizes=self.hidden_sizes   ,       depths=self.depths   ,       num_stages=self.num_stages   ,       hidden_act=self.hidden_act   ,       is_decoder=lowerCamelCase_   ,       initializer_range=self.initializer_range   ,       out_features=self.out_features   ,       out_indices=self.out_indices   ,       num_labels=self.num_labels   ,       )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Any:
														lowerCAmelCase__			  =      ConvNextVaModel(config=lowerCamelCase_  )
														model.to(lowerCamelCase_  )
														model.eval()
														lowerCAmelCase__			  =      model(lowerCamelCase_  )
														# expected last hidden states: B, C, H // 32, W // 32
														self.parent.assertEqual(
														    result.last_hidden_state.shape   ,       (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   ,       )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Any:
														lowerCAmelCase__			  =      ConvNextVaForImageClassification(lowerCamelCase_  )
														model.to(lowerCamelCase_  )
														model.eval()
														lowerCAmelCase__			  =      model(lowerCamelCase_   ,       labels=lowerCamelCase_  )
														self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.num_labels)  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				List[Any]:
														lowerCAmelCase__			  =      ConvNextVaBackbone(config=lowerCamelCase_  )
														model.to(lowerCamelCase_  )
														model.eval()
														lowerCAmelCase__			  =      model(lowerCamelCase_  )

														# verify hidden states
														self.parent.assertEqual(len(result.feature_maps  )   ,       len(config.out_features  )  )
														self.parent.assertListEqual(list(result.feature_maps[0].shape  )   ,       [self.batch_size, self.hidden_sizes[1], 4, 4]  )

														# verify channels
														self.parent.assertEqual(len(model.channels  )   ,       len(config.out_features  )  )
														self.parent.assertListEqual(model.channels   ,       config.hidden_sizes[1:]  )

														# verify backbone works with out_features=None
														lowerCAmelCase__			  =      None
														lowerCAmelCase__			  =      ConvNextVaBackbone(config=lowerCamelCase_  )
														model.to(lowerCamelCase_  )
														model.eval()
														lowerCAmelCase__			  =      model(lowerCamelCase_  )

														# verify feature maps
														self.parent.assertEqual(len(result.feature_maps  )   ,       1  )
														self.parent.assertListEqual(list(result.feature_maps[0].shape  )   ,       [self.batch_size, self.hidden_sizes[-1], 1, 1]  )

														# verify channels
														self.parent.assertEqual(len(model.channels  )   ,       1  )
														self.parent.assertListEqual(model.channels   ,       [config.hidden_sizes[-1]]  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      self.prepare_config_and_inputs()
														lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__			  =      config_and_inputs
														lowerCAmelCase__			  =      {'''pixel_values''': pixel_values}
														return config, inputs_dict







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      self.prepare_config_and_inputs()
														lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__			  =      config_and_inputs
														lowerCAmelCase__			  =      {'''pixel_values''': pixel_values, '''labels''': labels}
														return config, inputs_dict




@require_torch
class 						a__							(				a__							,							a__							,							unittest.TestCase    ):





							'''simple docstring'''






							lowercase__     :  int      		= (
							    (
							        ConvNextVaModel,
							        ConvNextVaForImageClassification,
							        ConvNextVaBackbone,
							    )
							    if is_torch_available()
							    else ()
							)
							lowercase__     :  Any      		= (
							    {"feature-extraction": ConvNextVaModel, "image-classification": ConvNextVaForImageClassification}
							    if is_torch_available()
							    else {}
							)

							lowercase__     :  Dict      		= False
							lowercase__     :  Any      		= False
							lowercase__     :  Dict      		= False
							lowercase__     :  List[Any]      		= False
							lowercase__     :  Optional[int]      		= False







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      ConvNextVaModelTester(self  )
														lowerCAmelCase__			  =      ConfigTester(self   ,       config_class=lowerCamelCase_   ,       has_text_modality=lowerCamelCase_   ,       hidden_size=37  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														self.create_and_test_config_common_properties()
														self.config_tester.create_and_test_config_to_json_string()
														self.config_tester.create_and_test_config_to_json_file()
														self.config_tester.create_and_test_config_from_and_save_pretrained()
														self.config_tester.create_and_test_config_with_num_labels()
														self.config_tester.check_config_can_be_init_without_params()
														self.config_tester.check_config_arguments_init()







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														return







							@unittest.skip(reason='''ConvNextV2 does not use inputs_embeds'''  )
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														pass







							@unittest.skip(reason='''ConvNextV2 does not support input and output embeddings'''  )
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														pass







							@unittest.skip(reason='''ConvNextV2 does not use feedforward chunking'''  )
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														pass







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														if not self.model_tester.is_training:
																					return

														for model_class in self.all_model_classes:
																					lowerCAmelCase__  ,   lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs_with_labels()
																					lowerCAmelCase__			  =      True

																					if model_class.__name__ in [
																					    *get_values(lowerCamelCase_  ),
																					    *get_values(lowerCamelCase_  ),
																					]:
																												continue

																					lowerCAmelCase__			  =      model_class(lowerCamelCase_  )
																					model.to(lowerCamelCase_  )
																					model.train()
																					lowerCAmelCase__			  =      self._prepare_for_class(lowerCamelCase_   ,       lowerCamelCase_   ,       return_labels=lowerCamelCase_  )
																					lowerCAmelCase__			  =      model(**lowerCamelCase_  ).loss
																					loss.backward()







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[str]:
														if not self.model_tester.is_training:
																					return

														for model_class in self.all_model_classes:
																					lowerCAmelCase__  ,   lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs_with_labels()
																					lowerCAmelCase__			  =      False
																					lowerCAmelCase__			  =      True

																					if (
																					    model_class.__name__
																					    in [*get_values(lowerCamelCase_  ), *get_values(lowerCamelCase_  )]
																					    or not model_class.supports_gradient_checkpointing
																					):
																												continue

																					lowerCAmelCase__			  =      model_class(lowerCamelCase_  )
																					model.to(lowerCamelCase_  )
																					model.gradient_checkpointing_enable()
																					model.train()
																					lowerCAmelCase__			  =      self._prepare_for_class(lowerCamelCase_   ,       lowerCamelCase_   ,       return_labels=lowerCamelCase_  )
																					lowerCAmelCase__			  =      model(**lowerCamelCase_  ).loss
																					loss.backward()







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__  ,   lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCAmelCase__			  =      model_class(lowerCamelCase_  )
																					lowerCAmelCase__			  =      inspect.signature(model.forward  )
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					lowerCAmelCase__			  =      [*signature.parameters.keys()]

																					lowerCAmelCase__			  =      ['''pixel_values''']
																					self.assertListEqual(arg_names[:1]   ,       lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[str]:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														def check_hidden_states_output(lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  ):
																					lowerCAmelCase__			  =      model_class(lowerCamelCase_  )
																					model.to(lowerCamelCase_  )
																					model.eval()

																					with torch.no_grad():
																												lowerCAmelCase__			  =      model(**self._prepare_for_class(lowerCamelCase_   ,       lowerCamelCase_  )  )

																					lowerCAmelCase__			  =      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																					lowerCAmelCase__			  =      self.model_tester.num_stages
																					self.assertEqual(len(lowerCamelCase_  )   ,       expected_num_stages + 1  )

																					# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
																					self.assertListEqual(
																					    list(hidden_states[0].shape[-2:]  )   ,       [self.model_tester.image_size // 4, self.model_tester.image_size // 4]   ,       )

														lowerCAmelCase__  ,   lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCAmelCase__			  =      True
																					check_hidden_states_output(lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )

																					# check that output_hidden_states also work using config
																					del inputs_dict["output_hidden_states"]
																					lowerCAmelCase__			  =      True

																					check_hidden_states_output(lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_  )







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCAmelCase__			  =      ConvNextVaModel.from_pretrained(lowerCamelCase_  )
																					self.assertIsNotNone(lowerCamelCase_  )


def    _snake_case  (	)		->       int:
							lowerCAmelCase__			  =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
							return image






@require_torch
@require_vision
class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							@cached_property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														return AutoImageProcessor.from_pretrained('''facebook/convnextv2-tiny-1k-224'''  ) if is_vision_available() else None







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      ConvNextVaForImageClassification.from_pretrained('''facebook/convnextv2-tiny-1k-224'''  ).to(lowerCamelCase_  )

														lowerCAmelCase__			  =      self.default_image_processor
														lowerCAmelCase__			  =      prepare_img()
														lowerCAmelCase__			  =      preprocessor(images=lowerCamelCase_   ,       return_tensors='''pt'''  ).to(lowerCamelCase_  )

														# forward pass
														with torch.no_grad():
																					lowerCAmelCase__			  =      model(**lowerCamelCase_  )

														# verify the logits
														lowerCAmelCase__			  =      torch.Size((1, 10_00)  )
														self.assertEqual(outputs.logits.shape   ,       lowerCamelCase_  )

														lowerCAmelCase__			  =      torch.tensor([0.9_996, 0.1_966, -0.4_386]  ).to(lowerCamelCase_  )
														self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,       lowerCamelCase_   ,       atol=1e-4  )  )



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

import cva
import numpy as np

# Parrameters
SCREAMING_SNAKE_CASE__     					=			(720, 1_280)  # Height, Width
SCREAMING_SNAKE_CASE__     					=			(0.4, 0.6)  # if height or width lower than this scale, drop it.
SCREAMING_SNAKE_CASE__     					=			1 / 100
SCREAMING_SNAKE_CASE__     					=			""
SCREAMING_SNAKE_CASE__     					=			""
SCREAMING_SNAKE_CASE__     					=			""
SCREAMING_SNAKE_CASE__     					=			250


def     lowercase	(    ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:str				     =						get_dataset(a     , a )
		for index in range(a ):
				SCREAMING_SNAKE_CASE_				:Any				     =						random.sample(range(len(a ) )     , 4 )
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						update_image_and_anno(
				    a     , a     , a     , a     , a     , filter_scale=a     , )

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


def     lowercase	(    a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						[]
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						[]
		for label_file in glob.glob(os.path.join(a     , "*.txt" ) ):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						label_file.split(os.sep )[-1].rsplit("."     , 1 )[0]
				with open(a ) as in_file:
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						in_file.readlines()
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						os.path.join(a     , F"{label_name}.jpg" )

				SCREAMING_SNAKE_CASE_				:Dict				     =						[]
				for obj_list in obj_lists:
						SCREAMING_SNAKE_CASE_				:Dict				     =						obj_list.rstrip("\n" ).split(" " )
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						float(obj[1] ) - float(obj[3] ) / 2
						SCREAMING_SNAKE_CASE_				:Dict				     =						float(obj[2] ) - float(obj[4] ) / 2
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						float(obj[1] ) + float(obj[3] ) / 2
						SCREAMING_SNAKE_CASE_				:List[str]				     =						float(obj[2] ) + float(obj[4] ) / 2

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


def     lowercase	(    a     , a     , a     , a     , a     , a = 0.0     , ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						np.zeros([output_size[0], output_size[1], 3]     , dtype=np.uinta )
		SCREAMING_SNAKE_CASE_				:Tuple				     =						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
		SCREAMING_SNAKE_CASE_				:List[str]				     =						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
		SCREAMING_SNAKE_CASE_				:Any				     =						int(scale_x * output_size[1] )
		SCREAMING_SNAKE_CASE_				:List[Any]				     =						int(scale_y * output_size[0] )

		SCREAMING_SNAKE_CASE_				:Any				     =						[]
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						[]
		for i, index in enumerate(a ):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						all_img_list[index]
				path_list.append(a )
				SCREAMING_SNAKE_CASE_				:Tuple				     =						all_annos[index]
				SCREAMING_SNAKE_CASE_				:Any				     =						cva.imread(a )
				if i == 0:  # top-left
						SCREAMING_SNAKE_CASE_				:int				     =						cva.resize(a     , (divid_point_x, divid_point_y) )
						SCREAMING_SNAKE_CASE_				:Any				     =						img
						for bbox in img_annos:
								SCREAMING_SNAKE_CASE_				:Tuple				     =						bbox[1] * scale_x
								SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						bbox[2] * scale_y
								SCREAMING_SNAKE_CASE_				:List[Any]				     =						bbox[3] * scale_x
								SCREAMING_SNAKE_CASE_				:Tuple				     =						bbox[4] * scale_y
								new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
				elif i == 1:  # top-right
						SCREAMING_SNAKE_CASE_				:Dict				     =						cva.resize(a     , (output_size[1] - divid_point_x, divid_point_y) )
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						img
						for bbox in img_annos:
								SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						scale_x + bbox[1] * (1 - scale_x)
								SCREAMING_SNAKE_CASE_				:Dict				     =						bbox[2] * scale_y
								SCREAMING_SNAKE_CASE_				:Optional[int]				     =						scale_x + bbox[3] * (1 - scale_x)
								SCREAMING_SNAKE_CASE_				:Tuple				     =						bbox[4] * scale_y
								new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
				elif i == 2:  # bottom-left
						SCREAMING_SNAKE_CASE_				:List[str]				     =						cva.resize(a     , (divid_point_x, output_size[0] - divid_point_y) )
						SCREAMING_SNAKE_CASE_				:int				     =						img
						for bbox in img_annos:
								SCREAMING_SNAKE_CASE_				:Tuple				     =						bbox[1] * scale_x
								SCREAMING_SNAKE_CASE_				:Dict				     =						scale_y + bbox[2] * (1 - scale_y)
								SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						bbox[3] * scale_x
								SCREAMING_SNAKE_CASE_				:List[str]				     =						scale_y + bbox[4] * (1 - scale_y)
								new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
				else:  # bottom-right
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						cva.resize(
						    a     , (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						img
						for bbox in img_annos:
								SCREAMING_SNAKE_CASE_				:Optional[int]				     =						scale_x + bbox[1] * (1 - scale_x)
								SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						scale_y + bbox[2] * (1 - scale_y)
								SCREAMING_SNAKE_CASE_				:Dict				     =						scale_x + bbox[3] * (1 - scale_x)
								SCREAMING_SNAKE_CASE_				:List[Any]				     =						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:
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						[
				    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	(    a ):



		'''simple docstring'''




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


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






