

import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 (
      MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
      MODEL_MAPPING,
      LevitForImageClassification,
      LevitForImageClassificationWithTeacher,
      LevitModel,
  )
  from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import LevitImageProcessor


class        UpperCamelCase_					(				_A  ):

    '''simple docstring'''






    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->List[str]:





           '''simple docstring'''

           A__   				=					self.config_class(**self.inputs_dict)
           self.parent.assertTrue(hasattr(UpperCAmelCase__      ,				'''hidden_sizes'''))
           self.parent.assertTrue(hasattr(UpperCAmelCase__      ,				'''num_attention_heads'''))


class        UpperCamelCase_					:

    '''simple docstring'''






    def __init__(    self   :			int      ,				UpperCAmelCase__   :			List[Any]      ,				UpperCAmelCase__   :			str=13      ,				UpperCAmelCase__   :			Tuple=64      ,				UpperCAmelCase__   :			Dict=3      ,				UpperCAmelCase__   :			str=3      ,				UpperCAmelCase__   :			Any=2      ,				UpperCAmelCase__   :			int=1      ,				UpperCAmelCase__   :			Any=16      ,				UpperCAmelCase__   :			Dict=[128, 256, 384]      ,				UpperCAmelCase__   :			Dict=[4, 6, 8]      ,				UpperCAmelCase__   :			Optional[int]=[2, 3, 4]      ,				UpperCAmelCase__   :			List[str]=[16, 16, 16]      ,				UpperCAmelCase__   :			Dict=0      ,				UpperCAmelCase__   :			Optional[int]=[2, 2, 2]      ,				UpperCAmelCase__   :			Union[str, Any]=[2, 2, 2]      ,				UpperCAmelCase__   :			Optional[int]=0.02      ,				UpperCAmelCase__   :			Dict=True      ,				UpperCAmelCase__   :			Any=True      ,				UpperCAmelCase__   :			Dict=2      ,				)		->List[Any]:





           '''simple docstring'''

           A__   				=					parent
           A__   				=					batch_size
           A__   				=					image_size
           A__   				=					num_channels
           A__   				=					kernel_size
           A__   				=					stride
           A__   				=					padding
           A__   				=					hidden_sizes
           A__   				=					num_attention_heads
           A__   				=					depths
           A__   				=					key_dim
           A__   				=					drop_path_rate
           A__   				=					patch_size
           A__   				=					attention_ratio
           A__   				=					mlp_ratio
           A__   				=					initializer_range
           A__   				=					[
               ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
               ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
           ]
           A__   				=					is_training
           A__   				=					use_labels
           A__   				=					num_labels
           A__   				=					initializer_range


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int])		->Optional[int]:





           '''simple docstring'''

           A__   				=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

           A__   				=					self.get_config()
           return config, pixel_values, labels


    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->List[Any]:





           '''simple docstring'''

           return LevitConfig(
               image_size=self.image_size      ,				num_channels=self.num_channels      ,				kernel_size=self.kernel_size      ,				stride=self.stride      ,				padding=self.padding      ,				patch_size=self.patch_size      ,				hidden_sizes=self.hidden_sizes      ,				num_attention_heads=self.num_attention_heads      ,				depths=self.depths      ,				key_dim=self.key_dim      ,				drop_path_rate=self.drop_path_rate      ,				mlp_ratio=self.mlp_ratio      ,				attention_ratio=self.attention_ratio      ,				initializer_range=self.initializer_range      ,				down_ops=self.down_ops      ,				)


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any]      ,				UpperCAmelCase__   :			List[Any]      ,				UpperCAmelCase__   :			Optional[int]      ,				UpperCAmelCase__   :			Optional[Any])		->int:





           '''simple docstring'''

           A__   				=					LevitModel(config=UpperCAmelCase__)
           model.to(UpperCAmelCase__)
           model.eval()
           A__   				=					model(UpperCAmelCase__)
           A__   				=					(self.image_size, self.image_size)
           A__   				=					image_size[0], image_size[1]
           for _ in range(4):
                  A__   				=					floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1)
                  A__   				=					floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1)
           self.parent.assertEqual(
               result.last_hidden_state.shape      ,				(self.batch_size, ceil(height / 4) * ceil(width / 4), self.hidden_sizes[-1])      ,				)


    def  SCREAMING_SNAKE_CASE							(    self   :			List[str]      ,				UpperCAmelCase__   :			List[Any]      ,				UpperCAmelCase__   :			Optional[int]      ,				UpperCAmelCase__   :			List[Any])		->Dict:





           '''simple docstring'''

           A__   				=					self.num_labels
           A__   				=					LevitForImageClassification(UpperCAmelCase__)
           model.to(UpperCAmelCase__)
           model.eval()
           A__   				=					model(UpperCAmelCase__      ,				labels=UpperCAmelCase__)
           self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.num_labels))





    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->str:





           '''simple docstring'''

           A__   				=					self.prepare_config_and_inputs()
           A__   				=					config_and_inputs
           A__   				=					{"pixel_values": pixel_values}
           return config, inputs_dict


@require_torch
class        UpperCamelCase_					(				_A	,      _A	,      unittest.TestCase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   (
        (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
        if is_torch_available()
        else ()
    )
    UpperCAmelCase__													=   (
        {
            '''feature-extraction''': LevitModel,
            '''image-classification''': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
        }
        if is_torch_available()
        else {}
    )

    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Any:





           '''simple docstring'''

           A__   				=					LevitModelTester(self)
           A__   				=					ConfigTester(self      ,				config_class=UpperCAmelCase__      ,				has_text_modality=UpperCAmelCase__      ,				hidden_size=37)


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Any:





           '''simple docstring'''

           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   :			str)		->Dict:





           '''simple docstring'''

           return


    @unittest.skip(reason='''Levit does not use inputs_embeds''')
    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->Any:





           '''simple docstring'''

           pass


    @unittest.skip(reason='''Levit does not support input and output embeddings''')
    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Dict:





           '''simple docstring'''

           pass


    @unittest.skip(reason='''Levit does not output attentions''')
    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->int:





           '''simple docstring'''

           pass


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->List[Any]:





           '''simple docstring'''

           A__   				=					self.model_tester.prepare_config_and_inputs_for_common()

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

                  A__   				=					["pixel_values"]
                  self.assertListEqual(arg_names[:1]      ,				UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Optional[int]:





           '''simple docstring'''

           def check_hidden_states_output(UpperCAmelCase__   :			Tuple      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			str):
                  A__   				=					model_class(UpperCAmelCase__)
                  model.to(UpperCAmelCase__)
                  model.eval()

                  with torch.no_grad():
                         A__   				=					model(**self._prepare_for_class(UpperCAmelCase__      ,				UpperCAmelCase__))

                  A__   				=					outputs.hidden_states

                  A__   				=					len(self.model_tester.depths) + 1
                  self.assertEqual(len(UpperCAmelCase__)      ,				UpperCAmelCase__)

                  A__   				=					(self.model_tester.image_size, self.model_tester.image_size)
                  A__   				=					image_size[0], image_size[1]
                  for _ in range(4):
                         A__   				=					floor(
                             (
                                 (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                                 / self.model_tester.stride
                             )
                             + 1)
                         A__   				=					floor(
                             (
                                 (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                                 / self.model_tester.stride
                             )
                             + 1)
                  # verify the first hidden states (first block)
                  self.assertListEqual(
                      list(hidden_states[0].shape[-2:])      ,				[
                          height * width,
                          self.model_tester.hidden_sizes[0],
                      ]      ,				)

           A__   				=					self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  A__   				=					True
                  check_hidden_states_output(UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__)

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

                  check_hidden_states_output(UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__)


    @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Optional[Any]:





           '''simple docstring'''

           pass


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple      ,				UpperCAmelCase__   :			Optional[Any]      ,				UpperCAmelCase__   :			Optional[int]      ,				UpperCAmelCase__   :			Union[str, Any]=False)		->str:





           '''simple docstring'''

           A__   				=					super()._prepare_for_class(UpperCAmelCase__      ,				UpperCAmelCase__      ,				return_labels=UpperCAmelCase__)

           if return_labels:
                  if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                         del inputs_dict["labels"]

           return inputs_dict


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Any:





           '''simple docstring'''

           A__   				=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Optional[Any]:





           '''simple docstring'''

           A__   				=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->List[str]:





           '''simple docstring'''

           if not self.model_tester.is_training:
                  return

           A__   				=					self.model_tester.prepare_config_and_inputs_for_common()
           A__   				=					True

           for model_class in self.all_model_classes:
                  # LevitForImageClassificationWithTeacher supports inference-only
                  if (
                      model_class in get_values(UpperCAmelCase__)
                      or model_class.__name__ == "LevitForImageClassificationWithTeacher"
                  ):
                         continue
                  A__   				=					model_class(UpperCAmelCase__)
                  model.to(UpperCAmelCase__)
                  model.train()
                  A__   				=					self._prepare_for_class(UpperCAmelCase__      ,				UpperCAmelCase__      ,				return_labels=UpperCAmelCase__)
                  A__   				=					model(**UpperCAmelCase__).loss
                  loss.backward()


    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->Dict:





           '''simple docstring'''

           A__   				=					self.model_tester.prepare_config_and_inputs_for_common()
           if not self.model_tester.is_training:
                  return

           A__   				=					False
           A__   				=					True

           for model_class in self.all_model_classes:
                  if model_class in get_values(UpperCAmelCase__) or not model_class.supports_gradient_checkpointing:
                         continue
                  # LevitForImageClassificationWithTeacher supports inference-only
                  if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                         continue
                  A__   				=					model_class(UpperCAmelCase__)
                  model.gradient_checkpointing_enable()
                  model.to(UpperCAmelCase__)
                  model.train()
                  A__   				=					self._prepare_for_class(UpperCAmelCase__      ,				UpperCAmelCase__      ,				return_labels=UpperCAmelCase__)
                  A__   				=					model(**UpperCAmelCase__).loss
                  loss.backward()


    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Optional[Any]:





           '''simple docstring'''

           A__   				=					self.model_tester.prepare_config_and_inputs_for_common()

           A__   				=					[
               {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
               {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
               {"title": "regression", "num_labels": 1, "dtype": torch.float},
           ]

           for model_class in self.all_model_classes:
                  if (
                      model_class
                      not in [
                          *get_values(UpperCAmelCase__),
                      ]
                      or model_class.__name__ == "LevitForImageClassificationWithTeacher"
                  ):
                         continue

                  for problem_type in problem_types:
                         with self.subTest(msg=f"""Testing {model_class} with {problem_type["title"]}"""):
                                A__   				=					problem_type["title"]
                                A__   				=					problem_type["num_labels"]

                                A__   				=					model_class(UpperCAmelCase__)
                                model.to(UpperCAmelCase__)
                                model.train()

                                A__   				=					self._prepare_for_class(UpperCAmelCase__      ,				UpperCAmelCase__      ,				return_labels=UpperCAmelCase__)

                                if problem_type["num_labels"] > 1:
                                       A__   				=					inputs["labels"].unsqueeze(1).repeat(1      ,				problem_type['''num_labels'''])

                                A__   				=					inputs["labels"].to(problem_type['''dtype'''])

                                # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                                # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                                # they have the same size." which is a symptom something in wrong for the regression problem.
                                # See https://github.com/huggingface/transformers/issues/11780
                                with warnings.catch_warnings(record=UpperCAmelCase__) as warning_list:
                                       A__   				=					model(**UpperCAmelCase__).loss
                                for w in warning_list:
                                       if "Using a target size that is different to the input size" in str(w.message):
                                              raise ValueError(
                                                  f"""Something is going wrong in the regression problem: intercepted {w.message}""")

                                loss.backward()





    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Optional[int]:





           '''simple docstring'''

           for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A__   				=					LevitModel.from_pretrained(UpperCAmelCase__)
                  self.assertIsNotNone(UpperCAmelCase__)







def SCREAMING_SNAKE_CASE       ( )     -> Dict:



       """simple docstring"""

       A__   				=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
       return image


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

    '''simple docstring'''






    @cached_property
    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Tuple:





           '''simple docstring'''

           return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0])





    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Any:





           '''simple docstring'''

           A__   				=					LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
               UpperCAmelCase__)

           A__   				=					self.default_image_processor
           A__   				=					prepare_img()
           A__   				=					image_processor(images=UpperCAmelCase__      ,				return_tensors='''pt''').to(UpperCAmelCase__)

           # forward pass
           with torch.no_grad():
                  A__   				=					model(**UpperCAmelCase__)

           # verify the logits
           A__   				=					torch.Size((1, 1_000))
           self.assertEqual(outputs.logits.shape      ,				UpperCAmelCase__)

           A__   				=					torch.tensor([1.0448, -0.3745, -1.8317]).to(UpperCAmelCase__)

           self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,				UpperCAmelCase__      ,				atol=1e-4))








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

import cva
import numpy as np

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







def SCREAMING_SNAKE_CASE       ( )     -> None:



       """simple docstring"""

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

              # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
              A__   				=					random_chars(32		)
              A__   				=					path.split(os.sep		)[-1].rsplit('''.'''				,       1		)[0]
              A__   				=					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}"""		)
              A__   				=					[]
              for anno in new_annos:
                     A__   				=					anno[3] - anno[1]
                     A__   				=					anno[4] - anno[2]
                     A__   				=					anno[1] + width / 2
                     A__   				=					anno[2] + height / 2
                     A__   				=					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 SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> tuple[list, list]:



       """simple docstring"""

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

              A__   				=					[]
              for obj_list in obj_lists:
                     A__   				=					obj_list.rstrip('''\n'''		).split(''' '''		)
                     A__   				=					float(obj[1]		) - float(obj[3]		) / 2
                     A__   				=					float(obj[2]		) - float(obj[4]		) / 2
                     A__   				=					float(obj[1]		) + float(obj[3]		) / 2
                     A__   				=					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 SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_ = 0.0				,       )     -> tuple[list, list, str]:



       """simple docstring"""

       A__   				=					np.zeros([output_size[0], output_size[1], 3]				,       dtype=np.uinta		)
       A__   				=					scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
       A__   				=					scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
       A__   				=					int(scale_x * output_size[1]		)
       A__   				=					int(scale_y * output_size[0]		)

       A__   				=					[]
       A__   				=					[]
       for i, index in enumerate(lowercase_		):
              A__   				=					all_img_list[index]
              path_list.append(lowercase_		)
              A__   				=					all_annos[index]
              A__   				=					cva.imread(lowercase_		)
              if i == 0:  # top-left
                     A__   				=					cva.resize(lowercase_				,       (divid_point_x, divid_point_y)		)
                     A__   				=					img
                     for bbox in img_annos:
                            A__   				=					bbox[1] * scale_x
                            A__   				=					bbox[2] * scale_y
                            A__   				=					bbox[3] * scale_x
                            A__   				=					bbox[4] * scale_y
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)
              elif i == 1:  # top-right
                     A__   				=					cva.resize(lowercase_				,       (output_size[1] - divid_point_x, divid_point_y)		)
                     A__   				=					img
                     for bbox in img_annos:
                            A__   				=					scale_x + bbox[1] * (1 - scale_x)
                            A__   				=					bbox[2] * scale_y
                            A__   				=					scale_x + bbox[3] * (1 - scale_x)
                            A__   				=					bbox[4] * scale_y
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)
              elif i == 2:  # bottom-left
                     A__   				=					cva.resize(lowercase_				,       (divid_point_x, output_size[0] - divid_point_y)		)
                     A__   				=					img
                     for bbox in img_annos:
                            A__   				=					bbox[1] * scale_x
                            A__   				=					scale_y + bbox[2] * (1 - scale_y)
                            A__   				=					bbox[3] * scale_x
                            A__   				=					scale_y + bbox[4] * (1 - scale_y)
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)
              else:  # bottom-right
                     A__   				=					cva.resize(
                         lowercase_				,       (output_size[1] - divid_point_x, output_size[0] - divid_point_y)		)
                     A__   				=					img
                     for bbox in img_annos:
                            A__   				=					scale_x + bbox[1] * (1 - scale_x)
                            A__   				=					scale_y + bbox[2] * (1 - scale_y)
                            A__   				=					scale_x + bbox[3] * (1 - scale_x)
                            A__   				=					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:
              A__   				=					[
                  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 SCREAMING_SNAKE_CASE       ( lowercase_		)     -> str:



       """simple docstring"""

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


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


