


'''simple docstring'''



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

import cva
import numpy as np

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



def      UpperCAmelCase__   (      )	->						None:
     __lowerCamelCase							,			__lowerCamelCase  :      Any														=						get_dataset(UpperCAmelCase_ ,       UpperCAmelCase_				)
     for index in range(UpperCAmelCase_				):
          __lowerCamelCase  :      Dict														=						random.sample(range(len(UpperCAmelCase_				)				) ,       4				)
          __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      int														=						update_image_and_anno(
              UpperCAmelCase_ ,       UpperCAmelCase_ ,       UpperCAmelCase_ ,       UpperCAmelCase_ ,       UpperCAmelCase_ ,       filter_scale=UpperCAmelCase_ ,       )

          # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
          __lowerCamelCase  :      Union[str, Any]														=						random_chars(32				)
          __lowerCamelCase  :      Any														=						path.split(os.sep				)[-1].rsplit('.' ,       1				)[0]
          __lowerCamelCase  :      Union[str, Any]														=						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}'				)
          __lowerCamelCase  :      List[str]														=						[]
          for anno in new_annos:
               __lowerCamelCase  :      Optional[int]														=						anno[3] - anno[1]
               __lowerCamelCase  :      Optional[Any]														=						anno[4] - anno[2]
               __lowerCamelCase  :      Optional[int]														=						anno[1] + width / 2
               __lowerCamelCase  :      Optional[Any]														=						anno[2] + height / 2
               __lowerCamelCase  :      List[Any]														=						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      UpperCAmelCase__   (      UpperCAmelCase_       :							str ,       UpperCAmelCase_       :							str				)	->						tuple[list, list]:
     __lowerCamelCase  :      int														=						[]
     __lowerCamelCase  :      Union[str, Any]														=						[]
     for label_file in glob.glob(os.path.join(UpperCAmelCase_ ,       '*.txt'				)				):
          __lowerCamelCase  :      Tuple														=						label_file.split(os.sep				)[-1].rsplit('.' ,       1				)[0]
          with open(UpperCAmelCase_				) as in_file:
               __lowerCamelCase  :      Tuple														=						in_file.readlines()
          __lowerCamelCase  :      Tuple														=						os.path.join(UpperCAmelCase_ ,       F'{label_name}.jpg'				)

          __lowerCamelCase  :      List[str]														=						[]
          for obj_list in obj_lists:
               __lowerCamelCase  :      List[Any]														=						obj_list.rstrip('\n'				).split(' '				)
               __lowerCamelCase  :      Dict														=						float(obj[1]				) - float(obj[3]				) / 2
               __lowerCamelCase  :      Tuple														=						float(obj[2]				) - float(obj[4]				) / 2
               __lowerCamelCase  :      List[Any]														=						float(obj[1]				) + float(obj[3]				) / 2
               __lowerCamelCase  :      Union[str, Any]														=						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      UpperCAmelCase__   (      UpperCAmelCase_       :							list ,       UpperCAmelCase_       :							list ,       UpperCAmelCase_       :							list[int] ,       UpperCAmelCase_       :							tuple[int, int] ,       UpperCAmelCase_       :							tuple[float, float] ,       UpperCAmelCase_       :							float = 0.0 ,       )	->						tuple[list, list, str]:
     __lowerCamelCase  :      Optional[Any]														=						np.zeros([output_size[0], output_size[1], 3] ,       dtype=np.uinta				)
     __lowerCamelCase  :      Any														=						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
     __lowerCamelCase  :      Union[str, Any]														=						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
     __lowerCamelCase  :      Any														=						int(scale_x * output_size[1]				)
     __lowerCamelCase  :      Optional[Any]														=						int(scale_y * output_size[0]				)

     __lowerCamelCase  :      List[str]														=						[]
     __lowerCamelCase  :      int														=						[]
     for i, index in enumerate(UpperCAmelCase_				):
          __lowerCamelCase  :      int														=						all_img_list[index]
          path_list.append(UpperCAmelCase_				)
          __lowerCamelCase  :      str														=						all_annos[index]
          __lowerCamelCase  :      List[Any]														=						cva.imread(UpperCAmelCase_				)
          if i == 0:  # top-left
               __lowerCamelCase  :      Union[str, Any]														=						cva.resize(UpperCAmelCase_ ,       (divid_point_x, divid_point_y)				)
               __lowerCamelCase  :      str														=						img
               for bbox in img_annos:
                    __lowerCamelCase  :      Any														=						bbox[1] * scale_x
                    __lowerCamelCase  :      Tuple														=						bbox[2] * scale_y
                    __lowerCamelCase  :      Tuple														=						bbox[3] * scale_x
                    __lowerCamelCase  :      List[Any]														=						bbox[4] * scale_y
                    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)
          elif i == 1:  # top-right
               __lowerCamelCase  :      Dict														=						cva.resize(UpperCAmelCase_ ,       (output_size[1] - divid_point_x, divid_point_y)				)
               __lowerCamelCase  :      str														=						img
               for bbox in img_annos:
                    __lowerCamelCase  :      List[str]														=						scale_x + bbox[1] * (1 - scale_x)
                    __lowerCamelCase  :      Any														=						bbox[2] * scale_y
                    __lowerCamelCase  :      str														=						scale_x + bbox[3] * (1 - scale_x)
                    __lowerCamelCase  :      Optional[Any]														=						bbox[4] * scale_y
                    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)
          elif i == 2:  # bottom-left
               __lowerCamelCase  :      Any														=						cva.resize(UpperCAmelCase_ ,       (divid_point_x, output_size[0] - divid_point_y)				)
               __lowerCamelCase  :      Dict														=						img
               for bbox in img_annos:
                    __lowerCamelCase  :      str														=						bbox[1] * scale_x
                    __lowerCamelCase  :      Optional[int]														=						scale_y + bbox[2] * (1 - scale_y)
                    __lowerCamelCase  :      Dict														=						bbox[3] * scale_x
                    __lowerCamelCase  :      List[Any]														=						scale_y + bbox[4] * (1 - scale_y)
                    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)
          else:  # bottom-right
               __lowerCamelCase  :      List[Any]														=						cva.resize(
                   UpperCAmelCase_ ,       (output_size[1] - divid_point_x, output_size[0] - divid_point_y)				)
               __lowerCamelCase  :      List[Any]														=						img
               for bbox in img_annos:
                    __lowerCamelCase  :      Any														=						scale_x + bbox[1] * (1 - scale_x)
                    __lowerCamelCase  :      int														=						scale_y + bbox[2] * (1 - scale_y)
                    __lowerCamelCase  :      str														=						scale_x + bbox[3] * (1 - scale_x)
                    __lowerCamelCase  :      str														=						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  :      Union[str, Any]														=						[
              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      UpperCAmelCase__   (      UpperCAmelCase_       :							int				)	->						str:
     assert number_char > 1, "The number of character should greater than 1"
     __lowerCamelCase  :      Any														=						ascii_lowercase + digits
     return "".join(random.choice(UpperCAmelCase_				) for _ in range(UpperCAmelCase_				)				)


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







"""simple docstring"""



import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402
class 						__UpperCAmelCase    (unittest.TestCase     ):







			'''simple docstring'''





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[str]								=			0





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[str]								=			AutoImageProcessor.from_pretrained("""openai/clip-vit-base-patch32"""							)
										self.assertIsInstance(snake_case_   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with tempfile.TemporaryDirectory() as tmpdirname:
																	A__       :     Optional[Any]								=			Path(snake_case_							) / """preprocessor_config.json"""
																	A__       :     Any								=			Path(snake_case_							) / """config.json"""
																	json.dump(
																	    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}   ,      open(snake_case_   ,      """w"""							)   ,      )
																	json.dump({"""model_type""": """clip"""}   ,      open(snake_case_   ,      """w"""							)							)

																	A__       :     Any								=			AutoImageProcessor.from_pretrained(snake_case_							)
																	self.assertIsInstance(snake_case_   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with tempfile.TemporaryDirectory() as tmpdirname:
																	A__       :     str								=			Path(snake_case_							) / """preprocessor_config.json"""
																	A__       :     Union[str, Any]								=			Path(snake_case_							) / """config.json"""
																	json.dump(
																	    {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}   ,      open(snake_case_   ,      """w"""							)   ,      )
																	json.dump({"""model_type""": """clip"""}   ,      open(snake_case_   ,      """w"""							)							)

																	A__       :     Optional[int]								=			AutoImageProcessor.from_pretrained(snake_case_							)
																	self.assertIsInstance(snake_case_   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with tempfile.TemporaryDirectory() as tmpdirname:
																	A__       :     Union[str, Any]								=			CLIPConfig()

																	# Create a dummy config file with image_proceesor_type
																	A__       :     Dict								=			Path(snake_case_							) / """preprocessor_config.json"""
																	A__       :     Optional[int]								=			Path(snake_case_							) / """config.json"""
																	json.dump(
																	    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}   ,      open(snake_case_   ,      """w"""							)   ,      )
																	json.dump({"""model_type""": """clip"""}   ,      open(snake_case_   ,      """w"""							)							)

																	# remove image_processor_type to make sure config.json alone is enough to load image processor locally
																	A__       :     List[str]								=			AutoImageProcessor.from_pretrained(snake_case_							).to_dict()

																	config_dict.pop("""image_processor_type"""							)
																	A__       :     str								=			CLIPImageProcessor(**snake_case_							)

																	# save in new folder
																	model_config.save_pretrained(snake_case_							)
																	config.save_pretrained(snake_case_							)

																	A__       :     Optional[int]								=			AutoImageProcessor.from_pretrained(snake_case_							)

																	# make sure private variable is not incorrectly saved
																	A__       :     Dict								=			json.loads(config.to_json_string()							)
																	self.assertTrue("""_processor_class""" not in dict_as_saved							)

										self.assertIsInstance(snake_case_   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with tempfile.TemporaryDirectory() as tmpdirname:
																	A__       :     List[Any]								=			Path(snake_case_							) / """preprocessor_config.json"""
																	json.dump(
																	    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}   ,      open(snake_case_   ,      """w"""							)   ,      )

																	A__       :     List[str]								=			AutoImageProcessor.from_pretrained(snake_case_							)
																	self.assertIsInstance(snake_case_   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with self.assertRaisesRegex(
										    snake_case_   ,      """clip-base is not a local folder and is not a valid model identifier"""							):
																	A__       :     Optional[Any]								=			AutoImageProcessor.from_pretrained("""clip-base"""							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with self.assertRaisesRegex(
										    snake_case_   ,      r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""							):
																	A__       :     Any								=			AutoImageProcessor.from_pretrained(snake_case_   ,      revision="""aaaaaa"""							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with self.assertRaisesRegex(
										    snake_case_   ,      """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."""   ,      ):
																	A__       :     Optional[Any]								=			AutoImageProcessor.from_pretrained("""hf-internal-testing/config-no-model"""							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with self.assertRaises(snake_case_							):
																	A__       :     str								=			AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor"""							)
										# If remote code is disabled, we can't load this config.
										with self.assertRaises(snake_case_							):
																	A__       :     int								=			AutoImageProcessor.from_pretrained(
																	    """hf-internal-testing/test_dynamic_image_processor"""   ,      trust_remote_code=snake_case_							)

										A__       :     Optional[int]								=			AutoImageProcessor.from_pretrained(
										    """hf-internal-testing/test_dynamic_image_processor"""   ,      trust_remote_code=snake_case_							)
										self.assertEqual(image_processor.__class__.__name__   ,      """NewImageProcessor"""							)

										# Test image processor can be reloaded.
										with tempfile.TemporaryDirectory() as tmp_dir:
																	image_processor.save_pretrained(snake_case_							)
																	A__       :     Optional[Any]								=			AutoImageProcessor.from_pretrained(snake_case_   ,      trust_remote_code=snake_case_							)
										self.assertEqual(reloaded_image_processor.__class__.__name__   ,      """NewImageProcessor"""							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										try:
																	AutoConfig.register("""custom"""   ,      snake_case_							)
																	AutoImageProcessor.register(snake_case_   ,      snake_case_							)
																	# Trying to register something existing in the Transformers library will raise an error
																	with self.assertRaises(snake_case_							):
																								AutoImageProcessor.register(snake_case_   ,      snake_case_							)

																	with tempfile.TemporaryDirectory() as tmpdirname:
																								A__       :     Tuple								=			Path(snake_case_							) / """preprocessor_config.json"""
																								A__       :     List[str]								=			Path(snake_case_							) / """config.json"""
																								json.dump(
																								    {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}   ,      open(snake_case_   ,      """w"""							)   ,      )
																								json.dump({"""model_type""": """clip"""}   ,      open(snake_case_   ,      """w"""							)							)

																								A__       :     Optional[int]								=			CustomImageProcessor.from_pretrained(snake_case_							)

																	# Now that the config is registered, it can be used as any other config with the auto-API
																	with tempfile.TemporaryDirectory() as tmp_dir:
																								image_processor.save_pretrained(snake_case_							)
																								A__       :     int								=			AutoImageProcessor.from_pretrained(snake_case_							)
																								self.assertIsInstance(snake_case_   ,      snake_case_							)

										finally:
																	if "custom" in CONFIG_MAPPING._extra_content:
																								del CONFIG_MAPPING._extra_content["custom"]
																	if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
																								del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										class 						__UpperCAmelCase    (__A     ):







													'''simple docstring'''
													_UpperCamelCase  :						Union[str, Any]					     =				True

										try:
																	AutoConfig.register("""custom"""   ,      snake_case_							)
																	AutoImageProcessor.register(snake_case_   ,      snake_case_							)
																	# If remote code is not set, the default is to use local
																	A__       :     int								=			AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor"""							)
																	self.assertEqual(image_processor.__class__.__name__   ,      """NewImageProcessor"""							)
																	self.assertTrue(image_processor.is_local							)

																	# If remote code is disabled, we load the local one.
																	A__       :     Any								=			AutoImageProcessor.from_pretrained(
																	    """hf-internal-testing/test_dynamic_image_processor"""   ,      trust_remote_code=snake_case_							)
																	self.assertEqual(image_processor.__class__.__name__   ,      """NewImageProcessor"""							)
																	self.assertTrue(image_processor.is_local							)

																	# If remote is enabled, we load from the Hub
																	A__       :     Optional[Any]								=			AutoImageProcessor.from_pretrained(
																	    """hf-internal-testing/test_dynamic_image_processor"""   ,      trust_remote_code=snake_case_							)
																	self.assertEqual(image_processor.__class__.__name__   ,      """NewImageProcessor"""							)
																	self.assertTrue(not hasattr(snake_case_   ,      """is_local"""							)							)

										finally:
																	if "custom" in CONFIG_MAPPING._extra_content:
																								del CONFIG_MAPPING._extra_content["custom"]
																	if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
																								del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]





