






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 				__A     (			unittest.TestCase ):





				"""simple docstring"""







				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Optional[Any]       		=0


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Optional[Any]       		=AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32'    )
									self.assertIsInstance(lowerCamelCase__							,  lowerCamelCase__    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									with tempfile.TemporaryDirectory() as tmpdirname:
														__UpperCamelCase      :					str       		=Path(lowerCamelCase__    ) / 'preprocessor_config.json'
														__UpperCamelCase      :					Optional[int]       		=Path(lowerCamelCase__    ) / 'config.json'
														json.dump(
														    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}							,  open(lowerCamelCase__							,  'w'    )							,  )
														json.dump({'model_type': 'clip'}							,  open(lowerCamelCase__							,  'w'    )    )

														__UpperCamelCase      :					List[Any]       		=AutoImageProcessor.from_pretrained(lowerCamelCase__    )
														self.assertIsInstance(lowerCamelCase__							,  lowerCamelCase__    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									with tempfile.TemporaryDirectory() as tmpdirname:
														__UpperCamelCase      :					Dict       		=Path(lowerCamelCase__    ) / 'preprocessor_config.json'
														__UpperCamelCase      :					Optional[int]       		=Path(lowerCamelCase__    ) / 'config.json'
														json.dump(
														    {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}							,  open(lowerCamelCase__							,  'w'    )							,  )
														json.dump({'model_type': 'clip'}							,  open(lowerCamelCase__							,  'w'    )    )

														__UpperCamelCase      :					Any       		=AutoImageProcessor.from_pretrained(lowerCamelCase__    )
														self.assertIsInstance(lowerCamelCase__							,  lowerCamelCase__    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									with tempfile.TemporaryDirectory() as tmpdirname:
														__UpperCamelCase      :					List[str]       		=CLIPConfig()

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

														# remove image_processor_type to make sure config.json alone is enough to load image processor locally
														__UpperCamelCase      :					Optional[Any]       		=AutoImageProcessor.from_pretrained(lowerCamelCase__    ).to_dict()

														config_dict.pop('image_processor_type'    )
														__UpperCamelCase      :					Any       		=CLIPImageProcessor(**lowerCamelCase__    )

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

														__UpperCamelCase      :					int       		=AutoImageProcessor.from_pretrained(lowerCamelCase__    )

														# make sure private variable is not incorrectly saved
														__UpperCamelCase      :					List[Any]       		=json.loads(config.to_json_string()    )
														self.assertTrue('_processor_class' not in dict_as_saved    )

									self.assertIsInstance(lowerCamelCase__							,  lowerCamelCase__    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									with tempfile.TemporaryDirectory() as tmpdirname:
														__UpperCamelCase      :					Any       		=Path(lowerCamelCase__    ) / 'preprocessor_config.json'
														json.dump(
														    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}							,  open(lowerCamelCase__							,  'w'    )							,  )

														__UpperCamelCase      :					str       		=AutoImageProcessor.from_pretrained(lowerCamelCase__    )
														self.assertIsInstance(lowerCamelCase__							,  lowerCamelCase__    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									with self.assertRaisesRegex(
									    lowerCamelCase__							,  'clip-base is not a local folder and is not a valid model identifier'    ):
														__UpperCamelCase      :					Tuple       		=AutoImageProcessor.from_pretrained('clip-base'    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									with self.assertRaisesRegex(
									    lowerCamelCase__							,  R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'    ):
														__UpperCamelCase      :					Optional[int]       		=AutoImageProcessor.from_pretrained(lowerCamelCase__							,  revision='aaaaaa'    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									with self.assertRaisesRegex(
									    lowerCamelCase__							,  'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'							,  ):
														__UpperCamelCase      :					Tuple       		=AutoImageProcessor.from_pretrained('hf-internal-testing/config-no-model'    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									with self.assertRaises(lowerCamelCase__    ):
														__UpperCamelCase      :					Tuple       		=AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor'    )
									# If remote code is disabled, we can't load this config.
									with self.assertRaises(lowerCamelCase__    ):
														__UpperCamelCase      :					Any       		=AutoImageProcessor.from_pretrained(
														    'hf-internal-testing/test_dynamic_image_processor'							,  trust_remote_code=lowerCamelCase__    )

									__UpperCamelCase      :					int       		=AutoImageProcessor.from_pretrained(
									    'hf-internal-testing/test_dynamic_image_processor'							,  trust_remote_code=lowerCamelCase__    )
									self.assertEqual(image_processor.__class__.__name__							,  'NewImageProcessor'    )

									# Test image processor can be reloaded.
									with tempfile.TemporaryDirectory() as tmp_dir:
														image_processor.save_pretrained(lowerCamelCase__    )
														__UpperCamelCase      :					Any       		=AutoImageProcessor.from_pretrained(lowerCamelCase__							,  trust_remote_code=lowerCamelCase__    )
									self.assertEqual(reloaded_image_processor.__class__.__name__							,  'NewImageProcessor'    )


				def       __lowercase							( self    ):




									"""simple docstring"""

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

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

																			__UpperCamelCase      :					Optional[Any]       		=CustomImageProcessor.from_pretrained(lowerCamelCase__    )

														# 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(lowerCamelCase__    )
																			__UpperCamelCase      :					Optional[int]       		=AutoImageProcessor.from_pretrained(lowerCamelCase__    )
																			self.assertIsInstance(lowerCamelCase__							,  lowerCamelCase__    )

									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       __lowercase							( self    ):




									"""simple docstring"""

									class 				__A     (			a ):





													"""simple docstring"""







													UpperCamelCase__ :      List[Any]			 =True

									try:
														AutoConfig.register('custom'							,  lowerCamelCase__    )
														AutoImageProcessor.register(lowerCamelCase__							,  lowerCamelCase__    )
														# If remote code is not set, the default is to use local
														__UpperCamelCase      :					Optional[Any]       		=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.
														__UpperCamelCase      :					Dict       		=AutoImageProcessor.from_pretrained(
														    'hf-internal-testing/test_dynamic_image_processor'							,  trust_remote_code=lowerCamelCase__    )
														self.assertEqual(image_processor.__class__.__name__							,  'NewImageProcessor'    )
														self.assertTrue(image_processor.is_local    )

														# If remote is enabled, we load from the Hub
														__UpperCamelCase      :					Dict       		=AutoImageProcessor.from_pretrained(
														    'hf-internal-testing/test_dynamic_image_processor'							,  trust_remote_code=lowerCamelCase__    )
														self.assertEqual(image_processor.__class__.__name__							,  'NewImageProcessor'    )
														self.assertTrue(not hasattr(lowerCamelCase__							,  '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]


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

import cva
import numpy as np

# Parrameters
__lowercase							 =					(720, 1280)  # Height, Width
__lowercase							 =					(0.4, 0.6)  # if height or width lower than this scale, drop it.
__lowercase							 =					1 / 100
__lowercase							 =					''''''
__lowercase							 =					''''''
__lowercase							 =					''''''
__lowercase							 =					250



def lowerCamelCase  (   ):
    '''simple docstring'''







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

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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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

        __UpperCamelCase     :int			  =		[]
        for obj_list in obj_lists:
            __UpperCamelCase     :Optional[int]			  =		obj_list.rstrip('''\n'''	).split(''' '''	)
            __UpperCamelCase     :Any			  =		float(obj[1]	) - float(obj[3]	) / 2
            __UpperCamelCase     :List[str]			  =		float(obj[2]	) - float(obj[4]	) / 2
            __UpperCamelCase     :Dict			  =		float(obj[1]	) + float(obj[3]	) / 2
            __UpperCamelCase     :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(SCREAMING_SNAKE_CASE	)
        labels.append(SCREAMING_SNAKE_CASE	)
    return img_paths, labels



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE = 0.0  ,			):
    '''simple docstring'''







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

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







    assert number_char > 1, "The number of character should greater than 1"
    __UpperCamelCase     :Optional[Any]			  =		ascii_lowercase + digits
    return "".join(random.choice(SCREAMING_SNAKE_CASE	) for _ in range(SCREAMING_SNAKE_CASE	)	)


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





