import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import CLIPSegProcessor, ViTImageProcessor


@require_vision
class 	lowerCamelCase_     (   unittest.TestCase ):






    '''simple docstring'''






    def 			UpperCamelCase__   (	self)       ->	List[str]:
        __UpperCamelCase     :int			  =		tempfile.mkdtemp()

        # fmt: off
        __UpperCamelCase     :int			  =		['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
        # fmt: on
        __UpperCamelCase     :Tuple			  =		dict(zip(__lowercase , range(len(__lowercase))))
        __UpperCamelCase     :List[Any]			  =		['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
        __UpperCamelCase     :Optional[Any]			  =		{'''unk_token''': '''<unk>'''}

        __UpperCamelCase     :List[str]			  =		os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''])
        __UpperCamelCase     :List[Any]			  =		os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''])
        with open(self.vocab_file , '''w''' , encoding='''utf-8''') as fp:
            fp.write(json.dumps(__lowercase) + '''\n''')
        with open(self.merges_file , '''w''' , encoding='''utf-8''') as fp:
            fp.write('''\n'''.join(__lowercase))

        __UpperCamelCase     :List[str]			  =		{
            '''do_resize''': True,
            '''size''': 20,
            '''do_center_crop''': True,
            '''crop_size''': 18,
            '''do_normalize''': True,
            '''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
            '''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
        }
        __UpperCamelCase     :Any			  =		os.path.join(self.tmpdirname , __lowercase)
        with open(self.image_processor_file , '''w''' , encoding='''utf-8''') as fp:
            json.dump(__lowercase , __lowercase)







    def 			UpperCamelCase__   (	self , **__lowercase)       ->	Any:
        return CLIPTokenizer.from_pretrained(self.tmpdirname , **__lowercase)







    def 			UpperCamelCase__   (	self , **__lowercase)       ->	Any:
        return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **__lowercase)







    def 			UpperCamelCase__   (	self , **__lowercase)       ->	List[str]:
        return ViTImageProcessor.from_pretrained(self.tmpdirname , **__lowercase)







    def 			UpperCamelCase__   (	self)       ->	List[str]:
        shutil.rmtree(self.tmpdirname)







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase     :Dict			  =		[np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta)]

        __UpperCamelCase     :int			  =		[Image.fromarray(np.moveaxis(__lowercase , 0 , -1)) for x in image_inputs]

        return image_inputs







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase     :Dict			  =		self.get_tokenizer()
        __UpperCamelCase     :str			  =		self.get_rust_tokenizer()
        __UpperCamelCase     :Optional[Any]			  =		self.get_image_processor()

        __UpperCamelCase     :List[Any]			  =		CLIPSegProcessor(tokenizer=__lowercase , image_processor=__lowercase)
        processor_slow.save_pretrained(self.tmpdirname)
        __UpperCamelCase     :Optional[int]			  =		CLIPSegProcessor.from_pretrained(self.tmpdirname , use_fast=__lowercase)

        __UpperCamelCase     :Union[str, Any]			  =		CLIPSegProcessor(tokenizer=__lowercase , image_processor=__lowercase)
        processor_fast.save_pretrained(self.tmpdirname)
        __UpperCamelCase     :Dict			  =		CLIPSegProcessor.from_pretrained(self.tmpdirname)

        self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab())
        self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab())
        self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab())
        self.assertIsInstance(processor_slow.tokenizer , __lowercase)
        self.assertIsInstance(processor_fast.tokenizer , __lowercase)

        self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string())
        self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string())
        self.assertIsInstance(processor_slow.image_processor , __lowercase)
        self.assertIsInstance(processor_fast.image_processor , __lowercase)







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        __UpperCamelCase     :Dict			  =		CLIPSegProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor())
        processor.save_pretrained(self.tmpdirname)

        __UpperCamelCase     :int			  =		self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''')
        __UpperCamelCase     :List[Any]			  =		self.get_image_processor(do_normalize=__lowercase , padding_value=1.0)

        __UpperCamelCase     :Optional[Any]			  =		CLIPSegProcessor.from_pretrained(
            self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=__lowercase , padding_value=1.0)

        self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab())
        self.assertIsInstance(processor.tokenizer , __lowercase)

        self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string())
        self.assertIsInstance(processor.image_processor , __lowercase)







    def 			UpperCamelCase__   (	self)       ->	Optional[Any]:
        __UpperCamelCase     :Optional[int]			  =		self.get_image_processor()
        __UpperCamelCase     :Optional[Any]			  =		self.get_tokenizer()

        __UpperCamelCase     :Dict			  =		CLIPSegProcessor(tokenizer=__lowercase , image_processor=__lowercase)

        __UpperCamelCase     :Dict			  =		self.prepare_image_inputs()

        __UpperCamelCase     :Union[str, Any]			  =		image_processor(__lowercase , return_tensors='''np''')
        __UpperCamelCase     :str			  =		processor(images=__lowercase , return_tensors='''np''')

        for key in input_feat_extract.keys():
            self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2)







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase     :Tuple			  =		self.get_image_processor()
        __UpperCamelCase     :Dict			  =		self.get_tokenizer()

        __UpperCamelCase     :str			  =		CLIPSegProcessor(tokenizer=__lowercase , image_processor=__lowercase)

        __UpperCamelCase     :Tuple			  =		'''lower newer'''

        __UpperCamelCase     :str			  =		processor(text=__lowercase)

        __UpperCamelCase     :Optional[Any]			  =		tokenizer(__lowercase)

        for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key] , encoded_processor[key])







    def 			UpperCamelCase__   (	self)       ->	Any:
        __UpperCamelCase     :str			  =		self.get_image_processor()
        __UpperCamelCase     :Tuple			  =		self.get_tokenizer()

        __UpperCamelCase     :Optional[Any]			  =		CLIPSegProcessor(tokenizer=__lowercase , image_processor=__lowercase)

        __UpperCamelCase     :Optional[int]			  =		'''lower newer'''
        __UpperCamelCase     :str			  =		self.prepare_image_inputs()

        __UpperCamelCase     :Any			  =		processor(text=__lowercase , images=__lowercase)

        self.assertListEqual(list(inputs.keys()) , ['''input_ids''', '''attention_mask''', '''pixel_values'''])

        # test if it raises when no input is passed
        with pytest.raises(__lowercase):
            processor()







    def 			UpperCamelCase__   (	self)       ->	Any:
        __UpperCamelCase     :str			  =		self.get_image_processor()
        __UpperCamelCase     :Tuple			  =		self.get_tokenizer()

        __UpperCamelCase     :List[Any]			  =		CLIPSegProcessor(tokenizer=__lowercase , image_processor=__lowercase)

        __UpperCamelCase     :Optional[Any]			  =		self.prepare_image_inputs()
        __UpperCamelCase     :Optional[Any]			  =		self.prepare_image_inputs()

        __UpperCamelCase     :List[Any]			  =		processor(images=__lowercase , visual_prompt=__lowercase)

        self.assertListEqual(list(inputs.keys()) , ['''pixel_values''', '''conditional_pixel_values'''])

        # test if it raises when no input is passed
        with pytest.raises(__lowercase):
            processor()







    def 			UpperCamelCase__   (	self)       ->	Dict:
        __UpperCamelCase     :Optional[Any]			  =		self.get_image_processor()
        __UpperCamelCase     :List[str]			  =		self.get_tokenizer()

        __UpperCamelCase     :Optional[Any]			  =		CLIPSegProcessor(tokenizer=__lowercase , image_processor=__lowercase)

        __UpperCamelCase     :int			  =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        __UpperCamelCase     :Optional[Any]			  =		processor.batch_decode(__lowercase)
        __UpperCamelCase     :Any			  =		tokenizer.batch_decode(__lowercase)

        self.assertListEqual(__lowercase , __lowercase)






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 ✅''')





