






import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin


class     __SCREAMING_SNAKE_CASE					(   _a		,					unittest.TestCase):
						_SCREAMING_SNAKE_CASE	:       Dict   =					DebertaTokenizer
						_SCREAMING_SNAKE_CASE	:       Optional[int]   =					True
						_SCREAMING_SNAKE_CASE	:       Optional[Any]   =					DebertaTokenizerFast






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												super().setUp()

												# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
												lowerCAmelCase__ 						=						[
												    'l',
												    'o',
												    'w',
												    'e',
												    'r',
												    's',
												    't',
												    'i',
												    'd',
												    'n',
												    '\u0120',
												    '\u0120l',
												    '\u0120n',
												    '\u0120lo',
												    '\u0120low',
												    'er',
												    '\u0120lowest',
												    '\u0120newer',
												    '\u0120wider',
												    '[UNK]',
												]
												lowerCAmelCase__ 						=						dict(zip(snake_case_     ,						range(len(snake_case_   )   )   )   )
												lowerCAmelCase__ 						=						['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
												lowerCAmelCase__ 						=						{'unk_token': '[UNK]'}

												lowerCAmelCase__ 						=						os.path.join(self.tmpdirname     ,						VOCAB_FILES_NAMES['vocab_file']   )
												lowerCAmelCase__ 						=						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(snake_case_   ) + '\n'   )
												with open(self.merges_file     ,						'w'     ,						encoding='utf-8'   ) as fp:
																		fp.write('\n'.join(snake_case_   )   )






						def       UpperCamelCase__      (	self     ,						**_UpperCamelCase   ):






												"""simple docstring"""
												kwargs.update(self.special_tokens_map   )
												return self.tokenizer_class.from_pretrained(self.tmpdirname     ,						**snake_case_   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__ 						=						'lower newer'
												lowerCAmelCase__ 						=						'lower newer'
												return input_text, output_text






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__ 						=						self.get_tokenizer()
												lowerCAmelCase__ 						=						'lower newer'
												lowerCAmelCase__ 						=						['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
												lowerCAmelCase__ 						=						tokenizer.tokenize(snake_case_   )
												self.assertListEqual(snake_case_     ,						snake_case_   )

												lowerCAmelCase__ 						=						tokens + [tokenizer.unk_token]
												lowerCAmelCase__ 						=						[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_   )     ,						snake_case_   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__ 						=						self.get_tokenizer()
												lowerCAmelCase__ 						=						tokenizer('Hello'     ,						'World'   )
												lowerCAmelCase__ 						=						[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
												self.assertListEqual(tokd['token_type_ids']     ,						snake_case_   )






						@slow
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__ 						=						self.tokenizer_class.from_pretrained('microsoft/deberta-base'   )

												lowerCAmelCase__ 						=						tokenizer.encode('sequence builders'     ,						add_special_tokens=snake_case_   )
												lowerCAmelCase__ 						=						tokenizer.encode('multi-sequence build'     ,						add_special_tokens=snake_case_   )

												lowerCAmelCase__ 						=						tokenizer.encode(
												    'sequence builders'     ,						add_special_tokens=snake_case_     ,						add_prefix_space=snake_case_   )
												lowerCAmelCase__ 						=						tokenizer.encode(
												    'sequence builders'     ,						'multi-sequence build'     ,						add_special_tokens=snake_case_     ,						add_prefix_space=snake_case_   )

												lowerCAmelCase__ 						=						tokenizer.build_inputs_with_special_tokens(snake_case_   )
												lowerCAmelCase__ 						=						tokenizer.build_inputs_with_special_tokens(snake_case_     ,						snake_case_   )

												assert encoded_sentence == encoded_text_from_decode
												assert encoded_pair == encoded_pair_from_decode







						@slow
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__ 						=						[self.tokenizer_class]
												if self.test_rust_tokenizer:
																		tokenizer_classes.append(self.rust_tokenizer_class   )

												for tokenizer_class in tokenizer_classes:
																		lowerCAmelCase__ 						=						tokenizer_class.from_pretrained('microsoft/deberta-base'   )

																		lowerCAmelCase__ 						=						[
																		    'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
																		    'ALBERT incorporates two parameter reduction techniques',
																		    'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
																		    ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
																		    ' vocabulary embedding.',
																		]

																		lowerCAmelCase__ 						=						tokenizer(snake_case_     ,						padding=snake_case_   )
																		lowerCAmelCase__ 						=						[tokenizer.decode(snake_case_     ,						skip_special_tokens=snake_case_   ) for seq in encoding['input_ids']]

																		# fmt: off
																		lowerCAmelCase__ 						=						{
																		    'input_ids': [
																		        [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																		        [1, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																		        [1, 1_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2]
																		    ],
																		    'token_type_ids': [
																		        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																		        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																		        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
																		    ],
																		    'attention_mask': [
																		        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																		        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																		        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
																		    ]
																		}
																		# fmt: on

																		lowerCAmelCase__ 						=						[
																		    'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
																		    'ALBERT incorporates two parameter reduction techniques',
																		    'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
																		    ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
																		    ' vocabulary embedding.',
																		]

																		self.assertDictEqual(encoding.data     ,						snake_case_   )

																		for expected, decoded in zip(snake_case_     ,						snake_case_   ):
																								self.assertEqual(snake_case_     ,						snake_case_   )













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

import cva
import numpy as np

# Parrameters
__snake_case    :    str         =     (7_20, 12_80)  # Height, Width
__snake_case    :    Dict         =     (0.4, 0.6)  # if height or width lower than this scale, drop it.
__snake_case    :    Dict         =     1 / 1_00
__snake_case    :    Optional[int]         =     """"""
__snake_case    :    Union[str, Any]         =     """"""
__snake_case    :    List[str]         =     """"""
__snake_case    :    List[str]         =     2_50







def 	_UpperCamelCase   (    )  ->   None:

						"""simple docstring"""



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

												# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
												lowerCAmelCase__    						=						random_chars(32		)
												lowerCAmelCase__    						=						path.split(os.sep		)[-1].rsplit('.'    ,       1		)[0]
												lowerCAmelCase__    						=						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__    						=						[]
												for anno in new_annos:
																		lowerCAmelCase__    						=						anno[3] - anno[1]
																		lowerCAmelCase__    						=						anno[4] - anno[2]
																		lowerCAmelCase__    						=						anno[1] + width / 2
																		lowerCAmelCase__    						=						anno[2] + height / 2
																		lowerCAmelCase__    						=						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]:

						"""simple docstring"""



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

												lowerCAmelCase__    						=						[]
												for obj_list in obj_lists:
																		lowerCAmelCase__    						=						obj_list.rstrip('\n'		).split(' '		)
																		lowerCAmelCase__    						=						float(obj[1]		) - float(obj[3]		) / 2
																		lowerCAmelCase__    						=						float(obj[2]		) - float(obj[4]		) / 2
																		lowerCAmelCase__    						=						float(obj[1]		) + float(obj[3]		) / 2
																		lowerCAmelCase__    						=						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]:

						"""simple docstring"""



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

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

						"""simple docstring"""



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


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





