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

import cva
import numpy as np

# Parrameters
__UpperCamelCase	: Any    		=  (720, 1280)  # Height, Width
__UpperCamelCase	: Optional[int]    		=  (0.4, 0.6)  # if height or width lower than this scale, drop it.
__UpperCamelCase	: int    		=  1 / 100
__UpperCamelCase	: int    		=  ''''''
__UpperCamelCase	: str    		=  ''''''
__UpperCamelCase	: int    		=  ''''''
__UpperCamelCase	: List[str]    		=  250
def    A			(					):
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					str           =							get_dataset(_UpperCAmelCase	,						_UpperCAmelCase		)
						for index in range(_UpperCAmelCase		):
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							random.sample(range(len(_UpperCAmelCase		)		)	,						4		)
												SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					int           =							update_image_and_anno(
												    _UpperCAmelCase	,						_UpperCAmelCase	,						_UpperCAmelCase	,						_UpperCAmelCase	,						_UpperCAmelCase	,						filter_scale=_UpperCAmelCase	,						)

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

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

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


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






import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
					from .tokenization_ta import TaTokenizer
else:
					__UpperCamelCase	: Dict    		=  None


__UpperCamelCase	: Tuple    		=  logging.get_logger(__name__)

__UpperCamelCase	: Optional[int]    		=  {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__UpperCamelCase	: Optional[int]    		=  {
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/tokenizer.json',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/tokenizer.json',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/tokenizer.json',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/tokenizer.json',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/tokenizer.json',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
__UpperCamelCase	: Union[str, Any]    		=  {
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	VOCAB_FILES_NAMES
						UpperCamelCase_  =	PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_  =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_  =	["""input_ids""", """attention_mask"""]
						UpperCamelCase_  =	TaTokenizer

						UpperCamelCase_  =	[]


						def __init__( self			:       str					,	UpperCamelCase__			:       Optional[int]=None					,	UpperCamelCase__			:       Dict=None					,	UpperCamelCase__			:       str="</s>"					,	UpperCamelCase__			:       str="<unk>"					,	UpperCamelCase__			:       Optional[int]="<pad>"					,	UpperCamelCase__			:       Optional[Any]=100					,	UpperCamelCase__			:       List[Any]=None					,	**UpperCamelCase__			:       str					,	):





												'''simple docstring'''





												if extra_ids > 0 and additional_special_tokens is None:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							[f"""<extra_id_{i}>""" for i in range(UpperCamelCase__		)]
												elif extra_ids > 0 and additional_special_tokens is not None:
																		# Check that we have the right number of extra special tokens
																		SCREAMING_SNAKE_CASE							:					int           =							len(set(filter(lambda UpperCamelCase__		: bool('''extra_id_''' in str(UpperCamelCase__		)		)					,	UpperCamelCase__		)		)		)
																		if extra_tokens != extra_ids:
																								raise ValueError(
																								    f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
																								    ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
																								    ''' tokens'''		)

												super().__init__(
												    UpperCamelCase__					,	tokenizer_file=UpperCamelCase__					,	eos_token=UpperCamelCase__					,	unk_token=UpperCamelCase__					,	pad_token=UpperCamelCase__					,	extra_ids=UpperCamelCase__					,	additional_special_tokens=UpperCamelCase__					,	**UpperCamelCase__					,	)

												SCREAMING_SNAKE_CASE							:					str           =							vocab_file
												SCREAMING_SNAKE_CASE							:					int           =							False if not self.vocab_file else True
												SCREAMING_SNAKE_CASE							:					str           =							extra_ids


						@staticmethod
						def  __A ( UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
																		if init_max_model_length is not None and init_max_model_length != max_model_length:
																								return init_max_model_length
																		elif init_max_model_length is None:
																								warnings.warn(
																								    '''This tokenizer was incorrectly instantiated with a model max length of'''
																								    f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
																								    ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
																								    ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
																								    f""" {pretrained_model_name_or_path} automatically truncating your input to"""
																								    f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
																								    f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
																								    ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
																								    ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''					,	UpperCamelCase__					,	)

												return max_model_length


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       Optional[str] = None		):





												'''simple docstring'''





												if not self.can_save_slow_tokenizer:
																		raise ValueError(
																		    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																		    '''tokenizer.'''		)

												if not os.path.isdir(UpperCamelCase__		):
																		logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
																		return
												SCREAMING_SNAKE_CASE							:					Any           =							os.path.join(
												    UpperCamelCase__					,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

												if os.path.abspath(self.vocab_file		) != os.path.abspath(UpperCamelCase__		):
																		copyfile(self.vocab_file					,	UpperCamelCase__		)
																		logger.info(f"""Copy vocab file to {out_vocab_file}"""		)

												return (out_vocab_file,)


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       List[int]					,	UpperCamelCase__			:       Optional[List[int]] = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[int]           =							token_ids_a + [self.eos_token_id]
												if token_ids_a is None:
																		return self.prefix_tokens + token_ids_a
												else:
																		SCREAMING_SNAKE_CASE							:					Tuple           =							token_ids_a + [self.eos_token_id]
																		return self.prefix_tokens + token_ids_a + token_ids_a


						def  __A ( self			:       Any					,	UpperCamelCase__			:       List[int]					,	UpperCamelCase__			:       Optional[List[int]] = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[self.eos_token_id]

												if token_ids_a is None:
																		return len(token_ids_a + eos		) * [0]
												return len(token_ids_a + eos + token_ids_a + eos		) * [0]


						def  __A ( self			:       Dict		):





												'''simple docstring'''





												return list(
												    set(filter(lambda UpperCamelCase__		: bool(re.search(r'''<extra_id_\d+>'''					,	UpperCamelCase__		)		) is not None					,	self.additional_special_tokens		)		)		)



						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





												return [self.convert_tokens_to_ids(UpperCamelCase__		) for token in self.get_sentinel_tokens()]





