


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


a					  =     logging.get_logger(__name__)

a					  =     '▁'

a					  =     {'vocab_file': 'sentencepiece.bpe.model'}

a					  =     {
    'vocab_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

a					  =     {
    'facebook/mbart-large-50-one-to-many-mmt': 1_0_2_4,
}

# fmt: off
a					  =     ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']







class        UpperCamelCase__   (	__magic_name__   ):
					__SCREAMING_SNAKE_CASE       :  List[Any]						=   VOCAB_FILES_NAMES
					__SCREAMING_SNAKE_CASE       :  int						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__SCREAMING_SNAKE_CASE       :  Union[str, Any]						=   PRETRAINED_VOCAB_FILES_MAP
					__SCREAMING_SNAKE_CASE       :  str						=   ['input_ids', 'attention_mask']

					__SCREAMING_SNAKE_CASE       :  List[int]						=   []
					__SCREAMING_SNAKE_CASE       :  List[int]						=   []

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






										'''simple docstring'''






										lowercase_  							=			AddedToken(UpperCamelCase__							,					lstrip=UpperCamelCase__							,					rstrip=UpperCamelCase__       ) if isinstance(UpperCamelCase__							,					UpperCamelCase__       ) else mask_token

										lowercase_  							=			{} if sp_model_kwargs is None else sp_model_kwargs

										lowercase_  							=			kwargs.get("""additional_special_tokens"""							,					[]       )
										kwargs["additional_special_tokens"] += [
										    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
										]

										super().__init__(
										    src_lang=UpperCamelCase__							,					tgt_lang=UpperCamelCase__							,					eos_token=UpperCamelCase__							,					unk_token=UpperCamelCase__							,					sep_token=UpperCamelCase__							,					cls_token=UpperCamelCase__							,					pad_token=UpperCamelCase__							,					mask_token=UpperCamelCase__							,					sp_model_kwargs=self.sp_model_kwargs							,					**UpperCamelCase__							,					)

										lowercase_  							=			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
										self.sp_model.Load(str(UpperCamelCase__       )       )
										lowercase_  							=			vocab_file

										# Original fairseq vocab and spm vocab must be "aligned":
										# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
										# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
										# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
										# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

										# Mimic fairseq token-to-id alignment for the first 4 token
										lowercase_  							=			{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

										# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
										lowercase_  							=			1

										lowercase_  							=			len(self.sp_model       )
										lowercase_  							=			{
										    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(UpperCamelCase__       )
										}
										lowercase_  							=			{v: k for k, v in self.lang_code_to_id.items()}
										lowercase_  							=			len(self.sp_model       ) + len(self.lang_code_to_id       ) + self.fairseq_offset

										self.fairseq_tokens_to_ids.update(self.lang_code_to_id       )
										lowercase_  							=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}

										lowercase_  							=			src_lang if src_lang is not None else """en_XX"""
										lowercase_  							=			self.lang_code_to_id[self._src_lang]
										lowercase_  							=			tgt_lang
										self.set_src_lang_special_tokens(self._src_lang       )

					@property
					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






										'''simple docstring'''






										return len(self.sp_model       ) + len(self.lang_code_to_id       ) + self.fairseq_offset + 1  # Plus 1 for the mask token

					@property
					def   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										return self._src_lang

					@src_lang.setter
					def   UpperCAmelCase__       (     self       :   List[Any]							,					UpperCamelCase__       :   str       ):






										'''simple docstring'''






										lowercase_  							=			new_src_lang
										self.set_src_lang_special_tokens(self._src_lang       )

					def __getstate__(     self       :   Dict       ):






										'''simple docstring'''






										lowercase_  							=			self.__dict__.copy()
										lowercase_  							=			None
										return state

					def __setstate__(     self       :   Union[str, Any]							,					UpperCamelCase__       :   Dict       ):






										'''simple docstring'''






										lowercase_  							=			d

										# for backward compatibility
										if not hasattr(self							,					"""sp_model_kwargs"""       ):
															lowercase_  							=			{}

										lowercase_  							=			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
										self.sp_model.Load(self.vocab_file       )

					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										lowercase_  							=			{self.convert_ids_to_tokens(UpperCamelCase__       ): i for i in range(self.vocab_size       )}
										vocab.update(self.added_tokens_encoder       )
										return vocab

					def   UpperCAmelCase__       (     self       :   List[Any]							,					UpperCamelCase__       :   str       ):






										'''simple docstring'''






										return self.sp_model.encode(UpperCamelCase__							,					out_type=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   List[Any]							,					UpperCamelCase__       :   str       ):






										'''simple docstring'''






										if token in self.fairseq_tokens_to_ids:
															return self.fairseq_tokens_to_ids[token]
										lowercase_  							=			self.sp_model.PieceToId(UpperCamelCase__       )

										# Need to return unknown token if the SP model returned 0
										return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

					def   UpperCAmelCase__       (     self       :   Dict							,					UpperCamelCase__       :   int       ):






										'''simple docstring'''






										if index in self.fairseq_ids_to_tokens:
															return self.fairseq_ids_to_tokens[index]
										return self.sp_model.IdToPiece(index - self.fairseq_offset       )

					def   UpperCAmelCase__       (     self       :   Union[str, Any]							,					UpperCamelCase__       :   Union[str, Any]       ):






										'''simple docstring'''






										lowercase_  							=			[]
										lowercase_  							=			""""""
										lowercase_  							=			False
										for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																				if not prev_is_special:
																									out_string += " "
																				out_string += self.sp_model.decode(UpperCamelCase__       ) + token
																				lowercase_  							=			True
																				lowercase_  							=			[]
															else:
																				current_sub_tokens.append(UpperCamelCase__       )
																				lowercase_  							=			False
										out_string += self.sp_model.decode(UpperCamelCase__       )
										return out_string.strip()

					def   UpperCAmelCase__       (     self       :   List[str]							,					UpperCamelCase__       :   str							,					UpperCamelCase__       :   Optional[str] = None       ):






										'''simple docstring'''






										if not os.path.isdir(UpperCamelCase__       ):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
															return
										lowercase_  							=			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__       ) and os.path.isfile(self.vocab_file       ):
															copyfile(self.vocab_file							,					UpperCamelCase__       )
										elif not os.path.isfile(self.vocab_file       ):
															with open(UpperCamelCase__							,					"""wb"""       ) as fi:
																				lowercase_  							=			self.sp_model.serialized_model_proto()
																				fi.write(UpperCamelCase__       )

										return (out_vocab_file,)

					def   UpperCAmelCase__       (     self       :   Union[str, Any]							,					UpperCamelCase__       :   List[int]							,					UpperCamelCase__       :   Optional[List[int]] = None							,					UpperCamelCase__       :   bool = False       ):






										'''simple docstring'''






										if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=UpperCamelCase__							,					token_ids_a=UpperCamelCase__							,					already_has_special_tokens=UpperCamelCase__       )

										lowercase_  							=			[1] * len(self.prefix_tokens       )
										lowercase_  							=			[1] * len(self.suffix_tokens       )
										if token_ids_a is None:
															return prefix_ones + ([0] * len(UpperCamelCase__       )) + suffix_ones
										return prefix_ones + ([0] * len(UpperCamelCase__       )) + ([0] * len(UpperCamelCase__       )) + suffix_ones

					def   UpperCAmelCase__       (     self       :   Dict							,					UpperCamelCase__       :   List[int]							,					UpperCamelCase__       :   Optional[List[int]] = None       ):






										'''simple docstring'''






										if token_ids_a is None:
															return self.prefix_tokens + token_ids_a + self.suffix_tokens
										# We don't expect to process pairs, but leave the pair logic for API consistency
										return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens

					def   UpperCAmelCase__       (     self       :   int							,					UpperCamelCase__       :   List[Any]							,					UpperCamelCase__       :   str							,					UpperCamelCase__       :   Optional[str]							,					UpperCamelCase__       :   Optional[str]							,					**UpperCamelCase__       :   Dict       ):






										'''simple docstring'''






										if src_lang is None or tgt_lang is None:
															raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""       )
										lowercase_  							=			src_lang
										lowercase_  							=			self(UpperCamelCase__							,					add_special_tokens=UpperCamelCase__							,					return_tensors=UpperCamelCase__							,					**UpperCamelCase__       )
										lowercase_  							=			self.convert_tokens_to_ids(UpperCamelCase__       )
										lowercase_  							=			tgt_lang_id
										return inputs

					def   UpperCAmelCase__       (     self       :   Union[str, Any]							,					UpperCamelCase__       :   List[str]							,					UpperCamelCase__       :   str = "en_XX"							,					UpperCamelCase__       :   Optional[List[str]] = None							,					UpperCamelCase__       :   str = "ro_RO"							,					**UpperCamelCase__       :   Optional[int]							,					):






										'''simple docstring'''






										lowercase_  							=			src_lang
										lowercase_  							=			tgt_lang
										return super().prepare_seqaseq_batch(UpperCamelCase__							,					UpperCamelCase__							,					**UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   List[str]       ):






										'''simple docstring'''






										return self.set_src_lang_special_tokens(self.src_lang       )

					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






										'''simple docstring'''






										return self.set_tgt_lang_special_tokens(self.tgt_lang       )

					def   UpperCAmelCase__       (     self       :   str							,					UpperCamelCase__       :   str       ):






										'''simple docstring'''






										lowercase_  							=			self.lang_code_to_id[src_lang]
										lowercase_  							=			[self.cur_lang_code_id]
										lowercase_  							=			[self.eos_token_id]







					def   UpperCAmelCase__       (     self       :   int							,					UpperCamelCase__       :   str       ):






										'''simple docstring'''






										lowercase_  							=			self.lang_code_to_id[tgt_lang]
										lowercase_  							=			[self.cur_lang_code_id]
										lowercase_  							=			[self.eos_token_id]






import cva
import numpy as np







class        UpperCamelCase__   :

					def __init__(     self       :   List[str]							,					UpperCamelCase__       :   float							,					UpperCamelCase__       :   int       ):






										'''simple docstring'''






										if k in (0.04, 0.06):
															lowercase_  							=			k
															lowercase_  							=			window_size
										else:
															raise ValueError("""invalid k value"""       )

					def __str__(     self       :   Optional[int]       ):






										'''simple docstring'''






										return str(self.k       )







					def   UpperCAmelCase__       (     self       :   Optional[Any]							,					UpperCamelCase__       :   str       ):






										'''simple docstring'''






										lowercase_  							=			cva.imread(UpperCamelCase__							,					0       )
										lowercase_							,					lowercase_  							=			img.shape
										lowercase_  							=			[]
										lowercase_  							=			img.copy()
										lowercase_  							=			cva.cvtColor(UpperCamelCase__							,					cva.COLOR_GRAY2RGB       )
										lowercase_							,					lowercase_  							=			np.gradient(UpperCamelCase__       )
										lowercase_  							=			dx**2
										lowercase_  							=			dy**2
										lowercase_  							=			dx * dy
										lowercase_  							=			0.04
										lowercase_  							=			self.window_size // 2
										for y in range(UpperCamelCase__							,					h - offset       ):
															for x in range(UpperCamelCase__							,					w - offset       ):
																				lowercase_  							=			ixx[
																				    y - offset : y + offset + 1, x - offset : x + offset + 1
																				].sum()
																				lowercase_  							=			iyy[
																				    y - offset : y + offset + 1, x - offset : x + offset + 1
																				].sum()
																				lowercase_  							=			ixy[
																				    y - offset : y + offset + 1, x - offset : x + offset + 1
																				].sum()

																				lowercase_  							=			(wxx * wyy) - (wxy**2)
																				lowercase_  							=			wxx + wyy
																				lowercase_  							=			det - k * (trace**2)
																				# Can change the value
																				if r > 0.5:
																									corner_list.append([x, y, r]       )
																									color_img.itemset((y, x, 0)							,					0       )
																									color_img.itemset((y, x, 1)							,					0       )
																									color_img.itemset((y, x, 2)							,					255       )
										return color_img, corner_list


if __name__ == "__main__":
							a					  =     HarrisCorner(0.04, 3)
							a  ,					a					  =     edge_detect.detect('path_to_image')
							cva.imwrite('detect.png', color_img)


