


import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


lowerCamelCase						=		logging.get_logger(__name__)

lowerCamelCase						=		'''▁'''

lowerCamelCase						=		{
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowerCamelCase						=		{
    '''vocab_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json''',
    },
    '''spm_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_config_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json''',
    },
}

lowerCamelCase						=		{
    '''facebook/m2m100_418M''': 1024,
}

# fmt: off
lowerCamelCase						=		{
    '''m2m100''': ['''af''', '''am''', '''ar''', '''ast''', '''az''', '''ba''', '''be''', '''bg''', '''bn''', '''br''', '''bs''', '''ca''', '''ceb''', '''cs''', '''cy''', '''da''', '''de''', '''el''', '''en''', '''es''', '''et''', '''fa''', '''ff''', '''fi''', '''fr''', '''fy''', '''ga''', '''gd''', '''gl''', '''gu''', '''ha''', '''he''', '''hi''', '''hr''', '''ht''', '''hu''', '''hy''', '''id''', '''ig''', '''ilo''', '''is''', '''it''', '''ja''', '''jv''', '''ka''', '''kk''', '''km''', '''kn''', '''ko''', '''lb''', '''lg''', '''ln''', '''lo''', '''lt''', '''lv''', '''mg''', '''mk''', '''ml''', '''mn''', '''mr''', '''ms''', '''my''', '''ne''', '''nl''', '''no''', '''ns''', '''oc''', '''or''', '''pa''', '''pl''', '''ps''', '''pt''', '''ro''', '''ru''', '''sd''', '''si''', '''sk''', '''sl''', '''so''', '''sq''', '''sr''', '''ss''', '''su''', '''sv''', '''sw''', '''ta''', '''th''', '''tl''', '''tn''', '''tr''', '''uk''', '''ur''', '''uz''', '''vi''', '''wo''', '''xh''', '''yi''', '''yo''', '''zh''', '''zu'''],
    '''wmt21''': ['''en''', '''ha''', '''is''', '''ja''', '''cs''', '''ru''', '''zh''', '''de''']
}





class  _a							(    _lowercase):
	_a  :  List[str]				   =							VOCAB_FILES_NAMES
	_a  :  List[Any]				   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_a  :  Union[str, Any]				   =							PRETRAINED_VOCAB_FILES_MAP
	_a  :  str				   =							['''input_ids''', '''attention_mask''']

	_a  :  List[int]				   =							[]
	_a  :  List[int]				   =							[]




	def __init__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Any		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Optional[int]=None		, _SCREAMING_SNAKE_CASE						:		str=None		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]="<s>"		, _SCREAMING_SNAKE_CASE						:		str="</s>"		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]="</s>"		, _SCREAMING_SNAKE_CASE						:		Dict="<pad>"		, _SCREAMING_SNAKE_CASE						:		List[str]="<unk>"		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]="m2m100"		, _SCREAMING_SNAKE_CASE						:		Optional[Dict[str, Any]] = None		, _SCREAMING_SNAKE_CASE						:		int=8		, **_SCREAMING_SNAKE_CASE						:		Optional[int]		, )->	None:
			lowerCAmelCase__       :      Tuple   					= {} if sp_model_kwargs is None else sp_model_kwargs

			lowerCAmelCase__       :      Optional[int]   					= language_codes
			lowerCAmelCase__       :      int   					= FAIRSEQ_LANGUAGE_CODES[language_codes]
			lowerCAmelCase__       :      Union[str, Any]   					= {lang_code: F'__{lang_code}__' for lang_code in fairseq_language_code}

			lowerCAmelCase__       :      Optional[Any]   					= kwargs.get('''additional_special_tokens'''		, []		)
			kwargs["additional_special_tokens"] += [
			    self.get_lang_token(_SCREAMING_SNAKE_CASE		)
			    for lang_code in fairseq_language_code
			    if self.get_lang_token(_SCREAMING_SNAKE_CASE		) not in kwargs["additional_special_tokens"]
			]

			super().__init__(
			    src_lang=_SCREAMING_SNAKE_CASE		, tgt_lang=_SCREAMING_SNAKE_CASE		, bos_token=_SCREAMING_SNAKE_CASE		, eos_token=_SCREAMING_SNAKE_CASE		, sep_token=_SCREAMING_SNAKE_CASE		, unk_token=_SCREAMING_SNAKE_CASE		, pad_token=_SCREAMING_SNAKE_CASE		, language_codes=_SCREAMING_SNAKE_CASE		, sp_model_kwargs=self.sp_model_kwargs		, num_madeup_words=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		, )

			lowerCAmelCase__       :      Optional[Any]   					= vocab_file
			lowerCAmelCase__       :      Optional[int]   					= load_json(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      List[Any]   					= {v: k for k, v in self.encoder.items()}
			lowerCAmelCase__       :      List[str]   					= spm_file
			lowerCAmelCase__       :      Dict   					= load_spm(_SCREAMING_SNAKE_CASE		, self.sp_model_kwargs		)

			lowerCAmelCase__       :      Union[str, Any]   					= len(self.encoder		)

			lowerCAmelCase__       :      List[Any]   					= {
			    self.get_lang_token(_SCREAMING_SNAKE_CASE		): self.encoder_size + i for i, lang_code in enumerate(_SCREAMING_SNAKE_CASE		)
			}
			lowerCAmelCase__       :      List[str]   					= {lang_code: self.encoder_size + i for i, lang_code in enumerate(_SCREAMING_SNAKE_CASE		)}
			lowerCAmelCase__       :      str   					= {v: k for k, v in self.lang_token_to_id.items()}

			lowerCAmelCase__       :      int   					= src_lang if src_lang is not None else '''en'''
			lowerCAmelCase__       :      List[Any]   					= tgt_lang
			lowerCAmelCase__       :      int   					= self.get_lang_id(self._src_lang		)
			self.set_src_lang_special_tokens(self._src_lang		)

			lowerCAmelCase__       :      Optional[Any]   					= num_madeup_words




	@property
	def 							UpperCAmelCase__( self						:		List[Any]		)->	int:
			return len(self.encoder		) + len(self.lang_token_to_id		)




	@property
	def 							UpperCAmelCase__( self						:		Tuple		)->	str:
			return self._src_lang




	@src_lang.setter
	def 							UpperCAmelCase__( self						:		Tuple		, _SCREAMING_SNAKE_CASE						:		str		)->	None:
			lowerCAmelCase__       :      str   					= new_src_lang
			self.set_src_lang_special_tokens(self._src_lang		)




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		str		)->	List[str]:
			return self.sp_model.encode(_SCREAMING_SNAKE_CASE		, out_type=_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		Tuple		, _SCREAMING_SNAKE_CASE						:		Any		)->	Optional[Any]:
			if token in self.lang_token_to_id:
					return self.lang_token_to_id[token]
			return self.encoder.get(_SCREAMING_SNAKE_CASE		, self.encoder[self.unk_token]		)




	def 							UpperCAmelCase__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		int		)->	str:
			if index in self.id_to_lang_token:
					return self.id_to_lang_token[index]
			return self.decoder.get(_SCREAMING_SNAKE_CASE		, self.unk_token		)




	def 							UpperCAmelCase__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		str		)->	Union[str, Any]:
			lowerCAmelCase__       :      str   					= []
			lowerCAmelCase__       :      str   					= ''''''
			for token in tokens:
					# make sure that special tokens are not decoded using sentencepiece model
					if token in self.all_special_tokens:
							out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE		) + token
							lowerCAmelCase__       :      List[str]   					= []
					else:
							current_sub_tokens.append(_SCREAMING_SNAKE_CASE		)
			out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE		)
			return out_string.strip()




	def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		List[int]		, _SCREAMING_SNAKE_CASE						:		Optional[List[int]] = None		, _SCREAMING_SNAKE_CASE						:		bool = False		)->	List[int]:

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

			lowerCAmelCase__       :      int   					= [1] * len(self.prefix_tokens		)
			lowerCAmelCase__       :      Tuple   					= [1] * len(self.suffix_tokens		)
			if token_ids_a is None:
					return prefix_ones + ([0] * len(_SCREAMING_SNAKE_CASE		)) + suffix_ones
			return prefix_ones + ([0] * len(_SCREAMING_SNAKE_CASE		)) + ([0] * len(_SCREAMING_SNAKE_CASE		)) + suffix_ones




	def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		List[int]		, _SCREAMING_SNAKE_CASE						:		Optional[List[int]] = None		)->	List[int]:
			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						:		List[Any]		)->	Dict:
			lowerCAmelCase__       :      Optional[int]   					= {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE		): i for i in range(self.vocab_size		)}
			vocab.update(self.added_tokens_encoder		)
			return vocab




	def __getstate__( self						:		str		)->	Dict:
			lowerCAmelCase__       :      Tuple   					= self.__dict__.copy()
			lowerCAmelCase__       :      Any   					= None
			return state




	def __setstate__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Dict		)->	None:
			lowerCAmelCase__       :      Dict   					= d

			# for backward compatibility
			if not hasattr(self		, '''sp_model_kwargs'''		):
					lowerCAmelCase__       :      Optional[int]   					= {}

			lowerCAmelCase__       :      List[Any]   					= load_spm(self.spm_file		, self.sp_model_kwargs		)




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		str		, _SCREAMING_SNAKE_CASE						:		Optional[str] = None		)->	Tuple[str]:
			lowerCAmelCase__       :      int   					= Path(_SCREAMING_SNAKE_CASE		)
			if not save_dir.is_dir():
					raise OSError(F'{save_directory} should be a directory'		)
			lowerCAmelCase__       :      List[str]   					= save_dir / (
			    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
			)
			lowerCAmelCase__       :      Any   					= save_dir / (
			    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
			)

			save_json(self.encoder		, _SCREAMING_SNAKE_CASE		)

			if os.path.abspath(self.spm_file		) != os.path.abspath(_SCREAMING_SNAKE_CASE		) and os.path.isfile(self.spm_file		):
					copyfile(self.spm_file		, _SCREAMING_SNAKE_CASE		)
			elif not os.path.isfile(self.spm_file		):
					with open(_SCREAMING_SNAKE_CASE		, '''wb'''		) as fi:
							lowerCAmelCase__       :      List[str]   					= self.sp_model.serialized_model_proto()
							fi.write(_SCREAMING_SNAKE_CASE		)

			return (str(_SCREAMING_SNAKE_CASE		), str(_SCREAMING_SNAKE_CASE		))




	def 							UpperCAmelCase__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		List[str]		, _SCREAMING_SNAKE_CASE						:		str = "en"		, _SCREAMING_SNAKE_CASE						:		Optional[List[str]] = None		, _SCREAMING_SNAKE_CASE						:		str = "ro"		, **_SCREAMING_SNAKE_CASE						:		str		, )->	BatchEncoding:
			lowerCAmelCase__       :      Dict   					= src_lang
			lowerCAmelCase__       :      Tuple   					= tgt_lang
			self.set_src_lang_special_tokens(self.src_lang		)
			return super().prepare_seqaseq_batch(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		Dict		, _SCREAMING_SNAKE_CASE						:		Any		, _SCREAMING_SNAKE_CASE						:		Optional[str]		, _SCREAMING_SNAKE_CASE						:		Optional[str]		, **_SCREAMING_SNAKE_CASE						:		str		)->	int:
			if src_lang is None or tgt_lang is None:
					raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''		)
			lowerCAmelCase__       :      Union[str, Any]   					= src_lang
			lowerCAmelCase__       :      Tuple   					= self(_SCREAMING_SNAKE_CASE		, add_special_tokens=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Tuple   					= self.get_lang_id(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      List[str]   					= tgt_lang_id
			return inputs




	def 							UpperCAmelCase__( self						:		Dict		)->	Union[str, Any]:
			self.set_src_lang_special_tokens(self.src_lang		)




	def 							UpperCAmelCase__( self						:		Optional[int]		)->	List[str]:
			self.set_tgt_lang_special_tokens(self.tgt_lang		)




	def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		str		)->	None:
			lowerCAmelCase__       :      Union[str, Any]   					= self.get_lang_token(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      List[Any]   					= self.lang_token_to_id[lang_token]
			lowerCAmelCase__       :      Any   					= [self.cur_lang_id]
			lowerCAmelCase__       :      Optional[int]   					= [self.eos_token_id]




	def 							UpperCAmelCase__( self						:		int		, _SCREAMING_SNAKE_CASE						:		str		)->	None:
			lowerCAmelCase__       :      Dict   					= self.get_lang_token(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Tuple   					= self.lang_token_to_id[lang_token]
			lowerCAmelCase__       :      Dict   					= [self.cur_lang_id]
			lowerCAmelCase__       :      Tuple   					= [self.eos_token_id]




	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		str		)->	str:
			return self.lang_code_to_token[lang]




	def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		str		)->	int:
			lowerCAmelCase__       :      List[str]   					= self.get_lang_token(_SCREAMING_SNAKE_CASE		)
			return self.lang_token_to_id[lang_token]






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      str   					= sentencepiece.SentencePieceProcessor(**_a	)
		spm.Load(str(_a	)	)
		return spm






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		with open(_a     ,       '''r'''	) as f:
				return json.load(_a	)






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		with open(_a     ,       '''w'''	) as f:
				json.dump(_a     ,       _a     ,       indent=2	)








from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[Any]   					= k_size // 2
		lowerCAmelCase__					,     lowerCAmelCase__       :      Union[str, Any]   					= mgrid[0 - center : k_size - center, 0 - center : k_size - center]
		lowerCAmelCase__       :      str   					= 1 / (2 * pi * sigma) * exp(-(square(_a	) + square(_a	)) / (2 * square(_a	))	)
		return g






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__					,     lowerCAmelCase__       :      int   					= image.shape[0], image.shape[1]
		# dst image height and width
		lowerCAmelCase__       :      Any   					= height - k_size + 1
		lowerCAmelCase__       :      Tuple   					= width - k_size + 1

		# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
		lowerCAmelCase__       :      int   					= zeros((dst_height * dst_width, k_size * k_size)	)
		lowerCAmelCase__       :      List[str]   					= 0
		for i, j in product(range(_a	)     ,       range(_a	)	):
				lowerCAmelCase__       :      Union[str, Any]   					= ravel(image[i : i + k_size, j : j + k_size]	)
				lowerCAmelCase__       :      List[Any]   					= window
				row += 1

		#  turn the kernel into shape(k*k, 1)
		lowerCAmelCase__       :      List[Any]   					= gen_gaussian_kernel(_a     ,       _a	)
		lowerCAmelCase__       :      str   					= ravel(_a	)

		# reshape and get the dst image
		lowerCAmelCase__       :      int   					= dot(_a     ,       _a	).reshape(_a     ,       _a	).astype(_a	)

		return dst


if __name__ == "__main__":
		# read original image
		lowerCamelCase						=		imread(R'''../image_data/lena.jpg''')
		# turn image in gray scale value
		lowerCamelCase						=		cvtColor(img, COLOR_BGR2GRAY)

		# get values with two different mask size
		lowerCamelCase						=		gaussian_filter(gray, 3, sigma=1)
		lowerCamelCase						=		gaussian_filter(gray, 5, sigma=0.8)

		# show result images
		imshow('''gaussian filter with 3x3 mask''', gaussianaxa)
		imshow('''gaussian filter with 5x5 mask''', gaussianaxa)
		waitKey()




