






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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_mbart import MBartTokenizer
else:
			_UpperCAmelCase			:		List[Any]   				=    None


_UpperCAmelCase			:		str   				=    logging.get_logger(__name__)


_UpperCAmelCase			:		Union[str, Any]   				=    {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

_UpperCAmelCase			:		Tuple   				=    {
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
        "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
    },
}

_UpperCAmelCase			:		int   				=    {
    "facebook/mbart-large-en-ro": 1_024,
    "facebook/mbart-large-cc25": 1_024,
}

# fmt: off
_UpperCAmelCase			:		Any   				=    ["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"]
class  lowerCAmelCase_       (				snake_case__					):
							UpperCamelCase_		:int		      =    VOCAB_FILES_NAMES
							UpperCamelCase_		:Any		      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase_		:Tuple		      =    PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase_		:List[Any]		      =    ['input_ids', 'attention_mask']
							UpperCamelCase_		:Any		      =    MBartTokenizer

							UpperCamelCase_		:List[int]		      =    []
							UpperCamelCase_		:List[int]		      =    []




							def __init__(   self     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=None ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=None ,							SCREAMING_SNAKE_CASE_     :   List[str]="<s>" ,							SCREAMING_SNAKE_CASE_     :   Optional[int]="</s>" ,							SCREAMING_SNAKE_CASE_     :   Any="</s>" ,							SCREAMING_SNAKE_CASE_     :   Tuple="<s>" ,							SCREAMING_SNAKE_CASE_     :   str="<unk>" ,							SCREAMING_SNAKE_CASE_     :   Dict="<pad>" ,							SCREAMING_SNAKE_CASE_     :   List[Any]="<mask>" ,							SCREAMING_SNAKE_CASE_     :   str=None ,							SCREAMING_SNAKE_CASE_     :   List[str]=None ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=None ,							**SCREAMING_SNAKE_CASE_     :   Optional[Any] ,							):
											# Mask token behave like a normal word, i.e. include the space before it
											lowerCAmelCase__      			=						AddedToken(SCREAMING_SNAKE_CASE_ ,							lstrip=SCREAMING_SNAKE_CASE_ ,							rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	) else mask_token

											super().__init__(
											    vocab_file=SCREAMING_SNAKE_CASE_ ,							tokenizer_file=SCREAMING_SNAKE_CASE_ ,							bos_token=SCREAMING_SNAKE_CASE_ ,							eos_token=SCREAMING_SNAKE_CASE_ ,							sep_token=SCREAMING_SNAKE_CASE_ ,							cls_token=SCREAMING_SNAKE_CASE_ ,							unk_token=SCREAMING_SNAKE_CASE_ ,							pad_token=SCREAMING_SNAKE_CASE_ ,							mask_token=SCREAMING_SNAKE_CASE_ ,							src_lang=SCREAMING_SNAKE_CASE_ ,							tgt_lang=SCREAMING_SNAKE_CASE_ ,							additional_special_tokens=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_ ,							)

											lowerCAmelCase__      			=						vocab_file
											lowerCAmelCase__      			=						False if not self.vocab_file else True

											lowerCAmelCase__      			=						FAIRSEQ_LANGUAGE_CODES.copy()

											if additional_special_tokens is not None:
															# Only add those special tokens if they are not already there.
															_additional_special_tokens.extend(
															    [t for t in additional_special_tokens if t not in _additional_special_tokens]	)

											self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens}	)
											lowerCAmelCase__      			=						{
											    lang_code: self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_	) for lang_code in FAIRSEQ_LANGUAGE_CODES
											}

											lowerCAmelCase__      			=						src_lang if src_lang is not None else '''en_XX'''
											lowerCAmelCase__      			=						self.convert_tokens_to_ids(self._src_lang	)
											lowerCAmelCase__      			=						tgt_lang
											self.set_src_lang_special_tokens(self._src_lang	)




							@property
							def 						__snake_case					(   self     :   int	):
											return self._src_lang




							@src_lang.setter
							def 						__snake_case					(   self     :   Dict ,							SCREAMING_SNAKE_CASE_     :   str	):
											lowerCAmelCase__      			=						new_src_lang
											self.set_src_lang_special_tokens(self._src_lang	)




							def 						__snake_case					(   self     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   List[int] ,							SCREAMING_SNAKE_CASE_     :   Optional[List[int]] = None	):
											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 						__snake_case					(   self     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   List[int] ,							SCREAMING_SNAKE_CASE_     :   Optional[List[int]] = None	):
											lowerCAmelCase__      			=						[self.sep_token_id]
											lowerCAmelCase__      			=						[self.cls_token_id]

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




							def 						__snake_case					(   self     :   str ,							SCREAMING_SNAKE_CASE_     :   int ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   Optional[str] ,							SCREAMING_SNAKE_CASE_     :   Optional[str] ,							**SCREAMING_SNAKE_CASE_     :   Tuple	):
											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__      			=						src_lang
											lowerCAmelCase__      			=						self(SCREAMING_SNAKE_CASE_ ,							add_special_tokens=SCREAMING_SNAKE_CASE_ ,							return_tensors=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						tgt_lang_id
											return inputs




							def 						__snake_case					(   self     :   Dict ,							SCREAMING_SNAKE_CASE_     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   str = "en_XX" ,							SCREAMING_SNAKE_CASE_     :   Optional[List[str]] = None ,							SCREAMING_SNAKE_CASE_     :   str = "ro_RO" ,							**SCREAMING_SNAKE_CASE_     :   int ,							):
											lowerCAmelCase__      			=						src_lang
											lowerCAmelCase__      			=						tgt_lang
											return super().prepare_seqaseq_batch(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   List[Any]	):
											return self.set_src_lang_special_tokens(self.src_lang	)




							def 						__snake_case					(   self     :   str	):
											return self.set_tgt_lang_special_tokens(self.tgt_lang	)




							def 						__snake_case					(   self     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   List[Any]	):
											lowerCAmelCase__      			=						self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						[]
											lowerCAmelCase__      			=						[self.eos_token_id, self.cur_lang_code]

											lowerCAmelCase__      			=						self.convert_ids_to_tokens(self.prefix_tokens	)
											lowerCAmelCase__      			=						self.convert_ids_to_tokens(self.suffix_tokens	)

											lowerCAmelCase__      			=						processors.TemplateProcessing(
											    single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str ,							pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str ,							special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,							self.prefix_tokens + self.suffix_tokens	)	) ,							)




							def 						__snake_case					(   self     :   Dict ,							SCREAMING_SNAKE_CASE_     :   str	):
											lowerCAmelCase__      			=						self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						[]
											lowerCAmelCase__      			=						[self.eos_token_id, self.cur_lang_code]

											lowerCAmelCase__      			=						self.convert_ids_to_tokens(self.prefix_tokens	)
											lowerCAmelCase__      			=						self.convert_ids_to_tokens(self.suffix_tokens	)

											lowerCAmelCase__      			=						processors.TemplateProcessing(
											    single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str ,							pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str ,							special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,							self.prefix_tokens + self.suffix_tokens	)	) ,							)




							def 						__snake_case					(   self     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   Optional[str] = None	):
											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(SCREAMING_SNAKE_CASE_	):
															logger.error(f'Vocabulary path ({save_directory}) should be a directory.'	)
															return
											lowerCAmelCase__      			=						os.path.join(
											    SCREAMING_SNAKE_CASE_ ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

											if os.path.abspath(self.vocab_file	) != os.path.abspath(SCREAMING_SNAKE_CASE_	):
															copyfile(self.vocab_file ,							SCREAMING_SNAKE_CASE_	)

											return (out_vocab_file,)













import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class  lowerCAmelCase_       :




							def __init__(   self     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   int ,							SCREAMING_SNAKE_CASE_     :   int	):
											if dst_width < 0 or dst_height < 0:
															raise ValueError('''Destination width/height should be > 0'''	)

											lowerCAmelCase__      			=						img
											lowerCAmelCase__      			=						img.shape[1]
											lowerCAmelCase__      			=						img.shape[0]
											lowerCAmelCase__      			=						dst_width
											lowerCAmelCase__      			=						dst_height

											lowerCAmelCase__      			=						self.src_w / self.dst_w
											lowerCAmelCase__      			=						self.src_h / self.dst_h

											lowerCAmelCase__      			=						lowerCAmelCase__      			=						(
											    np.ones((self.dst_h, self.dst_w, 3) ,							np.uinta	) * 255
											)




							def 						__snake_case					(   self     :   List[str]	):
											for i in range(self.dst_h	):
															for j in range(self.dst_w	):
																			lowerCAmelCase__      			=						self.img[self.get_y(SCREAMING_SNAKE_CASE_	)][self.get_x(SCREAMING_SNAKE_CASE_	)]




							def 						__snake_case					(   self     :   Any ,							SCREAMING_SNAKE_CASE_     :   int	):
											return int(self.ratio_x * x	)




							def 						__snake_case					(   self     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   int	):
											return int(self.ratio_y * y	)


if __name__ == "__main__":
			_UpperCAmelCase				,   _UpperCAmelCase			:		List[str]   				=    800, 600
			_UpperCAmelCase			:		Tuple   				=    imread("image_data/lena.jpg", 1)
			_UpperCAmelCase			:		str   				=    NearestNeighbour(im, dst_w, dst_h)
			n.process()

			imshow(
			    F'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
			)
			waitKey(0)
			destroyAllWindows()





