

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

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


snake_case__     							= logging.get_logger(__name__)

snake_case__     							= {
    """source_spm""": """source.spm""",
    """target_spm""": """target.spm""",
    """vocab""": """vocab.json""",
    """target_vocab_file""": """target_vocab.json""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

snake_case__     							= {
    """source_spm""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm"""
    },
    """target_spm""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm"""
    },
    """vocab""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json"""
    },
    """tokenizer_config_file""": {
        """Helsinki-NLP/opus-mt-en-de""": (
            """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json"""
        )
    },
}

snake_case__     							= {"""Helsinki-NLP/opus-mt-en-de""": 512}
snake_case__     							= {}



class     snake_case__  (      UpperCAmelCase__  ):
				__lowerCAmelCase		=      VOCAB_FILES_NAMES
				__lowerCAmelCase		=      PRETRAINED_VOCAB_FILES_MAP
				__lowerCAmelCase		=      PRETRAINED_INIT_CONFIGURATION
				__lowerCAmelCase		=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCAmelCase		=      ['input_ids', 'attention_mask']
				__lowerCAmelCase		=      re.compile('''>>.+<<'''  )  # type: re.Pattern
				def __init__(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase="<unk>"     ,      _UpperCAmelCase="</s>"     ,      _UpperCAmelCase="<pad>"     ,      _UpperCAmelCase=512     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase=False     ,      **_UpperCAmelCase     ,      ):




										"""simple docstring"""




										__magic_name__				:							Union[str, Any]				=      {} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
										    source_lang=lowerCamelCase__     ,      target_lang=lowerCamelCase__     ,      unk_token=lowerCamelCase__     ,      eos_token=lowerCamelCase__     ,      pad_token=lowerCamelCase__     ,      model_max_length=lowerCamelCase__     ,      sp_model_kwargs=self.sp_model_kwargs     ,      target_vocab_file=lowerCamelCase__     ,      separate_vocabs=lowerCamelCase__     ,      **lowerCamelCase__     ,      )
										assert Path(lowerCamelCase__      ).exists(), F"cannot find spm source {source_spm}"

										__magic_name__				:							Optional[int]				=      separate_vocabs
										__magic_name__				:							Dict				=      load_json(lowerCamelCase__      )
										if self.unk_token not in self.encoder:
																raise KeyError('<unk> token must be in vocab'      )
										assert self.pad_token in self.encoder

										if separate_vocabs:
																__magic_name__				:							Any				=      load_json(lowerCamelCase__      )
																__magic_name__				:							Tuple				=      {v: k for k, v in self.target_encoder.items()}
																__magic_name__				:							List[Any]				=      []
										else:
																__magic_name__				:							int				=      {v: k for k, v in self.encoder.items()}
																__magic_name__				:							list				=      [k for k in self.encoder if k.startswith('>>'      ) and k.endswith('<<'      )]

										__magic_name__				:							List[Any]				=      source_lang
										__magic_name__				:							int				=      target_lang
										__magic_name__				:							List[str]				=      [source_spm, target_spm]

										# load SentencePiece model for pre-processing
										__magic_name__				:							int				=      load_spm(lowerCamelCase__     ,      self.sp_model_kwargs      )
										__magic_name__				:							str				=      load_spm(lowerCamelCase__     ,      self.sp_model_kwargs      )
										__magic_name__				:							Optional[Any]				=      self.spm_source
										__magic_name__				:							Tuple				=      self.encoder

										# Multilingual target side: default to using first supported language code.

										self._setup_normalizer()
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										try:
																from sacremoses import MosesPunctNormalizer

																__magic_name__				:							str				=      MosesPunctNormalizer(self.source_lang      ).normalize
										except (ImportError, FileNotFoundError):
																warnings.warn('Recommended: pip install sacremoses.'      )
																__magic_name__				:							Any				=      lambda _UpperCAmelCase      : x
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										return self.punc_normalizer(lowerCamelCase__      ) if x else ""
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										return self.current_encoder.get(lowerCamelCase__     ,      self.current_encoder[self.unk_token]      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Dict				=      self.language_code_re.match(lowerCamelCase__      )
										__magic_name__				:							list				=      [match.group(0      )] if match else []
										return code, self.language_code_re.sub(''     ,      lowerCamelCase__      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							List[Any]				=      self.remove_language_code(lowerCamelCase__      )
										__magic_name__				:							Any				=      self.current_spm.encode(lowerCamelCase__     ,      out_type=lowerCamelCase__      )
										return code + pieces
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										return self.decoder.get(lowerCamelCase__     ,      self.unk_token      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      **_UpperCAmelCase      ):




										"""simple docstring"""




										return super().batch_decode(lowerCamelCase__     ,      **lowerCamelCase__      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      **_UpperCAmelCase      ):




										"""simple docstring"""




										return super().decode(lowerCamelCase__     ,      **lowerCamelCase__      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							List[str]				=      self.spm_source if self._decode_use_source_tokenizer else self.spm_target
										__magic_name__				:							List[str]				=      []
										__magic_name__				:							List[Any]				=      ""
										for token in tokens:
																# make sure that special tokens are not decoded using sentencepiece model
																if token in self.all_special_tokens:
																						out_string += sp_model.decode_pieces(lowerCamelCase__      ) + token + " "
																						__magic_name__				:							Union[str, Any]				=      []
																else:
																						current_sub_tokens.append(lowerCamelCase__      )
										out_string += sp_model.decode_pieces(lowerCamelCase__      )
										return out_string.strip()
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase=None      ):




										"""simple docstring"""




										if token_ids_a is None:
																return token_ids_a + [self.eos_token_id]
										# We don't expect to process pairs, but leave the pair logic for API consistency
										return token_ids_a + token_ids_a + [self.eos_token_id]
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Union[str, Any]				=      self.spm_source
										__magic_name__				:							Tuple				=      self.encoder
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							List[Any]				=      self.spm_target
										if self.separate_vocabs:
																__magic_name__				:							Tuple				=      self.target_encoder
				@property
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										return len(self.encoder      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase = None      ):




										"""simple docstring"""




										if not os.path.isdir(lowerCamelCase__      ):
																logger.error(F"Vocabulary path ({save_directory}) should be a directory"      )
																return
										__magic_name__				:							Tuple				=      []

										if self.separate_vocabs:
																__magic_name__				:							int				=      os.path.join(
																    lowerCamelCase__     ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab']     ,      )
																__magic_name__				:							Dict				=      os.path.join(
																    lowerCamelCase__     ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['target_vocab_file']     ,      )
																save_json(self.encoder     ,      lowerCamelCase__      )
																save_json(self.target_encoder     ,      lowerCamelCase__      )
																saved_files.append(lowerCamelCase__      )
																saved_files.append(lowerCamelCase__      )
										else:
																__magic_name__				:							int				=      os.path.join(
																    lowerCamelCase__     ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab']      )
																save_json(self.encoder     ,      lowerCamelCase__      )
																saved_files.append(lowerCamelCase__      )

										for spm_save_filename, spm_orig_path, spm_model in zip(
										    [VOCAB_FILES_NAMES['source_spm'], VOCAB_FILES_NAMES['target_spm']]     ,      self.spm_files     ,      [self.spm_source, self.spm_target]     ,      ):
																__magic_name__				:							List[str]				=      os.path.join(
																    lowerCamelCase__     ,      (filename_prefix + '-' if filename_prefix else '') + spm_save_filename      )
																if os.path.abspath(lowerCamelCase__      ) != os.path.abspath(lowerCamelCase__      ) and os.path.isfile(lowerCamelCase__      ):
																						copyfile(lowerCamelCase__     ,      lowerCamelCase__      )
																						saved_files.append(lowerCamelCase__      )
																elif not os.path.isfile(lowerCamelCase__      ):
																						with open(lowerCamelCase__     ,      'wb'      ) as fi:
																												__magic_name__				:							str				=      spm_model.serialized_model_proto()
																												fi.write(lowerCamelCase__      )
																						saved_files.append(lowerCamelCase__      )

										return tuple(lowerCamelCase__      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										return self.get_src_vocab()
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										return dict(self.encoder     ,      **self.added_tokens_encoder      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										return dict(self.target_encoder     ,      **self.added_tokens_decoder      )
				def __getstate__(  self      ):




										"""simple docstring"""




										__magic_name__				:							List[Any]				=      self.__dict__.copy()
										state.update(
										    {k: None for k in ['spm_source', 'spm_target', 'current_spm', 'punc_normalizer', 'target_vocab_file']}      )
										return state
				def __setstate__(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Union[str, Any]				=      d

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

										__magic_name__				:							Tuple				=      (load_spm(lowerCamelCase__     ,      self.sp_model_kwargs      ) for f in self.spm_files)
										__magic_name__				:							Any				=      self.spm_source
										self._setup_normalizer()
				def  UpperCamelCase_						(  self     ,      *_UpperCAmelCase     ,      **_UpperCAmelCase      ):




										"""simple docstring"""




										return 1
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Union[str, Any]				=      set(self.all_special_ids      )  # call it once instead of inside list comp
										all_special_ids.remove(self.unk_token_id      )  # <unk> is only sometimes special
										return [1 if x in all_special_ids else 0 for x in seq]





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = False      ):




										"""simple docstring"""




										if already_has_special_tokens:
																return self._special_token_mask(lowerCamelCase__      )
										elif token_ids_a is None:
																return self._special_token_mask(lowerCamelCase__      ) + [1]
										else:
																return self._special_token_mask(token_ids_a + token_ids_a      ) + [1]






def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Tuple      ,						lowercase__  :Optional[Any]				)       ->  Optional[int]:
						__magic_name__				:							Dict				=      sentencepiece.SentencePieceProcessor(**lowercase__				)
						spm.Load(lowercase__				)
						return spm
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[str]      ,						lowercase__  :List[str]				)       ->  int:
						with open(lowercase__      ,						'w'				) as f:
												json.dump(lowercase__      ,						lowercase__      ,						indent=2				)

def  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[Any]				)       ->  Optional[Any]:
						with open(lowercase__      ,						'r'				) as f:
												return json.load(lowercase__				)






from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
snake_case__     							= load_iris()
iris.keys()


print(F"""Target names: \n {iris.target_names} """)
print(F"""\n Features: \n {iris.feature_names}""")

# Train set e Test set
snake_case__							,     snake_case__							,     snake_case__							,     snake_case__     							= train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

snake_case__     							= KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
snake_case__     							= [[1, 2, 1, 4], [2, 3, 4, 5]]

snake_case__     							= knn.predict(X_new)

print(
    F"""\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"""
    F""" {iris['target_names'][prediction]}"""
)


