


"""simple docstring"""






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


lowercase  		=			logging.get_logger(__name__)

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

lowercase  		=			{
    """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"""
        )
    },
}

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


class 			SCREAMING_SNAKE_CASE_  (   __A):




							'''simple docstring'''





							__magic_name__					:     Union[str, Any]				  =  VOCAB_FILES_NAMES
							__magic_name__					:     Tuple				  =  PRETRAINED_VOCAB_FILES_MAP
							__magic_name__					:     List[str]				  =  PRETRAINED_INIT_CONFIGURATION
							__magic_name__					:     str				  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__magic_name__					:     Optional[int]				  =  ["""input_ids""", """attention_mask"""]
							__magic_name__					:     Optional[int]				  =  re.compile('''>>.+<<''')  # type: re.Pattern




							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__=None	,					lowerCamelCase__=None	,					lowerCamelCase__=None	,					lowerCamelCase__="<unk>"	,					lowerCamelCase__="</s>"	,					lowerCamelCase__="<pad>"	,					lowerCamelCase__=512	,					lowerCamelCase__ = None	,					lowerCamelCase__=False	,					**lowerCamelCase__	,					)   ->	None:







								'''simple docstring'''




								snake_case__  :						Tuple		=      {} 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}"""

								snake_case__  :						str		=      separate_vocabs
								snake_case__  :						Optional[int]		=      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:
									snake_case__  :						str		=      load_json(lowerCamelCase__)
									snake_case__  :						int		=      {v: k for k, v in self.target_encoder.items()}
									snake_case__  :						List[Any]		=      []
								else:
									snake_case__  :						Dict		=      {v: k for k, v in self.encoder.items()}
									snake_case__  :						Optional[int]		=      [k for k in self.encoder if k.startswith(">>") and k.endswith("<<")]

								snake_case__  :						Optional[int]		=      source_lang
								snake_case__  :						List[str]		=      target_lang
								snake_case__  :						str		=      [source_spm, target_spm]

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

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

								self._setup_normalizer()




							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								try:
									from sacremoses import MosesPunctNormalizer

									snake_case__  :						Any		=      MosesPunctNormalizer(self.source_lang).normalize
								except (ImportError, FileNotFoundError):
									warnings.warn("Recommended: pip install sacremoses.")
									snake_case__  :						int		=      lambda lowerCamelCase__: x




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	str:







								'''simple docstring'''




								return self.punc_normalizer(lowerCamelCase__) if x else ""




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	Optional[int]:







								'''simple docstring'''




								return self.current_encoder.get(lowerCamelCase__	,					self.current_encoder[self.unk_token])




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	List[str]:







								'''simple docstring'''




								snake_case__  :						Optional[Any]		=      self.language_code_re.match(lowerCamelCase__)
								snake_case__  :						Dict		=      [match.group(0)] if match else []
								return code, self.language_code_re.sub(""	,					lowerCamelCase__)




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	List[str]:







								'''simple docstring'''




								snake_case__,					snake_case__  :						str		=      self.remove_language_code(lowerCamelCase__)
								snake_case__  :						List[str]		=      self.current_spm.encode(lowerCamelCase__	,					out_type=lowerCamelCase__)
								return code + pieces




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	str:







								'''simple docstring'''




								return self.decoder.get(lowerCamelCase__	,					self.unk_token)




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					**lowerCamelCase__)   ->	Optional[int]:







								'''simple docstring'''




								return super().batch_decode(lowerCamelCase__	,					**lowerCamelCase__)




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					**lowerCamelCase__)   ->	Union[str, Any]:







								'''simple docstring'''




								return super().decode(lowerCamelCase__	,					**lowerCamelCase__)




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	str:







								'''simple docstring'''




								snake_case__  :						Optional[Any]		=      self.spm_source if self._decode_use_source_tokenizer else self.spm_target
								snake_case__  :						Any		=      []
								snake_case__  :						Tuple		=      ""
								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 + " "
										snake_case__  :						int		=      []
									else:
										current_sub_tokens.append(lowerCamelCase__)
								out_string += sp_model.decode_pieces(lowerCamelCase__)
								return out_string.strip()




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__=None)   ->	List[int]:







								'''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)   ->	str:







								'''simple docstring'''




								snake_case__  :						Dict		=      self.spm_source
								snake_case__  :						int		=      self.encoder




							def        UpperCAmelCase (						self)   ->	List[str]:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      self.spm_target
								if self.separate_vocabs:
									snake_case__  :						Dict		=      self.target_encoder




							@property
							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								return len(self.encoder)




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__ = None)   ->	Tuple[str]:







								'''simple docstring'''




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

								if self.separate_vocabs:
									snake_case__  :						List[Any]		=      os.path.join(
									    lowerCamelCase__	,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]	,					)
									snake_case__  :						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:
									snake_case__  :						List[Any]		=      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]	,					):
									snake_case__  :						List[Any]		=      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:
											snake_case__  :						List[Any]		=      spm_model.serialized_model_proto()
											fi.write(lowerCamelCase__)
										saved_files.append(lowerCamelCase__)

								return tuple(lowerCamelCase__)




							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								return self.get_src_vocab()




							def        UpperCAmelCase (						self)   ->	Optional[Any]:







								'''simple docstring'''




								return dict(self.encoder	,					**self.added_tokens_encoder)




							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								return dict(self.target_encoder	,					**self.added_tokens_decoder)




							def __getstate__(						self)   ->	Dict:







								'''simple docstring'''




								snake_case__  :						Dict		=      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	,					lowerCamelCase__)   ->	None:







								'''simple docstring'''




								snake_case__  :						List[str]		=      d

								# for backward compatibility
								if not hasattr(self	,					"sp_model_kwargs"):
									snake_case__  :						Union[str, Any]		=      {}

								snake_case__,					snake_case__  :						Any		=      (load_spm(lowerCamelCase__	,					self.sp_model_kwargs) for f in self.spm_files)
								snake_case__  :						int		=      self.spm_source
								self._setup_normalizer()




							def        UpperCAmelCase (						self	,					*lowerCamelCase__	,					**lowerCamelCase__)   ->	int:







								'''simple docstring'''




								return 1




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	Optional[Any]:







								'''simple docstring'''




								snake_case__  :						Optional[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	,					lowerCamelCase__	,					lowerCamelCase__ = None	,					lowerCamelCase__ = False)   ->	List[int]:







								'''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 			A__		(					_UpperCAmelCase    :      str				,    _UpperCAmelCase    :      Dict[str, Any]	)							->	List[str]:

	'''simple docstring'''


	snake_case__  :						Any		=      sentencepiece.SentencePieceProcessor(**a__	)
	spm.Load(a__	)
	return spm



def 			A__		(					_UpperCAmelCase    :      str				,    _UpperCAmelCase    :      str	)							->	Tuple:

	'''simple docstring'''


	with open(a__				,    "w"	) as f:
		json.dump(a__				,    a__				,    indent=2	)



def 			A__		(					_UpperCAmelCase    :      str	)							->	Union[str, Any]:

	'''simple docstring'''


	with open(a__				,    "r"	) as f:
		return json.load(a__	)











'''simple docstring'''
import numpy as np
def 							snake_case_    (  a__						:      Tuple     ):



    """simple docstring"""




    __lowercase							,__lowercase     							=		a.shape
    __lowercase     							=		min(a__       ,a__     )
    __lowercase     							=		np.eye(a__     )
    __lowercase     							=		a.copy()

    for k in range(t - 1     ):
        # select a column of modified matrix A':
        __lowercase     							=		r[k:, [k]]
        # construct first basis vector
        __lowercase     							=		np.zeros_like(a__     )
        __lowercase     							=		1.0
        # determine scaling factor
        __lowercase     							=		np.linalg.norm(a__     )
        # construct vector v for Householder reflection
        __lowercase     							=		x + np.sign(x[0]     ) * alpha * ea
        v /= np.linalg.norm(a__     )

        # construct the Householder matrix
        __lowercase     							=		np.eye(m - k     ) - 2.0 * v @ v.T
        # pad with ones and zeros as necessary
        __lowercase     							=		np.block([[np.eye(a__     ), np.zeros((k, m - k)     )], [np.zeros((m - k, k)     ), q_k]]     )

        __lowercase     							=		q @ q_k.T
        __lowercase     							=		q_k @ r

    return q, r


if __name__ == "__main__":
  import doctest

  doctest.testmod()




