

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


SCREAMING_SNAKE_CASE						:Dict      =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						:Optional[Any]      =			{
    """source_spm""": """source.spm""",
    """target_spm""": """target.spm""",
    """vocab""": """vocab.json""",
    """target_vocab_file""": """target_vocab.json""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

SCREAMING_SNAKE_CASE						:str      =			{
    """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"""
        )
    },
}

SCREAMING_SNAKE_CASE						:Any      =			{"""Helsinki-NLP/opus-mt-en-de""": 512}
SCREAMING_SNAKE_CASE						:Optional[int]      =			{}






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






     def __init__(							self						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase=None						,		_lowercase=None						,		_lowercase=None						,		_lowercase="<unk>"						,		_lowercase="</s>"						,		_lowercase="<pad>"						,		_lowercase=512						,		_lowercase = None						,		_lowercase=False						,		**_lowercase						,		)->			None:
         UpperCamelCase_          =		{} 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=_lowercase						,		target_lang=_lowercase						,		unk_token=_lowercase						,		eos_token=_lowercase						,		pad_token=_lowercase						,		model_max_length=_lowercase						,		sp_model_kwargs=self.sp_model_kwargs						,		target_vocab_file=_lowercase						,		separate_vocabs=_lowercase						,		**_lowercase						,		)
         assert Path(_lowercase     ).exists(), F"cannot find spm source {source_spm}"

         UpperCamelCase_          =		separate_vocabs
         UpperCamelCase_          =		load_json(_lowercase     )
         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:
             UpperCamelCase_          =		load_json(_lowercase     )
             UpperCamelCase_          =		{v: k for k, v in self.target_encoder.items()}
             UpperCamelCase_          =		[]
         else:
             UpperCamelCase_          =		{v: k for k, v in self.encoder.items()}
             UpperCamelCase_          =		[k for k in self.encoder if k.startswith(">>"     ) and k.endswith("<<"     )]

         UpperCamelCase_          =		source_lang
         UpperCamelCase_          =		target_lang
         UpperCamelCase_          =		[source_spm, target_spm]

         # load SentencePiece model for pre-processing
         UpperCamelCase_          =		load_spm(_lowercase						,		self.sp_model_kwargs     )
         UpperCamelCase_          =		load_spm(_lowercase						,		self.sp_model_kwargs     )
         UpperCamelCase_          =		self.spm_source
         UpperCamelCase_          =		self.encoder

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

         self._setup_normalizer()






     def      UpperCAmelCase_							(							self     )->			Any:
         try:
             from sacremoses import MosesPunctNormalizer

             UpperCamelCase_          =		MosesPunctNormalizer(self.source_lang     ).normalize
         except (ImportError, FileNotFoundError):
             warnings.warn("Recommended: pip install sacremoses."     )
             UpperCamelCase_          =		lambda _lowercase     : x






     def      UpperCAmelCase_							(							self						,		_lowercase     )->			str:
         return self.punc_normalizer(_lowercase     ) if x else ""






     def      UpperCAmelCase_							(							self						,		_lowercase     )->			Union[str, Any]:
         return self.current_encoder.get(_lowercase						,		self.current_encoder[self.unk_token]     )






     def      UpperCAmelCase_							(							self						,		_lowercase     )->			Optional[int]:
         UpperCamelCase_          =		self.language_code_re.match(_lowercase     )
         UpperCamelCase_          =		[match.group(0     )] if match else []
         return code, self.language_code_re.sub(""						,		_lowercase     )






     def      UpperCAmelCase_							(							self						,		_lowercase     )->			List[str]:
         UpperCamelCase_   ,    UpperCamelCase_          =		self.remove_language_code(_lowercase     )
         UpperCamelCase_          =		self.current_spm.encode(_lowercase						,		out_type=_lowercase     )
         return code + pieces






     def      UpperCAmelCase_							(							self						,		_lowercase     )->			str:
         return self.decoder.get(_lowercase						,		self.unk_token     )






     def      UpperCAmelCase_							(							self						,		_lowercase						,		**_lowercase     )->			int:
         return super().batch_decode(_lowercase						,		**_lowercase     )






     def      UpperCAmelCase_							(							self						,		_lowercase						,		**_lowercase     )->			Optional[int]:
         return super().decode(_lowercase						,		**_lowercase     )






     def      UpperCAmelCase_							(							self						,		_lowercase     )->			str:
         UpperCamelCase_          =		self.spm_source if self._decode_use_source_tokenizer else self.spm_target
         UpperCamelCase_          =		[]
         UpperCamelCase_          =		""
         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(_lowercase     ) + token + " "
                 UpperCamelCase_          =		[]
             else:
                 current_sub_tokens.append(_lowercase     )
         out_string += sp_model.decode_pieces(_lowercase     )
         return out_string.strip()






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase=None     )->			List[int]:
         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     )->			Tuple:
         UpperCamelCase_          =		self.spm_source
         UpperCamelCase_          =		self.encoder






     def      UpperCAmelCase_							(							self     )->			int:
         UpperCamelCase_          =		self.spm_target
         if self.separate_vocabs:
             UpperCamelCase_          =		self.target_encoder






     @property
     def      UpperCAmelCase_							(							self     )->			int:
         return len(self.encoder     )






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase = None     )->			Tuple[str]:
         if not os.path.isdir(_lowercase     ):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"     )
             return
         UpperCamelCase_          =		[]

         if self.separate_vocabs:
             UpperCamelCase_          =		os.path.join(
                 _lowercase						,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]						,		)
             UpperCamelCase_          =		os.path.join(
                 _lowercase						,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["target_vocab_file"]						,		)
             save_json(self.encoder						,		_lowercase     )
             save_json(self.target_encoder						,		_lowercase     )
             saved_files.append(_lowercase     )
             saved_files.append(_lowercase     )
         else:
             UpperCamelCase_          =		os.path.join(
                 _lowercase						,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]     )
             save_json(self.encoder						,		_lowercase     )
             saved_files.append(_lowercase     )

         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]						,		):
             UpperCamelCase_          =		os.path.join(
                 _lowercase						,		(filename_prefix + "-" if filename_prefix else "") + spm_save_filename     )
             if os.path.abspath(_lowercase     ) != os.path.abspath(_lowercase     ) and os.path.isfile(_lowercase     ):
                 copyfile(_lowercase						,		_lowercase     )
                 saved_files.append(_lowercase     )
             elif not os.path.isfile(_lowercase     ):
                 with open(_lowercase						,		"wb"     ) as fi:
                     UpperCamelCase_          =		spm_model.serialized_model_proto()
                     fi.write(_lowercase     )
                 saved_files.append(_lowercase     )

         return tuple(_lowercase     )






     def      UpperCAmelCase_							(							self     )->			Dict:
         return self.get_src_vocab()






     def      UpperCAmelCase_							(							self     )->			str:
         return dict(self.encoder						,		**self.added_tokens_encoder     )






     def      UpperCAmelCase_							(							self     )->			Optional[Any]:
         return dict(self.target_encoder						,		**self.added_tokens_decoder     )






     def __getstate__(							self     )->			Dict:
         UpperCamelCase_          =		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						,		_lowercase     )->			None:
         UpperCamelCase_          =		d

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

         UpperCamelCase_   ,    UpperCamelCase_          =		(load_spm(_lowercase						,		self.sp_model_kwargs     ) for f in self.spm_files)
         UpperCamelCase_          =		self.spm_source
         self._setup_normalizer()






     def      UpperCAmelCase_							(							self						,		*_lowercase						,		**_lowercase     )->			Tuple:
         return 1






     def      UpperCAmelCase_							(							self						,		_lowercase     )->			int:
         UpperCamelCase_          =		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						,		_lowercase						,		_lowercase = None						,		_lowercase = False     )->			List[int]:
         if already_has_special_tokens:
             return self._special_token_mask(_lowercase     )
         elif token_ids_a is None:
             return self._special_token_mask(_lowercase     ) + [1]
         else:
             return self._special_token_mask(token_ids_a + token_ids_a     ) + [1]




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		sentencepiece.SentencePieceProcessor:



    """simple docstring"""






    UpperCamelCase_          =		sentencepiece.SentencePieceProcessor(**SCREAMING_SNAKE_CASE_       )
    spm.Load(SCREAMING_SNAKE_CASE_       )
    return spm




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		None:



    """simple docstring"""






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




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       )->		Union[Dict, List]:



    """simple docstring"""






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



from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		torch.Tensor:



    """simple docstring"""






    if not inplace:
        UpperCamelCase_          =		ma + ma
    else:
        ma += ma

    return ma




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		torch.Tensor:



    """simple docstring"""






    UpperCamelCase_          =		-1 * len(SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		list(range(len(tensor.shape[:zero_index]       )       )       )
    return tensor.permute(first_inds + [zero_index + i for i in inds]       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		torch.Tensor:



    """simple docstring"""






    return t.reshape(t.shape[:-no_dims] + (-1,)       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 1E-4       )->		torch.Tensor:



    """simple docstring"""






    UpperCamelCase_          =		mask.expand(*value.shape       )
    return torch.sum(mask * value       ,		dim=SCREAMING_SNAKE_CASE_       ) / (eps + torch.sum(SCREAMING_SNAKE_CASE_       ,		dim=SCREAMING_SNAKE_CASE_       ))




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 2.3125       ,		SCREAMING_SNAKE_CASE_ = 21.6875       ,		SCREAMING_SNAKE_CASE_ = 6_4       )->		torch.Tensor:



    """simple docstring"""






    UpperCamelCase_          =		torch.linspace(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		no_bins - 1       ,		device=pts.device       )
    UpperCamelCase_          =		torch.sqrt(torch.sum((pts.unsqueeze(-2       ) - pts.unsqueeze(-3       )) ** 2       ,		dim=-1       )       )
    return torch.bucketize(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		dict:



    """simple docstring"""






    UpperCamelCase_          =		dicts[0]
    UpperCamelCase_          =		{}
    for k, v in first.items():
        UpperCamelCase_          =		[d[k] for d in dicts]
        if isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
            UpperCamelCase_          =		dict_multimap(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
        else:
            UpperCamelCase_          =		fn(SCREAMING_SNAKE_CASE_       )

    return new_dict




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		torch.Tensor:



    """simple docstring"""






    UpperCamelCase_          =		v_bins.view(((1,) * len(x.shape       )) + (len(SCREAMING_SNAKE_CASE_       ),)       )
    UpperCamelCase_          =		x[..., None] - reshaped_bins
    UpperCamelCase_          =		torch.argmin(torch.abs(SCREAMING_SNAKE_CASE_       )       ,		dim=-1       )
    return nn.functional.one_hot(SCREAMING_SNAKE_CASE_       ,		num_classes=len(SCREAMING_SNAKE_CASE_       )       ).float()




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 0       ,		SCREAMING_SNAKE_CASE_ = 0       )->		torch.Tensor:



    """simple docstring"""






    UpperCamelCase_          =		[]
    for i, s in enumerate(data.shape[:no_batch_dims]       ):
        UpperCamelCase_          =		torch.arange(SCREAMING_SNAKE_CASE_       )
        UpperCamelCase_          =		r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape       ) - i - 1)))       )
        ranges.append(SCREAMING_SNAKE_CASE_       )

    UpperCamelCase_          =		[slice(SCREAMING_SNAKE_CASE_       ) for _ in range(len(data.shape       ) - no_batch_dims       )]
    UpperCamelCase_          =		inds
    ranges.extend(SCREAMING_SNAKE_CASE_       )
    # Matt note: Editing this to get around the behaviour of using a list as an array index changing
    # in recent Numpy versions
    return data[tuple(SCREAMING_SNAKE_CASE_       )]


SCREAMING_SNAKE_CASE						:Dict      =			TypeVar("""T""")




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Dict[Any, Union[dict, list, tuple, Any]]:



    """simple docstring"""






    UpperCamelCase_          =		{}
    for k, v in dic.items():
        if isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
            UpperCamelCase_          =		dict_map(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
        else:
            UpperCamelCase_          =		tree_map(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )

    return new_dict




@overload
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Any:



    """simple docstring"""






    ...




@overload
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		dict:



    """simple docstring"""






    ...




@overload
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		list:



    """simple docstring"""






    ...




@overload
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		tuple:



    """simple docstring"""






    ...




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Optional[int]:



    """simple docstring"""






    if isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
        return dict_map(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    elif isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
        return [tree_map(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ) for x in tree]
    elif isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
        return tuple(tree_map(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ) for x in tree       )
    elif isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
        return fn(SCREAMING_SNAKE_CASE_       )
    else:
        print(type(SCREAMING_SNAKE_CASE_       )       )
        raise ValueError("Not supported"       )
SCREAMING_SNAKE_CASE						:Dict      =			partial(tree_map, leaf_type=torch.Tensor)
