
"""simple docstring"""



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

import sentencepiece as spm

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


UpperCAmelCase__          = '▁'

UpperCAmelCase__          = {'vocab_file': 'spiece.model'}

UpperCAmelCase__          = {
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'}
}

UpperCAmelCase__          = {
    'google/pegasus-xsum': 512,
}


UpperCAmelCase__          = logging.get_logger(__name__)

class       lowerCAmelCase__				(						A_	):
     __a						=					VOCAB_FILES_NAMES

     __a						=					VOCAB_FILES_NAMES
     __a						=					PRETRAINED_VOCAB_FILES_MAP
     __a						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __a						=					["""input_ids""", """attention_mask"""]







     def __init__(							self			:			Dict    ,      _lowerCamelCase			:			Dict    ,      _lowerCamelCase			:			Tuple="<pad>"    ,      _lowerCamelCase			:			Union[str, Any]="</s>"    ,      _lowerCamelCase			:			Optional[int]="<unk>"    ,      _lowerCamelCase			:			str="<mask_2>"    ,      _lowerCamelCase			:			Optional[int]="<mask_1>"    ,      _lowerCamelCase			:			int=None    ,      _lowerCamelCase			:			str=103    ,      _lowerCamelCase			:			Optional[Dict[str, Any]] = None    ,      **_lowerCamelCase			:			str    ,      ):
       _snake_case					     =       offset
       if additional_special_tokens is not None:
         if not isinstance(_lowerCamelCase    ,      _lowerCamelCase			):
           raise TypeError(
               f'''additional_special_tokens should be of type {type(_lowerCamelCase			)}, but is'''
               f''' {type(_lowerCamelCase			)}'''			)

         _snake_case					     =       (
             ([mask_token_sent] + additional_special_tokens)
             if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
             else additional_special_tokens
         )
         # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
         additional_special_tokens_extended += [
             f'''<unk_{i}>''' for i in range(len(_lowerCamelCase			)    ,      self.offset - 1			)
         ]

         if len(set(_lowerCamelCase			)			) != len(_lowerCamelCase			):
           raise ValueError(
               '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
               f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''			)
         _snake_case					     =       additional_special_tokens_extended
       else:
         _snake_case					     =       [mask_token_sent] if mask_token_sent is not None else []
         additional_special_tokens += [f'''<unk_{i}>''' for i in range(2    ,      self.offset			)]

       _snake_case					     =       {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           eos_token=_lowerCamelCase    ,      unk_token=_lowerCamelCase    ,      mask_token=_lowerCamelCase    ,      pad_token=_lowerCamelCase    ,      mask_token_sent=_lowerCamelCase    ,      offset=_lowerCamelCase    ,      additional_special_tokens=_lowerCamelCase    ,      sp_model_kwargs=self.sp_model_kwargs    ,      **_lowerCamelCase    ,      )
       _snake_case					     =       mask_token_sent
       _snake_case					     =       vocab_file
       _snake_case					     =       spm.SentencePieceProcessor(**self.sp_model_kwargs			)
       self.sp_model.Load(_lowerCamelCase			)

       # add special tokens to encoder dict
       _snake_case					     =       {
           0: self.pad_token,
           1: self.eos_token,
       }

       if self.mask_token_sent is not None:
         self.encoder.update(
             {
                 2: self.mask_token_sent,
                 3: self.mask_token,
             }			)

       if self.offset > 0:
         # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
         # mask_token_sent is already added to list -> so start at 1
         self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1    ,      self.offset - 1			)}			)

       _snake_case					     =       {v: k for k, v in self.encoder.items()}







     @property
     def      lowercase	(							self			:			Dict			):
       return len(self.sp_model			) + self.offset







     def      lowercase	(							self			:			str			):
       _snake_case					     =       {self.convert_ids_to_tokens(_lowerCamelCase			): i for i in range(self.vocab_size			)}
       vocab.update(self.added_tokens_encoder			)
       return vocab







     def __getstate__(							self			:			Union[str, Any]			):
       _snake_case					     =       self.__dict__.copy()
       _snake_case					     =       None
       return state







     def __setstate__(							self			:			Dict    ,      _lowerCamelCase			:			Optional[Any]			):
       _snake_case					     =       d

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

       _snake_case					     =       spm.SentencePieceProcessor(**self.sp_model_kwargs			)
       self.sp_model.Load(self.vocab_file			)







     def      lowercase	(							self			:			Dict    ,      _lowerCamelCase			:			str			):
       return self.sp_model.encode(_lowerCamelCase    ,      out_type=_lowerCamelCase			)







     def      lowercase	(							self			:			Tuple    ,      _lowerCamelCase			:			str			):
       if token in self.decoder:
         return self.decoder[token]
       elif token in self.added_tokens_decoder:
         return self.added_tokens_decoder[token]
       _snake_case					     =       self.sp_model.piece_to_id(_lowerCamelCase			)
       return sp_id + self.offset







     def      lowercase	(							self			:			Any    ,      _lowerCamelCase			:			int			):
       if index in self.encoder:
         return self.encoder[index]
       elif index in self.added_tokens_encoder:
         return self.added_tokens_encoder[index]
       else:
         _snake_case					     =       self.sp_model.IdToPiece(index - self.offset			)
       return token







     def      lowercase	(							self			:			List[Any]    ,      _lowerCamelCase			:			Dict			):
       _snake_case					     =       []
       _snake_case					     =       ''''''
       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(_lowerCamelCase			) + token
           _snake_case					     =       []
         else:
           current_sub_tokens.append(_lowerCamelCase			)
       out_string += self.sp_model.decode(_lowerCamelCase			)
       return out_string.strip()







     def      lowercase	(							self			:			List[str]    ,      _lowerCamelCase			:			List[Any]=False			):
       return 1







     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			Dict			):
       _snake_case					     =       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      lowercase	(							self			:			Any    ,      _lowerCamelCase			:			List    ,      _lowerCamelCase			:			Optional[List] = None    ,      _lowerCamelCase			:			bool = False			):
       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      lowercase	(							self			:			Optional[Any]    ,      _lowerCamelCase			:			Optional[int]    ,      _lowerCamelCase			:			Tuple=None			):
       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      lowercase	(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			Optional[str] = None			):
       if not os.path.isdir(_lowerCamelCase			):
         logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
         return
       _snake_case					     =       os.path.join(
           _lowerCamelCase    ,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

       if os.path.abspath(self.vocab_file			) != os.path.abspath(_lowerCamelCase			) and os.path.isfile(self.vocab_file			):
         copyfile(self.vocab_file    ,      _lowerCamelCase			)
       elif not os.path.isfile(self.vocab_file			):
         with open(_lowerCamelCase    ,      '''wb'''			) as fi:
           _snake_case					     =       self.sp_model.serialized_model_proto()
           fi.write(_lowerCamelCase			)

       return (out_vocab_file,)





"""simple docstring"""



# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

UpperCAmelCase__          = 'tiny-wmt19-en-ru'

# Build

# borrowed from a test
UpperCAmelCase__          = [
    'l',
    'o',
    'w',
    'e',
    'r',
    's',
    't',
    'i',
    'd',
    'n',
    'w</w>',
    'r</w>',
    't</w>',
    'lo',
    'low',
    'er</w>',
    'low</w>',
    'lowest</w>',
    'newer</w>',
    'wider</w>',
    '<unk>',
]
UpperCAmelCase__          = dict(zip(vocab, range(len(vocab))))
UpperCAmelCase__          = ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

with tempfile.TemporaryDirectory() as tmpdirname:
 UpperCAmelCase__          = Path(tmpdirname)
 UpperCAmelCase__          = build_dir / VOCAB_FILES_NAMES['src_vocab_file']
 UpperCAmelCase__          = build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
 UpperCAmelCase__          = build_dir / VOCAB_FILES_NAMES['merges_file']
 with open(src_vocab_file, 'w') as fp:
  fp.write(json.dumps(vocab_tokens))
 with open(tgt_vocab_file, 'w') as fp:
  fp.write(json.dumps(vocab_tokens))
 with open(merges_file, 'w') as fp:
  fp.write('\n'.join(merges))

 UpperCAmelCase__          = FSMTTokenizer(
     langs=['en', 'ru'],
     src_vocab_size=len(vocab),
     tgt_vocab_size=len(vocab),
     src_vocab_file=src_vocab_file,
     tgt_vocab_file=tgt_vocab_file,
     merges_file=merges_file,
 )

UpperCAmelCase__          = FSMTConfig(
    langs=['ru', 'en'],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

UpperCAmelCase__          = FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

# Test
UpperCAmelCase__          = tokenizer(['Making tiny model'], return_tensors='pt')
UpperCAmelCase__          = tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"Generated {mname_tiny}")

# Upload
# transformers-cli upload tiny-wmt19-en-ru



