
"""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=10_00,
    tgt_vocab_size=10_00,
    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



"""simple docstring"""



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


UpperCamelCase__          =	logging.get_logger(__name__)

UpperCamelCase__          =	{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase__          =	{
    'vocab_file': {
        'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_char_small': (
            'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_chinese_char_base': (
            'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_discriminator': (
            'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_generator': (
            'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
        ),
    }
}

UpperCamelCase__          =	{
    'junnyu/roformer_chinese_small': 15_36,
    'junnyu/roformer_chinese_base': 15_36,
    'junnyu/roformer_chinese_char_small': 5_12,
    'junnyu/roformer_chinese_char_base': 5_12,
    'junnyu/roformer_small_discriminator': 1_28,
    'junnyu/roformer_small_generator': 1_28,
}


UpperCamelCase__          =	{
    'junnyu/roformer_chinese_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_base': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
    'junnyu/roformer_small_discriminator': {'do_lower_case': True},
    'junnyu/roformer_small_generator': {'do_lower_case': True},
}





class 				a			(					lowercase      ):
      UpperCamelCase							:   int           =     VOCAB_FILES_NAMES
      UpperCamelCase							:   Dict           =     PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase							:   Union[str, Any]           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase							:   Tuple           =     PRETRAINED_INIT_CONFIGURATION
      UpperCamelCase							:   Optional[int]           =     RoFormerTokenizer



      def __init__(   self     ,	UpperCamelCase_=None     ,	UpperCamelCase_=None     ,	UpperCamelCase_=True     ,	UpperCamelCase_="[UNK]"     ,	UpperCamelCase_="[SEP]"     ,	UpperCamelCase_="[PAD]"     ,	UpperCamelCase_="[CLS]"     ,	UpperCamelCase_="[MASK]"     ,	UpperCamelCase_=True     ,	UpperCamelCase_=None     ,	**UpperCamelCase_     ,	):
          super().__init__(
              UpperCamelCase_     ,	tokenizer_file=UpperCamelCase_     ,	do_lower_case=UpperCamelCase_     ,	unk_token=UpperCamelCase_     ,	sep_token=UpperCamelCase_     ,	pad_token=UpperCamelCase_     ,	cls_token=UpperCamelCase_     ,	mask_token=UpperCamelCase_     ,	tokenize_chinese_chars=UpperCamelCase_     ,	strip_accents=UpperCamelCase_     ,	**UpperCamelCase_     ,	)

          UpperCAmelCase__	:      Union[str, Any]     							=					json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
          if (
              pre_tok_state.get('lowercase'     ,	UpperCamelCase_      ) != do_lower_case
              or pre_tok_state.get('strip_accents'     ,	UpperCamelCase_      ) != strip_accents
          ):
              UpperCAmelCase__	:      Any     							=					getattr(UpperCamelCase_     ,	pre_tok_state.pop('type'      )      )
              UpperCAmelCase__	:      str     							=					do_lower_case
              UpperCAmelCase__	:      Union[str, Any]     							=					strip_accents
              UpperCAmelCase__	:      Dict     							=					pre_tok_class(**UpperCamelCase_      )

          UpperCAmelCase__	:      Union[str, Any]     							=					do_lower_case



      def __getstate__(   self      ):
          UpperCAmelCase__	:      int     							=					self.__dict__.copy()
          UpperCAmelCase__	:      int     							=					BertPreTokenizer()
          return state



      def __setstate__(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					d
          UpperCAmelCase__	:      List[str]     							=					self.__dict__['_tokenizer'].get_vocab()
          UpperCAmelCase__	:      List[Any]     							=					PreTokenizer.custom(JiebaPreTokenizer(UpperCamelCase_      )      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=None      ):
          UpperCAmelCase__	:      List[Any]     							=					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

          if token_ids_a:
              output += token_ids_a + [self.sep_token_id]

          return output



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None      ):
          UpperCAmelCase__	:      int     							=					[self.sep_token_id]
          UpperCAmelCase__	:      Dict     							=					[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      ) * [0] + len(token_ids_a + sep      ) * [1]



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None      ):
          UpperCAmelCase__	:      Any     							=					self._tokenizer.model.save(UpperCamelCase_     ,	name=UpperCamelCase_      )
          return tuple(UpperCamelCase_      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=None     ,	UpperCamelCase_=None     ,	UpperCamelCase_=False     ,	**UpperCamelCase_     ,	):
          UpperCAmelCase__	:      int     							=					BertPreTokenizer()
          return super().save_pretrained(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      )

