

"""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

a					:Dict    = "tiny-wmt19-en-ru"

# Build

# borrowed from a test
a					:Tuple    = [
    "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>",
]
a					:List[str]    = dict(zip(vocab, range(len(vocab))))
a					:List[str]    = ["l o 123", "lo w 1456", "e r</w> 1789", ""]

with tempfile.TemporaryDirectory() as tmpdirname:
  a					:Optional[int]    = Path(tmpdirname)
  a					:Dict    = build_dir / VOCAB_FILES_NAMES["src_vocab_file"]
  a					:int    = build_dir / VOCAB_FILES_NAMES["tgt_vocab_file"]
  a					:List[Any]    = 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))

  a					:Optional[Any]    = 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,
  )

a					:Dict    = FSMTConfig(
    langs=["ru", "en"],
    src_vocab_size=1_000,
    tgt_vocab_size=1_000,
    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,
)

a					:Any    = FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
a					:str    = tokenizer(["Making tiny model"], return_tensors="pt")
a					:Union[str, Any]    = 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 os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_xlnet import XLNetTokenizer
else:
  a					:List[Any]    = None


a					:Optional[int]    = logging.get_logger(__name__)

a					:Union[str, Any]    = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a					:Optional[int]    = {
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
    },
}

a					:Dict    = {
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

a					:int    = "▁"

# Segments (not really needed)
a					:Dict    = 0
a					:Optional[int]    = 1
a					:Tuple    = 2
a					:List[str]    = 3
a					:Optional[Any]    = 4

class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Tuple       = VOCAB_FILES_NAMES
     _SCREAMING_SNAKE_CASE						:Union[str, Any]       = PRETRAINED_VOCAB_FILES_MAP
     _SCREAMING_SNAKE_CASE						:Tuple       = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _SCREAMING_SNAKE_CASE						:str       = """left"""
     _SCREAMING_SNAKE_CASE						:Optional[Any]       = XLNetTokenizer

     def __init__(						self	,    _a=None	,    _a=None	,    _a=False	,    _a=True	,    _a=False	,    _a="<s>"	,    _a="</s>"	,    _a="<unk>"	,    _a="<sep>"	,    _a="<pad>"	,    _a="<cls>"	,    _a="<mask>"	,    _a=["<eop>", "<eod>"]	,    **_a	,    )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	AddedToken(_a	,    lstrip=_a	,    rstrip=_a  ) if isinstance(_a	,    _a  ) else mask_token

           super().__init__(
               vocab_file=_a	,    tokenizer_file=_a	,    do_lower_case=_a	,    remove_space=_a	,    keep_accents=_a	,    bos_token=_a	,    eos_token=_a	,    unk_token=_a	,    sep_token=_a	,    pad_token=_a	,    cls_token=_a	,    mask_token=_a	,    additional_special_tokens=_a	,    **_a	,    )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	3
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	do_lower_case
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	remove_space
           SCREAMING_SNAKE_CASE__   :      int      		=	keep_accents
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	vocab_file
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	False if not self.vocab_file else True

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	[self.sep_token_id]
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	[self.cls_token_id]
           if token_ids_a is None:
                 return token_ids_a + sep + cls
           return token_ids_a + sep + token_ids_a + sep + cls

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	[self.sep_token_id]
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	[2]

           if token_ids_a is None:
                 return len(token_ids_a + sep  ) * [0] + cls_segment_id
           return len(token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1] + cls_segment_id





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


           """simple docstring"""


           if not self.can_save_slow_tokenizer:
                 raise ValueError(
                     """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                     """tokenizer."""  )

           if not os.path.isdir(_a  ):
                 logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
                 return
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	os.path.join(
               _a	,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )

           if os.path.abspath(self.vocab_file  ) != os.path.abspath(_a  ):
                 copyfile(self.vocab_file	,    _a  )

           return (out_vocab_file,)




