





# 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

_snake_case           =			"""tiny-wmt19-en-ru"""

# Build

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

with tempfile.TemporaryDirectory() as tmpdirname:
  _snake_case           =			Path(tmpdirname)
  _snake_case           =			build_dir / VOCAB_FILES_NAMES["""src_vocab_file"""]
  _snake_case           =			build_dir / VOCAB_FILES_NAMES["""tgt_vocab_file"""]
  _snake_case           =			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))

  _snake_case           =			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,
  )

_snake_case           =			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,
)

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

# Test
_snake_case           =			tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_snake_case           =			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













from __future__ import annotations

from collections import deque





class  lowerCAmelCase							:







  def __init__(      self  :List[Any]   ,							_lowercase  :list[str] ):






     '''simple docstring'''





     lowercase__			       =  []
     self.adlist.append(
         {"value": "", "next_states": [], "fail_state": 0, "output": []} )

     for keyword in keywords:
        self.add_keyword(_lowercase )
     self.set_fail_transitions()







  def   UpperCAmelCase					(      self  :str   ,							_lowercase  :int   ,							_lowercase  :str ):






     '''simple docstring'''





     for state in self.adlist[current_state]["next_states"]:
        if char == self.adlist[state]["value"]:
           return state
     return None







  def   UpperCAmelCase					(      self  :List[str]   ,							_lowercase  :str ):






     '''simple docstring'''





     lowercase__			       =  0
     for character in keyword:
        lowercase__			       =  self.find_next_state(_lowercase   ,							_lowercase )
        if next_state is None:
           self.adlist.append(
               {
                   "value": character,
                   "next_states": [],
                   "fail_state": 0,
                   "output": [],
               } )
           self.adlist[current_state]["next_states"].append(len(self.adlist ) - 1 )
           lowercase__			       =  len(self.adlist ) - 1
        else:
           lowercase__			       =  next_state
     self.adlist[current_state]["output"].append(_lowercase )







  def   UpperCAmelCase					(      self  :int ):






     '''simple docstring'''





     lowercase__			       =  deque()
     for node in self.adlist[0]["next_states"]:
        q.append(_lowercase )
        lowercase__			       =  0
     while q:
        lowercase__			       =  q.popleft()
        for child in self.adlist[r]["next_states"]:
           q.append(_lowercase )
           lowercase__			       =  self.adlist[r]["fail_state"]
           while (
               self.find_next_state(_lowercase   ,							self.adlist[child]["value"] ) is None
               and state != 0
           ):
              lowercase__			       =  self.adlist[state]["fail_state"]
           lowercase__			       =  self.find_next_state(
               _lowercase   ,							self.adlist[child]["value"] )
           if self.adlist[child]["fail_state"] is None:
              lowercase__			       =  0
           lowercase__			       =  (
               self.adlist[child]["output"]
               + self.adlist[self.adlist[child]["fail_state"]]["output"]
           )







  def   UpperCAmelCase					(      self  :Optional[Any]   ,							_lowercase  :str ):






     '''simple docstring'''





     lowercase__			       =  {}  # returns a dict with keywords and list of its occurrences
     lowercase__			       =  0
     for i in range(len(_lowercase ) ):
        while (
            self.find_next_state(_lowercase   ,							string[i] ) is None
            and current_state != 0
        ):
           lowercase__			       =  self.adlist[current_state]["fail_state"]
        lowercase__			       =  self.find_next_state(_lowercase   ,							string[i] )
        if next_state is None:
           lowercase__			       =  0
        else:
           lowercase__			       =  next_state
           for key in self.adlist[current_state]["output"]:
              if key not in result:
                 lowercase__			       =  []
              result[key].append(i - len(_lowercase ) + 1 )
     return result


if __name__ == "__main__":
  import doctest

  doctest.testmod()






