

# 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=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,
)

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









import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    '''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
    ''' StableDiffusionInpaintPipeline` instead.'''
)

