






'''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

lowerCAmelCase_           = 'tiny-wmt19-en-ru'

# Build

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

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

					lowerCAmelCase_           = 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,
					)

lowerCAmelCase_           = 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,
)

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

# Test
lowerCAmelCase_           = tokenizer(['Making tiny model'], return_tensors='pt')
lowerCAmelCase_           = 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 os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
lowerCAmelCase_           = '.'


if __name__ == "__main__":
					lowerCAmelCase_           = os.path.join(REPO_PATH, 'utils/documentation_tests.txt')
					lowerCAmelCase_           = []
					lowerCAmelCase_           = []
					with open(doctest_file_path) as fp:
										for line in fp:
															lowerCAmelCase_           = line.strip()
															lowerCAmelCase_           = os.path.join(REPO_PATH, line)
															if not (os.path.isfile(path) or os.path.isdir(path)):
																				non_existent_paths.append(line)
															all_paths.append(path)
					if len(non_existent_paths) > 0:
										lowerCAmelCase_           = '\n'.join(non_existent_paths)
										raise ValueError(f'''`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}''')
					if all_paths != sorted(all_paths):
										raise ValueError('Files in `utils/documentation_tests.txt` are not in alphabetical order.')





