


# 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=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,
)

__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






from __future__ import annotations




def        snake_case_					(  snake_case      ,							snake_case					)  ->    list[str]:
       if nth_term == "":
              return [""]
       lowercase__:       Tuple        =       int(snake_case					)
       lowercase__:       int        =       int(snake_case					)
       lowercase__:       list[str]        =       []
       for temp in range(int(snake_case					)					):
              series.append(f'1 / {pow(temp + 1      ,							int(snake_case					)					)}' if series else '1'					)
       return series


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 __lowerCAmelCase			=							int(input('''Enter the last number (nth term) of the P-Series'''))
 __lowerCAmelCase			=							int(input('''Enter the power for  P-Series'''))
 print('''Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p''')
 print(p_series(nth_term, power))


