




from __future__ import annotations

from collections.abc import Generator



def 						lowerCAmelCase		(		):




 """simple docstring"""







 UpperCAmelCase__							 =						{}
 UpperCAmelCase__							 =						2
 while True:
  UpperCAmelCase__							 =						factor_map.pop(_lowerCAmelCase						,							_lowerCAmelCase	)
  if factor:
   UpperCAmelCase__							 =						factor + prime
   while x in factor_map:
    x += factor
   UpperCAmelCase__							 =						factor
  else:
   UpperCAmelCase__							 =						prime
   yield prime
  prime += 1



def 						lowerCAmelCase		(		_lowerCAmelCase							:   float = 1E10	):




 """simple docstring"""







 UpperCAmelCase__							 =						sieve()

 UpperCAmelCase__							 =						1
 while True:
  UpperCAmelCase__							 =						next(_lowerCAmelCase	)
  if (2 * prime * n) > limit:
   return n
  # Ignore the next prime as the reminder will be 2.
  next(_lowerCAmelCase	)
  n += 2


if __name__ == "__main__":
  print(solution())











# 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 model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

_lowerCAmelCase						:   int		=   "facebook/wmt19-en-de"
_lowerCAmelCase						:   int		=   FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_lowerCAmelCase						:   Dict		=   FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        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						:   List[Any]		=   FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_lowerCAmelCase						:   Optional[Any]		=   tokenizer(["Making tiny model"], return_tensors="pt")
_lowerCAmelCase						:   Optional[Any]		=   tiny_model(**batch)

print("test output:", len(outputs.logits[0]))

# Save
_lowerCAmelCase						:   Optional[Any]		=   "tiny-wmt19-en-de"
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-de





