
"""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

A__      :	Optional[int]          =    """tiny-wmt19-en-ru"""

# Build

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

with tempfile.TemporaryDirectory() as tmpdirname:
    A__      :	str          =    Path(tmpdirname)
    A__      :	List[str]          =    build_dir / VOCAB_FILES_NAMES["""src_vocab_file"""]
    A__      :	Any          =    build_dir / VOCAB_FILES_NAMES["""tgt_vocab_file"""]
    A__      :	Optional[int]          =    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))

    A__      :	Optional[Any]          =    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,
    )

A__      :	Tuple          =    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,
)

A__      :	Tuple          =    FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

# Test
A__      :	Dict          =    tokenizer(["""Making tiny model"""], return_tensors="""pt""")
A__      :	str          =    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





"""simple docstring"""




from math import isqrt



def 							a__ (				lowerCAmelCase	:       int		):






     '''simple docstring'''
     UpperCAmelCase__							: Tuple 							=						[True] * max_number
     for i in range(2       ,    isqrt(max_number - 1		) + 1		):
          if is_prime[i]:
               for j in range(i**2       ,    lowerCAmelCase       ,    lowerCAmelCase		):
                    UpperCAmelCase__							: List[Any] 							=						False

     return [i for i in range(2       ,    lowerCAmelCase		) if is_prime[i]]



def 							a__ (				lowerCAmelCase	:       int = 10**8		):






     '''simple docstring'''
     UpperCAmelCase__							: Dict 							=						calculate_prime_numbers(max_number // 2		)

     UpperCAmelCase__							: Optional[int] 							=						0
     UpperCAmelCase__							: Optional[int] 							=						0
     UpperCAmelCase__							: Tuple 							=						len(lowerCAmelCase		) - 1
     while left <= right:
          while prime_numbers[left] * prime_numbers[right] >= max_number:
               right -= 1
          semiprimes_count += right - left + 1
          left += 1

     return semiprimes_count


if __name__ == "__main__":
    print(f"""{solution() = }""")



