





from collections import deque
def 							_lowerCamelCase(  UpperCamelCase__    : Tuple		)		->  Any:
    A       :       int							=						len(lowerCamelCase_		)
    A       :       Dict							=						deque()
    A       :       Optional[int]							=						[False for _ in range(lowerCamelCase_		)]
    A       :       Union[str, Any]							=						[-1 for _ in range(lowerCamelCase_		)]
    A       :       List[Any]							=						index_of[:]

    def strong_connect(UpperCamelCase__    : Union[str, Any]    ,       UpperCamelCase__    : Optional[Any]    ,       UpperCamelCase__    : Optional[int]		):
        A       :       List[str]							=						index  # the number when this node is seen
        A       :       Optional[Any]							=						index  # lowest rank node reachable from here
        index += 1
        stack.append(lowerCamelCase_		)
        A       :       Optional[Any]							=						True

        for w in g[v]:
            if index_of[w] == -1:
                A       :       Tuple							=						strong_connect(lowerCamelCase_    ,       lowerCamelCase_    ,       lowerCamelCase_		)
                A       :       Tuple							=						(
                    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                )
            elif on_stack[w]:
                A       :       List[str]							=						(
                    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                )

        if lowlink_of[v] == index_of[v]:
            A       :       Tuple							=						[]
            A       :       List[str]							=						stack.pop()
            A       :       List[str]							=						False
            component.append(lowerCamelCase_		)
            while w != v:
                A       :       str							=						stack.pop()
                A       :       List[str]							=						False
                component.append(lowerCamelCase_		)
            components.append(lowerCamelCase_		)
        return index

    A       :       int							=						[]
    for v in range(lowerCamelCase_		):
        if index_of[v] == -1:
            strong_connect(lowerCamelCase_    ,       0    ,       lowerCamelCase_		)

    return components
def 							_lowerCamelCase(  UpperCamelCase__    : Any    ,       UpperCamelCase__    : Tuple		)		->  Optional[Any]:
    A       :       Union[str, Any]							=						[[] for _ in range(lowerCamelCase_		)]
    for u, v in edges:
        g[u].append(lowerCamelCase_		)
    return g


if __name__ == "__main__":
     # Test
     snake_case_     =	7
     snake_case_     =	[0, 0, 1, 2, 3, 3, 4, 4, 6]
     snake_case_     =	[1, 3, 2, 0, 1, 4, 5, 6, 5]
     snake_case_     =	[(u, v) for u, v in zip(source, target)]
     snake_case_     =	create_graph(n_vertices, edges)

     assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)







'''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

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=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,
)

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
