




# 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

UpperCAmelCase_		:					Union[str, Any]          =  "tiny-wmt19-en-ru"

# Build

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

with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCAmelCase_		:					Union[str, Any]          =  Path(tmpdirname)
							UpperCAmelCase_		:					Tuple          =  build_dir / VOCAB_FILES_NAMES["src_vocab_file"]
							UpperCAmelCase_		:					List[str]          =  build_dir / VOCAB_FILES_NAMES["tgt_vocab_file"]
							UpperCAmelCase_		:					List[str]          =  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))

							UpperCAmelCase_		:					Tuple          =  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,
							)

UpperCAmelCase_		:					List[Any]          =  FSMTConfig(
    langs=["ru", "en"],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    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,
)

UpperCAmelCase_		:					List[Any]          =  FSMTForConditionalGeneration(config)
print(F"""num of params {tiny_model.num_parameters()}""")

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










from collections.abc import Sequence




def 		lowerCAmelCase_ (						lowerCamelCase = None ):
				if nums is None or not nums:
								raise ValueError("""Input sequence should not be empty""" )

				__magic_name__						:						str										=nums[0]
				for i in range(1						,  len(lowerCamelCase ) ):
								__magic_name__						:						Any										=nums[i]
								__magic_name__						:						Dict										=max(lowerCamelCase						,  ans + num						,  lowerCamelCase )

				return ans


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Try on a sample input from the user
							UpperCAmelCase_		:					List[str]          =  int(input("Enter number of elements : ").strip())
							UpperCAmelCase_		:					Tuple          =  list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
							print(max_subsequence_sum(array))




