

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=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    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









def 					lowerCamelCase_     (							UpperCAmelCase_							:     list	)						->				list:




	'''simple docstring'''




	if len(UpperCAmelCase_	) <= 1:
		return [tuple(UpperCAmelCase_	)]

	_UpperCamelCase   :		List[Any]			=						[]

	def generate(UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     list	):
		_UpperCamelCase   :		Optional[int]			=						[0] * n
		res.append(tuple(UpperCAmelCase_	)	)

		_UpperCamelCase   :		List[Any]			=						0
		while i < n:
			if c[i] < i:
				if i % 2 == 0:
					_UpperCamelCase	,		_UpperCamelCase   :		List[str]			=						arr[i], arr[0]
				else:
					_UpperCamelCase	,		_UpperCamelCase   :		List[str]			=						arr[i], arr[c[i]]
				res.append(tuple(UpperCAmelCase_	)	)
				c[i] += 1
				_UpperCamelCase   :		Tuple			=						0
			else:
				_UpperCamelCase   :		Tuple			=						0
				i += 1

	generate(len(UpperCAmelCase_	)							,       UpperCAmelCase_	)
	return res


if __name__ == "__main__":
							lowerCAmelCase__								=		input("""Enter numbers separated by a comma:\n""").strip()
							lowerCAmelCase__								=		[int(item) for item in user_input.split(""",""")]
							print(heaps(arr))




