






"""simple docstring"""
def        snake_case   (     A__		):
			UpperCAmelCase_  :				Dict      =      0

			# if input_string is "aba" than new_input_string become "a|b|a"
			UpperCAmelCase_  :				str      =      ""
			UpperCAmelCase_  :				Dict      =      ""

			# append each character + "|" in new_string for range(0, length-1)
			for i in input_string[: len(A__		) - 1]:
						new_input_string += i + "|"
			# append last character
			new_input_string += input_string[-1]

			# we will store the starting and ending of previous furthest ending palindromic
			# substring
			UpperCAmelCase_  :				Dict      =      0, 0

			# length[i] shows the length of palindromic substring with center i
			UpperCAmelCase_  :				Dict      =      [1 for i in range(len(A__		)		)]

			# for each character in new_string find corresponding palindromic string
			UpperCAmelCase_  :				List[str]      =      0
			for j in range(len(A__		)		):
						UpperCAmelCase_  :				List[Any]      =      1 if j > r else min(length[l + r - j] // 2			,r - j + 1		)
						while (
						    j - k >= 0
						    and j + k < len(A__		)
						    and new_input_string[k + j] == new_input_string[j - k]
						):
									k += 1

						UpperCAmelCase_  :				Tuple      =      2 * k - 1

						# does this string is ending after the previously explored end (that is r) ?
						# if yes the update the new r to the last index of this
						if j + k - 1 > r:
									UpperCAmelCase_  :				int      =      j - k + 1  # noqa: E741
									UpperCAmelCase_  :				int      =      j + k - 1

						# update max_length and start position
						if max_length < length[j]:
									UpperCAmelCase_  :				Union[str, Any]      =      length[j]
									UpperCAmelCase_  :				str      =      j

    # create that string
			UpperCAmelCase_  :				int      =      new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
			for i in s:
						if i != "|":
									output_string += i

			return output_string


if __name__ == "__main__":
			import doctest

			doctest.testmod()











"""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

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=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,
)

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



