


'''simple docstring'''

def 					lowercase__				(   __UpperCamelCase					)->				str:
      UpperCamelCase       						=      0

      # if input_string is "aba" than new_input_string become "a|b|a"
      UpperCamelCase       						=      ''''''
      UpperCamelCase       						=      ''''''

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

      # length[i] shows the length of palindromic substring with center i
      UpperCamelCase       						=      [1 for i in range(len(__UpperCamelCase					)					)]

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

            UpperCamelCase       						=      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       						=      j - k + 1  # noqa: E741
                  UpperCamelCase       						=      j + k - 1

            # update max_length and start position
            if max_length < length[j]:
                  UpperCamelCase       						=      length[j]
                  UpperCamelCase       						=      j

    # create that string
      UpperCamelCase       						=      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 model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

SCREAMING_SNAKE_CASE__											=     'facebook/wmt19-en-de'
SCREAMING_SNAKE_CASE__											=     FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
SCREAMING_SNAKE_CASE__											=     FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        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,
    )
)

SCREAMING_SNAKE_CASE__											=     FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
SCREAMING_SNAKE_CASE__											=     tokenizer(['Making tiny model'], return_tensors='pt')
SCREAMING_SNAKE_CASE__											=     tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
SCREAMING_SNAKE_CASE__											=     'tiny-wmt19-en-de'
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-de
