




from __future__ import annotations

import typing
from collections import Counter







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	typing.Counter[int]:



       """simple docstring"""

       A__							=   Counter()
       for base in range(1			,    max_perimeter + 1						):
              for perpendicular in range(lowercase_			,    max_perimeter + 1						):
                     A__							=   (base * base + perpendicular * perpendicular) ** 0.5
                     if hypotenuse == int(lowercase_						):
                            A__							=   int(base + perpendicular + hypotenuse						)
                            if perimeter > max_perimeter:
                                   continue
                            triplets[perimeter] += 1
       return triplets







def SCREAMING_SNAKE_CASE    (	lowercase_ = 1_000						)       ->	int:



       """simple docstring"""

       A__							=   pythagorean_triple(lowercase_						)
       return triplets.most_common(1						)[0][0]


if __name__ == "__main__":
  print(F'''Perimeter {solution()} has maximum solutions''')








# 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

_lowerCamelCase     :		Optional[Any]								=       """facebook/wmt19-en-de"""
_lowerCamelCase     :		Optional[Any]								=       FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_lowerCamelCase     :		int								=       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,
    )
)

_lowerCamelCase     :		Union[str, Any]								=       FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_lowerCamelCase     :		int								=       tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_lowerCamelCase     :		int								=       tiny_model(**batch)

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

# Save
_lowerCamelCase     :		str								=       """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


