




def SCREAMING_SNAKE_CASE    (	)       ->	list[list[int]]:



       """simple docstring"""

       return [list(range(1_000 - i			,    -1_000 - i			,    -1						)						) for i in range(1_000						)]


_lowerCamelCase     :		Any								=       generate_large_matrix()
_lowerCamelCase     :		List[str]								=       (
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       assert all(row == sorted(lowercase_			,    reverse=lowercase_						) for row in grid						)
       assert all(list(lowercase_						) == sorted(lowercase_			,    reverse=lowercase_						) for col in zip(*lowercase_						)						)







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	int:



       """simple docstring"""

       A__							=   0
       A__							=   len(lowercase_						) - 1

       # Edge cases such as no values or all numbers are negative.
       if not array or array[0] < 0:
              return 0

       while right + 1 > left:
              A__							=   (left + right) // 2
              A__							=   array[mid]

              # Num must be negative and the index must be greater than or equal to 0.
              if num < 0 and array[mid - 1] >= 0:
                     return mid

              if num >= 0:
                     A__							=   mid + 1
              else:
                     A__							=   mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
       return len(lowercase_						)







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	int:



       """simple docstring"""

       A__							=   0
       A__							=   len(grid[0]						)

       for i in range(len(lowercase_						)						):
              A__							=   find_negative_index(grid[i][:bound]						)
              total += bound
       return (len(lowercase_						) * len(grid[0]						)) - total







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	int:



       """simple docstring"""

       return len([number for row in grid for number in row if number < 0]						)







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	int:



       """simple docstring"""

       A__							=   0
       for row in grid:
              for i, number in enumerate(lowercase_						):
                     if number < 0:
                            total += len(lowercase_						) - i
                            break
       return total







def SCREAMING_SNAKE_CASE    (	)       ->	None:



       """simple docstring"""

       from timeit import timeit

       print('''Running benchmarks'''						)
       A__							=   (
           '''from __main__ import count_negatives_binary_search, '''
           '''count_negatives_brute_force, count_negatives_brute_force_with_break, grid'''
       )
       for func in (
           "count_negatives_binary_search",  # took 0.7727 seconds
           "count_negatives_brute_force_with_break",  # took 4.6505 seconds
           "count_negatives_brute_force",  # took 12.8160 seconds
       ):
              A__							=   timeit(f"""{func}(grid=grid)"""			,    setup=lowercase_			,    number=500						)
              print(f"""{func}() took {time:0.4f} seconds"""						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  benchmark()








# 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


