
"""simple docstring"""



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





    """simple docstring"""

    return [list(range(1_0_0_0 - i					,      -1_0_0_0 - i					,      -1      )      ) for i in range(1_0_0_0      )]


a					:	List[Any]							=		generate_large_matrix()
a					:	Optional[int]							=		(
    [[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  _UpperCamelCase	(						_A      )    ->  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  _UpperCamelCase	(						_A      )    ->  int:





    """simple docstring"""

    _UpperCAmelCase          =   0
    _UpperCAmelCase          =   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:
        _UpperCAmelCase          =   (left + right) // 2
        _UpperCAmelCase          =   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:
            _UpperCAmelCase          =   mid + 1
        else:
            _UpperCAmelCase          =   mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
    return len(_lowercase      )


def  _UpperCamelCase	(						_A      )    ->  int:





    """simple docstring"""

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

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


def  _UpperCamelCase	(						_A      )    ->  int:





    """simple docstring"""

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


def  _UpperCamelCase	(						_A      )    ->  int:





    """simple docstring"""

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


def  _UpperCamelCase	(						)    ->  None:





    """simple docstring"""

    from timeit import timeit

    print("""Running benchmarks"""      )
    _UpperCAmelCase          =   (
        '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
    ):
        _UpperCAmelCase          =   timeit(F"""{func}(grid=grid)"""					,      setup=_lowercase					,      number=5_0_0      )
        print(F"""{func}() took {time:0.4f} seconds"""      )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      benchmark()

"""simple docstring"""



from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class 		a_						( nn.Module	):







    def __init__(			self			:       List[str]     ,					__UpperCamelCase			:       int = 16     ,					__UpperCamelCase			:       int = 88     ,					__UpperCamelCase			:       Optional[int] = None     ,					__UpperCamelCase			:       int = 1     ,					__UpperCamelCase			:       float = 0.0     ,					__UpperCamelCase			:       int = 32     ,					__UpperCamelCase			:       Optional[int] = None     ,					__UpperCamelCase			:       bool = False     ,					__UpperCamelCase			:       Optional[int] = None     ,					__UpperCamelCase			:       Optional[int] = None     ,					__UpperCamelCase			:       str = "geglu"     ,					__UpperCamelCase			:       Optional[int] = None     ,					)		->Dict:






        '''simple docstring'''






        super().__init__()
        _UpperCAmelCase          =   nn.ModuleList(
            [
                TransformeraDModel(
                    num_attention_heads=__UpperCamelCase     ,					attention_head_dim=__UpperCamelCase     ,					in_channels=__UpperCamelCase     ,					num_layers=__UpperCamelCase     ,					dropout=__UpperCamelCase     ,					norm_num_groups=__UpperCamelCase     ,					cross_attention_dim=__UpperCamelCase     ,					attention_bias=__UpperCamelCase     ,					sample_size=__UpperCamelCase     ,					num_vector_embeds=__UpperCamelCase     ,					activation_fn=__UpperCamelCase     ,					num_embeds_ada_norm=__UpperCamelCase     ,					)
                for _ in range(2     )
            ]     )

        # Variables that can be set by a pipeline:

        # The ratio of transformer1 to transformer2's output states to be combined during inference
        _UpperCAmelCase          =   0.5

        # The shape of `encoder_hidden_states` is expected to be
        # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
        _UpperCAmelCase          =   [77, 2_57]

        # Which transformer to use to encode which condition.
        # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
        _UpperCAmelCase          =   [1, 0]







    def      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Union[str, Any]=None     ,					__UpperCamelCase			:       Union[str, Any]=None     ,					__UpperCamelCase			:       Tuple=None     ,					__UpperCamelCase			:       bool = True     ,					)		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   hidden_states

        _UpperCAmelCase          =   []
        _UpperCAmelCase          =   0
        # attention_mask is not used yet
        for i in range(2     ):
            # for each of the two transformers, pass the corresponding condition tokens
            _UpperCAmelCase          =   encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
            _UpperCAmelCase          =   self.transformer_index_for_condition[i]
            _UpperCAmelCase          =   self.transformers[transformer_index](
                __UpperCamelCase     ,					encoder_hidden_states=__UpperCamelCase     ,					timestep=__UpperCamelCase     ,					cross_attention_kwargs=__UpperCamelCase     ,					return_dict=__UpperCamelCase     ,					)[0]
            encoded_states.append(encoded_state - input_states     )
            tokens_start += self.condition_lengths[i]

        _UpperCAmelCase          =   encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
        _UpperCAmelCase          =   output_states + input_states

        if not return_dict:
            return (output_states,)

        return TransformeraDModelOutput(sample=__UpperCamelCase     )