
"""simple docstring"""





from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  torch.FloatTensor



class 						SCREAMING_SNAKE_CASE_ (						__a     ,				__a ):




		"""simple docstring"""





		@register_to_config
		def __init__(     self							,  lowerCAmelCase__ = 1_6							,  lowerCAmelCase__ = 8_8							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = 1							,  lowerCAmelCase__ = 0.0							,  lowerCAmelCase__ = 3_2							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = "geglu"							,  lowerCAmelCase__ = True							,  lowerCAmelCase__ = True							,  ):
				super().__init__()
				__SCREAMING_SNAKE_CASE            = num_attention_heads
				__SCREAMING_SNAKE_CASE            = attention_head_dim
				__SCREAMING_SNAKE_CASE            = num_attention_heads * attention_head_dim

				__SCREAMING_SNAKE_CASE            = in_channels

				__SCREAMING_SNAKE_CASE            = torch.nn.GroupNorm(num_groups=lowerCAmelCase__							,  num_channels=lowerCAmelCase__							,  eps=1E-6							,  affine=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = nn.Linear(lowerCAmelCase__							,  lowerCAmelCase__)

				# 3. Define transformers blocks
				__SCREAMING_SNAKE_CASE            = nn.ModuleList(
				    [
				        BasicTransformerBlock(
				            lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  dropout=lowerCAmelCase__							,  cross_attention_dim=lowerCAmelCase__							,  activation_fn=lowerCAmelCase__							,  attention_bias=lowerCAmelCase__							,  double_self_attention=lowerCAmelCase__							,  norm_elementwise_affine=lowerCAmelCase__							,  )
				        for d in range(lowerCAmelCase__)
				    ])

				__SCREAMING_SNAKE_CASE            = nn.Linear(lowerCAmelCase__							,  lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  lowerCAmelCase__=1							,  lowerCAmelCase__=None							,  lowerCAmelCase__ = True							,  ):
				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = hidden_states.shape
				__SCREAMING_SNAKE_CASE            = batch_frames // num_frames

				__SCREAMING_SNAKE_CASE            = hidden_states

				__SCREAMING_SNAKE_CASE            = hidden_states[None, :].reshape(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = hidden_states.permute(0							,  2							,  1							,  3							,  4)

				__SCREAMING_SNAKE_CASE            = self.norm(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = hidden_states.permute(0							,  3							,  4							,  2							,  1).reshape(batch_size * height * width							,  lowerCAmelCase__							,  lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = self.proj_in(lowerCAmelCase__)

				# 2. Blocks
				for block in self.transformer_blocks:
						__SCREAMING_SNAKE_CASE            = block(
						    lowerCAmelCase__							,  encoder_hidden_states=lowerCAmelCase__							,  timestep=lowerCAmelCase__							,  cross_attention_kwargs=lowerCAmelCase__							,  class_labels=lowerCAmelCase__							,  )

				# 3. Output
				__SCREAMING_SNAKE_CASE            = self.proj_out(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = (
				    hidden_states[None, None, :]
				    .reshape(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__)
				    .permute(0							,  3							,  4							,  1							,  2)
				    .contiguous()
				)
				__SCREAMING_SNAKE_CASE            = hidden_states.reshape(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = hidden_states + residual

				if not return_dict:
						return (output,)

				return TransformerTemporalModelOutput(sample=lowerCAmelCase__)









from __future__ import annotations

from math import ceil, floor, sqrt




def 	__UpperCamelCase	(  _lowerCAmelCase = 200_0000		)       ->  int:



    """simple docstring"""






    A :       list[int]          =   [0]
    A :       int

    for idx in range(1      ,     ceil(sqrt(target * 2		) * 1.1		)		):
        triangle_numbers.append(triangle_numbers[-1] + idx		)

    # we want this to be as close as possible to target
    A :       int          =   0
    # the area corresponding to the grid that gives the product closest to target
    A :       int          =   0
    # an estimate of b, using the quadratic formula
    A :       float
    # the largest integer less than b_estimate
    A :       int
    # the largest integer less than b_estimate
    A :       int
    # the triangle number corresponding to b_floor
    A :       int
    # the triangle number corresponding to b_ceil
    A :       int

    for idx_a, triangle_a in enumerate(triangle_numbers[1:]      ,     1		):
        A :       Union[str, Any]          =   (-1 + sqrt(1 + 8 * target / triangle_a		)) / 2
        A :       List[Any]          =   floor(_lowerCAmelCase		)
        A :       Tuple          =   ceil(_lowerCAmelCase		)
        A :       int          =   triangle_numbers[b_floor]
        A :       Dict          =   triangle_numbers[b_ceil]

        if abs(target - triangle_b_first_guess * triangle_a		) < abs(
            target - best_product		):
            A :       Optional[int]          =   triangle_b_first_guess * triangle_a
            A :       Optional[int]          =   idx_a * b_floor

        if abs(target - triangle_b_second_guess * triangle_a		) < abs(
            target - best_product		):
            A :       Tuple          =   triangle_b_second_guess * triangle_a
            A :       Tuple          =   idx_a * b_ceil

    return area


if __name__ == "__main__":
     print(F"""{solution() = }""")


