
"""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  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      torch.FloatTensor




class  __lowerCamelCase  ( lowerCAmelCase						,	lowerCAmelCase			):




				@register_to_config
				def __init__(				self							,						UpperCAmelCase = 16							,						UpperCAmelCase = 88							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase = 1							,						UpperCAmelCase = 0.0							,						UpperCAmelCase = 32							,						UpperCAmelCase = None							,						UpperCAmelCase = False							,						UpperCAmelCase = None							,						UpperCAmelCase = "geglu"							,						UpperCAmelCase = True							,						UpperCAmelCase = True							,						):
							super().__init__()
							lowerCamelCase_					=  num_attention_heads
							lowerCamelCase_					=  attention_head_dim
							lowerCamelCase_					=  num_attention_heads * attention_head_dim

							lowerCamelCase_					=  in_channels

							lowerCamelCase_					=  torch.nn.GroupNorm(num_groups=UpperCAmelCase							,						num_channels=UpperCAmelCase							,						eps=1e-6							,						affine=UpperCAmelCase   )
							lowerCamelCase_					=  nn.Linear(UpperCAmelCase							,						UpperCAmelCase   )

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

							lowerCamelCase_					=  nn.Linear(UpperCAmelCase							,						UpperCAmelCase   )





				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=1							,						UpperCAmelCase=None							,						UpperCAmelCase = True							,						):
							lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					=  hidden_states.shape
							lowerCamelCase_					=  batch_frames // num_frames

							lowerCamelCase_					=  hidden_states

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

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

							lowerCamelCase_					=  self.proj_in(UpperCAmelCase   )

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

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

							lowerCamelCase_					=  hidden_states + residual

							if not return_dict:
										return (output,)

							return TransformerTemporalModelOutput(sample=UpperCAmelCase   )







'''simple docstring'''



A__						:   dict[tuple[int, int, int], int]				=    {}



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							int				)	->						int:

     # if we are absent twice, or late 3 consecutive days,
     # no further prize strings are possible
     if late == 3 or absent == 2:
          return 0

     # if we have no days left, and have not failed any other rules,
     # we have a prize string
     if days == 0:
          return 1

     # No easy solution, so now we need to do the recursive calculation

     # First, check if the combination is already in the cache, and
     # if yes, return the stored value from there since we already
     # know the number of possible prize strings from this point on
     __lowerCamelCase  :      List[Any]														=						(days, absent, late)
     if key in cache:
          return cache[key]

     # now we calculate the three possible ways that can unfold from
     # this point on, depending on our attendance today

     # 1) if we are late (but not absent), the "absent" counter stays as
     # it is, but the "late" counter increases by one
     __lowerCamelCase  :      Tuple														=						_calculate(days - 1 ,       UpperCAmelCase_ ,       late + 1				)

     # 2) if we are absent, the "absent" counter increases by 1, and the
     # "late" counter resets to 0
     __lowerCamelCase  :      int														=						_calculate(days - 1 ,       absent + 1 ,       0				)

     # 3) if we are on time, this resets the "late" counter and keeps the
     # absent counter
     __lowerCamelCase  :      List[Any]														=						_calculate(days - 1 ,       UpperCAmelCase_ ,       0				)

     __lowerCamelCase  :      Optional[int]														=						state_late + state_absent + state_ontime

     __lowerCamelCase  :      Union[str, Any]														=						prizestrings
     return prizestrings



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int = 30				)	->						int:
     return _calculate(UpperCAmelCase_ ,       absent=0 ,       late=0				)


if __name__ == "__main__":
      print(solution())






