



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__):




		'''simple docstring'''



		snake_case_			=42



class  lowerCamelCase__    (							lowerCamelCase__   ,       lowerCamelCase__):




		'''simple docstring'''



		@register_to_config
		def __init__(self	,__lowerCamelCase = 16	,__lowerCamelCase = 88	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = 1	,__lowerCamelCase = 0.0	,__lowerCamelCase = 32	,__lowerCamelCase = None	,__lowerCamelCase = False	,__lowerCamelCase = None	,__lowerCamelCase = "geglu"	,__lowerCamelCase = True	,__lowerCamelCase = True	,)  ->    Union[str, Any]:

						"""simple docstring"""






						super().__init__()
						lowerCAmelCase__  :       List[str]				=  num_attention_heads
						lowerCAmelCase__  :       int				=  attention_head_dim
						lowerCAmelCase__  :       Any				=  num_attention_heads * attention_head_dim

						lowerCAmelCase__  :       Optional[Any]				=  in_channels

						lowerCAmelCase__  :       Optional[Any]				=  torch.nn.GroupNorm(num_groups=__lowerCamelCase	,num_channels=__lowerCamelCase	,eps=1e-6	,affine=__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[Any]				=  nn.Linear(__lowerCamelCase	,__lowerCamelCase				)

						# 3. Define transformers blocks
						lowerCAmelCase__  :       List[str]				=  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				)
						    ]				)

						lowerCAmelCase__  :       List[Any]				=  nn.Linear(__lowerCamelCase	,__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase=None	,__lowerCamelCase=None	,__lowerCamelCase=None	,__lowerCamelCase=1	,__lowerCamelCase=None	,__lowerCamelCase = True	,)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__  :       str				=  hidden_states.shape
						lowerCAmelCase__  :       Optional[int]				=  batch_frames // num_frames

						lowerCAmelCase__  :       Optional[int]				=  hidden_states

						lowerCAmelCase__  :       str				=  hidden_states[None, :].reshape(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)
						lowerCAmelCase__  :       List[Any]				=  hidden_states.permute(0	,2	,1	,3	,4				)

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

						lowerCAmelCase__  :       Optional[int]				=  self.proj_in(__lowerCamelCase				)

						# 2. Blocks
						for block in self.transformer_blocks:
										lowerCAmelCase__  :       Union[str, Any]				=  block(
										    __lowerCamelCase	,encoder_hidden_states=__lowerCamelCase	,timestep=__lowerCamelCase	,cross_attention_kwargs=__lowerCamelCase	,class_labels=__lowerCamelCase	,)

						# 3. Output
						lowerCAmelCase__  :       str				=  self.proj_out(__lowerCamelCase				)
						lowerCAmelCase__  :       List[Any]				=  (
						    hidden_states[None, None, :]
						    .reshape(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)
						    .permute(0	,3	,4	,1	,2				)
						    .contiguous()
						)
						lowerCAmelCase__  :       Optional[int]				=  hidden_states.reshape(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)

						lowerCAmelCase__  :       int				=  hidden_states + residual

						if not return_dict:
										return (output,)

						return TransformerTemporalModelOutput(sample=__lowerCamelCase				)






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Any ,lowerCamelCase_	:	Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       str				=  [0 for i in range(r + 1)]
				# nc0 = 1
				lowerCAmelCase__  :       Tuple				=  1
				for i in range(1 ,n + 1):
								# to compute current row from previous row.
								lowerCAmelCase__  :       Optional[int]				=  min(lowerCamelCase_ ,lowerCamelCase_)
								while j > 0:
												c[j] += c[j - 1]
												j -= 1
				return c[r]

print(binomial_coefficient(n=1_0, r=5))

