


'''simple docstring'''



import math

from numpy import inf
from scipy.integrate import quad



def      UpperCAmelCase__   (      UpperCAmelCase_       :							float				)	->						float:
     if num <= 0:
          raise ValueError('math domain error'				)

     return quad(UpperCAmelCase_ ,       0 ,       UpperCAmelCase_ ,       args=(UpperCAmelCase_)				)[0]



def      UpperCAmelCase__   (      UpperCAmelCase_       :							float ,       UpperCAmelCase_       :							float				)	->						float:
     return math.pow(UpperCAmelCase_ ,       z - 1				) * math.exp(-x				)


if __name__ == "__main__":
      from doctest import testmod

      testmod()













"""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  (	a			):
       """simple docstring"""




       __magic_name__	:torch.FloatTensor






class    _lowerCAmelCase  (	a    ,  a			):
       """simple docstring"""




       @register_to_config
       def __init__(       self       ,					__UpperCAmelCase = 1_6       ,					__UpperCAmelCase = 8_8       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = 1       ,					__UpperCAmelCase = 0.0       ,					__UpperCAmelCase = 3_2       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = False       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = "geglu"       ,					__UpperCAmelCase = True       ,					__UpperCAmelCase = True       ,					):



         '''simple docstring'''

         super().__init__()
         lowerCAmelCase__     :Dict											=				num_attention_heads
         lowerCAmelCase__     :Any											=				attention_head_dim
         lowerCAmelCase__     :Optional[int]											=				num_attention_heads * attention_head_dim

         lowerCAmelCase__     :Any											=				in_channels

         lowerCAmelCase__     :str											=				torch.nn.GroupNorm(num_groups=__UpperCAmelCase       ,					num_channels=__UpperCAmelCase       ,					eps=1E-6       ,					affine=__UpperCAmelCase )
         lowerCAmelCase__     :int											=				nn.Linear(__UpperCAmelCase       ,					__UpperCAmelCase )

         # 3. Define transformers blocks
         lowerCAmelCase__     :List[Any]											=				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__     :List[Any]											=				nn.Linear(__UpperCAmelCase       ,					__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=1       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase = True       ,					):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__     :int											=				hidden_states.shape
         lowerCAmelCase__     :Tuple											=				batch_frames // num_frames

         lowerCAmelCase__     :str											=				hidden_states

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

         lowerCAmelCase__     :Optional[int]											=				self.norm(__UpperCAmelCase )
         lowerCAmelCase__     :Optional[Any]											=				hidden_states.permute(0       ,					3       ,					4       ,					2       ,					1 ).reshape(batch_size * height * width       ,					__UpperCAmelCase       ,					__UpperCAmelCase )

         lowerCAmelCase__     :List[Any]											=				self.proj_in(__UpperCAmelCase )

         # 2. Blocks
         for block in self.transformer_blocks:
           lowerCAmelCase__     :Optional[int]											=				block(
               __UpperCAmelCase       ,					encoder_hidden_states=__UpperCAmelCase       ,					timestep=__UpperCAmelCase       ,					cross_attention_kwargs=__UpperCAmelCase       ,					class_labels=__UpperCAmelCase       ,					)

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

         lowerCAmelCase__     :List[Any]											=				hidden_states + residual

         if not return_dict:
           return (output,)

         return TransformerTemporalModelOutput(sample=__UpperCAmelCase )
