"""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       a_      (     lowerCamelCase__			):
					UpperCamelCase_ :	Tuple			=    42

class       a_      (     lowerCamelCase__ ,		lowerCamelCase__			):




					@register_to_config
					def __init__(	self						:     int  ,						snake_case__						:     List[Any] = 16  ,						snake_case__						:     List[Any] = 88  ,						snake_case__						:     Optional[int] = None  ,						snake_case__						:     Tuple = None  ,						snake_case__						:     List[Any] = 1  ,						snake_case__						:     Dict = 0.0  ,						snake_case__						:     Optional[Any] = 32  ,						snake_case__						:     Optional[Any] = None  ,						snake_case__						:     List[str] = False  ,						snake_case__						:     List[str] = None  ,						snake_case__						:     Any = "geglu"  ,						snake_case__						:     str = True  ,						snake_case__						:     str = 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=__lowerCamelCase  ,						num_channels=__lowerCamelCase  ,						eps=1E-6  ,						affine=__lowerCamelCase     )
							lowerCAmelCase__        =   nn.Linear(__lowerCamelCase  ,						__lowerCamelCase     )

							# 3. Define transformers blocks
							lowerCAmelCase__        =   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__        =   nn.Linear(__lowerCamelCase  ,						__lowerCamelCase     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     List[Any]  ,						snake_case__						:     int=None  ,						snake_case__						:     List[str]=None  ,						snake_case__						:     Any=None  ,						snake_case__						:     Dict=1  ,						snake_case__						:     int=None  ,						snake_case__						:     List[Any] = True  ,						):
							lowerCAmelCase__        =   hidden_states.shape
							lowerCAmelCase__        =   batch_frames // num_frames

							lowerCAmelCase__        =   hidden_states

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

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

							lowerCAmelCase__        =   self.proj_in(__lowerCamelCase     )

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

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

							lowerCAmelCase__        =   hidden_states + residual

							if not return_dict:
									return (output,)

							return TransformerTemporalModelOutput(sample=__lowerCamelCase     )





"""simple docstring"""




from math import pi, sqrt







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		if num <= 0:
				raise ValueError("""math domain error"""     )
		if num > 1_71.5:
				raise OverflowError("""math range error"""     )
		elif num - int(lowerCamelCase__     ) not in (0, 0.5):
				raise NotImplementedError("""num must be an integer or a half-integer"""     )
		elif num == 0.5:
				return sqrt(lowerCamelCase__     )
		else:
				return 1.0 if num == 1 else (num - 1) * gamma(num - 1     )







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		assert gamma(0.5     ) == sqrt(lowerCamelCase__     )
		assert gamma(1     ) == 1.0
		assert gamma(2     ) == 1.0


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

			testmod()
			__lowerCAmelCase    :							Dict													=						1.0
			while num:
						__lowerCAmelCase    :							Any													=						float(input("Gamma of: "))
						print(F"gamma({num}) = {gamma(num)}")
						print("\nEnter 0 to exit...")




