




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(		UpperCamelCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		42







class 						A(		UpperCamelCase							,   UpperCamelCase     ):



 '''simple docstring'''


 @register_to_config
 def __init__(       self      :						List[str]		,							A_      :						int = 16		,							A_      :						int = 88		,							A_      :						Optional[int] = None		,							A_      :						Optional[int] = None		,							A_      :						int = 1		,							A_      :						float = 0.0		,							A_      :						int = 32		,							A_      :						Optional[int] = None		,							A_      :						bool = False		,							A_      :						Optional[int] = None		,							A_      :						str = "geglu"		,							A_      :						bool = True		,							A_      :						bool = True		,							)     ->			Optional[Any]:

       """simple docstring"""






       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=A_		,							num_channels=A_		,							eps=1E-6		,							affine=A_						)
       lowerCamelCase_	  =					nn.Linear(A_		,							A_						)

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

       lowerCamelCase_	  =					nn.Linear(A_		,							A_						)





 def        a__				(       self      :						Union[str, Any]		,							A_      :						int		,							A_      :						Union[str, Any]=None		,							A_      :						str=None		,							A_      :						Optional[Any]=None		,							A_      :						str=1		,							A_      :						List[str]=None		,							A_      :						bool = True		,							)     ->			str:

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					hidden_states.shape
       lowerCamelCase_	  =					batch_frames // num_frames

       lowerCamelCase_	  =					hidden_states

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

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

       lowerCamelCase_	  =					self.proj_in(A_						)

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

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

       lowerCamelCase_	  =					hidden_states + residual

       if not return_dict:
             return (output,)

       return TransformerTemporalModelOutput(sample=A_						)









from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate







@dataclass(frozen=UpperCamelCase     )
class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		field(default='''language-modeling'''							,   metadata={'''include_in_asdict_even_if_is_default''': True}     )

 UpperCamelCase		     =		Features({'''text''': Value('''string'''     )}     )
 UpperCamelCase		     =		Features({}     )
 UpperCamelCase		     =		"text"

 @property
 def        a__				(       self      :						List[Any]						)     ->			Dict[str, str]:

       """simple docstring"""






       return {self.text_column: "text"}



