




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 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      42


class 				UpperCamelCase_    ( UpperCAmelCase__		,					UpperCAmelCase__     ):

    '''simple docstring'''






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





           '''simple docstring'''

           super().__init__()
           A__       =    num_attention_heads
           A__       =    attention_head_dim
           A__       =    num_attention_heads * attention_head_dim

           A__       =    in_channels

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

           # 3. Define transformers blocks
           A__       =    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__)
               ])

           A__       =    nn.Linear(UpperCAmelCase__					, UpperCAmelCase__)





    def        SCREAMING_SNAKE_CASE							(			self      :	Dict					, UpperCAmelCase__      :	Optional[int]					, UpperCAmelCase__      :	Dict=None					, UpperCAmelCase__      :	Optional[Any]=None					, UpperCAmelCase__      :	Tuple=None					, UpperCAmelCase__      :	Tuple=1					, UpperCAmelCase__      :	int=None					, UpperCAmelCase__      :	bool = True					, )						->List[str]:





           '''simple docstring'''

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

           A__       =    hidden_states

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

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

           A__       =    self.proj_in(UpperCAmelCase__)

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

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

           A__       =    hidden_states + residual

           if not return_dict:
                  return (output,)

           return TransformerTemporalModelOutput(sample=UpperCAmelCase__)









"""simple docstring"""




from copy import deepcopy

class  _UpperCAmelCase  :



      '''simple docstring'''

      def __init__(     self					,      snake_case_ = None					,      snake_case_ = None  ):

        """simple docstring"""

        if arr is None and size is not None:
          A_  :       Union[str, Any]										=  size
          A_  :       List[str]										=  [0] * size
        elif arr is not None:
          self.init(snake_case_  )
        else:
          raise ValueError('Either arr or size must be specified'  )

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        A_  :       Union[str, Any]										=  len(snake_case_  )
        A_  :       Optional[int]										=  deepcopy(snake_case_  )
        for i in range(1					,      self.size  ):
          A_  :       Optional[Any]										=  self.next_(snake_case_  )
          if j < self.size:
            self.tree[j] += self.tree[i]

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       int										=  self.tree[:]
        for i in range(self.size - 1					,      0					,      -1  ):
          A_  :       Optional[int]										=  self.next_(snake_case_  )
          if j < self.size:
            arr[j] -= arr[i]
        return arr

      @staticmethod
      def 					lowerCamelCase_			(     snake_case_  ):

        """simple docstring"""

        return index + (index & (-index))

      @staticmethod
      def 					lowerCamelCase_			(     snake_case_  ):

        """simple docstring"""

        return index - (index & (-index))

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        if index == 0:
          self.tree[0] += value
          return
        while index < self.size:
          self.tree[index] += value
          A_  :       List[str]										=  self.next_(snake_case_  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        self.add(snake_case_					,      value - self.get(snake_case_  )  )

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        if right == 0:
          return 0
        A_  :       Any										=  self.tree[0]
        right -= 1  # make right inclusive
        while right > 0:
          result += self.tree[right]
          A_  :       Tuple										=  self.prev(snake_case_  )
        return result

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        return self.prefix(snake_case_  ) - self.prefix(snake_case_  )

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        return self.query(snake_case_					,      index + 1  )

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        value -= self.tree[0]
        if value < 0:
          return -1

        A_  :       List[Any]										=  1  # Largest power of 2 <= size
        while j * 2 < self.size:
          j *= 2

        A_  :       Tuple										=  0

        while j > 0:
          if i + j < self.size and self.tree[i + j] <= value:
            value -= self.tree[i + j]
            i += j
          j //= 2
        return i


if __name__ == "__main__":
   import doctest

   doctest.testmod()