





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__  ):




      """simple docstring"""







      UpperCAmelCase__						=      42







class 			__A       (      lowerCamelCase__ ,lowerCamelCase__  ):




      """simple docstring"""







      @register_to_config
      def __init__(		self      ,							a__ = 16      ,							a__ = 88      ,							a__ = None      ,							a__ = None      ,							a__ = 1      ,							a__ = 0.0      ,							a__ = 32      ,							a__ = None      ,							a__ = False      ,							a__ = None      ,							a__ = "geglu"      ,							a__ = True      ,							a__ = True      ,							):



             """simple docstring"""





             super().__init__()
             _lowerCamelCase							:     List[Any]	=		num_attention_heads
             _lowerCamelCase							:     Union[str, Any]	=		attention_head_dim
             _lowerCamelCase							:     Tuple	=		num_attention_heads * attention_head_dim

             _lowerCamelCase							:     str	=		in_channels

             _lowerCamelCase							:     int	=		torch.nn.GroupNorm(num_groups=a__      ,							num_channels=a__      ,							eps=1e-6      ,							affine=a__)
             _lowerCamelCase							:     Tuple	=		nn.Linear(a__      ,							a__)

             # 3. Define transformers blocks
             _lowerCamelCase							:     List[Any]	=		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							:     Union[str, Any]	=		nn.Linear(a__      ,							a__)



      def 				__snake_case			(		self      ,							a__      ,							a__=None      ,							a__=None      ,							a__=None      ,							a__=1      ,							a__=None      ,							a__ = True      ,							):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase							:     Union[str, Any]	=		hidden_states.shape
             _lowerCamelCase							:     Dict	=		batch_frames // num_frames

             _lowerCamelCase							:     List[str]	=		hidden_states

             _lowerCamelCase							:     int	=		hidden_states[None, :].reshape(a__      ,							a__      ,							a__      ,							a__      ,							a__)
             _lowerCamelCase							:     Tuple	=		hidden_states.permute(0      ,							2      ,							1      ,							3      ,							4)

             _lowerCamelCase							:     Any	=		self.norm(a__)
             _lowerCamelCase							:     List[str]	=		hidden_states.permute(0      ,							3      ,							4      ,							2      ,							1).reshape(batch_size * height * width      ,							a__      ,							a__)

             _lowerCamelCase							:     List[str]	=		self.proj_in(a__)

             # 2. Blocks
             for block in self.transformer_blocks:
                    _lowerCamelCase							:     int	=		block(
                        a__      ,							encoder_hidden_states=a__      ,							timestep=a__      ,							cross_attention_kwargs=a__      ,							class_labels=a__      ,							)

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

             _lowerCamelCase							:     Tuple	=		hidden_states + residual

             if not return_dict:
                    return (output,)

             return TransformerTemporalModelOutput(sample=a__)








def    __UpperCAmelCase(      lowercase_      ):
       # if the collection is empty, returns empty
       if collection == []:
              return []

       # get some information about the collection
       _lowerCamelCase							:     Tuple	=		len(lowercase_      )
       _lowerCamelCase							:     List[str]	=		max(lowercase_      )
       _lowerCamelCase							:     Any	=		min(lowercase_      )

       # create the counting array
       _lowerCamelCase							:     str	=		coll_max + 1 - coll_min
       _lowerCamelCase							:     Union[str, Any]	=		[0] * counting_arr_length

       # count how much a number appears in the collection
       for number in collection:
              counting_arr[number - coll_min] += 1

       # sum each position with it's predecessors. now, counting_arr[i] tells
       # us how many elements <= i has in the collection
       for i in range(1 ,    lowercase_      ):
              _lowerCamelCase							:     Union[str, Any]	=		counting_arr[i] + counting_arr[i - 1]

       # create the output collection
       _lowerCamelCase							:     Dict	=		[0] * coll_len

       # place the elements in the output, respecting the original order (stable
       # sort) from end to begin, updating counting_arr
       for i in reversed(range(0 ,    lowercase_      )      ):
              _lowerCamelCase							:     Union[str, Any]	=		collection[i]
              counting_arr[collection[i] - coll_min] -= 1

       return ordered




def    __UpperCAmelCase(      lowercase_      ):
       return "".join([chr(lowercase_      ) for i in counting_sort([ord(lowercase_      ) for c in string]      )]      )


if __name__ == "__main__":
       # Test string sort
       assert counting_sort_string('thisisthestring') == "eghhiiinrsssttt"

       _lowerCamelCase			       =      input('Enter numbers separated by a comma:\n').strip()
       _lowerCamelCase			       =      [int(item) for item in user_input.split(',')]
       print(counting_sort(unsorted))

