





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block

@dataclass
class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    4_2

class SCREAMING_SNAKE_CASE_ (					lowercase_							,       lowercase_   ):





					"""simple docstring"""


					@register_to_config
					def __init__( self	,  _lowerCAmelCase = 6_5536	,  _lowerCAmelCase = None	,  _lowerCAmelCase = 2	,  _lowerCAmelCase = 2	,  _lowerCAmelCase = 0	,  _lowerCAmelCase = "fourier"	,  _lowerCAmelCase = True	,  _lowerCAmelCase = False	,  _lowerCAmelCase = 0.0	,  _lowerCAmelCase = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")	,  _lowerCAmelCase = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")	,  _lowerCAmelCase = "UNetMidBlock1D"	,  _lowerCAmelCase = None	,  _lowerCAmelCase = (32, 32, 64)	,  _lowerCAmelCase = None	,  _lowerCAmelCase = 8	,  _lowerCAmelCase = 1	,  _lowerCAmelCase = False	,  ):
										super().__init__()
										lowerCamelCase__       =       sample_size

										# time
										if time_embedding_type == "fourier":
															lowerCamelCase__       =       GaussianFourierProjection(
															    embedding_size=8	,  set_W_to_weight=_lowerCAmelCase	,  log=_lowerCAmelCase	,  flip_sin_to_cos=_lowerCAmelCase	)
															lowerCamelCase__       =       2 * block_out_channels[0]
										elif time_embedding_type == "positional":
															lowerCamelCase__       =       Timesteps(
															    block_out_channels[0]	,  flip_sin_to_cos=_lowerCAmelCase	,  downscale_freq_shift=_lowerCAmelCase	)
															lowerCamelCase__       =       block_out_channels[0]

										if use_timestep_embedding:
															lowerCamelCase__       =       block_out_channels[0] * 4
															lowerCamelCase__       =       TimestepEmbedding(
															    in_channels=_lowerCAmelCase	,  time_embed_dim=_lowerCAmelCase	,  act_fn=_lowerCAmelCase	,  out_dim=block_out_channels[0]	,  )

										lowerCamelCase__       =       nn.ModuleList([]	)
										lowerCamelCase__       =       None
										lowerCamelCase__       =       nn.ModuleList([]	)
										lowerCamelCase__       =       None

										# down
										lowerCamelCase__       =       in_channels
										for i, down_block_type in enumerate(_lowerCAmelCase	):
															lowerCamelCase__       =       output_channel
															lowerCamelCase__       =       block_out_channels[i]

															if i == 0:
																				input_channel += extra_in_channels

															lowerCamelCase__       =       i == len(_lowerCAmelCase	) - 1

															lowerCamelCase__       =       get_down_block(
															    _lowerCAmelCase	,  num_layers=_lowerCAmelCase	,  in_channels=_lowerCAmelCase	,  out_channels=_lowerCAmelCase	,  temb_channels=block_out_channels[0]	,  add_downsample=not is_final_block or downsample_each_block	,  )
															self.down_blocks.append(_lowerCAmelCase	)

										# mid
										lowerCamelCase__       =       get_mid_block(
										    _lowerCAmelCase	,  in_channels=block_out_channels[-1]	,  mid_channels=block_out_channels[-1]	,  out_channels=block_out_channels[-1]	,  embed_dim=block_out_channels[0]	,  num_layers=_lowerCAmelCase	,  add_downsample=_lowerCAmelCase	,  )

										# up
										lowerCamelCase__       =       list(reversed(_lowerCAmelCase	)	)
										lowerCamelCase__       =       reversed_block_out_channels[0]
										if out_block_type is None:
															lowerCamelCase__       =       out_channels
										else:
															lowerCamelCase__       =       block_out_channels[0]

										for i, up_block_type in enumerate(_lowerCAmelCase	):
															lowerCamelCase__       =       output_channel
															lowerCamelCase__       =       (
															    reversed_block_out_channels[i + 1] if i < len(_lowerCAmelCase	) - 1 else final_upsample_channels
															)

															lowerCamelCase__       =       i == len(_lowerCAmelCase	) - 1

															lowerCamelCase__       =       get_up_block(
															    _lowerCAmelCase	,  num_layers=_lowerCAmelCase	,  in_channels=_lowerCAmelCase	,  out_channels=_lowerCAmelCase	,  temb_channels=block_out_channels[0]	,  add_upsample=not is_final_block	,  )
															self.up_blocks.append(_lowerCAmelCase	)
															lowerCamelCase__       =       output_channel

										# out
										lowerCamelCase__       =       norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4	,  32	)
										lowerCamelCase__       =       get_out_block(
										    out_block_type=_lowerCAmelCase	,  num_groups_out=_lowerCAmelCase	,  embed_dim=block_out_channels[0]	,  out_channels=_lowerCAmelCase	,  act_fn=_lowerCAmelCase	,  fc_dim=block_out_channels[-1] // 4	,  )






					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = True	,  ):
										lowerCamelCase__       =       timestep
										if not torch.is_tensor(_lowerCAmelCase	):
															lowerCamelCase__       =       torch.tensor([timesteps]	,  dtype=torch.long	,  device=sample.device	)
										elif torch.is_tensor(_lowerCAmelCase	) and len(timesteps.shape	) == 0:
															lowerCamelCase__       =       timesteps[None].to(sample.device	)

										lowerCamelCase__       =       self.time_proj(_lowerCAmelCase	)
										if self.config.use_timestep_embedding:
															lowerCamelCase__       =       self.time_mlp(_lowerCAmelCase	)
										else:
															lowerCamelCase__       =       timestep_embed[..., None]
															lowerCamelCase__       =       timestep_embed.repeat([1, 1, sample.shape[2]]	).to(sample.dtype	)
															lowerCamelCase__       =       timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])	)

										# 2. down
										lowerCamelCase__       =       ()
										for downsample_block in self.down_blocks:
															lowerCamelCase__    ,		lowerCamelCase__       =       downsample_block(hidden_states=_lowerCAmelCase	,  temb=_lowerCAmelCase	)
															down_block_res_samples += res_samples

										# 3. mid
										if self.mid_block:
															lowerCamelCase__       =       self.mid_block(_lowerCAmelCase	,  _lowerCAmelCase	)

										# 4. up
										for i, upsample_block in enumerate(self.up_blocks	):
															lowerCamelCase__       =       down_block_res_samples[-1:]
															lowerCamelCase__       =       down_block_res_samples[:-1]
															lowerCamelCase__       =       upsample_block(_lowerCAmelCase	,  res_hidden_states_tuple=_lowerCAmelCase	,  temb=_lowerCAmelCase	)

										# 5. post-process
										if self.out_block:
															lowerCamelCase__       =       self.out_block(_lowerCAmelCase	,  _lowerCAmelCase	)

										if not return_dict:
															return (sample,)

										return UNetaDOutput(sample=_lowerCAmelCase	)













from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
A_												=     Lock()





def    __UpperCamelCase      (    a,     a,     a,     a,     a,     a,     a) ->Optional[int]:
					global process_lock

					# we perform n swaps since after n swaps we know we are sorted
					# we *could* stop early if we are sorted already, but it takes as long to
					# find out we are sorted as it does to sort the list with this algorithm
					for i in range(0,     10):
										if (i + position) % 2 == 0 and r_send is not None:
															# send your value to your right neighbor
															process_lock.acquire()
															r_send[1].send(a)
															process_lock.release()

															# receive your right neighbor's value
															process_lock.acquire()
															lowerCamelCase__       =       rr_cv[0].recv()
															process_lock.release()

															# take the lower value since you are on the left
															lowerCamelCase__       =       min(a,     a)
										elif (i + position) % 2 != 0 and l_send is not None:
															# send your value to your left neighbor
															process_lock.acquire()
															l_send[1].send(a)
															process_lock.release()

															# receive your left neighbor's value
															process_lock.acquire()
															lowerCamelCase__       =       lr_cv[0].recv()
															process_lock.release()

															# take the higher value since you are on the right
															lowerCamelCase__       =       max(a,     a)
    # after all swaps are performed, send the values back to main
					result_pipe[1].send(a)





def    __UpperCamelCase      (    a) ->Optional[Any]:
					lowerCamelCase__       =       []
					lowerCamelCase__       =       []
					# initialize the list of pipes where the values will be retrieved
					for _ in arr:
										result_pipe.append(Pipe())
					# creates the processes
					# the first and last process only have one neighbor so they are made outside
					# of the loop
					lowerCamelCase__       =       Pipe()
					lowerCamelCase__       =       Pipe()
					process_array_.append(
					    Process(
					        target=a,     args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]),     ))
					lowerCamelCase__       =       temp_rs
					lowerCamelCase__       =       temp_rr

					for i in range(1,     len(a) - 1):
										lowerCamelCase__       =       Pipe()
										lowerCamelCase__       =       Pipe()
										process_array_.append(
										    Process(
										        target=a,     args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]),     ))
										lowerCamelCase__       =       temp_rs
										lowerCamelCase__       =       temp_rr

					process_array_.append(
					    Process(
					        target=a,     args=(
					            len(a) - 1,
					            arr[len(a) - 1],
					            temp_ls,
					            None,
					            temp_lr,
					            None,
					            result_pipe[len(a) - 1],
					        ),     ))

					# start the processes
					for p in process_array_:
										p.start()

					# wait for the processes to end and write their values to the list
					for p in range(0,     len(a)):
										lowerCamelCase__       =       result_pipe[p][0].recv()
										process_array_[p].join()
					return arr





def    __UpperCamelCase      (    ) ->List[Any]:
					lowerCamelCase__       =       list(range(10,     0,     -1))
					print("Initial List")
					print(*a)
					lowerCamelCase__       =       odd_even_transposition(a)
					print("Sorted List\n")
					print(*a)


if __name__ == "__main__":
						main()

