





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     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			42





class     _UpperCAmelCase   (   _UpperCamelCase		,   _UpperCamelCase   ):

  """simple docstring"""


  @register_to_config
  def __init__(    self			:		List[Any] ,  lowerCAmelCase_			:		int = 6_5_5_3_6 ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		int = 2 ,  lowerCAmelCase_			:		int = 2 ,  lowerCAmelCase_			:		int = 0 ,  lowerCAmelCase_			:		str = "fourier" ,  lowerCAmelCase_			:		bool = True ,  lowerCAmelCase_			:		bool = False ,  lowerCAmelCase_			:		float = 0.0 ,  lowerCAmelCase_			:		Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") ,  lowerCAmelCase_			:		Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") ,  lowerCAmelCase_			:		Tuple[str] = "UNetMidBlock1D" ,  lowerCAmelCase_			:		str = None ,  lowerCAmelCase_			:		Tuple[int] = (3_2, 3_2, 6_4) ,  lowerCAmelCase_			:		str = None ,  lowerCAmelCase_			:		int = 8 ,  lowerCAmelCase_			:		int = 1 ,  lowerCAmelCase_			:		bool = False ,  )							->						Optional[int]:
     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 ,  3_2					)
     __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     lowercase		(    self			:		Optional[Any] ,  lowerCAmelCase_			:		torch.FloatTensor ,  lowerCAmelCase_			:		Union[torch.Tensor, float, int] ,  lowerCAmelCase_			:		bool = True ,  )							->						Union[UNetaDOutput, Tuple]:
     __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_					)








import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad



class     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  def     lowercase		(    self			:		Any					)							->						Optional[int]:
     __lowerCAmelCase							     =     1_0






  def     lowercase		(    self			:		int					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     [1, 2, 3, 4]
     __lowerCAmelCase							     =     [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
     self.assertEqual(truncate_or_pad(lowerCAmelCase_ ,  self.block_size ,  0					) ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		Optional[Any]					)							->						List[str]:
     __lowerCAmelCase							     =     [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
     __lowerCAmelCase							     =     [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
     self.assertEqual(truncate_or_pad(lowerCAmelCase_ ,  self.block_size ,  0					) ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		Any					)							->						Optional[Any]:
     __lowerCAmelCase							     =     [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0, 1_1, 1_2, 1_3]
     __lowerCAmelCase							     =     [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
     self.assertEqual(truncate_or_pad(lowerCAmelCase_ ,  self.block_size ,  0					) ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		List[str]					)							->						Any:
     __lowerCAmelCase							     =     'It was the year of Our Lord one thousand seven hundred and\n        seventy-five.\n\nSpiritual revelations were conceded to England at that\n        favoured period, as at this.'
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     process_story(lowerCAmelCase_					)
     self.assertEqual(lowerCAmelCase_ ,  []					)






  def     lowercase		(    self			:		Any					)							->						str:
     __lowerCAmelCase							     =     ''
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     process_story(lowerCAmelCase_					)
     self.assertEqual(lowerCAmelCase_ ,  []					)
     self.assertEqual(lowerCAmelCase_ ,  []					)






  def     lowercase		(    self			:		int					)							->						int:
     __lowerCAmelCase							     =     (
         'It was the year of Our Lord one thousand seven hundred and '
         'seventy-five\n\nSpiritual revelations were conceded to England '
         'at that favoured period, as at this.\n@highlight\n\nIt was the best of times'
     )
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     process_story(lowerCAmelCase_					)

     __lowerCAmelCase							     =     [
         'It was the year of Our Lord one thousand seven hundred and seventy-five.',
         'Spiritual revelations were conceded to England at that favoured period, as at this.',
     ]
     self.assertEqual(lowerCAmelCase_ ,  lowerCAmelCase_					)

     __lowerCAmelCase							     =     ['It was the best of times.']
     self.assertEqual(lowerCAmelCase_ ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		Dict					)							->						Any:
     __lowerCAmelCase							     =     torch.tensor([1, 2, 3, 4]					)
     __lowerCAmelCase							     =     torch.tensor([1, 1, 1, 1]					)
     np.testing.assert_array_equal(build_mask(lowerCAmelCase_ ,  0					).numpy() ,  expected.numpy()					)






  def     lowercase		(    self			:		List[Any]					)							->						Optional[int]:
     __lowerCAmelCase							     =     torch.tensor([1, 2, 3, 4, 2_3, 2_3, 2_3]					)
     __lowerCAmelCase							     =     torch.tensor([1, 1, 1, 1, 0, 0, 0]					)
     np.testing.assert_array_equal(build_mask(lowerCAmelCase_ ,  2_3					).numpy() ,  expected.numpy()					)






  def     lowercase		(    self			:		str					)							->						List[Any]:
     __lowerCAmelCase							     =     torch.tensor([8, 2, 3, 4, 1, 1, 1]					)
     __lowerCAmelCase							     =     torch.tensor([1, 1, 1, 1, 0, 0, 0]					)
     np.testing.assert_array_equal(build_mask(lowerCAmelCase_ ,  1					).numpy() ,  expected.numpy()					)






  def     lowercase		(    self			:		Optional[Any]					)							->						Optional[int]:
     __lowerCAmelCase							     =     1_0_1
     __lowerCAmelCase							     =     torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 1_0_1, 5, 6], [1, 1_0_1, 3, 4, 1_0_1, 6]]					)
     __lowerCAmelCase							     =     torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]					)

     __lowerCAmelCase							     =     compute_token_type_ids(lowerCAmelCase_ ,  lowerCAmelCase_					)
     np.testing.assert_array_equal(lowerCAmelCase_ ,  lowerCAmelCase_					)

