


'''simple docstring'''




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, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer







@dataclass
class 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: torch.FloatTensor







class 					a			(				snake_case__			,							snake_case__  ):
 '''simple docstring'''




 @register_to_config
 def __init__(    self		,      lowerCamelCase_ = 3		,      lowerCamelCase_ = 3		,      lowerCamelCase_ = ("DownEncoderBlock2D",)		,      lowerCamelCase_ = ("UpDecoderBlock2D",)		,      lowerCamelCase_ = (6_4,)		,      lowerCamelCase_ = 1		,      lowerCamelCase_ = "silu"		,      lowerCamelCase_ = 3		,      lowerCamelCase_ = 3_2		,      lowerCamelCase_ = 2_5_6		,      lowerCamelCase_ = 3_2		,      lowerCamelCase_ = None		,      lowerCamelCase_ = 0.18215		,      lowerCamelCase_ = "group"		,      )      ->    Any:
    super().__init__()

    # pass init params to Encoder
    _a					:  Tuple						=  Encoder(
        in_channels=snake_case__		,      out_channels=snake_case__		,      down_block_types=snake_case__		,      block_out_channels=snake_case__		,      layers_per_block=snake_case__		,      act_fn=snake_case__		,      norm_num_groups=snake_case__		,      double_z=snake_case__		,      )

    _a					:  str						=  vq_embed_dim if vq_embed_dim is not None else latent_channels

    _a					:  Tuple						=  nn.Convad(snake_case__		,      snake_case__		,      1      )
    _a					:  int						=  VectorQuantizer(snake_case__		,      snake_case__		,      beta=0.25		,      remap=snake_case__		,      sane_index_shape=snake_case__      )
    _a					:  Tuple						=  nn.Convad(snake_case__		,      snake_case__		,      1      )

    # pass init params to Decoder
    _a					:  Union[str, Any]						=  Decoder(
        in_channels=snake_case__		,      out_channels=snake_case__		,      up_block_types=snake_case__		,      block_out_channels=snake_case__		,      layers_per_block=snake_case__		,      act_fn=snake_case__		,      norm_num_groups=snake_case__		,      norm_type=snake_case__		,      )




 @apply_forward_hook
 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_ = True      )      ->    Any:
    _a					:  List[Any]						=  self.encoder(snake_case__      )
    _a					:  List[str]						=  self.quant_conv(snake_case__      )

    if not return_dict:
       return (h,)

    return VQEncoderOutput(latents=snake_case__      )




 @apply_forward_hook
 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_ = False		,      lowerCamelCase_ = True      )      ->    int:




    if not force_not_quantize:
       _a					:  Any						=  self.quantize(snake_case__      )
    else:
       _a					:  Tuple						=  h
    _a					:  int						=  self.post_quant_conv(snake_case__      )
    _a					:  Optional[int]						=  self.decoder(snake_case__		,      quant if self.config.norm_type == 'spatial' else None      )

    if not return_dict:
       return (dec,)

    return DecoderOutput(sample=snake_case__      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_ = True      )      ->    int:
    _a					:  Dict						=  sample
    _a					:  int						=  self.encode(snake_case__      ).latents
    _a					:  Any						=  self.decode(snake_case__      ).sample

    if not return_dict:
       return (dec,)

    return DecoderOutput(sample=snake_case__      )




'''simple docstring'''





from math import factorial

SCREAMING_SNAKE_CASE     =       {str(digit): factorial(digit) for digit in range(1_0)}







def snake_case_     (					lowercase__		):
  if not isinstance(lowercase__   ,       lowercase__		):
    raise TypeError("Parameter number must be int"		)

  if number < 0:
    raise ValueError("Parameter number must be greater than or equal to 0"		)

  # Converts number in string to iterate on its digits and adds its factorial.
  return sum(DIGIT_FACTORIAL[digit] for digit in str(lowercase__		)		)







def snake_case_     (					lowercase__ = 6_0   ,       lowercase__ = 1_0_0_0_0_0_0		):

  if not isinstance(lowercase__   ,       lowercase__		) or not isinstance(lowercase__   ,       lowercase__		):
    raise TypeError("Parameters chain_length and number_limit must be int"		)

  if chain_length <= 0 or number_limit <= 0:
    raise ValueError(
        "Parameters chain_length and number_limit must be greater than 0"		)

  # the counter for the chains with the exact desired length
  UpperCAmelCase__      :					Union[str, Any]				       =							0
  # the cached sizes of the previous chains
  UpperCAmelCase__      :					dict[int, int]				       =							{}

  for start_chain_element in range(1   ,       lowercase__		):
    # The temporary set will contain the elements of the chain
    UpperCAmelCase__      :					Any				       =							set()
    UpperCAmelCase__      :					int				       =							0

    # Stop computing the chain when you find a cached size, a repeating item or the
    # length is greater then the desired one.
    UpperCAmelCase__      :					List[str]				       =							start_chain_element
    while (
        chain_element not in chain_sets_lengths
        and chain_element not in chain_set
        and chain_set_length <= chain_length
    ):
      chain_set.add(lowercase__		)
      chain_set_length += 1
      UpperCAmelCase__      :					List[str]				       =							digit_factorial_sum(lowercase__		)

    if chain_element in chain_sets_lengths:
      chain_set_length += chain_sets_lengths[chain_element]

    UpperCAmelCase__      :					List[str]				       =							chain_set_length

    # If chain contains the exact amount of elements increase the counter
    if chain_set_length == chain_length:
      chains_counter += 1

  return chains_counter


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(F'{solution()}')



