


"""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      (      SCREAMING_SNAKE_CASE_ ):
    a_    :				torch.FloatTensor





class   _a      (      SCREAMING_SNAKE_CASE_		,		SCREAMING_SNAKE_CASE_ ):
    @register_to_config
    def __init__( self							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					int = 3							,					SCREAMING_SNAKE_CASE__							:					int = 3							,					SCREAMING_SNAKE_CASE__							:					Tuple[str] = ("DownEncoderBlock2D",)							,					SCREAMING_SNAKE_CASE__							:					Tuple[str] = ("UpDecoderBlock2D",)							,					SCREAMING_SNAKE_CASE__							:					Tuple[int] = (64,)							,					SCREAMING_SNAKE_CASE__							:					int = 1							,					SCREAMING_SNAKE_CASE__							:					str = "silu"							,					SCREAMING_SNAKE_CASE__							:					int = 3							,					SCREAMING_SNAKE_CASE__							:					int = 32							,					SCREAMING_SNAKE_CASE__							:					int = 2_56							,					SCREAMING_SNAKE_CASE__							:					int = 32							,					SCREAMING_SNAKE_CASE__							:					Optional[int] = None							,					SCREAMING_SNAKE_CASE__							:					float = 0.1_82_15							,					SCREAMING_SNAKE_CASE__							:					str = "group"							,					):
          super().__init__()

          # pass init params to Encoder
          lowerCamelCase__           =		Encoder(
              in_channels=SCREAMING_SNAKE_CASE__							,					out_channels=SCREAMING_SNAKE_CASE__							,					down_block_types=SCREAMING_SNAKE_CASE__							,					block_out_channels=SCREAMING_SNAKE_CASE__							,					layers_per_block=SCREAMING_SNAKE_CASE__							,					act_fn=SCREAMING_SNAKE_CASE__							,					norm_num_groups=SCREAMING_SNAKE_CASE__							,					double_z=SCREAMING_SNAKE_CASE__							,					)

          lowerCamelCase__           =		vq_embed_dim if vq_embed_dim is not None else latent_channels

          lowerCamelCase__           =		nn.Convad(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					1				)
          lowerCamelCase__           =		VectorQuantizer(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					beta=0.25							,					remap=SCREAMING_SNAKE_CASE__							,					sane_index_shape=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		nn.Convad(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					1				)

          # pass init params to Decoder
          lowerCamelCase__           =		Decoder(
              in_channels=SCREAMING_SNAKE_CASE__							,					out_channels=SCREAMING_SNAKE_CASE__							,					up_block_types=SCREAMING_SNAKE_CASE__							,					block_out_channels=SCREAMING_SNAKE_CASE__							,					layers_per_block=SCREAMING_SNAKE_CASE__							,					act_fn=SCREAMING_SNAKE_CASE__							,					norm_num_groups=SCREAMING_SNAKE_CASE__							,					norm_type=SCREAMING_SNAKE_CASE__							,					)
    @apply_forward_hook
    def      _UpperCamelCase       ( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					torch.FloatTensor							,					SCREAMING_SNAKE_CASE__							:					bool = True				):
          lowerCamelCase__           =		self.encoder(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		self.quant_conv(SCREAMING_SNAKE_CASE__				)

          if not return_dict:
                return (h,)

          return VQEncoderOutput(latents=SCREAMING_SNAKE_CASE__				)
    @apply_forward_hook
    def      _UpperCamelCase       ( self							:					Any							,					SCREAMING_SNAKE_CASE__							:					torch.FloatTensor							,					SCREAMING_SNAKE_CASE__							:					bool = False							,					SCREAMING_SNAKE_CASE__							:					bool = True				):
          # also go through quantization layer
          if not force_not_quantize:
                lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		self.quantize(SCREAMING_SNAKE_CASE__				)
          else:
                lowerCamelCase__           =		h
          lowerCamelCase__           =		self.post_quant_conv(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		self.decoder(SCREAMING_SNAKE_CASE__							,					quant if self.config.norm_type == 'spatial' else None				)

          if not return_dict:
                return (dec,)

          return DecoderOutput(sample=SCREAMING_SNAKE_CASE__				)


    def      _UpperCamelCase       ( self							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					torch.FloatTensor							,					SCREAMING_SNAKE_CASE__							:					bool = True				):
          lowerCamelCase__           =		sample
          lowerCamelCase__           =		self.encode(SCREAMING_SNAKE_CASE__				).latents
          lowerCamelCase__           =		self.decode(SCREAMING_SNAKE_CASE__				).sample

          if not return_dict:
                return (dec,)

          return DecoderOutput(sample=SCREAMING_SNAKE_CASE__				)







"""simple docstring"""


from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

_snake_case          =							TypeVar("KEY")
_snake_case          =							TypeVar("VAL")



@dataclass(frozen=SCREAMING_SNAKE_CASE_		,		slots=SCREAMING_SNAKE_CASE_ )
class   _a      (      Generic[KEY, VAL] ):
    a_    :				KEY
    a_    :				VAL





class   _a      (      _Item ):
    def __init__( self							:					List[str]				):
          super().__init__(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)


    def __bool__( self							:					str				):
          return False





_snake_case          =							_DeletedItem()



class   _a      (      MutableMapping[KEY, VAL] ):
    def __init__( self							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					int = 8							,					SCREAMING_SNAKE_CASE__							:					float = 0.75				):
          lowerCamelCase__           =		initial_block_size
          lowerCamelCase__           =		[None] * initial_block_size
          assert 0.0 < capacity_factor < 1.0
          lowerCamelCase__           =		capacity_factor
          lowerCamelCase__           =		0
    def      _UpperCamelCase       ( self							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					KEY				):
          return hash(SCREAMING_SNAKE_CASE__				) % len(self._buckets				)
    def      _UpperCamelCase       ( self							:					str							,					SCREAMING_SNAKE_CASE__							:					int				):
          return (ind + 1) % len(self._buckets				)
    def      _UpperCamelCase       ( self							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					KEY							,					SCREAMING_SNAKE_CASE__							:					VAL				):
          lowerCamelCase__           =		self._buckets[ind]
          if not stored:
                lowerCamelCase__           =		_Item(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
                self._len += 1
                return True
          elif stored.key == key:
                lowerCamelCase__           =		_Item(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
                return True
          else:
                return False
    def      _UpperCamelCase       ( self							:					Dict				):
          lowerCamelCase__           =		len(self._buckets				) * self._capacity_factor
          return len(self				) >= int(SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					int				):
          if len(self._buckets				) <= self._initial_block_size:
                return False
          lowerCamelCase__           =		len(self._buckets				) * self._capacity_factor / 2
          return len(self				) < limit
    def      _UpperCamelCase       ( self							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					int				):
          lowerCamelCase__           =		self._buckets
          lowerCamelCase__           =		[None] * new_size
          lowerCamelCase__           =		0
          for item in old_buckets:
                if item:
                      self._add_item(item.key							,					item.val				)
    def      _UpperCamelCase       ( self							:					List[str]				):
          self._resize(len(self._buckets				) * 2				)
    def      _UpperCamelCase       ( self							:					Optional[int]				):
          self._resize(len(self._buckets				) // 2				)
    def      _UpperCamelCase       ( self							:					Any							,					SCREAMING_SNAKE_CASE__							:					KEY				):
          lowerCamelCase__           =		self._get_bucket_index(SCREAMING_SNAKE_CASE__				)
          for _ in range(len(self._buckets				)				):
                yield ind
                lowerCamelCase__           =		self._get_next_ind(SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					KEY							,					SCREAMING_SNAKE_CASE__							:					VAL				):
          for ind in self._iterate_buckets(SCREAMING_SNAKE_CASE__				):
                if self._try_set(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				):
                      break
    def __setitem__( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					KEY							,					SCREAMING_SNAKE_CASE__							:					VAL				):
          if self._is_full():
                self._size_up()

          self._add_item(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
    def __delitem__( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					KEY				):
          for ind in self._iterate_buckets(SCREAMING_SNAKE_CASE__				):
                lowerCamelCase__           =		self._buckets[ind]
                if item is None:
                      raise KeyError(SCREAMING_SNAKE_CASE__				)
                if item is _deleted:
                      continue
                if item.key == key:
                      lowerCamelCase__           =		_deleted
                      self._len -= 1
                      break
          if self._is_sparse():
                self._size_down()
    def __getitem__( self							:					str							,					SCREAMING_SNAKE_CASE__							:					KEY				):
          for ind in self._iterate_buckets(SCREAMING_SNAKE_CASE__				):
                lowerCamelCase__           =		self._buckets[ind]
                if item is None:
                      break
                if item is _deleted:
                      continue
                if item.key == key:
                      return item.val
          raise KeyError(SCREAMING_SNAKE_CASE__				)
    def __len__( self							:					List[Any]				):
          return self._len
    def __iter__( self							:					Optional[int]				):
          yield from (item.key for item in self._buckets if item)


    def __repr__( self							:					str				):
          lowerCamelCase__           =		' ,'.join(
              F'{item.key}: {item.val}' for item in self._buckets if item				)
          return F'HashMap({val_string})'



