
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 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							torch.FloatTensor







class 					lowerCamelCase		(  A_							,			A_  ):





     @register_to_config
     def __init__(self				:   Optional[int]    ,   _A				:   int = 3    ,   _A				:   int = 3    ,   _A				:   Tuple[str] = ("DownEncoderBlock2D",)    ,   _A				:   Tuple[str] = ("UpDecoderBlock2D",)    ,   _A				:   Tuple[int] = (6_4,)    ,   _A				:   int = 1    ,   _A				:   str = "silu"    ,   _A				:   int = 3    ,   _A				:   int = 3_2    ,   _A				:   int = 2_5_6    ,   _A				:   int = 3_2    ,   _A				:   Optional[int] = None    ,   _A				:   float = 0.1_82_15    ,   _A				:   str = "group"    ,   )						->						List[str]:
         super().__init__()

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

         snake_case								= vq_embed_dim if vq_embed_dim is not None else latent_channels

         snake_case								= nn.Convad(_A    ,   _A    ,   1       )
         snake_case								= VectorQuantizer(_A    ,   _A    ,   beta=0.25    ,   remap=_A    ,   sane_index_shape=_A       )
         snake_case								= nn.Convad(_A    ,   _A    ,   1       )

         # pass init params to Decoder
         snake_case								= Decoder(
             in_channels=_A    ,   out_channels=_A    ,   up_block_types=_A    ,   block_out_channels=_A    ,   layers_per_block=_A    ,   act_fn=_A    ,   norm_num_groups=_A    ,   norm_type=_A    ,   )





     @apply_forward_hook
     def 		UpperCAmelCase(self				:   int    ,   _A				:   torch.FloatTensor    ,   _A				:   bool = True       )						->						VQEncoderOutput:
         snake_case								= self.encoder(_A       )
         snake_case								= self.quant_conv(_A       )

         if not return_dict:
             return (h,)

         return VQEncoderOutput(latents=_A       )





     @apply_forward_hook
     def 		UpperCAmelCase(self				:   Union[str, Any]    ,   _A				:   torch.FloatTensor    ,   _A				:   bool = False    ,   _A				:   bool = True       )						->						Union[DecoderOutput, torch.FloatTensor]:
         # also go through quantization layer
         if not force_not_quantize:
             snake_case					,							snake_case					,							snake_case								= self.quantize(_A       )
         else:
             snake_case								= h
         snake_case								= self.post_quant_conv(_A       )
         snake_case								= self.decoder(_A    ,   quant if self.config.norm_type == "spatial" else None       )

         if not return_dict:
             return (dec,)

         return DecoderOutput(sample=_A       )





     def 		UpperCAmelCase(self				:   Optional[Any]    ,   _A				:   torch.FloatTensor    ,   _A				:   bool = True       )						->						Union[DecoderOutput, torch.FloatTensor]:
         snake_case								= sample
         snake_case								= self.encode(_A       ).latents
         snake_case								= self.decode(_A       ).sample

         if not return_dict:
             return (dec,)

         return DecoderOutput(sample=_A       )



from __future__ import annotations



def 						lowercase_      (       A__			,		A__			,		A__		)							->							int | float:
    """simple docstring"""

    if len(A__		) == 0:
        raise ValueError("find_max() arg is an empty sequence"		)
    if (
        left >= len(A__		)
        or left < -len(A__		)
        or right >= len(A__		)
        or right < -len(A__		)
    ):
        raise IndexError("list index out of range"		)
    if left == right:
        return nums[left]
    snake_case								= (left + right) >> 1  # the middle
    snake_case								= find_max(A__			,		A__			,		A__		)  # find max in range[left, mid]
    snake_case								= find_max(A__			,		mid + 1			,		A__		)  # find max in range[mid + 1, right]

    return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)

