





"""simple docstring"""





SCREAMING_SNAKE_CASE__					=   "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						bytes			):







      '''simple docstring'''




      if not isinstance(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			):
            lowerCAmelCase  						=       F'a bytes-like object is required, not \'{data.__class__.__name__}\''
            raise TypeError(SCREAMING_SNAKE_CASE			)

      lowerCAmelCase  						=       """""".join(bin(SCREAMING_SNAKE_CASE			)[2:].zfill(8			) for byte in data			)

      lowerCAmelCase  						=       len(SCREAMING_SNAKE_CASE			) % 6 != 0

      if padding_needed:
            # The padding that will be added later
            lowerCAmelCase  						=       B"""=""" * ((6 - len(SCREAMING_SNAKE_CASE			) % 6) // 2)

            # Append binary_stream with arbitrary binary digits (0's by default) to make its
            # length a multiple of 6.
            binary_stream += "0" * (6 - len(SCREAMING_SNAKE_CASE			) % 6)
      else:
            lowerCAmelCase  						=       B""""""

      # Encode every 6 binary digits to their corresponding Base64 character
      return (
          "".join(
              B64_CHARSET[int(binary_stream[index : index + 6]							,						2			)]
              for index in range(0							,						len(SCREAMING_SNAKE_CASE			)							,						6			)			).encode()
          + padding
      )


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str			):







      '''simple docstring'''




      if not isinstance(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			) and not isinstance(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			):
            lowerCAmelCase  						=       (
                """argument should be a bytes-like object or ASCII string, """
                F'not \'{encoded_data.__class__.__name__}\''
            )
            raise TypeError(SCREAMING_SNAKE_CASE			)

      # In case encoded_data is a bytes-like object, make sure it contains only
      # ASCII characters so we convert it to a string object
      if isinstance(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			):
            try:
                  lowerCAmelCase  						=       encoded_data.decode("""utf-8"""			)
            except UnicodeDecodeError:
                  raise ValueError("""base64 encoded data should only contain ASCII characters"""			)

      lowerCAmelCase  						=       encoded_data.count("""="""			)

      # Check if the encoded string contains non base64 characters
      if padding:
            assert all(
                char in B64_CHARSET for char in encoded_data[:-padding]			), "Invalid base64 character(s) found."
      else:
            assert all(
                char in B64_CHARSET for char in encoded_data			), "Invalid base64 character(s) found."

      # Check the padding
      assert len(SCREAMING_SNAKE_CASE			) % 4 == 0 and padding < 3, "Incorrect padding"

      if padding:
            # Remove padding if there is one
            lowerCAmelCase  						=       encoded_data[:-padding]

            lowerCAmelCase  						=       """""".join(
                bin(B64_CHARSET.index(SCREAMING_SNAKE_CASE			)			)[2:].zfill(6			) for char in encoded_data			)[: -padding * 2]
      else:
            lowerCAmelCase  						=       """""".join(
                bin(B64_CHARSET.index(SCREAMING_SNAKE_CASE			)			)[2:].zfill(6			) for char in encoded_data			)

      lowerCAmelCase  						=       [
          int(binary_stream[index : index + 8]							,						2			)
          for index in range(0							,						len(SCREAMING_SNAKE_CASE			)							,						8			)
      ]

      return bytes(SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()













"""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 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      42







class 		lowercase					(					_UpperCAmelCase     ,  _UpperCAmelCase					):





      @register_to_config
      def __init__(       self      ,						lowercase = 3      ,						lowercase = 3      ,						lowercase = ("DownEncoderBlock2D",)      ,						lowercase = ("UpDecoderBlock2D",)      ,						lowercase = (64,)      ,						lowercase = 1      ,						lowercase = "silu"      ,						lowercase = 3      ,						lowercase = 32      ,						lowercase = 256      ,						lowercase = 32      ,						lowercase = None      ,						lowercase = 0.18_215      ,						lowercase = "group"      ,						)		->     Union[str, Any]:
            super().__init__()

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

            lowerCAmelCase  						=       vq_embed_dim if vq_embed_dim is not None else latent_channels

            lowerCAmelCase  						=       nn.Convad(lowercase      ,						lowercase      ,						1 )
            lowerCAmelCase  						=       VectorQuantizer(lowercase      ,						lowercase      ,						beta=0.25      ,						remap=lowercase      ,						sane_index_shape=lowercase )
            lowerCAmelCase  						=       nn.Convad(lowercase      ,						lowercase      ,						1 )

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





      @apply_forward_hook
      def     _snake_case			(       self      ,						lowercase      ,						lowercase = True )		->     VQEncoderOutput:
            lowerCAmelCase  						=       self.encoder(lowercase )
            lowerCAmelCase  						=       self.quant_conv(lowercase )

            if not return_dict:
                  return (h,)

            return VQEncoderOutput(latents=lowercase )





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

            if not return_dict:
                  return (dec,)

            return DecoderOutput(sample=lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = True )		->     Union[DecoderOutput, torch.FloatTensor]:
            lowerCAmelCase  						=       sample
            lowerCAmelCase  						=       self.encode(lowercase ).latents
            lowerCAmelCase  						=       self.decode(lowercase ).sample

            if not return_dict:
                  return (dec,)

            return DecoderOutput(sample=lowercase )






