
'''simple docstring'''





from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE     =       logging.get_logger(__name__)






class 							UpperCAmelCase_							(    A				):

 '''simple docstring'''
 lowercase_					:    Optional[int]				=							["pixel_values"]







 def __init__(					self		:  Dict			,				snake_case__		:  bool = True			,				snake_case__		:  Union[int, float] = 1 / 2_55			,				snake_case__		:  bool = True			,				snake_case__		:  int = 8			,				**snake_case__		:  Optional[Any]			,				):



   '''simple docstring'''




   super().__init__(**snake_case__ )

   UpperCAmelCase__      :					str				       =							do_rescale
   UpperCAmelCase__      :					int				       =							rescale_factor
   UpperCAmelCase__      :					Tuple				       =							do_pad
   UpperCAmelCase__      :					Any				       =							pad_size







 def 							UpperCamelCase  (					self		:  List[Any]			,				snake_case__		:  np.ndarray			,				snake_case__		:  float			,				snake_case__		:  Optional[Union[str, ChannelDimension]] = None			,				**snake_case__		:  Tuple ):



   '''simple docstring'''




   return rescale(snake_case__			,				scale=snake_case__			,				data_format=snake_case__			,				**snake_case__ )







 def 							UpperCamelCase  (					self		:  Dict			,				snake_case__		:  np.ndarray			,				snake_case__		:  int			,				snake_case__		:  Optional[Union[str, ChannelDimension]] = None ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					int				       =							get_image_size(snake_case__ )
   UpperCAmelCase__      :					int				       =							(old_height // size + 1) * size - old_height
   UpperCAmelCase__      :					Any				       =							(old_width // size + 1) * size - old_width

   return pad(snake_case__			,				((0, pad_height), (0, pad_width))			,				mode="symmetric"			,				data_format=snake_case__ )







 def 							UpperCamelCase  (					self		:  Tuple			,				snake_case__		:  ImageInput			,				snake_case__		:  Optional[bool] = None			,				snake_case__		:  Optional[float] = None			,				snake_case__		:  Optional[bool] = None			,				snake_case__		:  Optional[int] = None			,				snake_case__		:  Optional[Union[str, TensorType]] = None			,				snake_case__		:  Union[str, ChannelDimension] = ChannelDimension.FIRST			,				**snake_case__		:  List[Any]			,				):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							do_rescale if do_rescale is not None else self.do_rescale
   UpperCAmelCase__      :					Optional[int]				       =							rescale_factor if rescale_factor is not None else self.rescale_factor
   UpperCAmelCase__      :					Tuple				       =							do_pad if do_pad is not None else self.do_pad
   UpperCAmelCase__      :					Dict				       =							pad_size if pad_size is not None else self.pad_size

   UpperCAmelCase__      :					Dict				       =							make_list_of_images(snake_case__ )

   if not valid_images(snake_case__ ):
     raise ValueError(
         "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
         "torch.Tensor, tf.Tensor or jax.ndarray." )

   if do_rescale and rescale_factor is None:
     raise ValueError("Rescale factor must be specified if do_rescale is True." )

   # All transformations expect numpy arrays.
   UpperCAmelCase__      :					List[Any]				       =							[to_numpy_array(snake_case__ ) for image in images]

   if do_rescale:
     UpperCAmelCase__      :					int				       =							[self.rescale(image=snake_case__			,				scale=snake_case__ ) for image in images]

   if do_pad:
     UpperCAmelCase__      :					List[str]				       =							[self.pad(snake_case__			,				size=snake_case__ ) for image in images]

   UpperCAmelCase__      :					Dict				       =							[to_channel_dimension_format(snake_case__			,				snake_case__ ) for image in images]

   UpperCAmelCase__      :					Any				       =							{"pixel_values": images}
   return BatchFeature(data=snake_case__			,				tensor_type=snake_case__ )





'''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 							UpperCAmelCase_							(    A				):

 '''simple docstring'''
 lowercase_					:    torch.FloatTensor






class 							UpperCAmelCase_							(    A					,				A				):

 '''simple docstring'''







 @register_to_config
 def __init__(					self		:  List[str]			,				snake_case__		:  int = 3			,				snake_case__		:  int = 3			,				snake_case__		:  Tuple[str] = ("DownEncoderBlock2D",)			,				snake_case__		:  Tuple[str] = ("UpDecoderBlock2D",)			,				snake_case__		:  Tuple[int] = (64,)			,				snake_case__		:  int = 1			,				snake_case__		:  str = "silu"			,				snake_case__		:  int = 3			,				snake_case__		:  int = 32			,				snake_case__		:  int = 2_56			,				snake_case__		:  int = 32			,				snake_case__		:  Optional[int] = None			,				snake_case__		:  float = 0.18215			,				snake_case__		:  str = "group"			,				):



   '''simple docstring'''




   super().__init__()

   # pass init params to Encoder
   UpperCAmelCase__      :					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__			,				)

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

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

   # pass init params to Decoder
   UpperCAmelCase__      :					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		:  Union[str, Any]			,				snake_case__		:  torch.FloatTensor			,				snake_case__		:  bool = True ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[Any]				       =							self.encoder(snake_case__ )
   UpperCAmelCase__      :					List[str]				       =							self.quant_conv(snake_case__ )

   if not return_dict:
     return (h,)

   return VQEncoderOutput(latents=snake_case__ )







 @apply_forward_hook
 def 							UpperCamelCase  (					self		:  List[str]			,				snake_case__		:  torch.FloatTensor			,				snake_case__		:  bool = False			,				snake_case__		:  bool = True ):



   '''simple docstring'''




   if not force_not_quantize:
     UpperCAmelCase__       ,			UpperCAmelCase__       ,			UpperCAmelCase__      :					Any				       =							self.quantize(snake_case__ )
   else:
     UpperCAmelCase__      :					Tuple				       =							h
   UpperCAmelCase__      :					int				       =							self.post_quant_conv(snake_case__ )
   UpperCAmelCase__      :					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		:  Any			,				snake_case__		:  torch.FloatTensor			,				snake_case__		:  bool = True ):



   '''simple docstring'''




   UpperCAmelCase__      :					Dict				       =							sample
   UpperCAmelCase__      :					int				       =							self.encode(snake_case__ ).latents
   UpperCAmelCase__      :					Any				       =							self.decode(snake_case__ ).sample

   if not return_dict:
     return (dec,)

   return DecoderOutput(sample=snake_case__ )



