






"""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      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				torch.FloatTensor






class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	@register_to_config
	def __init__(self     ,     lowerCAmelCase_ = 3     ,     lowerCAmelCase_ = 3     ,     lowerCAmelCase_ = ("DownEncoderBlock2D",)     ,     lowerCAmelCase_ = ("UpDecoderBlock2D",)     ,     lowerCAmelCase_ = (64,)     ,     lowerCAmelCase_ = 1     ,     lowerCAmelCase_ = "silu"     ,     lowerCAmelCase_ = 3     ,     lowerCAmelCase_ = 32     ,     lowerCAmelCase_ = 256     ,     lowerCAmelCase_ = 32     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = 0.18215     ,     lowerCAmelCase_ = "group"     ,     ):
		super().__init__()

		# pass init params to Encoder
		A_		:						str	    =   Encoder(
		    in_channels=lowerCAmelCase_     ,     out_channels=lowerCAmelCase_     ,     down_block_types=lowerCAmelCase_     ,     block_out_channels=lowerCAmelCase_     ,     layers_per_block=lowerCAmelCase_     ,     act_fn=lowerCAmelCase_     ,     norm_num_groups=lowerCAmelCase_     ,     double_z=lowerCAmelCase_     ,     )

		A_		:						Tuple	    =   vq_embed_dim if vq_embed_dim is not None else latent_channels

		A_		:						List[Any]	    =   nn.Convad(lowerCAmelCase_     ,     lowerCAmelCase_     ,     1							)
		A_		:						int	    =   VectorQuantizer(lowerCAmelCase_     ,     lowerCAmelCase_     ,     beta=0.25     ,     remap=lowerCAmelCase_     ,     sane_index_shape=lowerCAmelCase_							)
		A_		:						List[str]	    =   nn.Convad(lowerCAmelCase_     ,     lowerCAmelCase_     ,     1							)

		# pass init params to Decoder
		A_		:						str	    =   Decoder(
		    in_channels=lowerCAmelCase_     ,     out_channels=lowerCAmelCase_     ,     up_block_types=lowerCAmelCase_     ,     block_out_channels=lowerCAmelCase_     ,     layers_per_block=lowerCAmelCase_     ,     act_fn=lowerCAmelCase_     ,     norm_num_groups=lowerCAmelCase_     ,     norm_type=lowerCAmelCase_     ,     )





	@apply_forward_hook
	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = True							):
		A_		:						Any	    =   self.encoder(lowerCAmelCase_							)
		A_		:						Optional[int]	    =   self.quant_conv(lowerCAmelCase_							)

		if not return_dict:
			return (h,)

		return VQEncoderOutput(latents=lowerCAmelCase_							)





	@apply_forward_hook
	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = False     ,     lowerCAmelCase_ = True							):
		# also go through quantization layer
		if not force_not_quantize:
			A_    ,     A_    ,     A_		:						int	    =   self.quantize(lowerCAmelCase_							)
		else:
			A_		:						Dict	    =   h
		A_		:						int	    =   self.post_quant_conv(lowerCAmelCase_							)
		A_		:						Any	    =   self.decoder(lowerCAmelCase_     ,     quant if self.config.norm_type == """spatial""" else None							)

		if not return_dict:
			return (dec,)

		return DecoderOutput(sample=lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = True							):
		A_		:						Tuple	    =   sample
		A_		:						Any	    =   self.encode(lowerCAmelCase_							).latents
		A_		:						Any	    =   self.decode(lowerCAmelCase_							).sample

		if not return_dict:
			return (dec,)

		return DecoderOutput(sample=lowerCAmelCase_							)









"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Optional[int]    =				["""image_processor""", """tokenizer"""]
	_A       :				str    =				"""AutoImageProcessor"""
	_A       :				Union[str, Any]    =				"""AutoTokenizer"""





	def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		super().__init__(lowerCAmelCase_     ,     lowerCAmelCase_							)
		A_		:						Union[str, Any]	    =   self.image_processor





	def __call__(self     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     **lowerCAmelCase_							):

		if text is None and images is None:
			raise ValueError("""You have to specify either text or images. Both cannot be none."""							)

		if text is not None:
			A_		:						Optional[int]	    =   self.tokenizer(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

		if images is not None:
			A_		:						List[Any]	    =   self.image_processor(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

		if text is not None and images is not None:
			A_		:						int	    =   image_features.pixel_values
			return encoding
		elif text is not None:
			return encoding
		else:
			return BatchEncoding(data=dict(**lowerCAmelCase_							)     ,     tensor_type=lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return self.tokenizer.batch_decode(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return self.tokenizer.decode(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





	@property
	def    lowerCamelCase(self							):
		return ["input_ids", "attention_mask", "pixel_values"]

