





"""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    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:torch.FloatTensor






class    _lowerCAmelCase  (	a    ,  a			):
       """simple docstring"""




       @register_to_config
       def __init__(       self       ,					__UpperCAmelCase = 3       ,					__UpperCAmelCase = 3       ,					__UpperCAmelCase = ("DownEncoderBlock2D",)       ,					__UpperCAmelCase = ("UpDecoderBlock2D",)       ,					__UpperCAmelCase = (6_4,)       ,					__UpperCAmelCase = 1       ,					__UpperCAmelCase = "silu"       ,					__UpperCAmelCase = 3       ,					__UpperCAmelCase = 3_2       ,					__UpperCAmelCase = 2_5_6       ,					__UpperCAmelCase = 3_2       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = 0.1_82_15       ,					__UpperCAmelCase = "group"       ,					):



         '''simple docstring'''

         super().__init__()

         # pass init params to Encoder
         lowerCAmelCase__     :Optional[Any]											=				Encoder(
             in_channels=__UpperCAmelCase       ,					out_channels=__UpperCAmelCase       ,					down_block_types=__UpperCAmelCase       ,					block_out_channels=__UpperCAmelCase       ,					layers_per_block=__UpperCAmelCase       ,					act_fn=__UpperCAmelCase       ,					norm_num_groups=__UpperCAmelCase       ,					double_z=__UpperCAmelCase       ,					)

         lowerCAmelCase__     :Optional[int]											=				vq_embed_dim if vq_embed_dim is not None else latent_channels

         lowerCAmelCase__     :List[str]											=				nn.Convad(__UpperCAmelCase       ,					__UpperCAmelCase       ,					1 )
         lowerCAmelCase__     :List[str]											=				VectorQuantizer(__UpperCAmelCase       ,					__UpperCAmelCase       ,					beta=0.25       ,					remap=__UpperCAmelCase       ,					sane_index_shape=__UpperCAmelCase )
         lowerCAmelCase__     :Union[str, Any]											=				nn.Convad(__UpperCAmelCase       ,					__UpperCAmelCase       ,					1 )

         # pass init params to Decoder
         lowerCAmelCase__     :Tuple											=				Decoder(
             in_channels=__UpperCAmelCase       ,					out_channels=__UpperCAmelCase       ,					up_block_types=__UpperCAmelCase       ,					block_out_channels=__UpperCAmelCase       ,					layers_per_block=__UpperCAmelCase       ,					act_fn=__UpperCAmelCase       ,					norm_num_groups=__UpperCAmelCase       ,					norm_type=__UpperCAmelCase       ,					)






       @apply_forward_hook
       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = True ):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				self.encoder(__UpperCAmelCase )
         lowerCAmelCase__     :List[Any]											=				self.quant_conv(__UpperCAmelCase )

         if not return_dict:
           return (h,)

         return VQEncoderOutput(latents=__UpperCAmelCase )






       @apply_forward_hook
       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = False       ,					__UpperCAmelCase = True ):



         '''simple docstring'''

         if not force_not_quantize:
           lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__     :Union[str, Any]											=				self.quantize(__UpperCAmelCase )
         else:
           lowerCAmelCase__     :List[str]											=				h
         lowerCAmelCase__     :Dict											=				self.post_quant_conv(__UpperCAmelCase )
         lowerCAmelCase__     :Tuple											=				self.decoder(__UpperCAmelCase       ,					quant if self.config.norm_type == 'spatial' else None )

         if not return_dict:
           return (dec,)

         return DecoderOutput(sample=__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = True ):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				sample
         lowerCAmelCase__     :Optional[int]											=				self.encode(__UpperCAmelCase ).latents
         lowerCAmelCase__     :Tuple											=				self.decode(__UpperCAmelCase ).sample

         if not return_dict:
           return (dec,)

         return DecoderOutput(sample=__UpperCAmelCase )







import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      ['image_processor', 'tokenizer']
 __UpperCamelCase       =      'BlipImageProcessor'
 __UpperCamelCase       =      'AutoTokenizer'

 def __init__(  self,		A_,		A_,		A_		)	->       str:
    super().__init__(A_,		A_		)

    # add QFormer tokenizer
    UpperCAmelCase__						=qformer_tokenizer

 def __call__(  self,		A_ = None,		A_ = None,		A_ = True,		A_ = False,		A_ = None,		A_ = None,		A_ = 0,		A_ = None,		A_ = None,		A_ = False,		A_ = False,		A_ = False,		A_ = False,		A_ = False,		A_ = True,		A_ = None,		**A_,		)	->       BatchFeature:
    if images is None and text is None:
       raise ValueError("You have to specify at least images or text."		)

    UpperCAmelCase__						=BatchFeature()

    if text is not None:
       UpperCAmelCase__						=self.tokenizer(
           text=A_,		add_special_tokens=A_,		padding=A_,		truncation=A_,		max_length=A_,		stride=A_,		pad_to_multiple_of=A_,		return_attention_mask=A_,		return_overflowing_tokens=A_,		return_special_tokens_mask=A_,		return_offsets_mapping=A_,		return_token_type_ids=A_,		return_length=A_,		verbose=A_,		return_tensors=A_,		**A_,		)
       encoding.update(A_		)
       UpperCAmelCase__						=self.qformer_tokenizer(
           text=A_,		add_special_tokens=A_,		padding=A_,		truncation=A_,		max_length=A_,		stride=A_,		pad_to_multiple_of=A_,		return_attention_mask=A_,		return_overflowing_tokens=A_,		return_special_tokens_mask=A_,		return_offsets_mapping=A_,		return_token_type_ids=A_,		return_length=A_,		verbose=A_,		return_tensors=A_,		**A_,		)
       UpperCAmelCase__						=qformer_text_encoding.pop("input_ids"		)
       UpperCAmelCase__						=qformer_text_encoding.pop("attention_mask"		)

    if images is not None:
       UpperCAmelCase__						=self.image_processor(A_,		return_tensors=A_		)
       encoding.update(A_		)

    return encoding

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       List[Any]:
    return self.tokenizer.batch_decode(*A_,		**A_		)

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       int:
    return self.tokenizer.decode(*A_,		**A_		)

 @property
 # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
 def  __UpperCAmelCase						(  self		)	->       List[str]:
    UpperCAmelCase__						=self.tokenizer.model_input_names
    UpperCAmelCase__						=self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)

 def  __UpperCAmelCase						(  self,		A_,		**A_		)	->       str:
    if os.path.isfile(A_		):
       raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""		)
    os.makedirs(A_,		exist_ok=A_		)
    UpperCAmelCase__						=os.path.join(A_,		"qformer_tokenizer"		)
    self.qformer_tokenizer.save_pretrained(A_		)
    return super().save_pretrained(A_,		**A_		)






 @classmethod
 def  __UpperCAmelCase						(  cls,		A_,		**A_		)	->       int:
    UpperCAmelCase__						=AutoTokenizer.from_pretrained(A_,		subfolder="qformer_tokenizer"		)
    UpperCAmelCase__						=cls._get_arguments_from_pretrained(A_,		**A_		)
    args.append(A_		)
    return cls(*A_		)



