





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 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   Tuple		      =     ['''image_processor''', '''tokenizer''']
				SCREAMING_SNAKE_CASE__     :   Any		      =     '''BlipImageProcessor'''
				SCREAMING_SNAKE_CASE__     :   Optional[int]		      =     '''AutoTokenizer'''






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										super().__init__(_lowerCAmelCase			, _lowerCAmelCase					)

										# add QFormer tokenizer
										__SCREAMING_SNAKE_CASE:				Any           =   qformer_tokenizer






				def __call__(  self			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = True			, _lowerCAmelCase = False			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = 0			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = False			, _lowerCAmelCase = False			, _lowerCAmelCase = False			, _lowerCAmelCase = False			, _lowerCAmelCase = False			, _lowerCAmelCase = True			, _lowerCAmelCase = None			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										if images is None and text is None:
																raise ValueError('''You have to specify at least images or text.'''					)

										__SCREAMING_SNAKE_CASE:				Tuple           =   BatchFeature()

										if text is not None:
																__SCREAMING_SNAKE_CASE:				Tuple           =   self.tokenizer(
																    text=_lowerCAmelCase			, add_special_tokens=_lowerCAmelCase			, padding=_lowerCAmelCase			, truncation=_lowerCAmelCase			, max_length=_lowerCAmelCase			, stride=_lowerCAmelCase			, pad_to_multiple_of=_lowerCAmelCase			, return_attention_mask=_lowerCAmelCase			, return_overflowing_tokens=_lowerCAmelCase			, return_special_tokens_mask=_lowerCAmelCase			, return_offsets_mapping=_lowerCAmelCase			, return_token_type_ids=_lowerCAmelCase			, return_length=_lowerCAmelCase			, verbose=_lowerCAmelCase			, return_tensors=_lowerCAmelCase			, **_lowerCAmelCase			, )
																encoding.update(_lowerCAmelCase					)
																__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self.qformer_tokenizer(
																    text=_lowerCAmelCase			, add_special_tokens=_lowerCAmelCase			, padding=_lowerCAmelCase			, truncation=_lowerCAmelCase			, max_length=_lowerCAmelCase			, stride=_lowerCAmelCase			, pad_to_multiple_of=_lowerCAmelCase			, return_attention_mask=_lowerCAmelCase			, return_overflowing_tokens=_lowerCAmelCase			, return_special_tokens_mask=_lowerCAmelCase			, return_offsets_mapping=_lowerCAmelCase			, return_token_type_ids=_lowerCAmelCase			, return_length=_lowerCAmelCase			, verbose=_lowerCAmelCase			, return_tensors=_lowerCAmelCase			, **_lowerCAmelCase			, )
																__SCREAMING_SNAKE_CASE:				Optional[Any]           =   qformer_text_encoding.pop('''input_ids'''					)
																__SCREAMING_SNAKE_CASE:				Any           =   qformer_text_encoding.pop('''attention_mask'''					)

										if images is not None:
																__SCREAMING_SNAKE_CASE:				List[Any]           =   self.image_processor(_lowerCAmelCase			, return_tensors=_lowerCAmelCase					)
																encoding.update(_lowerCAmelCase					)

										return encoding






				def 			snake_case_    (  self			, *_lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




										return self.tokenizer.batch_decode(*_lowerCAmelCase			, **_lowerCAmelCase					)






				def 			snake_case_    (  self			, *_lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




										return self.tokenizer.decode(*_lowerCAmelCase			, **_lowerCAmelCase					)






				@property
				# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   self.tokenizer.model_input_names
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




										if os.path.isfile(_lowerCAmelCase					):
																raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""					)
										os.makedirs(_lowerCAmelCase			, exist_ok=_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   os.path.join(_lowerCAmelCase			, '''qformer_tokenizer'''					)
										self.qformer_tokenizer.save_pretrained(_lowerCAmelCase					)
										return super().save_pretrained(_lowerCAmelCase			, **_lowerCAmelCase					)






				@classmethod
				def 			snake_case_    (  cls			, _lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Tuple           =   AutoTokenizer.from_pretrained(_lowerCAmelCase			, subfolder='''qformer_tokenizer'''					)
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   cls._get_arguments_from_pretrained(_lowerCAmelCase			, **_lowerCAmelCase					)
										args.append(_lowerCAmelCase					)
										return cls(*_lowerCAmelCase					)






import flax.linen as nn
import jax
import jax.numpy as jnp





class 					__lowerCAmelCase	(   nn.Module				):
  _a		     =    42
  _a		     =    jnp.floataa




  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        _lowercase      			=nn.Conv(
            self.out_channels				,  kernel_size=(3, 3)				,  strides=(1, 1)				,  padding=((1, 1), (1, 1))				,  dtype=self.dtype				,  )




  def __call__(							self				,  lowerCAmelCase   )    ->					Any:
        '''simple docstring'''






        _lowercase			,			_lowercase			,			_lowercase			,			_lowercase      			=hidden_states.shape
        _lowercase      			=jax.image.resize(
            lowerCAmelCase				,  shape=(batch, height * 2, width * 2, channels)				,  method='nearest'				,  )
        _lowercase      			=self.conv(lowerCAmelCase   )
        return hidden_states





class 					__lowerCAmelCase	(   nn.Module				):
  _a		     =    42
  _a		     =    jnp.floataa




  def     A__	(							self   )    ->					Any:
        '''simple docstring'''






        _lowercase      			=nn.Conv(
            self.out_channels				,  kernel_size=(3, 3)				,  strides=(2, 2)				,  padding=((1, 1), (1, 1))				,  dtype=self.dtype				,  )




  def __call__(							self				,  lowerCAmelCase   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			=self.conv(lowerCAmelCase   )
        return hidden_states





class 					__lowerCAmelCase	(   nn.Module				):
  _a		     =    42
  _a		     =    None
  _a		     =    0.0
  _a		     =    None
  _a		     =    jnp.floataa




  def     A__	(							self   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			=self.in_channels if self.out_channels is None else self.out_channels

        _lowercase      			=nn.GroupNorm(num_groups=32				,  epsilon=1e-5   )
        _lowercase      			=nn.Conv(
            lowerCAmelCase				,  kernel_size=(3, 3)				,  strides=(1, 1)				,  padding=((1, 1), (1, 1))				,  dtype=self.dtype				,  )

        _lowercase      			=nn.Dense(lowerCAmelCase				,  dtype=self.dtype   )

        _lowercase      			=nn.GroupNorm(num_groups=32				,  epsilon=1e-5   )
        _lowercase      			=nn.Dropout(self.dropout_prob   )
        _lowercase      			=nn.Conv(
            lowerCAmelCase				,  kernel_size=(3, 3)				,  strides=(1, 1)				,  padding=((1, 1), (1, 1))				,  dtype=self.dtype				,  )

        _lowercase      			=self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

        _lowercase      			=None
        if use_nin_shortcut:
              _lowercase      			=nn.Conv(
                  lowerCAmelCase				,  kernel_size=(1, 1)				,  strides=(1, 1)				,  padding='VALID'				,  dtype=self.dtype				,  )




  def __call__(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase=True   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=hidden_states
        _lowercase      			=self.norma(lowerCAmelCase   )
        _lowercase      			=nn.swish(lowerCAmelCase   )
        _lowercase      			=self.conva(lowerCAmelCase   )

        _lowercase      			=self.time_emb_proj(nn.swish(lowerCAmelCase   )   )
        _lowercase      			=jnp.expand_dims(jnp.expand_dims(lowerCAmelCase				,  1   )				,  1   )
        _lowercase      			=hidden_states + temb

        _lowercase      			=self.norma(lowerCAmelCase   )
        _lowercase      			=nn.swish(lowerCAmelCase   )
        _lowercase      			=self.dropout(lowerCAmelCase				,  lowerCAmelCase   )
        _lowercase      			=self.conva(lowerCAmelCase   )

        if self.conv_shortcut is not None:
              _lowercase      			=self.conv_shortcut(lowerCAmelCase   )

        return hidden_states + residual


