from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 		_lowerCamelCase		( UpperCamelCase_     ):
    __a						=   ["image_processor", "tokenizer"]
    __a						=   "BlipImageProcessor"
    __a						=   "AutoTokenizer"
    def __init__(	self			,			lowerCAmelCase			,			lowerCAmelCase )							->       Optional[int]:
     SCREAMING_SNAKE_CASE__: Optional[int]=   False
     super().__init__(lowerCAmelCase			,			lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: List[Any]=   self.image_processor
    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			,			)							->       BatchEncoding:
     if images is None and text is None:
      raise ValueError('''You have to specify either images or text.''' )

     # Get only text
     if images is None:
      SCREAMING_SNAKE_CASE__: str=   self.tokenizer
      SCREAMING_SNAKE_CASE__: Optional[Any]=   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			,			)
      return text_encoding

     # add pixel_values
     SCREAMING_SNAKE_CASE__: Optional[int]=   self.image_processor(lowerCAmelCase			,			return_tensors=lowerCAmelCase )

     if text is not None:
      SCREAMING_SNAKE_CASE__: List[str]=   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			,			)
     else:
      SCREAMING_SNAKE_CASE__: Optional[Any]=   None

     if text_encoding is not None:
      encoding_image_processor.update(lowerCAmelCase )

     return encoding_image_processor
    def     UpperCamelCase_					(	self			,			*lowerCAmelCase			,			**lowerCAmelCase )							->       List[str]:
     return self.tokenizer.batch_decode(*lowerCAmelCase			,			**lowerCAmelCase )
    def     UpperCamelCase_					(	self			,			*lowerCAmelCase			,			**lowerCAmelCase )							->       List[str]:
     return self.tokenizer.decode(*lowerCAmelCase			,			**lowerCAmelCase )



    @property
    # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
    def     UpperCamelCase_					(	self )							->       Dict:
     SCREAMING_SNAKE_CASE__: List[Any]=   self.tokenizer.model_input_names
     SCREAMING_SNAKE_CASE__: Optional[int]=   self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )









'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp


class 							_UpperCAmelCase    (		nn.Module      ):
    __lowerCamelCase: int
    __lowerCamelCase: jnp.dtype									=     jnp.floataa




    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  nn.Conv(
           self.out_channels	,  kernel_size=(3, 3)	,  strides=(1, 1)	,  padding=((1, 1), (1, 1))	,  dtype=self.dtype	,  )





    def __call__(      self	:							List[str]	,  a	:							Optional[int]      ):






       '''simple docstring'''






       lowercase_     ,		lowercase_     ,		lowercase_     ,		lowercase_						:     Optional[Any]							     =  hidden_states.shape
       lowercase_						:     Tuple							     =  jax.image.resize(
           a	,  shape=(batch, height * 2, width * 2, channels)	,  method="nearest"	,  )
       lowercase_						:     List[Any]							     =  self.conv(a      )
       return hidden_states


class 							_UpperCAmelCase    (		nn.Module      ):
    __lowerCamelCase: int
    __lowerCamelCase: jnp.dtype									=     jnp.floataa




    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






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





    def __call__(      self	:							Any	,  a	:							int      ):






       '''simple docstring'''






       lowercase_						:     Any							     =  self.conv(a      )
       return hidden_states


class 							_UpperCAmelCase    (		nn.Module      ):
    __lowerCamelCase: int
    __lowerCamelCase: int									=     None
    __lowerCamelCase: float									=     0.0
    __lowerCamelCase: bool									=     None
    __lowerCamelCase: jnp.dtype									=     jnp.floataa




    def 					lowerCAmelCase__					(      self	:							Optional[int]      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  self.in_channels if self.out_channels is None else self.out_channels

       lowercase_						:     Optional[int]							     =  nn.GroupNorm(num_groups=3_2	,  epsilon=1e-5      )
       lowercase_						:     Tuple							     =  nn.Conv(
           a	,  kernel_size=(3, 3)	,  strides=(1, 1)	,  padding=((1, 1), (1, 1))	,  dtype=self.dtype	,  )

       lowercase_						:     List[str]							     =  nn.Dense(a	,  dtype=self.dtype      )

       lowercase_						:     Optional[int]							     =  nn.GroupNorm(num_groups=3_2	,  epsilon=1e-5      )
       lowercase_						:     Any							     =  nn.Dropout(self.dropout_prob      )
       lowercase_						:     Dict							     =  nn.Conv(
           a	,  kernel_size=(3, 3)	,  strides=(1, 1)	,  padding=((1, 1), (1, 1))	,  dtype=self.dtype	,  )

       lowercase_						:     Tuple							     =  self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

       lowercase_						:     Optional[Any]							     =  None
       if use_nin_shortcut:
          lowercase_						:     Union[str, Any]							     =  nn.Conv(
              a	,  kernel_size=(1, 1)	,  strides=(1, 1)	,  padding="VALID"	,  dtype=self.dtype	,  )





    def __call__(      self	:							List[str]	,  a	:							str	,  a	:							Dict	,  a	:							List[str]=True      ):






       '''simple docstring'''






       lowercase_						:     Dict							     =  hidden_states
       lowercase_						:     int							     =  self.norma(a      )
       lowercase_						:     List[Any]							     =  nn.swish(a      )
       lowercase_						:     Dict							     =  self.conva(a      )

       lowercase_						:     Optional[int]							     =  self.time_emb_proj(nn.swish(a      )      )
       lowercase_						:     Tuple							     =  jnp.expand_dims(jnp.expand_dims(a	,  1      )	,  1      )
       lowercase_						:     List[str]							     =  hidden_states + temb

       lowercase_						:     Optional[Any]							     =  self.norma(a      )
       lowercase_						:     Any							     =  nn.swish(a      )
       lowercase_						:     List[str]							     =  self.dropout(a	,  a      )
       lowercase_						:     int							     =  self.conva(a      )

       if self.conv_shortcut is not None:
          lowercase_						:     List[str]							     =  self.conv_shortcut(a      )

       return hidden_states + residual




