





'''simple docstring'''




import math

import tensorflow as tf
from packaging import version






def        _lowerCAmelCase			(  __snake_case  : int			)     ->     int:
     __A						: Any      =       tf.convert_to_tensor(__snake_case			)
     __A						: int      =       0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0			) ,							x.dtype			)			))

     return x * cdf






def        _lowerCAmelCase			(  __snake_case  : Optional[int]			)     ->     Any:
     __A						: Union[str, Any]      =       tf.convert_to_tensor(__snake_case			)
     __A						: Tuple      =       tf.cast(math.pi ,							x.dtype			)
     __A						: Tuple      =       tf.cast(0.044_715 ,							x.dtype			)
     __A						: int      =       0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi			) * (x + coeff * tf.pow(__snake_case ,							3			))			))

     return x * cdf






def        _lowerCAmelCase			(  __snake_case  : List[str]			)     ->     Tuple:
     __A						: str      =       tf.convert_to_tensor(__snake_case			)

     return x * tf.tanh(tf.math.softplus(__snake_case			)			)






def        _lowerCAmelCase			(  __snake_case  : Any			)     ->     List[str]:
     __A						: Any      =       tf.convert_to_tensor(__snake_case			)
     __A						: Optional[Any]      =       tf.cast(0.044_715 ,							x.dtype			)
     __A						: int      =       tf.cast(0.7_978_845_608 ,							x.dtype			)

     return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)			))






def        _lowerCAmelCase			(  __snake_case  : Any			)     ->     Tuple:
     __A						: List[str]      =       tf.convert_to_tensor(__snake_case			)
     __A						: Optional[int]      =       tf.cast(1.702 ,							x.dtype			)
     return x * tf.math.sigmoid(coeff * x			)






def        _lowerCAmelCase			(  __snake_case  : List[str]			)     ->     Tuple:
     return tf.clip_by_value(_gelu(__snake_case			) ,							-10 ,							10			)






def        _lowerCAmelCase			(  __snake_case  : Dict ,							__snake_case  : Union[str, Any]=-1			)     ->     List[str]:
     __A ,__A						: Dict      =       tf.split(__snake_case ,							2 ,							axis=__snake_case			)
     return a * tf.math.sigmoid(__snake_case			)


if version.parse(tf.version.VERSION) >= version.parse('''2.4'''):






  def        _lowerCAmelCase			(  __snake_case  : List[str]			)     ->     List[Any]:
       return tf.keras.activations.gelu(__snake_case ,							approximate=__snake_case			)

  lowercase__    :      List[Any]						=    tf.keras.activations.gelu







  lowercase__    :      Any						=    approximate_gelu_wrap
else:
  lowercase__    :      Dict						=    _gelu
  lowercase__    :      List[Any]						=    _gelu_new


lowercase__    :      Union[str, Any]						=    {
    '''gelu''': gelu,
    '''gelu_10''': gelu_aa,
    '''gelu_fast''': gelu_fast,
    '''gelu_new''': gelu_new,
    '''glu''': glu,
    '''mish''': mish,
    '''quick_gelu''': quick_gelu,
    '''relu''': tf.keras.activations.relu,
    '''sigmoid''': tf.keras.activations.sigmoid,
    '''silu''': tf.keras.activations.swish,
    '''swish''': tf.keras.activations.swish,
    '''tanh''': tf.keras.activations.tanh,
}







def        _lowerCAmelCase			(  __snake_case  : Optional[Any]			)     ->     Optional[int]:
     if activation_string in ACTaFN:
          return ACTaFN[activation_string]
     else:
          raise KeyError(f'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()			)}'			)






'''simple docstring'''




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  SCREAMING_SNAKE_CASE    (a__							):
     lowerCAmelCase					=       ['''image_processor''', '''tokenizer''']
     lowerCAmelCase					=       '''BlipImageProcessor'''
     lowerCAmelCase					=       ('''BertTokenizer''', '''BertTokenizerFast''')


     def __init__(						self						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: int      =       False
          super().__init__(_UpperCAmelCase						,	_UpperCAmelCase)
          __A						: Optional[int]      =       self.image_processor


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

          '''simple docstring'''






          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:
               __A						: int      =       self.tokenizer
               __A						: Optional[Any]      =       self.tokenizer(
                   text=_UpperCAmelCase						,	add_special_tokens=_UpperCAmelCase						,	padding=_UpperCAmelCase						,	truncation=_UpperCAmelCase						,	max_length=_UpperCAmelCase						,	stride=_UpperCAmelCase						,	pad_to_multiple_of=_UpperCAmelCase						,	return_attention_mask=_UpperCAmelCase						,	return_overflowing_tokens=_UpperCAmelCase						,	return_special_tokens_mask=_UpperCAmelCase						,	return_offsets_mapping=_UpperCAmelCase						,	return_token_type_ids=_UpperCAmelCase						,	return_length=_UpperCAmelCase						,	verbose=_UpperCAmelCase						,	return_tensors=_UpperCAmelCase						,	**_UpperCAmelCase						,	)
               return text_encoding

          # add pixel_values
          __A						: List[Any]      =       self.image_processor(_UpperCAmelCase						,	return_tensors=_UpperCAmelCase)

          if text is not None:
               __A						: Optional[Any]      =       self.tokenizer(
                   text=_UpperCAmelCase						,	add_special_tokens=_UpperCAmelCase						,	padding=_UpperCAmelCase						,	truncation=_UpperCAmelCase						,	max_length=_UpperCAmelCase						,	stride=_UpperCAmelCase						,	pad_to_multiple_of=_UpperCAmelCase						,	return_attention_mask=_UpperCAmelCase						,	return_overflowing_tokens=_UpperCAmelCase						,	return_special_tokens_mask=_UpperCAmelCase						,	return_offsets_mapping=_UpperCAmelCase						,	return_token_type_ids=_UpperCAmelCase						,	return_length=_UpperCAmelCase						,	verbose=_UpperCAmelCase						,	return_tensors=_UpperCAmelCase						,	**_UpperCAmelCase						,	)
          else:
               __A						: int      =       None

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

          return encoding_image_processor


     def 							SCREAMING_SNAKE_CASE  (						self						,	*_UpperCAmelCase						,	**_UpperCAmelCase):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self						,	*_UpperCAmelCase						,	**_UpperCAmelCase):

          '''simple docstring'''






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


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[str]      =       self.tokenizer.model_input_names
          __A						: Tuple      =       self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))