






import math

import tensorflow as tf
from packaging import version


def 				__lowerCamelCase     (							__lowerCAmelCase							:					List[str]							)     ->  str:
   __UpperCamelCase	:			Tuple					=					tf.convert_to_tensor(__lowerCAmelCase							)
   __UpperCamelCase	:			Optional[Any]					=					0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0							)					,							x.dtype							)							))

   return x * cdf


def 				__lowerCamelCase     (							__lowerCAmelCase							:					str							)     ->  Any:
   __UpperCamelCase	:			Optional[Any]					=					tf.convert_to_tensor(__lowerCAmelCase							)
   __UpperCamelCase	:			str					=					tf.cast(math.pi					,							x.dtype							)
   __UpperCamelCase	:			List[Any]					=					tf.cast(0.044715					,							x.dtype							)
   __UpperCamelCase	:			int					=					0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi							) * (x + coeff * tf.pow(__lowerCAmelCase					,							3							))							))

   return x * cdf


def 				__lowerCamelCase     (							__lowerCAmelCase							:					int							)     ->  Optional[int]:
   __UpperCamelCase	:			Dict					=					tf.convert_to_tensor(__lowerCAmelCase							)

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


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Optional[int]							)     ->  Tuple:
   __UpperCamelCase	:			List[str]					=					tf.convert_to_tensor(__lowerCAmelCase							)
   __UpperCamelCase	:			Optional[Any]					=					tf.cast(0.044715					,							x.dtype							)
   __UpperCamelCase	:			int					=					tf.cast(0.7978845608					,							x.dtype							)

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


def 				__lowerCamelCase     (							__lowerCAmelCase							:					List[str]							)     ->  Optional[int]:
   __UpperCamelCase	:			List[Any]					=					tf.convert_to_tensor(__lowerCAmelCase							)
   __UpperCamelCase	:			List[Any]					=					tf.cast(1.702					,							x.dtype							)
   return x * tf.math.sigmoid(coeff * x							)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Dict							)     ->  Union[str, Any]:
   return tf.clip_by_value(_gelu(__lowerCAmelCase							)					,							-10					,							10							)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					List[Any]					,							__lowerCAmelCase							:					Optional[Any]=-1							)     ->  Tuple:
   __UpperCamelCase	,     __UpperCamelCase	:			Any					=					tf.split(__lowerCAmelCase					,							2					,							axis=__lowerCAmelCase							)
   return a * tf.math.sigmoid(__lowerCAmelCase							)


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


       def 				__lowerCamelCase     (							__lowerCAmelCase							:					Tuple							)     ->  List[Any]:
          return tf.keras.activations.gelu(__lowerCAmelCase					,							approximate=__lowerCAmelCase							)

       UpperCamelCase    							=      tf.keras.activations.gelu



       UpperCamelCase    							=      approximate_gelu_wrap
else:
       UpperCamelCase    							=      _gelu
       UpperCamelCase    							=      _gelu_new


UpperCamelCase    							=      {
    '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     (							__lowerCAmelCase							:					str							)     ->  Union[str, Any]:
   if activation_string in ACTaFN:
      return ACTaFN[activation_string]
   else:
      raise KeyError(f'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()							)}'							)











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 		_A						( UpperCAmelCase_ ):
  lowercase_						:			Tuple						=					['''image_processor''', '''tokenizer''']
  lowercase_						:			str						=					'''BlipImageProcessor'''
  lowercase_						:			str						=					('''BertTokenizer''', '''BertTokenizerFast''')

  def __init__(    self						:	List[str]   ,				lowerCamelCase__						:	str   ,				lowerCamelCase__						:	Union[str, Any]			):
     """simple docstring"""

     __UpperCamelCase	:			Optional[int]					=					False
     super().__init__(lowerCamelCase__   ,				lowerCamelCase__			)
     __UpperCamelCase	:			Tuple					=					self.image_processor

  def __call__(    self						:	Optional[Any]   ,				lowerCamelCase__						:	ImageInput = None   ,				lowerCamelCase__						:	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,				lowerCamelCase__						:	bool = True   ,				lowerCamelCase__						:	Union[bool, str, PaddingStrategy] = False   ,				lowerCamelCase__						:	Union[bool, str, TruncationStrategy] = None   ,				lowerCamelCase__						:	Optional[int] = None   ,				lowerCamelCase__						:	int = 0   ,				lowerCamelCase__						:	Optional[int] = None   ,				lowerCamelCase__						:	Optional[bool] = None   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = True   ,				lowerCamelCase__						:	Optional[Union[str, TensorType]] = None   ,				**lowerCamelCase__						:	int   ,				):
     """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:
        __UpperCamelCase	:			Optional[int]					=					self.tokenizer
        __UpperCamelCase	:			Dict					=					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
     __UpperCamelCase	:			str					=					self.image_processor(lowerCamelCase__   ,				return_tensors=lowerCamelCase__			)

     if text is not None:
        __UpperCamelCase	:			Optional[int]					=					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:
        __UpperCamelCase	:			Optional[Any]					=					None

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

     return encoding_image_processor

  def    a					(    self						:	List[str]   ,				*lowerCamelCase__						:	Any   ,				**lowerCamelCase__						:	List[Any]			):
     """simple docstring"""

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

  def    a					(    self						:	List[str]   ,				*lowerCamelCase__						:	int   ,				**lowerCamelCase__						:	Tuple			):
     """simple docstring"""

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

  @property
  def    a					(    self						:	Any			):
     """simple docstring"""

     __UpperCamelCase	:			str					=					self.tokenizer.model_input_names
     __UpperCamelCase	:			str					=					self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)



