





'''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 				a_       (    _lowerCAmelCase							):
     __A								=      ["image_processor", "tokenizer"]
     __A								=      "BlipImageProcessor"
     __A								=      "AutoTokenizer"





     def __init__(			self		: List[str]     ,				lowercase		: Any     ,				lowercase		: List[str]						):





        """simple docstring"""




        lowercase_      :List[Any]         =			False
        super().__init__(lowercase     ,				lowercase						)
        lowercase_      :Dict         =			self.image_processor





     def __call__(			self		: Tuple     ,				lowercase		: ImageInput = None     ,				lowercase		: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,				lowercase		: bool = True     ,				lowercase		: Union[bool, str, PaddingStrategy] = False     ,				lowercase		: Union[bool, str, TruncationStrategy] = None     ,				lowercase		: Optional[int] = None     ,				lowercase		: int = 0     ,				lowercase		: Optional[int] = None     ,				lowercase		: Optional[bool] = None     ,				lowercase		: bool = False     ,				lowercase		: bool = False     ,				lowercase		: bool = False     ,				lowercase		: bool = False     ,				lowercase		: bool = False     ,				lowercase		: bool = True     ,				lowercase		: Optional[Union[str, TensorType]] = None     ,				**lowercase		: Optional[Any]     ,				):





        """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:
           lowercase_      :Union[str, Any]         =			self.tokenizer
           lowercase_      :Dict         =			self.tokenizer(
               text=lowercase     ,				add_special_tokens=lowercase     ,				padding=lowercase     ,				truncation=lowercase     ,				max_length=lowercase     ,				stride=lowercase     ,				pad_to_multiple_of=lowercase     ,				return_attention_mask=lowercase     ,				return_overflowing_tokens=lowercase     ,				return_special_tokens_mask=lowercase     ,				return_offsets_mapping=lowercase     ,				return_token_type_ids=lowercase     ,				return_length=lowercase     ,				verbose=lowercase     ,				return_tensors=lowercase     ,				**lowercase     ,				)
           return text_encoding

        # add pixel_values
        lowercase_      :Tuple         =			self.image_processor(lowercase     ,				return_tensors=lowercase						)

        if text is not None:
           lowercase_      :Tuple         =			self.tokenizer(
               text=lowercase     ,				add_special_tokens=lowercase     ,				padding=lowercase     ,				truncation=lowercase     ,				max_length=lowercase     ,				stride=lowercase     ,				pad_to_multiple_of=lowercase     ,				return_attention_mask=lowercase     ,				return_overflowing_tokens=lowercase     ,				return_special_tokens_mask=lowercase     ,				return_offsets_mapping=lowercase     ,				return_token_type_ids=lowercase     ,				return_length=lowercase     ,				verbose=lowercase     ,				return_tensors=lowercase     ,				**lowercase     ,				)
        else:
           lowercase_      :List[str]         =			None

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

        return encoding_image_processor





     def 							lowercase__				(			self		: Tuple     ,				*lowercase		: Optional[Any]     ,				**lowercase		: int						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: Optional[Any]     ,				*lowercase		: Optional[int]     ,				**lowercase		: List[Any]						):





        """simple docstring"""




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







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





        """simple docstring"""




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









'''simple docstring'''


from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


lowerCAmelCase :					Tuple 							=logging.get_logger(__name__)
def 							UpperCAmelCase_  (						__lowerCamelCase : Union[tf.Tensor, np.ndarray]			):
   if isinstance(__lowerCamelCase     ,np.ndarray			):
      return list(tensor.shape			)

   lowercase_      :Optional[int]         =			tf.shape(__lowerCamelCase			)

   if tensor.shape == tf.TensorShape(__lowerCamelCase			):
      return dynamic

   lowercase_      :Union[str, Any]         =			tensor.shape.as_list()

   return [dynamic[i] if s is None else s for i, s in enumerate(__lowerCamelCase			)]
def 							UpperCAmelCase_  (						__lowerCamelCase : tf.Tensor     ,__lowerCamelCase : Optional[int] = None     ,__lowerCamelCase : Optional[str] = None			):
   return tf.nn.softmax(logits=logits + 1e-9     ,axis=__lowerCamelCase     ,name=__lowerCamelCase			)
def 							UpperCAmelCase_  (						__lowerCamelCase : str     ,__lowerCamelCase : List[Any]     ,__lowerCamelCase : Any     ,__lowerCamelCase : List[str]=1e-5     ,__lowerCamelCase : List[str]=-1			):
   # This is a very simplified functional layernorm, designed to duplicate
   # the functionality of PyTorch nn.functional.layer_norm when this is needed to port
   # models in Transformers.

   if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__lowerCamelCase     ,__lowerCamelCase			):
      raise NotImplementedError("Only 1D weight and bias tensors are supported for now, with only a single axis."			)

   # Get mean and variance on the axis to be normalized
   lowercase_      ,							lowercase_      :List[str]         =			tf.nn.moments(__lowerCamelCase     ,axes=[axis]     ,keepdims=__lowerCamelCase			)

   if axis != -1:
      # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
      # on every dimension except axis
      lowercase_      :Union[str, Any]         =			[1] * inputs.shape.rank
      lowercase_      :Optional[Any]         =			shape_list(__lowerCamelCase			)[axis]
      lowercase_      :List[str]         =			tf.reshape(__lowerCamelCase     ,__lowerCamelCase			)
      lowercase_      :Dict         =			tf.reshape(__lowerCamelCase     ,__lowerCamelCase			)

   # Compute layer normalization using the batch_normalization
   # function.
   lowercase_      :Union[str, Any]         =			tf.nn.batch_normalization(
       __lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase     ,offset=__lowerCamelCase     ,scale=__lowerCamelCase     ,variance_epsilon=__lowerCamelCase     ,)
   return outputs
def 							UpperCAmelCase_  (						__lowerCamelCase : Optional[int]     ,__lowerCamelCase : Union[str, Any]=0     ,__lowerCamelCase : Dict=-1			):
   # Replicates the behavior of torch.flatten in TF

   # If end_dim or start_dim is negative, count them from the end
   if end_dim < 0:
      end_dim += input.shape.rank
   if start_dim < 0:
      start_dim += input.shape.rank

   if start_dim == end_dim:
      return input

   lowercase_      :Optional[int]         =			tf.shape(__lowerCamelCase			)
   lowercase_      :Optional[int]         =			tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]			)
   lowercase_      :List[str]         =			tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]     ,axis=0			)
   return tf.reshape(__lowerCamelCase     ,__lowerCamelCase			)
def 							UpperCAmelCase_  (						__lowerCamelCase : tf.Tensor			):
   if not isinstance(__lowerCamelCase     ,tf.Tensor			):
      lowercase_      :str         =			tf.convert_to_tensor(__lowerCamelCase			)  # Catches stray NumPy inputs
   if encoder_attention_mask.shape.rank == 3:
      lowercase_      :List[Any]         =			encoder_attention_mask[:, None, :, :]
   if encoder_attention_mask.shape.rank == 2:
      lowercase_      :Optional[int]         =			encoder_attention_mask[:, None, None, :]
   # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
   # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
   # /transformer/transformer_layers.py#L270
   # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
   # encoder_extended_attention_mask.transpose(-1, -2))
   lowercase_      :str         =			(
       tf.cast(1     ,encoder_attention_mask.dtype			) - encoder_extended_attention_mask
   ) * encoder_extended_attention_mask.dtype.min

   return encoder_extended_attention_mask
def 							UpperCAmelCase_  (						__lowerCamelCase : tf.Tensor     ,__lowerCamelCase : int     ,__lowerCamelCase : str = "input_ids"			):
   tf.debugging.assert_less(
       __lowerCamelCase     ,tf.cast(__lowerCamelCase     ,dtype=tensor.dtype			)     ,message=(
           F'The maximum value of {tensor_name} ({tf.math.reduce_max(__lowerCamelCase			)}) must be smaller than the embedding '
           F'layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'
       )     ,)
def 							UpperCAmelCase_  (						__lowerCamelCase : List[str]     ,__lowerCamelCase : Tuple     ,__lowerCamelCase : Dict			):
   lowercase_      :int         =			6_45_12
   # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
   # because in that case even chunking the array would not make the saving
   # possible.
   lowercase_      :Union[str, Any]         =			[x for x in data if len(__lowerCamelCase			) > HDF5_OBJECT_HEADER_LIMIT]

   # Expecting this to never be true.
   if bad_attributes:
      raise RuntimeError(
          "The following attributes cannot be saved to HDF5 file because "
          F'they are larger than {HDF5_OBJECT_HEADER_LIMIT} '
          F'bytes: {bad_attributes}'			)

   lowercase_      :Union[str, Any]         =			np.asarray(__lowerCamelCase			)

   lowercase_      :Optional[int]         =			1
   lowercase_      :int         =			np.array_split(__lowerCamelCase     ,__lowerCamelCase			)

   # This will never loop forever thanks to the test above.
   while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data			):
      num_chunks += 1
      lowercase_      :List[Any]         =			np.array_split(__lowerCamelCase     ,__lowerCamelCase			)

   if num_chunks > 1:
      for chunk_id, chunk_data in enumerate(__lowerCamelCase			):
         lowercase_      :int         =			chunk_data
   else:
      lowercase_      :Tuple         =			data
def 							UpperCAmelCase_  (						__lowerCamelCase : str     ,__lowerCamelCase : Tuple			):
   if name in group.attrs:
      lowercase_      :Optional[Any]         =			[n.decode("utf8"			) if hasattr(__lowerCamelCase     ,"decode"			) else n for n in group.attrs[name]]
   else:
      lowercase_      :List[str]         =			[]
      lowercase_      :str         =			0
      while "%s%d" % (name, chunk_id) in group.attrs:
         data.extend(
             [n.decode("utf8"			) if hasattr(__lowerCamelCase     ,"decode"			) else n for n in group.attrs["%s%d" % (name, chunk_id)]]			)
         chunk_id += 1
   return data


def 							UpperCAmelCase_  (						__lowerCamelCase : str			):

   def _expand_single_ad_tensor(__lowerCamelCase : Tuple			):
      if isinstance(__lowerCamelCase     ,tf.Tensor			) and t.shape.rank == 1:
         return tf.expand_dims(__lowerCamelCase     ,axis=-1			)
      return t

   return tf.nest.map_structure(_expand_single_ad_tensor     ,__lowerCamelCase			)


