



'''simple docstring'''


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


lowerCAmelCase :					int 							=logging.get_logger(__name__)
class 				a_       (    _lowerCAmelCase							):
     __A								=      ["pixel_values"]





     def __init__(			self		: List[Any]     ,				lowercase		: bool = True     ,				lowercase		: Dict[str, int] = None     ,				lowercase		: PILImageResampling = PILImageResampling.BICUBIC     ,				lowercase		: bool = True     ,				lowercase		: Union[int, float] = 1 / 255     ,				lowercase		: bool = True     ,				lowercase		: Optional[Union[float, List[float]]] = None     ,				lowercase		: Optional[Union[float, List[float]]] = None     ,				lowercase		: bool = True     ,				**lowercase		: List[str]     ,				):





        """simple docstring"""




        super().__init__(**lowercase						)
        lowercase_      :List[str]         =			size if size is not None else {"height": 384, "width": 384}
        lowercase_      :int         =			get_size_dict(lowercase     ,				default_to_square=lowercase						)

        lowercase_      :Tuple         =			do_resize
        lowercase_      :str         =			size
        lowercase_      :Any         =			resample
        lowercase_      :List[str]         =			do_rescale
        lowercase_      :Optional[Any]         =			rescale_factor
        lowercase_      :Optional[int]         =			do_normalize
        lowercase_      :Optional[Any]         =			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        lowercase_      :Tuple         =			image_std if image_std is not None else OPENAI_CLIP_STD
        lowercase_      :str         =			do_convert_rgb





     def 							lowercase__				(			self		: int     ,				lowercase		: np.ndarray     ,				lowercase		: Dict[str, int]     ,				lowercase		: PILImageResampling = PILImageResampling.BICUBIC     ,				lowercase		: Optional[Union[str, ChannelDimension]] = None     ,				**lowercase		: int     ,				):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			get_size_dict(lowercase     ,				default_to_square=lowercase						)
        if "height" not in size or "width" not in size:
           raise ValueError(F'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'						)
        lowercase_      :List[Any]         =			(size["height"], size["width"])
        return resize(lowercase     ,				size=lowercase     ,				resample=lowercase     ,				data_format=lowercase     ,				**lowercase						)





     def 							lowercase__				(			self		: Union[str, Any]     ,				lowercase		: np.ndarray     ,				lowercase		: Union[int, float]     ,				lowercase		: Optional[Union[str, ChannelDimension]] = None     ,				**lowercase		: Optional[int]     ,				):





        """simple docstring"""




        return rescale(lowercase     ,				scale=lowercase     ,				data_format=lowercase     ,				**lowercase						)





     def 							lowercase__				(			self		: Tuple     ,				lowercase		: np.ndarray     ,				lowercase		: Union[float, List[float]]     ,				lowercase		: Union[float, List[float]]     ,				lowercase		: Optional[Union[str, ChannelDimension]] = None     ,				**lowercase		: List[Any]     ,				):





        """simple docstring"""




        return normalize(lowercase     ,				mean=lowercase     ,				std=lowercase     ,				data_format=lowercase     ,				**lowercase						)







     def 							lowercase__				(			self		: Tuple     ,				lowercase		: ImageInput     ,				lowercase		: Optional[bool] = None     ,				lowercase		: Optional[Dict[str, int]] = None     ,				lowercase		: PILImageResampling = None     ,				lowercase		: Optional[bool] = None     ,				lowercase		: Optional[float] = None     ,				lowercase		: Optional[bool] = None     ,				lowercase		: Optional[Union[float, List[float]]] = None     ,				lowercase		: Optional[Union[float, List[float]]] = None     ,				lowercase		: Optional[Union[str, TensorType]] = None     ,				lowercase		: bool = None     ,				lowercase		: ChannelDimension = ChannelDimension.FIRST     ,				**lowercase		: Optional[int]     ,				):





        """simple docstring"""




        lowercase_      :Tuple         =			do_resize if do_resize is not None else self.do_resize
        lowercase_      :Tuple         =			resample if resample is not None else self.resample
        lowercase_      :Tuple         =			do_rescale if do_rescale is not None else self.do_rescale
        lowercase_      :int         =			rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase_      :str         =			do_normalize if do_normalize is not None else self.do_normalize
        lowercase_      :Optional[Any]         =			image_mean if image_mean is not None else self.image_mean
        lowercase_      :List[str]         =			image_std if image_std is not None else self.image_std
        lowercase_      :Optional[Any]         =			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        lowercase_      :Optional[int]         =			size if size is not None else self.size
        lowercase_      :Dict         =			get_size_dict(lowercase     ,				default_to_square=lowercase						)

        lowercase_      :int         =			make_list_of_images(lowercase						)

        if not valid_images(lowercase						):
           raise ValueError(
               "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
               "torch.Tensor, tf.Tensor or jax.ndarray."						)

        if do_resize and size is None or resample is None:
           raise ValueError("Size and resample must be specified if do_resize is True."						)

        if do_rescale and rescale_factor is None:
           raise ValueError("Rescale factor must be specified if do_rescale is True."						)

        if do_normalize and (image_mean is None or image_std is None):
           raise ValueError("Image mean and std must be specified if do_normalize is True."						)

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
           lowercase_      :Optional[Any]         =			[convert_to_rgb(lowercase						) for image in images]

        # All transformations expect numpy arrays.
        lowercase_      :int         =			[to_numpy_array(lowercase						) for image in images]

        if do_resize:
           lowercase_      :Any         =			[self.resize(image=lowercase     ,				size=lowercase     ,				resample=lowercase						) for image in images]

        if do_rescale:
           lowercase_      :List[str]         =			[self.rescale(image=lowercase     ,				scale=lowercase						) for image in images]

        if do_normalize:
           lowercase_      :Optional[Any]         =			[self.normalize(image=lowercase     ,				mean=lowercase     ,				std=lowercase						) for image in images]

        lowercase_      :str         =			[to_channel_dimension_format(lowercase     ,				lowercase						) for image in images]

        lowercase_      :Any         =			BatchFeature(data={"pixel_values": images}     ,				tensor_type=lowercase						)

        return encoded_outputs









'''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			)


