


"""simple docstring"""





from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


snake_case__						:   Optional[Any]			 = logging.get_logger(__name__)


def    _snake_case  (  _snake_case :	Union[tf.Tensor, np.ndarray]	):
      if isinstance(_snake_case       ,      np.ndarray	):
            return list(tensor.shape	)

      lowerCAmelCase    :		Any				=      tf.shape(_snake_case	)

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

      lowerCAmelCase    :		Optional[int]				=      tensor.shape.as_list()

      return [dynamic[i] if s is None else s for i, s in enumerate(_snake_case	)]


def    _snake_case  (  _snake_case :	tf.Tensor       ,      _snake_case :	Optional[int] = None       ,      _snake_case :	Optional[str] = None	):
      return tf.nn.softmax(logits=logits + 1E-9       ,      axis=_snake_case       ,      name=_snake_case	)


def    _snake_case  (  _snake_case :	List[str]       ,      _snake_case :	Dict       ,      _snake_case :	Optional[int]       ,      _snake_case :	Any=1E-5       ,      _snake_case :	Union[str, Any]=-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(_snake_case       ,      _snake_case	):
            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
      lowerCAmelCase,					lowerCAmelCase    :		Tuple				=      tf.nn.moments(_snake_case       ,      axes=[axis]       ,      keepdims=_snake_case	)

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

      # Compute layer normalization using the batch_normalization
      # function.
      lowerCAmelCase    :		Dict				=      tf.nn.batch_normalization(
          _snake_case       ,      _snake_case       ,      _snake_case       ,      offset=_snake_case       ,      scale=_snake_case       ,      variance_epsilon=_snake_case       ,      )
      return outputs


def    _snake_case  (  _snake_case :	Dict       ,      _snake_case :	Optional[int]=0       ,      _snake_case :	Tuple=-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

      lowerCAmelCase    :		Optional[Any]				=      tf.shape(_snake_case	)
      lowerCAmelCase    :		Tuple				=      tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]	)
      lowerCAmelCase    :		str				=      tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]       ,      axis=0	)
      return tf.reshape(_snake_case       ,      _snake_case	)


def    _snake_case  (  _snake_case :	tf.Tensor	):
      if not isinstance(_snake_case       ,      tf.Tensor	):
            lowerCAmelCase    :		Any				=      tf.convert_to_tensor(_snake_case	)  # Catches stray NumPy inputs
      if encoder_attention_mask.shape.rank == 3:
            lowerCAmelCase    :		Optional[int]				=      encoder_attention_mask[:, None, :, :]
      if encoder_attention_mask.shape.rank == 2:
            lowerCAmelCase    :		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))
      lowerCAmelCase    :		Any				=      (
          tf.cast(1       ,      encoder_attention_mask.dtype	) - encoder_extended_attention_mask
      ) * encoder_extended_attention_mask.dtype.min

      return encoder_extended_attention_mask


def    _snake_case  (  _snake_case :	tf.Tensor       ,      _snake_case :	int       ,      _snake_case :	str = "input_ids"	):
      tf.debugging.assert_less(
          _snake_case       ,      tf.cast(_snake_case       ,      dtype=tensor.dtype	)       ,      message=(
              f'''The maximum value of {tensor_name} ({tf.math.reduce_max(_snake_case	)}) must be smaller than the embedding '''
              f'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
          )       ,      )


def    _snake_case  (  _snake_case :	List[Any]       ,      _snake_case :	Optional[int]       ,      _snake_case :	str	):
      lowerCAmelCase    :		Tuple				=      64512
      # 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.
      lowerCAmelCase    :		Optional[Any]				=      [x for x in data if len(_snake_case	) > 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}'''	)

      lowerCAmelCase    :		Optional[int]				=      np.asarray(_snake_case	)

      lowerCAmelCase    :		Tuple				=      1
      lowerCAmelCase    :		Optional[Any]				=      np.array_split(_snake_case       ,      _snake_case	)

      # 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
            lowerCAmelCase    :		Optional[int]				=      np.array_split(_snake_case       ,      _snake_case	)

      if num_chunks > 1:
            for chunk_id, chunk_data in enumerate(_snake_case	):
                  lowerCAmelCase    :		int				=      chunk_data
      else:
            lowerCAmelCase    :		List[Any]				=      data


def    _snake_case  (  _snake_case :	str       ,      _snake_case :	Union[str, Any]	):
      if name in group.attrs:
            lowerCAmelCase    :		Union[str, Any]				=      [n.decode('''utf8'''	) if hasattr(_snake_case       ,      '''decode'''	) else n for n in group.attrs[name]]
      else:
            lowerCAmelCase    :		Dict				=      []
            lowerCAmelCase    :		List[str]				=      0
            while "%s%d" % (name, chunk_id) in group.attrs:
                  data.extend(
                      [n.decode('''utf8'''	) if hasattr(_snake_case       ,      '''decode'''	) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]]	)
                  chunk_id += 1
      return data






def    _snake_case  (  _snake_case :	Optional[int]	):

      def _expand_single_ad_tensor(_snake_case :	List[str]	):
            if isinstance(_snake_case       ,      tf.Tensor	) and t.shape.rank == 1:
                  return tf.expand_dims(_snake_case       ,      axis=-1	)
            return t

      return tf.nest.map_structure(_expand_single_ad_tensor       ,      _snake_case	)










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase	:							Tuple				={
    '''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
    '''tokenization_luke''': ['''LukeTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase	:							Union[str, Any]				=[
     '''LUKE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''LukeForEntityClassification''',
     '''LukeForEntityPairClassification''',
     '''LukeForEntitySpanClassification''',
     '''LukeForMultipleChoice''',
     '''LukeForQuestionAnswering''',
     '''LukeForSequenceClassification''',
     '''LukeForTokenClassification''',
     '''LukeForMaskedLM''',
     '''LukeModel''',
     '''LukePreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
 from .tokenization_luke import LukeTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_luke import (
      LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
      LukeForEntityClassification,
      LukeForEntityPairClassification,
      LukeForEntitySpanClassification,
      LukeForMaskedLM,
      LukeForMultipleChoice,
      LukeForQuestionAnswering,
      LukeForSequenceClassification,
      LukeForTokenClassification,
      LukeModel,
      LukePreTrainedModel,
  )

else:
 import sys

 lowerCamelCase	:							Dict				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)