

'''simple docstring'''



from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


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







def    a__       (  a__						):




     """simple docstring"""


     if isinstance(a__      ,    np.ndarray						):
          return list(tensor.shape						)

     __SCREAMING_SNAKE_CASE     =      tf.shape(a__						)

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

     __SCREAMING_SNAKE_CASE     =      tensor.shape.as_list()

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







def    a__       (  a__      ,    a__ = None      ,    a__ = None						):




     """simple docstring"""


     return tf.nn.softmax(logits=logits + 1E-9      ,    axis=a__      ,    name=a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__=1E-5      ,    a__=-1						):




     """simple docstring"""


     if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(a__      ,    a__						):
          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
     __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      tf.nn.moments(a__      ,    axes=[axis]      ,    keepdims=a__						)

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

     # Compute layer normalization using the batch_normalization
     # function.
     __SCREAMING_SNAKE_CASE     =      tf.nn.batch_normalization(
         a__      ,    a__      ,    a__      ,    offset=a__      ,    scale=a__      ,    variance_epsilon=a__      ,    )
     return outputs







def    a__       (  a__      ,    a__=0      ,    a__=-1						):




     """simple docstring"""


     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

     __SCREAMING_SNAKE_CASE     =      tf.shape(a__						)
     __SCREAMING_SNAKE_CASE     =      tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]						)
     __SCREAMING_SNAKE_CASE     =      tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]      ,    axis=0						)
     return tf.reshape(a__      ,    a__						)







def    a__       (  a__						):




     """simple docstring"""


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

     return encoder_extended_attention_mask







def    a__       (  a__      ,    a__      ,    a__ = "input_ids"						):




     """simple docstring"""


     tf.debugging.assert_less(
         a__      ,    tf.cast(a__      ,    dtype=tensor.dtype						)      ,    message=(
             F'The maximum value of {tensor_name} ({tf.math.reduce_max(a__						)}) must be smaller than the embedding '
             F'layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'
         )      ,    )







def    a__       (  a__      ,    a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      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.
     __SCREAMING_SNAKE_CASE     =      [x for x in data if len(a__						) > 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}'						)

     __SCREAMING_SNAKE_CASE     =      np.asarray(a__						)

     __SCREAMING_SNAKE_CASE     =      1
     __SCREAMING_SNAKE_CASE     =      np.array_split(a__      ,    a__						)

     # 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
          __SCREAMING_SNAKE_CASE     =      np.array_split(a__      ,    a__						)

     if num_chunks > 1:
          for chunk_id, chunk_data in enumerate(a__						):
               __SCREAMING_SNAKE_CASE     =      chunk_data
     else:
          __SCREAMING_SNAKE_CASE     =      data







def    a__       (  a__      ,    a__						):




     """simple docstring"""


     if name in group.attrs:
          __SCREAMING_SNAKE_CASE     =      [n.decode("""utf8"""						) if hasattr(a__      ,    """decode"""						) else n for n in group.attrs[name]]
     else:
          __SCREAMING_SNAKE_CASE     =      []
          __SCREAMING_SNAKE_CASE     =      0
          while "%s%d" % (name, chunk_id) in group.attrs:
               data.extend(
                   [n.decode("""utf8"""						) if hasattr(a__      ,    """decode"""						) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]]						)
               chunk_id += 1
     return data







def    a__       (  a__						):




     """simple docstring"""


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

     return tf.nest.map_structure(_expand_single_ad_tensor      ,    a__						)









'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase :       Union[str, Any]			   =     {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase :       Union[str, Any]			   =     [
      'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
      'SEWForCTC',
      'SEWForSequenceClassification',
      'SEWModel',
      'SEWPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_sew import (
        SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
        SEWForCTC,
        SEWForSequenceClassification,
        SEWModel,
        SEWPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase :       int			   =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






