




"""simple docstring"""


from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


A_      =    logging.get_logger(__name__)


def  UpperCAmelCase__	(snake_case__							: List[str]   ):
  """simple docstring"""

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

  _snake_case							:		int								=  tf.shape(a__   )

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

  _snake_case							:		List[Any]								=  tensor.shape.as_list()

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


def  UpperCAmelCase__	(snake_case__							: Union[str, Any]		,       snake_case__							: Union[str, Any] = None		,       snake_case__							: str = None   ):
  """simple docstring"""

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


def  UpperCAmelCase__	(snake_case__							: Any		,       snake_case__							: Optional[Any]		,       snake_case__							: Optional[Any]		,       snake_case__							: Optional[int]=1e-5		,       snake_case__							: List[Any]=-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
  _snake_case		, _snake_case							:		Tuple								=  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
    _snake_case							:		Optional[Any]								=  [1] * inputs.shape.rank
    _snake_case							:		Optional[Any]								=  shape_list(a__   )[axis]
    _snake_case							:		Tuple								=  tf.reshape(a__		,       a__   )
    _snake_case							:		Tuple								=  tf.reshape(a__		,       a__   )

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


def  UpperCAmelCase__	(snake_case__							: Optional[int]		,       snake_case__							: Union[str, Any]=0		,       snake_case__							: Dict=-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

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


def  UpperCAmelCase__	(snake_case__							: Tuple   ):
  """simple docstring"""

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

  return encoder_extended_attention_mask


def  UpperCAmelCase__	(snake_case__							: str		,       snake_case__							: Union[str, Any]		,       snake_case__							: Dict = "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  UpperCAmelCase__	(snake_case__							: List[Any]		,       snake_case__							: str		,       snake_case__							: int   ):
  """simple docstring"""

  _snake_case							:		Any								=  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.
  _snake_case							:		Optional[int]								=  [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}"   )

  _snake_case							:		Tuple								=  np.asarray(a__   )

  _snake_case							:		List[Any]								=  1
  _snake_case							:		Optional[int]								=  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
    _snake_case							:		List[str]								=  np.array_split(a__		,       a__   )

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


def  UpperCAmelCase__	(snake_case__							: List[str]		,       snake_case__							: Tuple   ):
  """simple docstring"""

  if name in group.attrs:
    _snake_case							:		Union[str, Any]								=  [n.decode("""utf8"""   ) if hasattr(a__		,       """decode"""   ) else n for n in group.attrs[name]]
  else:
    _snake_case							:		Dict								=  []
    _snake_case							:		Optional[Any]								=  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  UpperCAmelCase__	(snake_case__							: int   ):
  """simple docstring"""

  def _expand_single_ad_tensor(snake_case__							: List[str]   ):
    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"""


def  UpperCAmelCase__	(snake_case__							: int = 10_00   ):
  """simple docstring"""

  _snake_case		, _snake_case							:		List[Any]								=  1, 1
  _snake_case							:		str								=  []
  for i in range(1		,       n + 1   ):
    _snake_case							:		Any								=  prev_numerator + 2 * prev_denominator
    _snake_case							:		Optional[Any]								=  prev_numerator + prev_denominator
    if len(str(snake_case__   )   ) > len(str(snake_case__   )   ):
      result.append(snake_case__   )
    _snake_case							:		int								=  numerator
    _snake_case							:		Any								=  denominator

  return len(snake_case__   )


if __name__ == "__main__":
     print(F'''{solution() = }''')


