




"""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__							:      Union[tf.Tensor, np.ndarray]					):
  """simple docstring"""

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

  _snake_case							:	List[Any]			=						tf.shape(snake_case__					)

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

  _snake_case							:	int			=						tensor.shape.as_list()

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


def       UpperCAmelCase__ (snake_case__							:      tf.Tensor	,     snake_case__							:      Optional[int] = None	,     snake_case__							:      Optional[str] = None					):
  """simple docstring"""

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


def       UpperCAmelCase__ (snake_case__							:      Union[str, Any]	,     snake_case__							:      Any	,     snake_case__							:      Any	,     snake_case__							:      Dict=1e-5	,     snake_case__							:      int=-1					):
  """simple docstring"""

  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
  _snake_case	,      _snake_case							:	int			=						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
    _snake_case							:	Optional[Any]			=						[1] * inputs.shape.rank
    _snake_case							:	Optional[Any]			=						shape_list(snake_case__					)[axis]
    _snake_case							:	Optional[int]			=						tf.reshape(snake_case__	,     snake_case__					)
    _snake_case							:	List[str]			=						tf.reshape(snake_case__	,     snake_case__					)

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


def       UpperCAmelCase__ (snake_case__							:      str	,     snake_case__							:      Any=0	,     snake_case__							:      Tuple=-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							:	Any			=						tf.shape(snake_case__					)
  _snake_case							:	Any			=						tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]					)
  _snake_case							:	int			=						tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]	,     axis=0					)
  return tf.reshape(snake_case__	,     snake_case__					)


def       UpperCAmelCase__ (snake_case__							:      tf.Tensor					):
  """simple docstring"""

  if not isinstance(snake_case__	,     tf.Tensor					):
    _snake_case							:	Optional[int]			=						tf.convert_to_tensor(snake_case__					)  # Catches stray NumPy inputs
  if encoder_attention_mask.shape.rank == 3:
    _snake_case							:	int			=						encoder_attention_mask[:, None, :, :]
  if encoder_attention_mask.shape.rank == 2:
    _snake_case							:	List[str]			=						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							:	Optional[int]			=						(
      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__							:      tf.Tensor	,     snake_case__							:      int	,     snake_case__							:      str = "input_ids"					):
  """simple docstring"""

  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       UpperCAmelCase__ (snake_case__							:      Tuple	,     snake_case__							:      int	,     snake_case__							:      Optional[int]					):
  """simple docstring"""

  _snake_case							:	Union[str, 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							:	List[str]			=						[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}"					)

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

  _snake_case							:	Dict			=						1
  _snake_case							:	Dict			=						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
    _snake_case							:	int			=						np.array_split(snake_case__	,     snake_case__					)

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


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

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

  def _expand_single_ad_tensor(snake_case__							:      Union[str, Any]					):
    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__					)








"""simple docstring"""


from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

A_      			=       (
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
A_      			=       [ord(letter) for letter in string.ascii_lowercase]
A_      			=       {ord(char) for char in VALID_CHARS}

A_      			=       ["the", "be", "to", "of", "and", "in", "that", "have"]


def       UpperCAmelCase__ (snake_case__							:      list[int]	,     snake_case__							:      tuple[int, ...]					):
  """simple docstring"""

  _snake_case							:	str			=						""
  _snake_case							:	int
  _snake_case							:	int
  _snake_case							:	int

  for keychar, cipherchar in zip(cycle(snake_case__					)	,     snake_case__					):
    _snake_case							:	List[str]			=						cipherchar ^ keychar
    if decodedchar not in VALID_INTS:
      return None
    decoded += chr(snake_case__					)

  return decoded


def       UpperCAmelCase__ (snake_case__							:      list[int]					):
  """simple docstring"""

  _snake_case							:	list[str]			=						[]
  for key in product(snake_case__	,     repeat=3					):
    _snake_case							:	List[Any]			=						try_key(snake_case__	,     snake_case__					)
    if encoded is not None:
      possibles.append(snake_case__					)
  return possibles


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

  return [possible for possible in possibles if common_word in possible.lower()]


def       UpperCAmelCase__ (snake_case__							:      str = "p059_cipher.txt"					):
  """simple docstring"""

  _snake_case							:	list[int]
  _snake_case							:	list[str]
  _snake_case							:	str
  _snake_case							:	str
  _snake_case							:	str			=						Path(snake_case__					).parent.joinpath(snake_case__					).read_text(encoding="""utf-8"""					)

  _snake_case							:	List[Any]			=						[int(snake_case__					) for number in data.strip().split(""","""					)]

  _snake_case							:	Optional[Any]			=						filter_valid_chars(snake_case__					)
  for common_word in COMMON_WORDS:
    _snake_case							:	Union[str, Any]			=						filter_common_word(snake_case__	,     snake_case__					)
    if len(snake_case__					) == 1:
      break

  _snake_case							:	Optional[int]			=						possibles[0]
  return sum(ord(snake_case__					) for char in decoded_text					)


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


