


from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


UpperCamelCase								=	logging.get_logger(__name__)


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  if isinstance(SCREAMING_SNAKE_CASE ,			np.ndarray       ):
    return list(tensor.shape       )

  A_      :				List[str]		     =					tf.shape(SCREAMING_SNAKE_CASE       )

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

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

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


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE = None ,			SCREAMING_SNAKE_CASE = None       ):
  return tf.nn.softmax(logits=logits + 1e-9 ,			axis=SCREAMING_SNAKE_CASE ,			name=SCREAMING_SNAKE_CASE       )


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE=1e-5 ,			SCREAMING_SNAKE_CASE=-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(SCREAMING_SNAKE_CASE ,			SCREAMING_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
  A_     ,       A_      :				Union[str, Any]		     =					tf.nn.moments(SCREAMING_SNAKE_CASE ,			axes=[axis] ,			keepdims=SCREAMING_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
    A_      :				str		     =					[1] * inputs.shape.rank
    A_      :				Optional[Any]		     =					shape_list(SCREAMING_SNAKE_CASE       )[axis]
    A_      :				Tuple		     =					tf.reshape(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
    A_      :				int		     =					tf.reshape(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )

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


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE=0 ,			SCREAMING_SNAKE_CASE=-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

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


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  if not isinstance(SCREAMING_SNAKE_CASE ,			tf.Tensor       ):
    A_      :				List[str]		     =					tf.convert_to_tensor(SCREAMING_SNAKE_CASE       )  # Catches stray NumPy inputs
  if encoder_attention_mask.shape.rank == 3:
    A_      :				List[str]		     =					encoder_attention_mask[:, None, :, :]
  if encoder_attention_mask.shape.rank == 2:
    A_      :				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))
  A_      :				Union[str, Any]		     =					(
      tf.cast(1 ,			encoder_attention_mask.dtype       ) - encoder_extended_attention_mask
  ) * encoder_extended_attention_mask.dtype.min

  return encoder_extended_attention_mask


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE = "input_ids"       ):
  tf.debugging.assert_less(
      SCREAMING_SNAKE_CASE ,			tf.cast(SCREAMING_SNAKE_CASE ,			dtype=tensor.dtype       ) ,			message=(
          f'''The maximum value of {tensor_name} ({tf.math.reduce_max(SCREAMING_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   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				Any		     =					64_512
  # 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.
  A_      :				Any		     =					[x for x in data if len(SCREAMING_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}'''       )

  A_      :				List[Any]		     =					np.asarray(SCREAMING_SNAKE_CASE       )

  A_      :				int		     =					1
  A_      :				Any		     =					np.array_split(SCREAMING_SNAKE_CASE ,			SCREAMING_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
    A_      :				Tuple		     =					np.array_split(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )

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


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







def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):

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

  return tf.nest.map_structure(_expand_single_ad_tensor ,			SCREAMING_SNAKE_CASE       )








from math import isclose, sqrt


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				List[str]		     =					point_y / 4 / point_x
  A_      :				Union[str, Any]		     =					2 * normal_gradient / (1 + normal_gradient * normal_gradient)
  A_      :				int		     =					(1 - normal_gradient * normal_gradient) / (
      1 + normal_gradient * normal_gradient
  )
  A_      :				List[str]		     =					(sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

  # to find the next point, solve the simultaeneous equations:
  # y^2 + 4x^2 = 100
  # y - b = m * (x - a)
  # ==> A x^2 + B x + C = 0
  A_      :				List[str]		     =					outgoing_gradient**2 + 4
  A_      :				List[str]		     =					2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
  A_      :				Any		     =					(point_y - outgoing_gradient * point_x) ** 2 - 100

  A_      :				str		     =					(
      -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term       )
  ) / (2 * quadratic_term)
  A_      :				Any		     =					(
      -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term       )
  ) / (2 * quadratic_term)

  # two solutions, one of which is our input point
  A_      :				int		     =					x_minus if isclose(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) else x_plus
  A_      :				Any		     =					point_y + outgoing_gradient * (next_x - point_x)

  return next_x, next_y, outgoing_gradient


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE = 1.4 ,			SCREAMING_SNAKE_CASE = -9.6       ):
  A_      :				int		     =					0
  A_      :				float		     =					first_x_coord
  A_      :				float		     =					first_y_coord
  A_      :				float		     =					(1_0.1 - point_y) / (0.0 - point_x)

  while not (-0.0_1 <= point_x <= 0.0_1 and point_y > 0):
    A_     ,       A_     ,       A_      :				List[str]		     =					next_point(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
    num_reflections += 1

  return num_reflections


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




