Delete private most_specific_compatible_type helper
PiperOrigin-RevId: 435706841
diff --git a/tensorflow/python/framework/type_spec.py b/tensorflow/python/framework/type_spec.py
index 8ae980d..192dadc 100644
--- a/tensorflow/python/framework/type_spec.py
+++ b/tensorflow/python/framework/type_spec.py
@@ -15,7 +15,6 @@
"""Type specifications for TensorFlow APIs."""
import abc
-import collections
import functools
import re
from typing import List, Optional, Sequence, Any
@@ -566,101 +565,6 @@
return a.is_compatible_with(b)
return a == b
- # TODO(b/221459366): Remove after usages are removed.
- @staticmethod
- def __most_specific_compatible_type_serialization(a, b):
- """Helper for most_specific_compatible_type.
-
- Combines two type serializations as follows:
-
- * If they are both tuples of the same length, then recursively combine
- the respective tuple elements.
- * If they are both dicts with the same keys, then recursively combine
- the respective dict elements.
- * If they are both TypeSpecs, then combine using
- TypeSpec.most_specific_compatible_type.
- * If they are both TensorShapes, then combine using
- TensorShape.most_specific_compatible_shape.
- * If they are both TensorSpecs with the same dtype, then combine using
- TensorShape.most_specific_compatible_shape to combine shapes.
- * If they are equal, then return a.
- * If none of the above, then raise a ValueError.
-
- Args:
- a: A serialized TypeSpec or nested component from a serialized TypeSpec.
- b: A serialized TypeSpec or nested component from a serialized TypeSpec.
-
- Returns:
- A value with the same type and structure as `a` and `b`.
-
- Raises:
- ValueError: If `a` and `b` are incompatible.
- """
- if not TypeSpec.__same_types(a, b):
- raise ValueError(
- f"Encountered incompatible types while determining the most specific "
- f"compatible type. "
- f"The Python type structures of `a` and `b` are different. "
- f"`a` : {a!r} `b` : {b!r}")
- if nest.is_namedtuple(a):
- assert a._fields == b._fields # Implied by __same_types(a, b).
- return type(a)(*[
- TypeSpec.__most_specific_compatible_type_serialization(x, y)
- for (x, y) in zip(a, b)
- ])
- if isinstance(a, (list, tuple)):
- if len(a) != len(b):
- raise ValueError(
- f"Encountered incompatible types while determining the most specific "
- f"compatible type. "
- f"Type spec structure `a` has a length of {len(a)} and "
- f"type spec structure `b` has a different length of {len(b)}."
- f"`a` : {a!r} `b` : {b!r}")
- return tuple(
- TypeSpec.__most_specific_compatible_type_serialization(x, y)
- for (x, y) in zip(a, b))
- if isinstance(a, collections.OrderedDict):
- a_keys, b_keys = a.keys(), b.keys()
- if len(a) != len(b) or a_keys != b_keys:
- raise ValueError(
- f"Encountered incompatible types while determining the most specific "
- f"compatible type. "
- f"Type spec structure `a` has keys {a_keys} and "
- f"type spec structure `b` has different keys {b_keys}."
- f"`a` : {a!r} `b` : {b!r}")
- return collections.OrderedDict([
- (k,
- TypeSpec.__most_specific_compatible_type_serialization(a[k], b[k]))
- for k in a_keys
- ])
- if isinstance(a, dict):
- a_keys, b_keys = sorted(a.keys()), sorted(b.keys())
- if len(a) != len(b) or a_keys != b_keys:
- raise ValueError(
- f"Encountered incompatible types while determining the most specific "
- f"compatible type. "
- f"Type spec structure `a` has keys {a_keys} and "
- f"type spec structure `b` has different keys {b_keys}."
- f"`a` : {a!r} `b` : {b!r}")
- return {
- k: TypeSpec.__most_specific_compatible_type_serialization(a[k], b[k])
- for k in a_keys
- }
- if isinstance(a, tensor_shape.TensorShape):
- return a.most_specific_compatible_shape(b)
- if isinstance(a, list):
- raise AssertionError(
- f"{type(a).__name__}._serialize() should not return list values.")
- if isinstance(a, TypeSpec):
- return a.most_specific_compatible_type(b)
- if a != b:
- raise ValueError(
- f"Encountered incompatible types while determining the most specific "
- f"compatible type. "
- f"Type spec structure `a` and `b` are different. "
- f"`a` : {a!r} `b` : {b!r}")
- return a
-
class TypeSpecBatchEncoder(object, metaclass=abc.ABCMeta):
"""Class used to encode and decode composite tensor values for batching.
