fix: latest versions of typing dont support Text instead str is recommended

Author: init-22

algorithmic_efficiency/halton.py

@@ -10,13 +10,13 @@
 import functools
 import itertools
 import math
-from typing import Any, Callable, Dict, List, Sequence, Text, Tuple, Union
+from typing import Any, Callable, Dict, List, Sequence, Tuple, Union
 
 from absl import logging
 from numpy import random
 
-_SweepSequence = List[Dict[Text, Any]]
-_GeneratorFn = Callable[[float], Tuple[Text, float]]
+_SweepSequence = List[Dict[str, Any]]
+_GeneratorFn = Callable[[float], Tuple[str, float]]
 
 
 def generate_primes(n: int) -> List[int]:
@@ -195,10 +195,10 @@ def generate_sequence(num_samples: int,
   return halton_sequence
 
 
-def _generate_double_point(name: Text,
+def _generate_double_point(name: str,
                            min_val: float,
                            max_val: float,
-                           scaling: Text,
+                           scaling: str,
                            halton_point: float) -> Tuple[str, float]:
   """Generate a float hyperparameter value from a Halton sequence point."""
   if scaling not in ['linear', 'log']:
@@ -234,7 +234,7 @@ def interval(start: int, end: int) -> Tuple[int, int]:
   return start, end
 
 
-def loguniform(name: Text, range_endpoints: Tuple[int, int]) -> _GeneratorFn:
+def loguniform(name: str, range_endpoints: Tuple[int, int]) -> _GeneratorFn:
   min_val, max_val = range_endpoints
   return functools.partial(_generate_double_point,
                            name,
@@ -244,7 +244,7 @@ def loguniform(name: Text, range_endpoints: Tuple[int, int]) -> _GeneratorFn:
 
 
 def uniform(
-    name: Text, search_points: Union[_DiscretePoints,
+    name: str, search_points: Union[_DiscretePoints,
                                      Tuple[int, int]]) -> _GeneratorFn:
   if isinstance(search_points, _DiscretePoints):
     return functools.partial(_generate_discrete_point,

algorithmic_efficiency/workloads/wmt/wmt_jax/workload.py

@@ -16,7 +16,7 @@
 
 from algorithmic_efficiency import param_utils
 from algorithmic_efficiency import spec
-from algorithmic_efficiency.workloads.wmt import bleu
+#from algorithmic_efficiency.workloads.wmt import bleu
 from algorithmic_efficiency.workloads.wmt.wmt_jax import decode
 from algorithmic_efficiency.workloads.wmt.wmt_jax import models
 from algorithmic_efficiency.workloads.wmt.workload import BaseWmtWorkload

algorithmic_efficiency/workloads/wmt/wmt_pytorch/workload.py

@@ -16,7 +16,7 @@
 from algorithmic_efficiency import param_utils
 from algorithmic_efficiency import pytorch_utils
 from algorithmic_efficiency import spec
-from algorithmic_efficiency.workloads.wmt import bleu
+#from algorithmic_efficiency.workloads.wmt import bleu
 from algorithmic_efficiency.workloads.wmt.wmt_pytorch import decode
 from algorithmic_efficiency.workloads.wmt.wmt_pytorch.models import Transformer
 from algorithmic_efficiency.workloads.wmt.workload import BaseWmtWorkload