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Add Pyrefly suppressions #1296

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4 changes: 2 additions & 2 deletions tensorflow_model_optimization/python/core/common/keras/compression/algorithms/epr_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -182,9 +182,9 @@ def test_reduces_model_size_at_reasonable_accuracy(self):
self.assertGreater(accuracy, .9)

with self.subTest("compressed_weights_are_smaller"):
original_size = sum(
original_size = sum( # pyrefly: ignore[missing-attribute]
map(get_weight_size_in_bytes, model.weights)).numpy()
compressed_size = sum(
compressed_size = sum( # pyrefly: ignore[missing-attribute]
map(get_weight_size_in_bytes, compressed_model.weights)).numpy()
self.assertLess(compressed_size, 0.01 * original_size)

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2 changes: 1 addition & 1 deletion tensorflow_model_optimization/python/core/common/keras/compression/internal/optimize.py
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Expand Up @@ -360,7 +360,7 @@ def create_layer_for_training(layer, algorithm):

input_shape = layer.input_shape

compressible_weights = algorithm.get_compressible_weights(layer)
compressible_weights = algorithm.get_compressible_weights(layer) # pyrefly: ignore[missing-attribute]

# Clone layer for two reasons:
#
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4 changes: 2 additions & 2 deletions tensorflow_model_optimization/python/core/common/keras/compression/schedules.py
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Expand Up @@ -87,7 +87,7 @@ def __init__(self,
self.begin_step = begin_step
self.end_value = end_value
self.decay_steps = decay_steps
self.end_step = self.begin_step + self.decay_steps
self.end_step = self.begin_step + self.decay_steps # pyrefly: ignore[unsupported-operation]
self.exponent = exponent
self.dtype = dtype

Expand All @@ -111,7 +111,7 @@ def _during_decay(self, step: Union[int, tf.Tensor]) -> tf.Tensor:
"""Return decayed scheduled value."""

with tf.name_scope(self.name or "PolynomialDecay"):
local_steps = tf.cast(step - self.begin_step, dtype=tf.float32)
local_steps = tf.cast(step - self.begin_step, dtype=tf.float32) # pyrefly: ignore[unsupported-operation]
decay_term = tf.math.divide(local_steps,
tf.cast(self.decay_steps, dtype=tf.float32))
total_delta = tf.cast(self.start_value - self.end_value, dtype=tf.float32)
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