Fix make fix-copies with type annotations (#13586)
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@ -109,6 +109,10 @@ def _compute_mask_indices(
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# scatter indices to mask
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spec_aug_mask = spec_aug_mask.scatter(1, spec_aug_mask_idxs, True)
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if attention_mask is not None:
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# make sure padded input ids cannot be masked
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spec_aug_mask = torch.where(attention_mask.bool(), spec_aug_mask, False)
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return spec_aug_mask
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@ -258,7 +258,7 @@ def _compute_mask_indices(
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tf.ones_like(spec_aug_mask_idxs), spec_aug_mask_idxs, spec_aug_mask.shape
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)
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return tf.cast(spec_aug_mask, tf.float32)
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return spec_aug_mask
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# Copied from transformers.models.bart.modeling_tf_bart._expand_mask
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@ -508,9 +508,9 @@ class TFRobertaMainLayer(tf.keras.layers.Layer):
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if inputs["input_ids"] is not None and inputs["inputs_embeds"] is not None:
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raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
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elif inputs["input_ids"] is not None:
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input_shape = shape_list(tensor=inputs["input_ids"])
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input_shape = shape_list(inputs["input_ids"])
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elif inputs["inputs_embeds"] is not None:
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input_shape = shape_list(tensor=inputs["inputs_embeds"])[:-1]
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input_shape = shape_list(inputs["inputs_embeds"])[:-1]
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else:
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raise ValueError("You have to specify either input_ids or inputs_embeds")
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@ -52,7 +52,7 @@ LOCALIZED_READMES = {
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def _should_continue(line, indent):
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return line.startswith(indent) or len(line) <= 1 or re.search(r"^\s*\):\s*$", line) is not None
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return line.startswith(indent) or len(line) <= 1 or re.search(r"^\s*\)(\s*->.*:|:)\s*$", line) is not None
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def find_code_in_transformers(object_name):
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