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edit//Qwen3-TTS-test//qwen_tts//core//models//processing_qwen3_tts.py

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+# coding=utf-8
+# Copyright 2026 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.processing_utils import ProcessingKwargs, ProcessorMixin
+
+
+class Qwen3TTSProcessorKwargs(ProcessingKwargs, total=False):
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+            "padding_side": "left",
+        }
+    }
+
+class Qwen3TTSProcessor(ProcessorMixin):
+    r"""
+    Constructs a Qwen3TTS processor.
+
+    Args:
+        tokenizer ([`Qwen2TokenizerFast`], *optional*):
+            The text tokenizer.
+        chat_template (`Optional[str]`, *optional*):
+            The Jinja template to use for formatting the conversation. If not provided, the default chat template is used.
+    """
+
+    attributes = ["tokenizer"]
+    tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast")
+
+    def __init__(
+        self, tokenizer=None, chat_template=None
+    ):
+        super().__init__(tokenizer, chat_template=chat_template)
+
+    def __call__(self, text=None, **kwargs) -> BatchFeature:
+        """
+        Main method to prepare for the model one or several sequences(s) and audio(s). This method forwards the `text`
+        and `kwargs` arguments to Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text` is not `None` to encode
+        the text. 
+
+        Args:
+            text (`str`, `List[str]`, `List[List[str]]`):
+                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+        """
+
+        if text is None:
+            raise ValueError("You need to specify either a `text` input to process.")
+
+        output_kwargs = self._merge_kwargs(
+            Qwen3TTSProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+        if not isinstance(text, list):
+            text = [text]
+
+        texts_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
+
+        return BatchFeature(
+            data={**texts_inputs},
+            tensor_type=kwargs.get("return_tensors"),
+        )
+
+    def batch_decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
+        refer to the docstring of this method for more information.
+        """
+        return self.tokenizer.batch_decode(*args, **kwargs)
+
+    def decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
+        the docstring of this method for more information.
+        """
+        return self.tokenizer.decode(*args, **kwargs)
+
+    def apply_chat_template(self, conversations, chat_template=None, **kwargs):
+        if isinstance(conversations[0], dict):
+            conversations = [conversations]
+        return super().apply_chat_template(conversations, chat_template, **kwargs)
+
+    @property
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        return list(
+            dict.fromkeys(
+                tokenizer_input_names
+            )
+        )
+
+
+__all__ = ["Qwen3TTSProcessor"]