247 lines
11 KiB
Python
247 lines
11 KiB
Python
# coding=utf-8
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# Copyright 2025 The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Processor class for EVOLLA.
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"""
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import os
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from typing import Optional, Union
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from ...feature_extraction_utils import BatchFeature
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from ...processing_utils import (
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ProcessorMixin,
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)
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from ..auto import AutoTokenizer
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PROTEIN_VALID_KEYS = ["aa_seq", "foldseek", "msa"]
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class EvollaProcessor(ProcessorMixin):
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r"""
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Constructs a EVOLLA processor which wraps a LLama tokenizer and SaProt tokenizer (EsmTokenizer) into a single processor.
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[`EvollaProcessor`] offers all the functionalities of [`EsmTokenizer`] and [`LlamaTokenizerFast`]. See the
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docstring of [`~EvollaProcessor.__call__`] and [`~EvollaProcessor.decode`] for more information.
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Args:
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protein_tokenizer (`EsmTokenizer`):
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An instance of [`EsmTokenizer`]. The protein tokenizer is a required input.
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tokenizer (`LlamaTokenizerFast`, *optional*):
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An instance of [`LlamaTokenizerFast`]. The tokenizer is a required input.
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protein_max_length (`int`, *optional*, defaults to 1024):
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The maximum length of the sequence to be generated.
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text_max_length (`int`, *optional*, defaults to 512):
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The maximum length of the text to be generated.
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"""
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attributes = ["protein_tokenizer", "tokenizer"]
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valid_kwargs = ["sequence_max_length"]
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# protein_tokenizer_class = "EsmTokenizer"
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# tokenizer_class = "LlamaTokenizerFast"
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protein_tokenizer_class = "AutoTokenizer"
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tokenizer_class = "AutoTokenizer"
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protein_tokenizer_dir_name = "protein_tokenizer"
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# tokenizer_dir_name = "text_tokenizer"
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def __init__(self, protein_tokenizer, tokenizer=None, protein_max_length=1024, text_max_length=512, **kwargs):
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if protein_tokenizer is None:
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raise ValueError("You need to specify an `protein_tokenizer`.")
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if tokenizer is None:
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raise ValueError("You need to specify a `tokenizer`.")
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super().__init__(protein_tokenizer, tokenizer)
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self.tokenizer.pad_token = "<|reserved_special_token_0|>"
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self.protein_max_length = protein_max_length
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self.text_max_length = text_max_length
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def process_proteins(self, proteins, protein_max_length=1024):
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sa_sequences = []
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for protein in proteins:
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aa_seq = protein.get("aa_seq")
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foldseek = protein.get("foldseek")
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sa_sequence = "".join([s.upper() + f.lower() for s, f in zip(aa_seq, foldseek)])
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sa_sequences.append(sa_sequence)
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sa_tokens = self.protein_tokenizer.batch_encode_plus(
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sa_sequences, return_tensors="pt", truncation=True, max_length=protein_max_length, padding=True
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)
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return sa_tokens
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def process_text(
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self,
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texts,
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text_max_length: int = 512,
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):
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prompts = []
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for messages in texts:
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prompt = self.tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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)
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prompts.append(prompt)
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prompt_inputs = self.tokenizer(
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prompts,
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add_special_tokens=False,
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return_tensors="pt",
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padding="longest",
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truncation=True,
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max_length=text_max_length,
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)
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return prompt_inputs
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def __call__(
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self,
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proteins: Optional[Union[list[dict], dict]] = None,
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messages_list: Optional[Union[list[list[dict]], list[dict]]] = None,
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protein_max_length: Optional[int] = None,
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text_max_length: Optional[int] = None,
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**kwargs,
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):
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r"""This method takes batched or non-batched proteins and messages_list and converts them into format that can be used by
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the model.
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Args:
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proteins (`Union[List[dict], dict]`):
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A list of dictionaries or a single dictionary containing the following keys:
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- `"aa_seq"` (`str`) -- The amino acid sequence of the protein.
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- `"foldseek"` (`str`) -- The foldseek string of the protein.
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messages_list (`Union[List[List[dict]], List[dict]]`):
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A list of lists of dictionaries or a list of dictionaries containing the following keys:
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- `"role"` (`str`) -- The role of the message.
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- `"content"` (`str`) -- The content of the message.
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protein_max_length (`int`, *optional*, defaults to 1024):
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The maximum length of the sequence to be generated.
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text_max_length (`int`, *optional*, defaults to 512):
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The maximum length of the text.
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Return:
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a dict with following keys:
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- `protein_input_ids` (`torch.Tensor` of shape `(batch_size, sequence_length)`) -- The input IDs for the protein sequence.
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- `protein_attention_mask` (`torch.Tensor` of shape `(batch_size, sequence_length)`) -- The attention mask for the protein sequence.
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- `text_input_ids` (`torch.Tensor` of shape `(batch_size, sequence_length)`) -- The input IDs for the text sequence.
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- `text_attention_mask` (`torch.Tensor` of shape `(batch_size, sequence_length)`) -- The attention mask for the text sequence.
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"""
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# proteins and messages_list should be provided
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if proteins is None or messages_list is None:
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raise ValueError("You need to specify `messages_list` and `proteins`.")
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protein_max_length = protein_max_length if protein_max_length is not None else self.protein_max_length
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text_max_length = text_max_length if text_max_length is not None else self.text_max_length
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# proteins should be List[dict]
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if isinstance(proteins, dict):
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proteins = [proteins]
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# messages_list should be List[List[dict]]
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if isinstance(messages_list, (list, tuple)) and not isinstance(messages_list[0], (list, tuple)):
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messages_list = [messages_list]
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# Check if batched proteins are in the correct format
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if isinstance(proteins, (list, tuple)) and not all(isinstance(p, dict) for p in proteins):
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raise ValueError("The proteins should be a list of dictionaries, but not all elements are dictionaries.")
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if isinstance(proteins, (list, tuple)) and not all(
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all(k in PROTEIN_VALID_KEYS for k in p.keys()) for p in proteins
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):
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raise ValueError(
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"There should be a list of dictionaries with keys: "
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f"{', '.join(PROTEIN_VALID_KEYS)} for each protein."
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f"But got: {proteins}"
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)
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# Check if batched messages_list is in the correct format
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if isinstance(messages_list, (list, tuple)):
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for messages in messages_list:
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if not isinstance(messages, (list, tuple)):
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raise ValueError(f"Each messages in messages_list should be a list instead of {type(messages)}.")
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if not all(isinstance(m, dict) for m in messages):
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raise ValueError(
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"Each message in messages_list should be a list of dictionaries, but not all elements are dictionaries."
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)
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if any(len(m.keys()) != 2 for m in messages) or any(
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set(m.keys()) != {"role", "content"} for m in messages
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):
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raise ValueError(
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"Each message in messages_list should be a list of dictionaries with two keys: 'role' and 'content'."
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f"But got: {messages}"
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)
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else:
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raise ValueError(
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f"The messages_list should be a list of lists of dictionaries, but it's {type(messages_list)}."
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)
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sa_tokens = self.process_proteins(proteins, protein_max_length)
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text_tokens = self.process_text(messages_list, text_max_length)
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return BatchFeature(
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data={
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"protein_input_ids": sa_tokens["input_ids"],
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"protein_attention_mask": sa_tokens["attention_mask"],
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"input_ids": text_tokens["input_ids"],
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"attention_mask": text_tokens["attention_mask"],
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}
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)
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def batch_decode(self, *args, **kwargs):
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return self.tokenizer.batch_decode(*args, **kwargs)
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def decode(self, *args, **kwargs):
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return self.tokenizer.decode(*args, **kwargs)
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def protein_batch_decode(self, *args, **kwargs):
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return self.protein_tokenizer.batch_decode(*args, **kwargs)
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def protein_decode(self, *args, **kwargs):
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return self.protein_tokenizer.decode(*args, **kwargs)
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# overwrite to save the protein tokenizer in a separate folder
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# Adapted from instructblip.processing_instructblip.py (https://github.com/huggingface/transformers/blob/9b479a245b793cac2a8b2e87c6d8e81bb24e20c4/src/transformers/models/instructblip/processing_instructblip.py#L191-L221)
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def save_pretrained(self, save_directory, **kwargs):
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# only save the protein tokenizer in sub_dir
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self.protein_tokenizer.save_pretrained(os.path.join(save_directory, self.protein_tokenizer_dir_name))
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# we modify the attributes so that only the text tokenizer are saved in the main folder
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protein_tokenizer_present = "protein_tokenizer" in self.attributes
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# find the correct position of it in the attributes list
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protein_tokenizer_index = self.attributes.index("protein_tokenizer") if protein_tokenizer_present else None
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if protein_tokenizer_present and protein_tokenizer_index is not None:
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self.attributes.remove("protein_tokenizer")
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outputs = super().save_pretrained(save_directory, **kwargs)
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if protein_tokenizer_present and protein_tokenizer_index is not None:
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self.attributes.insert(protein_tokenizer_index, "protein_tokenizer")
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return outputs
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# overwirte to load the protein tokenizer from a separate folder
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# Adapted from instructblip.processing_instructblip.py (https://github.com/huggingface/transformers/blob/9b479a245b793cac2a8b2e87c6d8e81bb24e20c4/src/transformers/models/instructblip/processing_instructblip.py#L191-L221)
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
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processor = super().from_pretrained(pretrained_model_name_or_path, **kwargs)
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# if return_unused_kwargs a tuple is returned where the second element is 'unused_kwargs'
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if isinstance(processor, tuple):
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processor = processor[0]
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protein_tokenizer = AutoTokenizer.from_pretrained(
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pretrained_model_name_or_path, subfolder=cls.protein_tokenizer_dir_name
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)
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processor.protein_tokenizer = protein_tokenizer
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return processor
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__all__ = ["EvollaProcessor"]
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