Update model.py

model.py

@@ -1,353 +1,714 @@
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-import torch
-from modules.file import ExcelFileWriter
-import os
-
-from abc import ABC, abstractmethod
-from typing import List
-import re
-
-class FilterPipeline():
-    def __init__(self, filter_list):
-        self._filter_list:List[Filter] = filter_list
-
-    def append(self, filter):
-        self._filter_list.append(filter)
-
-    def batch_encoder(self, inputs):
-        for filter in self._filter_list:
-            inputs = filter.encoder(inputs)
-        return inputs
-    
-    def batch_decoder(self, inputs):
-        for filter in reversed(self._filter_list):
-            inputs = filter.decoder(inputs)
-        return inputs
-
-class Filter(ABC):
-    def __init__(self):
-        self.name = 'filter'
-        self.code = []
-    @abstractmethod
-    def encoder(self, inputs):
-        pass
-
-    @abstractmethod
-    def decoder(self, inputs):
-        pass
-
-class SpecialTokenFilter(Filter):
-    def __init__(self):
-        self.name = 'special token filter'
-        self.code = []
-        self.special_tokens = ['!', '！']
-    
-    def encoder(self, inputs):
-        filtered_inputs = []
-        self.code = []
-        for i, input_str in enumerate(inputs):
-            if not all(char in self.special_tokens for char in input_str):
-                filtered_inputs.append(input_str)
-            else:
-                self.code.append([i, input_str])
-        return filtered_inputs
-    
-    def decoder(self, inputs):
-        original_inputs = inputs.copy()
-        for removed_indice in self.code:
-            original_inputs.insert(removed_indice[0], removed_indice[1])
-        return original_inputs
-
-class SperSignFilter(Filter):
-    def __init__(self):
-        self.name = 's persign filter'
-        self.code = []
-    
-    def encoder(self, inputs):
-        encoded_inputs = []
-        self.code = []  # 清空 self.code
-        for i, input_str in enumerate(inputs):
-            if 's%' in input_str:
-                encoded_str = input_str.replace('s%', '*')
-                self.code.append(i)  # 将包含 's%' 的字符串的索引存储到 self.code 中
-            else:
-                encoded_str = input_str
-            encoded_inputs.append(encoded_str)
-        return encoded_inputs
-    
-    def decoder(self, inputs):
-        decoded_inputs = inputs.copy()
-        for i in self.code:
-            decoded_inputs[i] = decoded_inputs[i].replace('*', 's%')  # 使用 self.code 中的索引还原原始字符串
-        return decoded_inputs
-    
-class SimilarFilter(Filter):
-    def __init__(self):
-        self.name = 'similar filter'
-        self.code = []
-    
-    def is_similar(self, str1, str2):
-        # 判断两个字符串是否相似（只有数字上有区别）
-        pattern = re.compile(r'\d+')
-        return pattern.sub('', str1) == pattern.sub('', str2)
-    
-    def encoder(self, inputs):
-        encoded_inputs = []
-        self.code = []  # 清空 self.code
-        i = 0
-        while i < len(inputs):
-            encoded_inputs.append(inputs[i])
-            similar_strs = [inputs[i]]
-            j = i + 1
-            while j < len(inputs) and self.is_similar(inputs[i], inputs[j]):
-                similar_strs.append(inputs[j])
-                j += 1
-            if len(similar_strs) > 1:
-                self.code.append((i, similar_strs))  # 将相似字符串的起始索引和实际字符串列表存储到 self.code 中
-            i = j
-        return encoded_inputs
-    
-    def decoder(self, inputs):
-        decoded_inputs = []
-        index = 0
-        for i, similar_strs in self.code:
-            decoded_inputs.extend(inputs[index:i])
-            decoded_inputs.extend(similar_strs)  # 直接将实际的相似字符串添加到 decoded_inputs 中
-            index = i + 1
-        decoded_inputs.extend(inputs[index:])
-        return decoded_inputs
-
-script_dir = os.path.dirname(os.path.abspath(__file__))
-parent_dir = os.path.dirname(os.path.dirname(os.path.dirname(script_dir)))
-
-class Model():
-    def __init__(self, modelname, selected_lora_model, selected_gpu):
-        def get_gpu_index(gpu_info, target_gpu_name):
-            """
-            从 GPU 信息中获取目标 GPU 的索引
-            Args:
-                gpu_info (list): 包含 GPU 名称的列表
-                target_gpu_name (str): 目标 GPU 的名称
-
-            Returns:
-                int: 目标 GPU 的索引，如果未找到则返回 -1
-            """
-            for i, name in enumerate(gpu_info):
-                if target_gpu_name.lower() in name.lower():
-                    return i
-            return -1
-        if selected_gpu != "cpu":
-            gpu_count = torch.cuda.device_count()
-            gpu_info = [torch.cuda.get_device_name(i) for i in range(gpu_count)]
-            selected_gpu_index = get_gpu_index(gpu_info, selected_gpu)
-            self.device_name = f"cuda:{selected_gpu_index}"
-        else:
-            self.device_name = "cpu"
-        print("device_name", self.device_name)
-        self.model = AutoModelForSeq2SeqLM.from_pretrained(modelname).to(self.device_name)
-        self.tokenizer = AutoTokenizer.from_pretrained(modelname)
-        # self.translator = pipeline('translation', model=self.original_model, tokenizer=self.tokenizer, src_lang=original_language, tgt_lang=target_language, device=device)
-    
-    def generate(self, inputs, original_language, target_languages, max_batch_size):
-        filter_list = [SpecialTokenFilter(), SperSignFilter(), SimilarFilter()]
-        filter_pipeline = FilterPipeline(filter_list)
-        def language_mapping(original_language):
-            d = {
-                "Achinese (Arabic script)": "ace_Arab",
-                "Achinese (Latin script)": "ace_Latn",
-                "Mesopotamian Arabic": "acm_Arab",
-                "Ta'izzi-Adeni Arabic": "acq_Arab",
-                "Tunisian Arabic": "aeb_Arab",
-                "Afrikaans": "afr_Latn",
-                "South Levantine Arabic": "ajp_Arab",
-                "Akan": "aka_Latn",
-                "Amharic": "amh_Ethi",
-                "North Levantine Arabic": "apc_Arab",
-                "Standard Arabic": "arb_Arab",
-                "Najdi Arabic": "ars_Arab",
-                "Moroccan Arabic": "ary_Arab",
-                "Egyptian Arabic": "arz_Arab",
-                "Assamese": "asm_Beng",
-                "Asturian": "ast_Latn",
-                "Awadhi": "awa_Deva",
-                "Central Aymara": "ayr_Latn",
-                "South Azerbaijani": "azb_Arab",
-                "North Azerbaijani": "azj_Latn",
-                "Bashkir": "bak_Cyrl",
-                "Bambara": "bam_Latn",
-                "Balinese": "ban_Latn",
-                "Belarusian": "bel_Cyrl",
-                "Bemba": "bem_Latn",
-                "Bengali": "ben_Beng",
-                "Bhojpuri": "bho_Deva",
-                "Banjar (Arabic script)": "bjn_Arab",
-                "Banjar (Latin script)": "bjn_Latn",
-                "Tibetan": "bod_Tibt",
-                "Bosnian": "bos_Latn",
-                "Buginese": "bug_Latn",
-                "Bulgarian": "bul_Cyrl",
-                "Catalan": "cat_Latn",
-                "Cebuano": "ceb_Latn",
-                "Czech": "ces_Latn",
-                "Chokwe": "cjk_Latn",
-                "Central Kurdish": "ckb_Arab",
-                "Crimean Tatar": "crh_Latn",
-                "Welsh": "cym_Latn",
-                "Danish": "dan_Latn",
-                "German": "deu_Latn",
-                "Dinka": "dik_Latn",
-                "Jula": "dyu_Latn",
-                "Dzongkha": "dzo_Tibt",
-                "Greek": "ell_Grek",
-                "English": "eng_Latn",
-                "Esperanto": "epo_Latn",
-                "Estonian": "est_Latn",
-                "Basque": "eus_Latn",
-                "Ewe": "ewe_Latn",
-                "Faroese": "fao_Latn",
-                "Persian": "pes_Arab",
-                "Fijian": "fij_Latn",
-                "Finnish": "fin_Latn",
-                "Fon": "fon_Latn",
-                "French": "fra_Latn",
-                "Friulian": "fur_Latn",
-                "Nigerian Fulfulde": "fuv_Latn",
-                "Scottish Gaelic": "gla_Latn",
-                "Irish": "gle_Latn",
-                "Galician": "glg_Latn",
-                "Guarani": "grn_Latn",
-                "Gujarati": "guj_Gujr",
-                "Haitian Creole": "hat_Latn",
-                "Hausa": "hau_Latn",
-                "Hebrew": "heb_Hebr",
-                "Hindi": "hin_Deva",
-                "Chhattisgarhi": "hne_Deva",
-                "Croatian": "hrv_Latn",
-                "Hungarian": "hun_Latn",
-                "Armenian": "hye_Armn",
-                "Igbo": "ibo_Latn",
-                "Iloko": "ilo_Latn",
-                "Indonesian": "ind_Latn",
-                "Icelandic": "isl_Latn",
-                "Italian": "ita_Latn",
-                "Javanese": "jav_Latn",
-                "Japanese": "jpn_Jpan",
-                "Kabyle": "kab_Latn",
-                "Kachin": "kac_Latn",
-                "Arabic": "ar_AR",
-                "Chinese": "zho_Hans", 
-                "Spanish": "spa_Latn",
-                "Dutch": "nld_Latn", 
-                "Kazakh": "kaz_Cyrl", 
-                "Korean": "kor_Hang", 
-                "Lithuanian": "lit_Latn",
-                "Malayalam": "mal_Mlym", 
-                "Marathi": "mar_Deva", 
-                "Nepali": "ne_NP", 
-                "Polish": "pol_Latn", 
-                "Portuguese": "por_Latn", 
-                "Russian": "rus_Cyrl", 
-                "Sinhala": "sin_Sinh",
-                "Tamil": "tam_Taml", 
-                "Turkish": "tur_Latn", 
-                "Ukrainian": "ukr_Cyrl", 
-                "Urdu": "urd_Arab", 
-                "Vietnamese": "vie_Latn", 
-                "Thai":"tha_Thai"
-            }
-            return d[original_language]
-        def process_gpu_translate_result(temp_outputs):
-            outputs = []
-            for temp_output in temp_outputs:
-                length = len(temp_output[0]["generated_translation"])
-                for i in range(length):
-                    temp = []
-                    for trans in temp_output:
-                        temp.append({
-                            "target_language": trans["target_language"],
-                            "generated_translation": trans['generated_translation'][i],
-                        })
-                    outputs.append(temp)
-            excel_writer = ExcelFileWriter()
-            excel_writer.write_text(os.path.join(parent_dir,r"temp/empty.xlsx"), outputs, 'A', 1, len(outputs))
-        self.tokenizer.src_lang = language_mapping(original_language)
-        if self.device_name == "cpu":
-            # Tokenize input
-            input_ids = self.tokenizer(inputs, return_tensors="pt", padding=True, max_length=128).to(self.device_name)
-            output = []
-            for target_language in target_languages:
-                # Get language code for the target language
-                target_lang_code = self.tokenizer.lang_code_to_id[language_mapping(target_language)]
-                # Generate translation
-                generated_tokens = self.model.generate(
-                    **input_ids,
-                    forced_bos_token_id=target_lang_code,
-                    max_length=128
-                )
-                generated_translation = self.tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
-                # Append result to output
-                output.append({
-                    "target_language": target_language,
-                    "generated_translation": generated_translation,
-                })
-            outputs = []
-            length = len(output[0]["generated_translation"])
-            for i in range(length):
-                temp = []
-                for trans in output:
-                    temp.append({
-                        "target_language": trans["target_language"],
-                        "generated_translation": trans['generated_translation'][i],
-                    })
-                outputs.append(temp)
-            return outputs
-        else:
-            # 最大批量大小 = 可用 GPU 内存字节数 / 4 / （张量大小 + 可训练参数）
-            # max_batch_size = 10
-            # Ensure batch size is within model limits:
-            print("length of inputs: ",len(inputs))
-            batch_size = min(len(inputs), int(max_batch_size))
-            batches = [inputs[i:i + batch_size] for i in range(0, len(inputs), batch_size)]
-            print("length of batches size: ", len(batches))
-            temp_outputs = []
-            processed_num = 0
-            for index, batch in enumerate(batches):
-                # Tokenize input
-                batch = filter_pipeline.batch_encoder(batch)
-                print(">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>")
-                print(batch)
-                input_ids = self.tokenizer(batch, return_tensors="pt", padding=True).to(self.device_name)
-                temp = []
-                for target_language in target_languages:
-                    target_lang_code = self.tokenizer.lang_code_to_id[language_mapping(target_language)]
-                    generated_tokens = self.model.generate(
-                        **input_ids,
-                        forced_bos_token_id=target_lang_code,
-                    )
-                    generated_translation = self.tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
-                    print(generated_translation)
-                    generated_translation = filter_pipeline.batch_decoder(generated_translation)
-                    # Append result to output
-                    temp.append({
-                        "target_language": target_language,
-                        "generated_translation": generated_translation,
-                    })
-                input_ids.to('cpu')
-                del input_ids
-                temp_outputs.append(temp)
-                processed_num += len(batch)
-                if (index + 1) * max_batch_size // 1000 - index  * max_batch_size // 1000 == 1:
-                    print("Already processed number: ", len(temp_outputs))
-                    process_gpu_translate_result(temp_outputs)
-            outputs = []
-            for temp_output in temp_outputs:
-                length = len(temp_output[0]["generated_translation"])
-                for i in range(length):
-                    temp = []
-                    for trans in temp_output:
-                        temp.append({
-                            "target_language": trans["target_language"],
-                            "generated_translation": trans['generated_translation'][i],
-                        })
-                    outputs.append(temp)
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+import torch
+from modules.file import ExcelFileWriter
+import os
+
+from abc import ABC, abstractmethod
+from typing import List
+import re
+
+class FilterPipeline():
+    def __init__(self, filter_list):
+        self._filter_list:List[Filter] = filter_list
+
+    def append(self, filter):
+        self._filter_list.append(filter)
+
+    def batch_encoder(self, inputs):from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+import torch
+from modules.file import ExcelFileWriter
+import os
+
+from abc import ABC, abstractmethod
+from typing import List
+import re
+
+class FilterPipeline():
+    def __init__(self, filter_list):
+        self._filter_list:List[Filter] = filter_list
+
+    def append(self, filter):
+        self._filter_list.append(filter)
+
+    def batch_encoder(self, inputs):
+        for filter in self._filter_list:
+            inputs = filter.encoder(inputs)
+        return inputs
+    
+    def batch_decoder(self, inputs):
+        for filter in reversed(self._filter_list):
+            inputs = filter.decoder(inputs)
+        return inputs
+
+class Filter(ABC):
+    def __init__(self):
+        self.name = 'filter'
+        self.code = []
+    @abstractmethod
+    def encoder(self, inputs):
+        pass
+
+    @abstractmethod
+    def decoder(self, inputs):
+        pass
+
+class SpecialTokenFilter(Filter):
+    def __init__(self):
+        self.name = 'special token filter'
+        self.code = []
+        self.special_tokens = ['!', '！', '-']
+    
+    def encoder(self, inputs):
+        filtered_inputs = []
+        self.code = []
+        for i, input_str in enumerate(inputs):
+            if not all(char in self.special_tokens for char in input_str):
+                filtered_inputs.append(input_str)
+            else:
+                self.code.append([i, input_str])
+        return filtered_inputs
+    
+    def decoder(self, inputs):
+        original_inputs = inputs.copy()
+        for removed_indice in self.code:
+            original_inputs.insert(removed_indice[0], removed_indice[1])
+        return original_inputs
+
+class SperSignFilter(Filter):
+    def __init__(self):
+        self.name = 's persign filter'
+        self.code = []
+    
+    def encoder(self, inputs):
+        encoded_inputs = []
+        self.code = []  # 清空 self.code
+        for i, input_str in enumerate(inputs):
+            if 's%' in input_str:
+                encoded_str = input_str.replace('s%', '*')
+                self.code.append(i)  # 将包含 's%' 的字符串的索引存储到 self.code 中
+            else:
+                encoded_str = input_str
+            encoded_inputs.append(encoded_str)
+        return encoded_inputs
+    
+    def decoder(self, inputs):
+        decoded_inputs = inputs.copy()
+        for i in self.code:
+            decoded_inputs[i] = decoded_inputs[i].replace('*', 's%')  # 使用 self.code 中的索引还原原始字符串
+        return decoded_inputs
+    
+class SimilarFilter(Filter):
+    def __init__(self):
+        self.name = 'similar filter'
+        self.code = []
+    
+    def is_similar(self, str1, str2):
+        # 判断两个字符串是否相似（只有数字上有区别）
+        pattern = re.compile(r'\d+')
+        return pattern.sub('', str1) == pattern.sub('', str2)
+    
+    def encoder(self, inputs):
+        encoded_inputs = []
+        self.code = []  # 清空 self.code
+        i = 0
+        while i < len(inputs):
+            encoded_inputs.append(inputs[i])
+            similar_strs = [inputs[i]]
+            j = i + 1
+            while j < len(inputs) and self.is_similar(inputs[i], inputs[j]):
+                similar_strs.append(inputs[j])
+                j += 1
+            if len(similar_strs) > 1:
+                self.code.append((i, similar_strs))  # 将相似字符串的起始索引和实际字符串列表存储到 self.code 中
+            i = j
+        return encoded_inputs
+    
+    def decoder(self, inputs:List):
+        decoded_inputs = inputs
+        for i, similar_strs in self.code:
+            pattern = re.compile(r'\d+')
+            for j in range(len(similar_strs)):
+                if pattern.search(similar_strs[j]):
+                    number = re.findall(r'\d+', similar_strs[j])[0]  # 获取相似字符串的数字部分
+                    new_str = pattern.sub(number, inputs[i])  # 将新字符串的数字部分替换为相似字符串的数字部分
+                else:
+                    new_str = inputs[i]  # 如果相似字符串不含数字，直接使用新字符串
+                if j > 0:
+                    decoded_inputs.insert(i+j, new_str)
+        return decoded_inputs
+
+script_dir = os.path.dirname(os.path.abspath(__file__))
+parent_dir = os.path.dirname(os.path.dirname(os.path.dirname(script_dir)))
+
+class Model():
+    def __init__(self, modelname, selected_lora_model, selected_gpu):
+        def get_gpu_index(gpu_info, target_gpu_name):
+            """
+            从 GPU 信息中获取目标 GPU 的索引
+            Args:
+                gpu_info (list): 包含 GPU 名称的列表
+                target_gpu_name (str): 目标 GPU 的名称
+
+            Returns:
+                int: 目标 GPU 的索引，如果未找到则返回 -1
+            """
+            for i, name in enumerate(gpu_info):
+                if target_gpu_name.lower() in name.lower():
+                    return i
+            return -1
+        if selected_gpu != "cpu":
+            gpu_count = torch.cuda.device_count()
+            gpu_info = [torch.cuda.get_device_name(i) for i in range(gpu_count)]
+            selected_gpu_index = get_gpu_index(gpu_info, selected_gpu)
+            self.device_name = f"cuda:{selected_gpu_index}"
+        else:
+            self.device_name = "cpu"
+        print("device_name", self.device_name)
+        self.model = AutoModelForSeq2SeqLM.from_pretrained(modelname).to(self.device_name)
+        self.tokenizer = AutoTokenizer.from_pretrained(modelname)
+        # self.translator = pipeline('translation', model=self.original_model, tokenizer=self.tokenizer, src_lang=original_language, tgt_lang=target_language, device=device)
+    
+    def generate(self, inputs, original_language, target_languages, max_batch_size):
+        filter_list = [SpecialTokenFilter(), SperSignFilter(), SimilarFilter()]
+        filter_pipeline = FilterPipeline(filter_list)
+        def language_mapping(original_language):
+            d = {
+                "Achinese (Arabic script)": "ace_Arab",
+                "Achinese (Latin script)": "ace_Latn",
+                "Mesopotamian Arabic": "acm_Arab",
+                "Ta'izzi-Adeni Arabic": "acq_Arab",
+                "Tunisian Arabic": "aeb_Arab",
+                "Afrikaans": "afr_Latn",
+                "South Levantine Arabic": "ajp_Arab",
+                "Akan": "aka_Latn",
+                "Amharic": "amh_Ethi",
+                "North Levantine Arabic": "apc_Arab",
+                "Standard Arabic": "arb_Arab",
+                "Najdi Arabic": "ars_Arab",
+                "Moroccan Arabic": "ary_Arab",
+                "Egyptian Arabic": "arz_Arab",
+                "Assamese": "asm_Beng",
+                "Asturian": "ast_Latn",
+                "Awadhi": "awa_Deva",
+                "Central Aymara": "ayr_Latn",
+                "South Azerbaijani": "azb_Arab",
+                "North Azerbaijani": "azj_Latn",
+                "Bashkir": "bak_Cyrl",
+                "Bambara": "bam_Latn",
+                "Balinese": "ban_Latn",
+                "Belarusian": "bel_Cyrl",
+                "Bemba": "bem_Latn",
+                "Bengali": "ben_Beng",
+                "Bhojpuri": "bho_Deva",
+                "Banjar (Arabic script)": "bjn_Arab",
+                "Banjar (Latin script)": "bjn_Latn",
+                "Tibetan": "bod_Tibt",
+                "Bosnian": "bos_Latn",
+                "Buginese": "bug_Latn",
+                "Bulgarian": "bul_Cyrl",
+                "Catalan": "cat_Latn",
+                "Cebuano": "ceb_Latn",
+                "Czech": "ces_Latn",
+                "Chokwe": "cjk_Latn",
+                "Central Kurdish": "ckb_Arab",
+                "Crimean Tatar": "crh_Latn",
+                "Welsh": "cym_Latn",
+                "Danish": "dan_Latn",
+                "German": "deu_Latn",
+                "Dinka": "dik_Latn",
+                "Jula": "dyu_Latn",
+                "Dzongkha": "dzo_Tibt",
+                "Greek": "ell_Grek",
+                "English": "eng_Latn",
+                "Esperanto": "epo_Latn",
+                "Estonian": "est_Latn",
+                "Basque": "eus_Latn",
+                "Ewe": "ewe_Latn",
+                "Faroese": "fao_Latn",
+                "Persian": "pes_Arab",
+                "Fijian": "fij_Latn",
+                "Finnish": "fin_Latn",
+                "Fon": "fon_Latn",
+                "French": "fra_Latn",
+                "Friulian": "fur_Latn",
+                "Nigerian Fulfulde": "fuv_Latn",
+                "Scottish Gaelic": "gla_Latn",
+                "Irish": "gle_Latn",
+                "Galician": "glg_Latn",
+                "Guarani": "grn_Latn",
+                "Gujarati": "guj_Gujr",
+                "Haitian Creole": "hat_Latn",
+                "Hausa": "hau_Latn",
+                "Hebrew": "heb_Hebr",
+                "Hindi": "hin_Deva",
+                "Chhattisgarhi": "hne_Deva",
+                "Croatian": "hrv_Latn",
+                "Hungarian": "hun_Latn",
+                "Armenian": "hye_Armn",
+                "Igbo": "ibo_Latn",
+                "Iloko": "ilo_Latn",
+                "Indonesian": "ind_Latn",
+                "Icelandic": "isl_Latn",
+                "Italian": "ita_Latn",
+                "Javanese": "jav_Latn",
+                "Japanese": "jpn_Jpan",
+                "Kabyle": "kab_Latn",
+                "Kachin": "kac_Latn",
+                "Arabic": "ar_AR",
+                "Chinese": "zho_Hans", 
+                "Spanish": "spa_Latn",
+                "Dutch": "nld_Latn", 
+                "Kazakh": "kaz_Cyrl", 
+                "Korean": "kor_Hang", 
+                "Lithuanian": "lit_Latn",
+                "Malayalam": "mal_Mlym", 
+                "Marathi": "mar_Deva", 
+                "Nepali": "ne_NP", 
+                "Polish": "pol_Latn", 
+                "Portuguese": "por_Latn", 
+                "Russian": "rus_Cyrl", 
+                "Sinhala": "sin_Sinh",
+                "Tamil": "tam_Taml", 
+                "Turkish": "tur_Latn", 
+                "Ukrainian": "ukr_Cyrl", 
+                "Urdu": "urd_Arab", 
+                "Vietnamese": "vie_Latn", 
+                "Thai":"tha_Thai"
+            }
+            return d[original_language]
+        def process_gpu_translate_result(temp_outputs):
+            outputs = []
+            for temp_output in temp_outputs:
+                length = len(temp_output[0]["generated_translation"])
+                for i in range(length):
+                    temp = []
+                    for trans in temp_output:
+                        temp.append({
+                            "target_language": trans["target_language"],
+                            "generated_translation": trans['generated_translation'][i],
+                        })
+                    outputs.append(temp)
+            excel_writer = ExcelFileWriter()
+            excel_writer.write_text(os.path.join(parent_dir,r"temp/empty.xlsx"), outputs, 'A', 1, len(outputs))
+        self.tokenizer.src_lang = language_mapping(original_language)
+        if self.device_name == "cpu":
+            # Tokenize input
+            input_ids = self.tokenizer(inputs, return_tensors="pt", padding=True, max_length=128).to(self.device_name)
+            output = []
+            for target_language in target_languages:
+                # Get language code for the target language
+                target_lang_code = self.tokenizer.lang_code_to_id[language_mapping(target_language)]
+                # Generate translation
+                generated_tokens = self.model.generate(
+                    **input_ids,
+                    forced_bos_token_id=target_lang_code,
+                    max_length=128
+                )
+                generated_translation = self.tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+                # Append result to output
+                output.append({
+                    "target_language": target_language,
+                    "generated_translation": generated_translation,
+                })
+            outputs = []
+            length = len(output[0]["generated_translation"])
+            for i in range(length):
+                temp = []
+                for trans in output:
+                    temp.append({
+                        "target_language": trans["target_language"],
+                        "generated_translation": trans['generated_translation'][i],
+                    })
+                outputs.append(temp)
+            return outputs
+        else:
+            # 最大批量大小 = 可用 GPU 内存字节数 / 4 / （张量大小 + 可训练参数）
+            # max_batch_size = 10
+            # Ensure batch size is within model limits:
+            print("length of inputs: ",len(inputs))
+            batch_size = min(len(inputs), int(max_batch_size))
+            batches = [inputs[i:i + batch_size] for i in range(0, len(inputs), batch_size)]
+            print("length of batches size: ", len(batches))
+            temp_outputs = []
+            processed_num = 0
+            for index, batch in enumerate(batches):
+                # Tokenize input
+                print(">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>")
+                print(len(batch))
+                print(batch)
+                batch = filter_pipeline.batch_encoder(batch)
+                print(batch)
+                input_ids = self.tokenizer(batch, return_tensors="pt", padding=True).to(self.device_name)
+                temp = []
+                for target_language in target_languages:
+                    target_lang_code = self.tokenizer.lang_code_to_id[language_mapping(target_language)]
+                    generated_tokens = self.model.generate(
+                        **input_ids,
+                        forced_bos_token_id=target_lang_code,
+                    )
+                    generated_translation = self.tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+                    
+                    print(generated_translation)
+                    generated_translation = filter_pipeline.batch_decoder(generated_translation)
+                    print(generated_translation)
+                    print(len(generated_translation))
+                    # Append result to output
+                    temp.append({
+                        "target_language": target_language,
+                        "generated_translation": generated_translation,
+                    })
+                input_ids.to('cpu')
+                del input_ids
+                temp_outputs.append(temp)
+                processed_num += len(batch)
+                if (index + 1) * max_batch_size // 1000 - index  * max_batch_size // 1000 == 1:
+                    print("Already processed number: ", len(temp_outputs))
+                    process_gpu_translate_result(temp_outputs)
+            outputs = []
+            for temp_output in temp_outputs:
+                length = len(temp_output[0]["generated_translation"])
+                for i in range(length):
+                    temp = []
+                    for trans in temp_output:
+                        temp.append({
+                            "target_language": trans["target_language"],
+                            "generated_translation": trans['generated_translation'][i],
+                        })
+                    outputs.append(temp)
+            return outputs
+        for filter in self._filter_list:
+            inputs = filter.encoder(inputs)
+        return inputs
+    
+    def batch_decoder(self, inputs):
+        for filter in reversed(self._filter_list):
+            inputs = filter.decoder(inputs)
+        return inputs
+
+class Filter(ABC):
+    def __init__(self):
+        self.name = 'filter'
+        self.code = []
+    @abstractmethod
+    def encoder(self, inputs):
+        pass
+
+    @abstractmethod
+    def decoder(self, inputs):
+        pass
+
+class SpecialTokenFilter(Filter):
+    def __init__(self):
+        self.name = 'special token filter'
+        self.code = []
+        self.special_tokens = ['!', '！']
+    
+    def encoder(self, inputs):
+        filtered_inputs = []
+        self.code = []
+        for i, input_str in enumerate(inputs):
+            if not all(char in self.special_tokens for char in input_str):
+                filtered_inputs.append(input_str)
+            else:
+                self.code.append([i, input_str])
+        return filtered_inputs
+    
+    def decoder(self, inputs):
+        original_inputs = inputs.copy()
+        for removed_indice in self.code:
+            original_inputs.insert(removed_indice[0], removed_indice[1])
+        return original_inputs
+
+class SperSignFilter(Filter):
+    def __init__(self):
+        self.name = 's persign filter'
+        self.code = []
+    
+    def encoder(self, inputs):
+        encoded_inputs = []
+        self.code = []  # 清空 self.code
+        for i, input_str in enumerate(inputs):
+            if 's%' in input_str:
+                encoded_str = input_str.replace('s%', '*')
+                self.code.append(i)  # 将包含 's%' 的字符串的索引存储到 self.code 中
+            else:
+                encoded_str = input_str
+            encoded_inputs.append(encoded_str)
+        return encoded_inputs
+    
+    def decoder(self, inputs):
+        decoded_inputs = inputs.copy()
+        for i in self.code:
+            decoded_inputs[i] = decoded_inputs[i].replace('*', 's%')  # 使用 self.code 中的索引还原原始字符串
+        return decoded_inputs
+    
+class SimilarFilter(Filter):
+    def __init__(self):
+        self.name = 'similar filter'
+        self.code = []
+    
+    def is_similar(self, str1, str2):
+        # 判断两个字符串是否相似（只有数字上有区别）
+        pattern = re.compile(r'\d+')
+        return pattern.sub('', str1) == pattern.sub('', str2)
+    
+    def encoder(self, inputs):
+        encoded_inputs = []
+        self.code = []  # 清空 self.code
+        i = 0
+        while i < len(inputs):
+            encoded_inputs.append(inputs[i])
+            similar_strs = [inputs[i]]
+            j = i + 1
+            while j < len(inputs) and self.is_similar(inputs[i], inputs[j]):
+                similar_strs.append(inputs[j])
+                j += 1
+            if len(similar_strs) > 1:
+                self.code.append((i, similar_strs))  # 将相似字符串的起始索引和实际字符串列表存储到 self.code 中
+            i = j
+        return encoded_inputs
+    
+    def decoder(self, inputs):
+        decoded_inputs = []
+        index = 0
+        for i, similar_strs in self.code:
+            decoded_inputs.extend(inputs[index:i])
+            decoded_inputs.extend(similar_strs)  # 直接将实际的相似字符串添加到 decoded_inputs 中
+            index = i + 1
+        decoded_inputs.extend(inputs[index:])
+        return decoded_inputs
+
+script_dir = os.path.dirname(os.path.abspath(__file__))
+parent_dir = os.path.dirname(os.path.dirname(os.path.dirname(script_dir)))
+
+class Model():
+    def __init__(self, modelname, selected_lora_model, selected_gpu):
+        def get_gpu_index(gpu_info, target_gpu_name):
+            """
+            从 GPU 信息中获取目标 GPU 的索引
+            Args:
+                gpu_info (list): 包含 GPU 名称的列表
+                target_gpu_name (str): 目标 GPU 的名称
+
+            Returns:
+                int: 目标 GPU 的索引，如果未找到则返回 -1
+            """
+            for i, name in enumerate(gpu_info):
+                if target_gpu_name.lower() in name.lower():
+                    return i
+            return -1
+        if selected_gpu != "cpu":
+            gpu_count = torch.cuda.device_count()
+            gpu_info = [torch.cuda.get_device_name(i) for i in range(gpu_count)]
+            selected_gpu_index = get_gpu_index(gpu_info, selected_gpu)
+            self.device_name = f"cuda:{selected_gpu_index}"
+        else:
+            self.device_name = "cpu"
+        print("device_name", self.device_name)
+        self.model = AutoModelForSeq2SeqLM.from_pretrained(modelname).to(self.device_name)
+        self.tokenizer = AutoTokenizer.from_pretrained(modelname)
+        # self.translator = pipeline('translation', model=self.original_model, tokenizer=self.tokenizer, src_lang=original_language, tgt_lang=target_language, device=device)
+    
+    def generate(self, inputs, original_language, target_languages, max_batch_size):
+        filter_list = [SpecialTokenFilter(), SperSignFilter(), SimilarFilter()]
+        filter_pipeline = FilterPipeline(filter_list)
+        def language_mapping(original_language):
+            d = {
+                "Achinese (Arabic script)": "ace_Arab",
+                "Achinese (Latin script)": "ace_Latn",
+                "Mesopotamian Arabic": "acm_Arab",
+                "Ta'izzi-Adeni Arabic": "acq_Arab",
+                "Tunisian Arabic": "aeb_Arab",
+                "Afrikaans": "afr_Latn",
+                "South Levantine Arabic": "ajp_Arab",
+                "Akan": "aka_Latn",
+                "Amharic": "amh_Ethi",
+                "North Levantine Arabic": "apc_Arab",
+                "Standard Arabic": "arb_Arab",
+                "Najdi Arabic": "ars_Arab",
+                "Moroccan Arabic": "ary_Arab",
+                "Egyptian Arabic": "arz_Arab",
+                "Assamese": "asm_Beng",
+                "Asturian": "ast_Latn",
+                "Awadhi": "awa_Deva",
+                "Central Aymara": "ayr_Latn",
+                "South Azerbaijani": "azb_Arab",
+                "North Azerbaijani": "azj_Latn",
+                "Bashkir": "bak_Cyrl",
+                "Bambara": "bam_Latn",
+                "Balinese": "ban_Latn",
+                "Belarusian": "bel_Cyrl",
+                "Bemba": "bem_Latn",
+                "Bengali": "ben_Beng",
+                "Bhojpuri": "bho_Deva",
+                "Banjar (Arabic script)": "bjn_Arab",
+                "Banjar (Latin script)": "bjn_Latn",
+                "Tibetan": "bod_Tibt",
+                "Bosnian": "bos_Latn",
+                "Buginese": "bug_Latn",
+                "Bulgarian": "bul_Cyrl",
+                "Catalan": "cat_Latn",
+                "Cebuano": "ceb_Latn",
+                "Czech": "ces_Latn",
+                "Chokwe": "cjk_Latn",
+                "Central Kurdish": "ckb_Arab",
+                "Crimean Tatar": "crh_Latn",
+                "Welsh": "cym_Latn",
+                "Danish": "dan_Latn",
+                "German": "deu_Latn",
+                "Dinka": "dik_Latn",
+                "Jula": "dyu_Latn",
+                "Dzongkha": "dzo_Tibt",
+                "Greek": "ell_Grek",
+                "English": "eng_Latn",
+                "Esperanto": "epo_Latn",
+                "Estonian": "est_Latn",
+                "Basque": "eus_Latn",
+                "Ewe": "ewe_Latn",
+                "Faroese": "fao_Latn",
+                "Persian": "pes_Arab",
+                "Fijian": "fij_Latn",
+                "Finnish": "fin_Latn",
+                "Fon": "fon_Latn",
+                "French": "fra_Latn",
+                "Friulian": "fur_Latn",
+                "Nigerian Fulfulde": "fuv_Latn",
+                "Scottish Gaelic": "gla_Latn",
+                "Irish": "gle_Latn",
+                "Galician": "glg_Latn",
+                "Guarani": "grn_Latn",
+                "Gujarati": "guj_Gujr",
+                "Haitian Creole": "hat_Latn",
+                "Hausa": "hau_Latn",
+                "Hebrew": "heb_Hebr",
+                "Hindi": "hin_Deva",
+                "Chhattisgarhi": "hne_Deva",
+                "Croatian": "hrv_Latn",
+                "Hungarian": "hun_Latn",
+                "Armenian": "hye_Armn",
+                "Igbo": "ibo_Latn",
+                "Iloko": "ilo_Latn",
+                "Indonesian": "ind_Latn",
+                "Icelandic": "isl_Latn",
+                "Italian": "ita_Latn",
+                "Javanese": "jav_Latn",
+                "Japanese": "jpn_Jpan",
+                "Kabyle": "kab_Latn",
+                "Kachin": "kac_Latn",
+                "Arabic": "ar_AR",
+                "Chinese": "zho_Hans", 
+                "Spanish": "spa_Latn",
+                "Dutch": "nld_Latn", 
+                "Kazakh": "kaz_Cyrl", 
+                "Korean": "kor_Hang", 
+                "Lithuanian": "lit_Latn",
+                "Malayalam": "mal_Mlym", 
+                "Marathi": "mar_Deva", 
+                "Nepali": "ne_NP", 
+                "Polish": "pol_Latn", 
+                "Portuguese": "por_Latn", 
+                "Russian": "rus_Cyrl", 
+                "Sinhala": "sin_Sinh",
+                "Tamil": "tam_Taml", 
+                "Turkish": "tur_Latn", 
+                "Ukrainian": "ukr_Cyrl", 
+                "Urdu": "urd_Arab", 
+                "Vietnamese": "vie_Latn", 
+                "Thai":"tha_Thai"
+            }
+            return d[original_language]
+        def process_gpu_translate_result(temp_outputs):
+            outputs = []
+            for temp_output in temp_outputs:
+                length = len(temp_output[0]["generated_translation"])
+                for i in range(length):
+                    temp = []
+                    for trans in temp_output:
+                        temp.append({
+                            "target_language": trans["target_language"],
+                            "generated_translation": trans['generated_translation'][i],
+                        })
+                    outputs.append(temp)
+            excel_writer = ExcelFileWriter()
+            excel_writer.write_text(os.path.join(parent_dir,r"temp/empty.xlsx"), outputs, 'A', 1, len(outputs))
+        self.tokenizer.src_lang = language_mapping(original_language)
+        if self.device_name == "cpu":
+            # Tokenize input
+            input_ids = self.tokenizer(inputs, return_tensors="pt", padding=True, max_length=128).to(self.device_name)
+            output = []
+            for target_language in target_languages:
+                # Get language code for the target language
+                target_lang_code = self.tokenizer.lang_code_to_id[language_mapping(target_language)]
+                # Generate translation
+                generated_tokens = self.model.generate(
+                    **input_ids,
+                    forced_bos_token_id=target_lang_code,
+                    max_length=128
+                )
+                generated_translation = self.tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+                # Append result to output
+                output.append({
+                    "target_language": target_language,
+                    "generated_translation": generated_translation,
+                })
+            outputs = []
+            length = len(output[0]["generated_translation"])
+            for i in range(length):
+                temp = []
+                for trans in output:
+                    temp.append({
+                        "target_language": trans["target_language"],
+                        "generated_translation": trans['generated_translation'][i],
+                    })
+                outputs.append(temp)
+            return outputs
+        else:
+            # 最大批量大小 = 可用 GPU 内存字节数 / 4 / （张量大小 + 可训练参数）
+            # max_batch_size = 10
+            # Ensure batch size is within model limits:
+            print("length of inputs: ",len(inputs))
+            batch_size = min(len(inputs), int(max_batch_size))
+            batches = [inputs[i:i + batch_size] for i in range(0, len(inputs), batch_size)]
+            print("length of batches size: ", len(batches))
+            temp_outputs = []
+            processed_num = 0
+            for index, batch in enumerate(batches):
+                # Tokenize input
+                batch = filter_pipeline.batch_encoder(batch)
+                print(">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>")
+                print(batch)
+                input_ids = self.tokenizer(batch, return_tensors="pt", padding=True).to(self.device_name)
+                temp = []
+                for target_language in target_languages:
+                    target_lang_code = self.tokenizer.lang_code_to_id[language_mapping(target_language)]
+                    generated_tokens = self.model.generate(
+                        **input_ids,
+                        forced_bos_token_id=target_lang_code,
+                    )
+                    generated_translation = self.tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+                    print(generated_translation)
+                    generated_translation = filter_pipeline.batch_decoder(generated_translation)
+                    # Append result to output
+                    temp.append({
+                        "target_language": target_language,
+                        "generated_translation": generated_translation,
+                    })
+                input_ids.to('cpu')
+                del input_ids
+                temp_outputs.append(temp)
+                processed_num += len(batch)
+                if (index + 1) * max_batch_size // 1000 - index  * max_batch_size // 1000 == 1:
+                    print("Already processed number: ", len(temp_outputs))
+                    process_gpu_translate_result(temp_outputs)
+            outputs = []
+            for temp_output in temp_outputs:
+                length = len(temp_output[0]["generated_translation"])
+                for i in range(length):
+                    temp = []
+                    for trans in temp_output:
+                        temp.append({
+                            "target_language": trans["target_language"],
+                            "generated_translation": trans['generated_translation'][i],
+                        })
+                    outputs.append(temp)
             return outputs