| import os
|
| from modules import shared, utils
|
| from pathlib import Path
|
| import requests
|
| import tqdm
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| import json
|
|
|
| '''
|
| def get_gpu_memory_usage(rank):
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| return {
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| 'total': round(torch.cuda.get_device_properties(rank).total_memory / (1024**3), 2),
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| 'max': round(torch.cuda.max_memory_allocated(rank) / (1024**3), 2),
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| 'reserved': round(torch.cuda.memory_reserved(rank) / (1024**3), 2),
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| 'allocated': round(torch.cuda.memory_allocated(rank) / (1024**3), 2)
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| }
|
| '''
|
|
|
| def list_subfoldersByTime(directory):
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|
|
| if not directory.endswith('/'):
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| directory += '/'
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| subfolders = []
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| subfolders.append('None')
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| path = directory
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| name_list = os.listdir(path)
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| full_list = [os.path.join(path,i) for i in name_list]
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| time_sorted_list = sorted(full_list, key=os.path.getmtime,reverse=True)
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|
|
| for entry in time_sorted_list:
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| if os.path.isdir(entry):
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| entry_str = f"{entry}"
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| full_path = entry_str
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| entry_str = entry_str.replace('\\','/')
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| entry_str = entry_str.replace(f"{directory}", "")
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| subfolders.append(entry_str)
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|
|
| return subfolders
|
|
|
| def get_available_loras_local(_sortedByTime):
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|
|
| model_dir = shared.args.lora_dir
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| subfolders = []
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| if _sortedByTime:
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| subfolders = list_subfoldersByTime(model_dir)
|
| else:
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| subfolders = utils.get_available_loras()
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|
|
| return subfolders
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|
|
|
|
|
|
|
|
| def split_sentences(text: str, cutoff_len: int):
|
| sentences = []
|
| sentence = ''
|
| delimiters = ['. ', '? ', '! ', '... ', '.\n', '?\n', '!\n','...\n','</s>','<//>']
|
| abbreviations = ['Mr. ', 'Mrs. ', 'Dr. ', 'Ms. ', 'St. ', 'Prof. ', 'Jr. ', 'Ltd. ', 'Capt. ', 'Col. ', 'Gen. ', 'Ave. ', 'Blvd. ', 'Co. ', 'Corp. ', 'Dept. ', 'Est. ', 'Gov. ', 'Inc. ', 'Ph.D. ', 'Univ. ']
|
| errors = 0
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| max_cut = cutoff_len-1
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| prev_char = ''
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|
|
| for char in text:
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| sentence += char
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|
|
|
|
| if (any(sentence.endswith(delimiter) for delimiter in delimiters) and
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| not (prev_char.isupper() and len(sentence) >= 3 and sentence[-3] != ' ') and
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| not any(sentence.endswith(abbreviation) for abbreviation in abbreviations)):
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| tokens = shared.tokenizer.encode(sentence)
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|
|
| if len(tokens) > max_cut:
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| tokens = tokens[:max_cut]
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| sentence = shared.tokenizer.decode(tokens, skip_special_tokens=True)
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| errors = errors + 1
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|
|
| sentences.append({'text': sentence, 'size': len(tokens)})
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|
|
| sentence = ''
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|
|
| prev_char = char
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|
|
| if sentence:
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| tokens = shared.tokenizer.encode(sentence)
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| if len(tokens) > max_cut:
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| tokens = tokens[:max_cut]
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| sentence = shared.tokenizer.decode(tokens, skip_special_tokens=True)
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| errors = errors + 1
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|
|
| sentences.append({'text': sentence, 'size': len(tokens)})
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|
|
| if errors > 0:
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| print(f"Trimmed sentences beyond Cutoff Length: {errors}")
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|
|
| return sentences
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
| def precise_cut(text: str, overlap: bool, min_chars_cut: int, eos_to_hc: bool, cutoff_len: int, hard_cut_string: str, debug_slicer:bool):
|
|
|
| EOSX_str = '<//>'
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| EOS_str = '</s>'
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| print("Precise raw text slicer: ON")
|
|
|
| cut_string = hard_cut_string.replace('\\n', '\n')
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| text = text.replace(cut_string, EOSX_str)
|
| sentences = split_sentences(text, cutoff_len)
|
|
|
| print(f"Sentences: {len(sentences)}")
|
| sentencelist = []
|
| currentSentence = ''
|
| totalLength = 0
|
| max_cut = cutoff_len-1
|
| half_cut = cutoff_len//2
|
| halfcut_length = 0
|
|
|
| edgeindex = []
|
| half_index = 0
|
|
|
| for index, item in enumerate(sentences):
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|
|
| if halfcut_length+ item['size'] < half_cut:
|
| halfcut_length += item['size']
|
| half_index = index
|
| else:
|
| edgeindex.append(half_index)
|
| halfcut_length = -2 * max_cut
|
|
|
|
|
| if totalLength + item['size'] < max_cut and not currentSentence.endswith(EOSX_str):
|
| currentSentence += item['text']
|
| totalLength += item['size']
|
| else:
|
|
|
| if len(currentSentence.strip()) > min_chars_cut:
|
| sentencelist.append(currentSentence.strip())
|
|
|
| currentSentence = item['text']
|
| totalLength = item['size']
|
| halfcut_length = item['size']
|
|
|
| if len(currentSentence.strip()) > min_chars_cut:
|
| sentencelist.append(currentSentence.strip())
|
|
|
| unique_blocks = len(sentencelist)
|
| print(f"Text Blocks: {unique_blocks}")
|
|
|
|
|
|
|
| if overlap:
|
| for edge_idx in edgeindex:
|
| currentSentence = ''
|
| totalLength = 0
|
|
|
| for item in sentences[edge_idx:]:
|
| if totalLength + item['size'] < max_cut:
|
| currentSentence += item['text']
|
| totalLength += item['size']
|
| else:
|
|
|
| if currentSentence.endswith(EOSX_str) and len(currentSentence.strip()) > min_chars_cut:
|
| sentencelist.append(currentSentence.strip())
|
|
|
| elif EOSX_str not in currentSentence and len(currentSentence.strip()) > min_chars_cut:
|
| sentencelist.append(currentSentence.strip())
|
|
|
| currentSentence = ''
|
| totalLength = 0
|
| break
|
|
|
| print(f"+ Overlapping blocks: {len(sentencelist)-unique_blocks}")
|
|
|
| num_EOS = 0
|
| for i in range(len(sentencelist)):
|
| if eos_to_hc:
|
| sentencelist[i] = sentencelist[i].replace(EOSX_str, EOS_str)
|
| else:
|
| sentencelist[i] = sentencelist[i].replace(EOSX_str, '')
|
|
|
|
|
| sentencelist[i] = sentencelist[i].replace("</s></s>", EOS_str)
|
| num_EOS += sentencelist[i].count(EOS_str)
|
|
|
| if num_EOS > 0:
|
| print(f"+ EOS count: {num_EOS}")
|
|
|
|
|
| sentencelist = [item for item in sentencelist if item.strip() != "</s>"]
|
| sentencelist = [item for item in sentencelist if item.strip() != ""]
|
|
|
|
|
| if debug_slicer:
|
|
|
| Path('logs').mkdir(exist_ok=True)
|
| sentencelist_dict = {index: sentence for index, sentence in enumerate(sentencelist)}
|
| output_file = "logs/sentencelist.json"
|
| with open(output_file, 'w') as f:
|
| json.dump(sentencelist_dict, f,indent=2)
|
|
|
| print("Saved sentencelist.json in logs folder")
|
|
|
| return sentencelist
|
|
|
|
|
| def sliding_block_cut(text: str, min_chars_cut: int, eos_to_hc: bool, cutoff_len: int, hard_cut_string: str, debug_slicer:bool):
|
|
|
| EOSX_str = '<//>'
|
| EOS_str = '</s>'
|
| print("Mega Block Overlap: ON")
|
|
|
| cut_string = hard_cut_string.replace('\\n', '\n')
|
| text = text.replace(cut_string, EOSX_str)
|
| sentences = split_sentences(text, cutoff_len)
|
|
|
| print(f"Sentences: {len(sentences)}")
|
| sentencelist = []
|
|
|
| max_cut = cutoff_len-1
|
|
|
|
|
| advancing_to = 0
|
|
|
| prev_block_lastsentence = ""
|
|
|
|
|
| for i in range(len(sentences)):
|
| totalLength = 0
|
| currentSentence = ''
|
| lastsentence = ""
|
|
|
| if i >= advancing_to:
|
| for k in range(i, len(sentences)):
|
|
|
| current_length = sentences[k]['size']
|
|
|
| if totalLength + current_length <= max_cut and not currentSentence.endswith(EOSX_str):
|
| currentSentence += sentences[k]['text']
|
| totalLength += current_length
|
| lastsentence = sentences[k]['text']
|
| else:
|
| if len(currentSentence.strip()) > min_chars_cut:
|
| if prev_block_lastsentence!=lastsentence:
|
| sentencelist.append(currentSentence.strip())
|
| prev_block_lastsentence = lastsentence
|
|
|
| advancing_to = 0
|
| if currentSentence.endswith(EOSX_str):
|
| advancing_to = k
|
|
|
| currentSentence = ""
|
| totalLength = 0
|
| break
|
|
|
| if currentSentence != "":
|
| if len(currentSentence.strip()) > min_chars_cut:
|
| sentencelist.append(currentSentence.strip())
|
|
|
| unique_blocks = len(sentencelist)
|
| print(f"Text Blocks: {unique_blocks}")
|
| num_EOS = 0
|
| for i in range(len(sentencelist)):
|
| if eos_to_hc:
|
| sentencelist[i] = sentencelist[i].replace(EOSX_str, EOS_str)
|
| else:
|
| sentencelist[i] = sentencelist[i].replace(EOSX_str, '')
|
|
|
|
|
| sentencelist[i] = sentencelist[i].replace("</s></s>", EOS_str)
|
| num_EOS += sentencelist[i].count(EOS_str)
|
|
|
| if num_EOS > 0:
|
| print(f"+ EOS count: {num_EOS}")
|
|
|
|
|
| sentencelist = [item for item in sentencelist if item.strip() != "</s>"]
|
| sentencelist = [item for item in sentencelist if item.strip() != ""]
|
|
|
|
|
| if debug_slicer:
|
|
|
| Path('logs').mkdir(exist_ok=True)
|
| sentencelist_dict = {index: sentence for index, sentence in enumerate(sentencelist)}
|
| output_file = "logs/sentencelist.json"
|
| with open(output_file, 'w') as f:
|
| json.dump(sentencelist_dict, f,indent=2)
|
|
|
| print("Saved sentencelist.json in logs folder")
|
|
|
| return sentencelist
|
|
|
|
|
|
|
|
|
| def download_file_from_url(url, overwrite, output_dir_in, valid_extensions = {'.txt', '.json'}):
|
| try:
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| session = requests.Session()
|
| headers = {}
|
| mode = 'wb'
|
| filename = url.split('/')[-1]
|
|
|
| output_dir = str(output_dir_in)
|
|
|
| local_filename = os.path.join(output_dir, filename)
|
|
|
|
|
| overw = ''
|
| if os.path.exists(local_filename):
|
| if not overwrite:
|
| yield f"File '{local_filename}' already exists. Aborting."
|
| return
|
| else:
|
| overw = ' [Overwrite existing]'
|
|
|
| filename_lower = filename.lower()
|
|
|
|
|
| file_extension = os.path.splitext(filename_lower)[-1]
|
|
|
| if file_extension not in valid_extensions:
|
| yield f"Invalid file extension: {file_extension}. Only {valid_extensions} files are supported."
|
| return
|
|
|
| with session.get(url, stream=True, headers=headers, timeout=10) as r:
|
| r.raise_for_status()
|
|
|
|
|
|
|
| block_size = 1024 * 4
|
| with open(local_filename, mode) as f:
|
| count = 0
|
| for data in r.iter_content(block_size):
|
| f.write(data)
|
| count += len(data)
|
|
|
| yield f"Downloaded: {count} " + overw
|
|
|
|
|
| if os.path.exists(local_filename):
|
| downloaded_size = os.path.getsize(local_filename)
|
| if downloaded_size > 0:
|
| yield f"File '{filename}' downloaded to '{output_dir}' ({downloaded_size} bytes)."
|
| print("File Downloaded")
|
| else:
|
| print("Downloaded file is zero")
|
| yield f"Failed. Downloaded file size is zero)."
|
| else:
|
| print(f"Error: {local_filename} failed to download.")
|
| yield f"Error: {local_filename} failed to download"
|
|
|
| except Exception as e:
|
| print(f"An error occurred: {e}")
|
| yield f"An error occurred: {e}"
|
|
|
| finally:
|
|
|
| session.close()
|
|
|
|
|