hello.
Please help me, I do not know how to solve my problem problem.
I run and completed without errors
`python synthesizer_preprocess_audio.py <datasets_root>`
`python synthesizer_preprocess_embeds.py <datasets_root>/SV2TTS/synthesizer`
but after typing `python synthesizer_train.py my_run <datasets_root>/SV2TTS/synthesizer`
shows me a long error

```
Arguments:
    name:                   my_run
    synthesizer_root:       C:\Users\matve\Documents\Tacotron\datasets\SV2TTS\synthesizer
    models_dir:             synthesizer/saved_models/
    mode:                   synthesis
    GTA:                    True
    restore:                True
    summary_interval:       2500
    embedding_interval:     10000
    checkpoint_interval:    2000
    eval_interval:          100000
    tacotron_train_steps:   2000000
    tf_log_level:           1
    slack_url:              None
    hparams:

Checkpoint path: synthesizer/saved_models/logs-my_run\taco_pretrained\tacotron_model.ckpt
Loading training data from: C:\Users\matve\Documents\Tacotron\datasets\SV2TTS\synthesizer\train.txt
Using model: Tacotron
Hyperparameters:
  allow_clipping_in_normalization: True
  attention_dim: 128
  attention_filters: 32
  attention_kernel: (31,)
  cbhg_conv_channels: 128
  cbhg_highway_units: 128
  cbhg_highwaynet_layers: 4
  cbhg_kernels: 8
  cbhg_pool_size: 2
  cbhg_projection: 256
  cbhg_projection_kernel_size: 3
  cbhg_rnn_units: 128
  cleaners: english_cleaners
  clip_for_wavenet: True
  clip_mels_length: True
  cross_entropy_pos_weight: 20
  cumulative_weights: True
  decoder_layers: 2
  decoder_lstm_units: 1024
  embedding_dim: 512
  enc_conv_channels: 512
  enc_conv_kernel_size: (5,)
  enc_conv_num_layers: 3
  encoder_lstm_units: 256
  fmax: 7600
  fmin: 55
  frame_shift_ms: None
  griffin_lim_iters: 60
  hop_size: 200
  mask_decoder: False
  mask_encoder: True
  max_abs_value: 4.0
  max_iters: 2000
  max_mel_frames: 900
  min_level_db: -100
  n_fft: 800
  natural_eval: False
  normalize_for_wavenet: True
  num_mels: 80
  outputs_per_step: 2
  postnet_channels: 512
  postnet_kernel_size: (5,)
  postnet_num_layers: 5
  power: 1.5
  predict_linear: False
  preemphasis: 0.97
  preemphasize: True
  prenet_layers: [256, 256]
  ref_level_db: 20
  rescale: True
  rescaling_max: 0.9
  sample_rate: 16000
  signal_normalization: True
  silence_min_duration_split: 0.4
  silence_threshold: 2
  smoothing: False
  speaker_embedding_size: 256
  split_on_cpu: True
  stop_at_any: True
  symmetric_mels: True
  tacotron_adam_beta1: 0.9
  tacotron_adam_beta2: 0.999
  tacotron_adam_epsilon: 1e-06
  tacotron_batch_size: 36
  tacotron_clip_gradients: True
  tacotron_data_random_state: 1234
  tacotron_decay_learning_rate: True
  tacotron_decay_rate: 0.5
  tacotron_decay_steps: 50000
  tacotron_dropout_rate: 0.5
  tacotron_final_learning_rate: 1e-05
  tacotron_gpu_start_idx: 0
  tacotron_initial_learning_rate: 0.001
  tacotron_num_gpus: 1
  tacotron_random_seed: 5339
  tacotron_reg_weight: 1e-07
  tacotron_scale_regularization: False
  tacotron_start_decay: 50000
  tacotron_swap_with_cpu: False
  tacotron_synthesis_batch_size: 128
  tacotron_teacher_forcing_decay_alpha: 0.0
  tacotron_teacher_forcing_decay_steps: 280000
  tacotron_teacher_forcing_final_ratio: 0.0
  tacotron_teacher_forcing_init_ratio: 1.0
  tacotron_teacher_forcing_mode: constant
  tacotron_teacher_forcing_ratio: 1.0
  tacotron_teacher_forcing_start_decay: 10000
  tacotron_test_batches: None
  tacotron_test_size: 0.05
  tacotron_zoneout_rate: 0.1
  train_with_GTA: False
  trim_fft_size: 512
  trim_hop_size: 128
  trim_top_db: 23
  use_lws: False
  utterance_min_duration: 1.6
  win_size: 800
Loaded metadata for 290550 examples (366.70 hours)
initialisation done /gpu:0
Initialized Tacotron model. Dimensions (? = dynamic shape):
  Train mode:               True
  Eval mode:                False
  GTA mode:                 False
  Synthesis mode:           False
  Input:                    (?, ?)
  device:                   0
  embedding:                (?, ?, 512)
  enc conv out:             (?, ?, 512)
  encoder out (cond):       (?, ?, 768)
  decoder out:              (?, ?, 80)
  residual out:             (?, ?, 512)
  projected residual out:   (?, ?, 80)
  mel out:                  (?, ?, 80)
  <stop_token> out:         (?, ?)
  Tacotron Parameters       28.439 Million.
initialisation done /gpu:0
Initialized Tacotron model. Dimensions (? = dynamic shape):
  Train mode:               False
  Eval mode:                True
  GTA mode:                 False
  Synthesis mode:           False
  Input:                    (?, ?)
  device:                   0
  embedding:                (?, ?, 512)
  enc conv out:             (?, ?, 512)
  encoder out (cond):       (?, ?, 768)
  decoder out:              (?, ?, 80)
  residual out:             (?, ?, 512)
  projected residual out:   (?, ?, 80)
  mel out:                  (?, ?, 80)
  <stop_token> out:         (?, ?)
  Tacotron Parameters       28.439 Million.
Tacotron training set to a maximum of 2000000 steps
Loading checkpoint synthesizer/saved_models/logs-my_run\taco_pretrained\tacotron_model.ckpt-0

Generated 64 train batches of size 36 in 3.626 sec
Step       1 [5.798 sec/step, loss=14.85899, avg_loss=14.85899]
Step       1 [5.798 sec/step, loss=14.85899, avg_loss=14.85899]

Saving Model Character Embeddings visualization..
Tacotron Character embeddings have been updated on tensorboard!
Step       2 [3.362 sec/step, loss=11.10468, avg_loss=12.98183]
Step       2 [3.362 sec/step, loss=11.10468, avg_loss=12.98183]

Generated 403 test batches of size 36 in 15.574 sec
Exiting due to exception: 2 root error(s) found.
  (0) Resource exhausted: OOM when allocating tensor with shape[36,512,1,702] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
	 [[node Tacotron_model/inference/postnet_convolutions/conv_layer_1_postnet_convolutions/conv1d/conv1d (defined at e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\framework\ops.py:1748) ]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

	 [[Tacotron_model/clip_by_global_norm/mul_30/_479]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

  (1) Resource exhausted: OOM when allocating tensor with shape[36,512,1,702] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
	 [[node Tacotron_model/inference/postnet_convolutions/conv_layer_1_postnet_convolutions/conv1d/conv1d (defined at e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\framework\ops.py:1748) ]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

0 successful operations.
0 derived errors ignored.

Original stack trace for 'Tacotron_model/inference/postnet_convolutions/conv_layer_1_postnet_convolutions/conv1d/conv1d':
  File "synthesizer_train.py", line 55, in <module>
    tacotron_train(args, log_dir, hparams)
  File "C:\Users\matve\Documents\Tacotron\Real-Time-Voice-Cloning\synthesizer\train.py", line 392, in tacotron_train
    return train(log_dir, args, hparams)
  File "C:\Users\matve\Documents\Tacotron\Real-Time-Voice-Cloning\synthesizer\train.py", line 148, in train
    model, stats = model_train_mode(args, feeder, hparams, global_step)
  File "C:\Users\matve\Documents\Tacotron\Real-Time-Voice-Cloning\synthesizer\train.py", line 91, in model_train_mode
    is_training=True, split_infos=feeder.split_infos)
  File "C:\Users\matve\Documents\Tacotron\Real-Time-Voice-Cloning\synthesizer\models\tacotron.py", line 230, in initialize
    residual = postnet(decoder_output)
  File "C:\Users\matve\Documents\Tacotron\Real-Time-Voice-Cloning\synthesizer\models\modules.py", line 406, in __call__
    "conv_layer_{}_".format(i + 1) + self.scope)
  File "C:\Users\matve\Documents\Tacotron\Real-Time-Voice-Cloning\synthesizer\models\modules.py", line 420, in conv1d
    padding="same")
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\util\deprecation.py", line 324, in new_func
    return func(*args, **kwargs)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\layers\convolutional.py", line 218, in conv1d
    return layer.apply(inputs)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\util\deprecation.py", line 324, in new_func
    return func(*args, **kwargs)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py", line 1700, in apply
    return self.__call__(inputs, *args, **kwargs)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\layers\base.py", line 548, in __call__
    outputs = super(Layer, self).__call__(inputs, *args, **kwargs)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py", line 854, in __call__
    outputs = call_fn(cast_inputs, *args, **kwargs)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\autograph\impl\api.py", line 234, in wrapper
    return converted_call(f, options, args, kwargs)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\autograph\impl\api.py", line 439, in converted_call
    return _call_unconverted(f, args, kwargs, options)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\autograph\impl\api.py", line 330, in _call_unconverted
    return f(*args, **kwargs)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\keras\layers\convolutional.py", line 387, in call
    return super(Conv1D, self).call(inputs)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\keras\layers\convolutional.py", line 197, in call
    outputs = self._convolution_op(inputs, self.kernel)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 1134, in __call__
    return self.conv_op(inp, filter)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 639, in __call__
    return self.call(inp, filter)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 238, in __call__
    name=self.name)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 227, in _conv1d
    name=name)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\util\deprecation.py", line 574, in new_func
    return func(*args, **kwargs)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\util\deprecation.py", line 574, in new_func
    return func(*args, **kwargs)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 1681, in conv1d
    name=name)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\ops\gen_nn_ops.py", line 1071, in conv2d
    data_format=data_format, dilations=dilations, name=name)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\framework\op_def_library.py", line 794, in _apply_op_helper
    op_def=op_def)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\util\deprecation.py", line 507, in new_func
    return func(*args, **kwargs)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\framework\ops.py", line 3357, in create_op
    attrs, op_def, compute_device)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\framework\ops.py", line 3426, in _create_op_internal
    op_def=op_def)
  File "e:\ProgramData\Miniconda3\lib\site-packages\tensorflow_core\python\framework\ops.py", line 1748, in __init__
    self._traceback = tf_stack.extract_stack()

2021-02-05 20:02:33.232435: W tensorflow/core/kernels/queue_base.cc:277] _1_datafeeder/eval_queue: Skipping cancelled enqueue attempt with queue not closed
2021-02-05 20:02:33.232577: W tensorflow/core/kernels/queue_base.cc:277] _0_datafeeder/input_queue: Skipping cancelled enqueue attempt with queue not closed

```
I think it can't use the memory of my GTX 1660 super .Tell the noob what to do


Python 3.10.12 is not supported

When I ran python3.10 -m pip install numpy==1.20.3 on linux mint, I got an error while I was trying to install it. But it was totally fine when I used python3.8
![12](https://github.com/CorentinJ/Real-Time-Voice-Cloning/assets/100217654/99071c68-bf38-4ffe-b789-9d292ed539a5)


[Errno 2] No such file or directory: 'encoder/_sources.txt'

I have this problem, but I can't understand what does this file contain? There is not _sources.txt in this repo


Tensorflow.contrib file missing  what to do

0
and
2
这里是指导是doc
问题原因是依赖的库的版本


Getting an exception when browsing for files

For some reason, importing mp3 files is not working.  Anyone got an idea on why this might be the case.?


A couple inquiries about the colab version

So I have a setup using a copy of the colaboratory version, but I want to be able to generate a few sentences at a time without having to generate per sentence.

I understand that commas and periods don't work, but in the demonstration video it was mentioned that line breaks are a way to get around this for now... however that's done in the toolbox application. How would it be done in code?

I've tried \n but I assume that's only for print related arguments... but I'm fairly new to Python so excuse my ignorance.

On top of this, how could I improve the voice in colab? In regards to training, it's mentioned that a decent session requires around 500gb or more... since I don't exactly have that in colab, is there another way to go about doing this?

I've tried the code with the input being longer than 10 seconds, but apparently if the input is more than 10 seconds or so the voice seems more jittery than it would be if it were capped at 10 seconds. I absolutely applaud this repo but I just really need to understand it a bit better... Thanks in advance.


Not code-savy but want to experiment with code

I have Python Spyder downloaded, but I do not know much about coding, or how to get to the stage where I can add audio and synthesize it. What would you recommend?


i cant install NVIDIA CUDA

I can't install NVIDIA CUDA even though I followed everything that [this guide](https://poorlydocumented.com/2019/11/installing-corentinjs-real-time-voice-cloning-project-on-windows-10-from-scratch/l) told me to do. I also have tried searching for this problem on the internet, but none of them solves my problem. I also have provided the image of the error [here](https://imgur.com/a/fYkiBYQ).


New Audio Issue: Assertion Failed

This was working yesterday fine, and no big changes were made.
However, today starting up the demo toolbox loaded:
Assertion failed!

Program: C:\Users\paul1\AppData\Local\Programs\Python\Python37\python.exe
File: src/hostapi/wdmks/pa_win_wdmks.c, Line 1061

Expression: FALSE

I have tried reinstalling visual studio as well, but to no avail. Any thoughts on this would be deeply appreciated.


[Bug]: Non checkpoints found. Can't run without a checkpoint.

### Is there an existing issue for this?

- [X] I have searched the existing issues and checked the recent builds/commits

### What happened?

During the installation (windows), an error occurs :
```
venv "G:\Dev\stable-diffusion-webui\venv\Scripts\Python.exe"
Python 3.10.8 (tags/v3.10.8:aaaf517, Oct 11 2022, 16:50:30) [MSC v.1933 64 bit (AMD64)]
Commit hash: 9e78d2c419732711e984c4478af15ece121d64fd
Installing requirements for Web UI
Launching Web UI with arguments:
No module 'xformers'. Proceeding without it.
No checkpoints found. When searching for checkpoints, looked at:
 - file G:\Dev\stable-diffusion-webui\model.ckpt
 - directory G:\Dev\stable-diffusion-webui\models\Stable-diffusion
Can't run without a checkpoint. Find and place a .ckpt file into any of those locations. The program will exit.
```

### Steps to reproduce the problem

Launch webui-user.bat

### What should have happened?

Installation complete

### Commit where the problem happens

9e78d2c419732711e984c4478af15ece121d64fd

### What platforms do you use to access UI ?

Windows

### What browsers do you use to access the UI ?

Google Chrome

### Command Line Arguments

_No response_

### Additional information, context and logs

_No response_


[Bug]: ModuleNotFoundError: No module named 'pytorch_lightning.utilities.distributed'

### Is there an existing issue for this?

- [X] I have searched the existing issues and checked the recent builds/commits

### What happened?

Launching Web UI with arguments: --share --disable-safe-unpickle --no-half-vae --xformers --enable-insecure-extension --gradio-queue
2023-06-27 13:53:22.297173: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-06-27 13:53:23.287285: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
╭───────────────────── Traceback (most recent call last) ──────────────────────╮
│ /content/microsoftexcel/launch.py:38 in <module>                             │
│                                                                              │
│   35                                                                         │
│   36                                                                         │
│   37 if __name__ == "__main__":                                              │
│ ❱ 38 │   main()                                                              │
│   39                                                                         │
│                                                                              │
│ /content/microsoftexcel/launch.py:34 in main                                 │
│                                                                              │
│   31 │   if args.test_server:                                                │
│   32 │   │   configure_for_tests()                                           │
│   33 │                                                                       │
│ ❱ 34 │   start()                                                             │
│   35                                                                         │
│   36                                                                         │
│   37 if __name__ == "__main__":                                              │
│                                                                              │
│ /content/microsoftexcel/modules/launch_utils.py:340 in start                 │
│                                                                              │
│   337                                                                        │
│   338 def start():                                                           │
│   339 │   print(f"Launching {'API server' if '--nowebui' in sys.argv else 'W │
│ ❱ 340 │   import webui                                                       │
│   341 │   if '--nowebui' in sys.argv:                                        │
│   342 │   │   webui.api_only()                                               │
│   343 │   else:                                                              │
│                                                                              │
│ /content/microsoftexcel/webui.py:42 in <module>                              │
│                                                                              │
│    39 startup_timer.record("import ldm")                                     │
│    40                                                                        │
│    41 from modules import extra_networks                                     │
│ ❱  42 from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, │
│    43                                                                        │
│    44 # Truncate version number of nightly/local build of PyTorch to not cau │
│    45 if ".dev" in torch.__version__ or "+git" in torch.__version__:         │
│                                                                              │
│ /content/microsoftexcel/modules/call_queue.py:5 in <module>                  │
│                                                                              │
│     2 import threading                                                       │
│     3 import time                                                            │
│     4                                                                        │
│ ❱   5 from modules import shared, progress, errors                           │
│     6                                                                        │
│     7 queue_lock = threading.Lock()                                          │
│     8                                                                        │
│                                                                              │
│ /content/microsoftexcel/modules/shared.py:18 in <module>                     │
│                                                                              │
│    15 import modules.devices as devices                                      │
│    16 from modules import localization, script_loading, errors, ui_component │
│    17 from modules.paths_internal import models_path, script_path, data_path │
│ ❱  18 from ldm.models.diffusion.ddpm import LatentDiffusion                  │
│    19 from typing import Optional                                            │
│    20                                                                        │
│    21 demo = None                                                            │
│                                                                              │
│ /content/microsoftexcel/repositories/stable-diffusion-stability-ai/ldm/model │
│ s/diffusion/ddpm.py:20 in <module>                                           │
│                                                                              │
│     17 import itertools                                                      │
│     18 from tqdm import tqdm                                                 │
│     19 from torchvision.utils import make_grid                               │
│ ❱   20 from pytorch_lightning.utilities.distributed import rank_zero_only    │
│     21 from omegaconf import ListConfig                                      │
│     22                                                                       │
│     23 from ldm.util import log_txt_as_img, exists, default, ismap, isimage, │
╰──────────────────────────────────────────────────────────────────────────────╯
ModuleNotFoundError: No module named 'pytorch_lightning.utilities.distributed'

### Steps to reproduce the problem

1. on colab
2. try to use the new 1.4.0 release
3. error

### What should have happened?

no error

### Version or Commit where the problem happens

1.4.0

### What Python version are you running on ?

None

### What platforms do you use to access the UI ?

Other/Cloud

### What device are you running WebUI on?

_No response_

### Cross attention optimization

Automatic

### What browsers do you use to access the UI ?

Google Chrome

### Command Line Arguments

```Shell
!COMMANDLINE_ARGS="--share --disable-safe-unpickle --no-half-vae --xformers --enable-insecure-extension --gradio-queue" REQS_FILE="requirements.txt" python launch.py
```

### List of extensions

sd-webui-tunnels
controlnet
openpose-editor
posex
a1111-sd-webui-tagcomplete
supermerger
ultimate-upscale-for-automatic1111
a111 locon extension
images browser

### Console logs

```Shell
**truncated on colab**

  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100  1217  100  1217    0     0   3699      0 --:--:-- --:--:-- --:--:--  3699
100 1722k  100 1722k    0     0   670k      0  0:00:02  0:00:02 --:--:-- 1355k
Archive:  /content/microsoftexcel.zip
   creating: microsoftexcel/
  inflating: microsoftexcel/.eslintignore
  inflating: microsoftexcel/.eslintrc.js
  inflating: microsoftexcel/.git-blame-ignore-revs
   creating: microsoftexcel/.github/
   creating: microsoftexcel/.github/ISSUE_TEMPLATE/
  inflating: microsoftexcel/.github/ISSUE_TEMPLATE/bug_report.yml
  inflating: microsoftexcel/.github/ISSUE_TEMPLATE/config.yml
  inflating: microsoftexcel/.github/ISSUE_TEMPLATE/feature_request.yml
  inflating: microsoftexcel/.github/pull_request_template.md
   creating: microsoftexcel/.github/workflows/
  inflating: microsoftexcel/.github/workflows/on_pull_request.yaml
  inflating: microsoftexcel/.github/workflows/run_tests.yaml
  inflating: microsoftexcel/.gitignore
  inflating: microsoftexcel/.pylintrc
  inflating: microsoftexcel/CHANGELOG.md
  inflating: microsoftexcel/CODEOWNERS
   creating: microsoftexcel/configs/
  inflating: microsoftexcel/configs/alt-diffusion-inference.yaml
  inflating: microsoftexcel/configs/instruct-pix2pix.yaml
  inflating: microsoftexcel/configs/v1-inference.yaml
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  inflating: embeddings/CyberRealistic_Negative-neg.pt
  inflating: embeddings/easynegative.safetensors
  inflating: embeddings/EasyNegativeV2.safetensors
  inflating: embeddings/epiCNegative.pt
  inflating: embeddings/epiCRealism.pt
  inflating: embeddings/FastNegativeEmbedding.pt
  inflating: embeddings/HyperStylizeV6.pt
  inflating: embeddings/nartfixer.pt
  inflating: embeddings/negative_hand-neg.pt
  inflating: embeddings/nfixer.pt
  inflating: embeddings/ng_deepnegative_v1_75t.pt
  inflating: embeddings/nrealfixer.pt
  inflating: embeddings/pureerosface_v1.pt
  inflating: embeddings/rmadanegative402_sd15-neg.pt
  inflating: embeddings/ulzzang-6500-v1.1.bin
  inflating: embeddings/ulzzang-6500.pt
  inflating: embeddings/UnrealisticDream.pt
  inflating: embeddings/verybadimagenegative_v1.3.pt
/content/microsoftexcel/models/ESRGAN
Archive:  /content/microsoftexcel/models/ESRGAN/upscalers.zip
  inflating: 4x-UltraSharp.pth
  inflating: 4x_foolhardy_Remacri.pth
/content
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100  1133  100  1133    0     0   4800      0 --:--:-- --:--:-- --:--:--  4800
100 4067M  100 4067M    0     0   221M      0  0:00:18  0:00:18 --:--:--  242M
/content/microsoftexcel
fatal: not a git repository (or any of the parent directories): .git
fatal: not a git repository (or any of the parent directories): .git
Python 3.10.12 (main, Jun  7 2023, 12:45:35) [GCC 9.4.0]
Version: ## 1.4.0
Commit hash: <none>
Installing gfpgan
Installing clip
Installing open_clip
Installing xformers
Cloning Stable Diffusion into /content/microsoftexcel/repositories/stable-diffusion-stability-ai...
Cloning K-diffusion into /content/microsoftexcel/repositories/k-diffusion...
Cloning CodeFormer into /content/microsoftexcel/repositories/CodeFormer...
Cloning BLIP into /content/microsoftexcel/repositories/BLIP...
Installing requirements for CodeFormer
Installing requirements
Installing sd-webui-controlnet requirement: mediapipe
Installing sd-webui-controlnet requirement: svglib
Installing sd-webui-controlnet requirement: fvcore

Installing pycloudflared

Installing diffusers

Launching Web UI with arguments: --share --disable-safe-unpickle --no-half-vae --xformers --enable-insecure-extension --gradio-queue
2023-06-27 13:53:22.297173: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-06-27 13:53:23.287285: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
╭───────────────────── Traceback (most recent call last) ──────────────────────╮
│ /content/microsoftexcel/launch.py:38 in <module>                             │
│                                                                              │
│   35                                                                         │
│   36                                                                         │
│   37 if __name__ == "__main__":                                              │
│ ❱ 38 │   main()                                                              │
│   39                                                                         │
│                                                                              │
│ /content/microsoftexcel/launch.py:34 in main                                 │
│                                                                              │
│   31 │   if args.test_server:                                                │
│   32 │   │   configure_for_tests()                                           │
│   33 │                                                                       │
│ ❱ 34 │   start()                                                             │
│   35                                                                         │
│   36                                                                         │
│   37 if __name__ == "__main__":                                              │
│                                                                              │
│ /content/microsoftexcel/modules/launch_utils.py:340 in start                 │
│                                                                              │
│   337                                                                        │
│   338 def start():                                                           │
│   339 │   print(f"Launching {'API server' if '--nowebui' in sys.argv else 'W │
│ ❱ 340 │   import webui                                                       │
│   341 │   if '--nowebui' in sys.argv:                                        │
│   342 │   │   webui.api_only()                                               │
│   343 │   else:                                                              │
│                                                                              │
│ /content/microsoftexcel/webui.py:42 in <module>                              │
│                                                                              │
│    39 startup_timer.record("import ldm")                                     │
│    40                                                                        │
│    41 from modules import extra_networks                                     │
│ ❱  42 from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, │
│    43                                                                        │
│    44 # Truncate version number of nightly/local build of PyTorch to not cau │
│    45 if ".dev" in torch.__version__ or "+git" in torch.__version__:         │
│                                                                              │
│ /content/microsoftexcel/modules/call_queue.py:5 in <module>                  │
│                                                                              │
│     2 import threading                                                       │
│     3 import time                                                            │
│     4                                                                        │
│ ❱   5 from modules import shared, progress, errors                           │
│     6                                                                        │
│     7 queue_lock = threading.Lock()                                          │
│     8                                                                        │
│                                                                              │
│ /content/microsoftexcel/modules/shared.py:18 in <module>                     │
│                                                                              │
│    15 import modules.devices as devices                                      │
│    16 from modules import localization, script_loading, errors, ui_component │
│    17 from modules.paths_internal import models_path, script_path, data_path │
│ ❱  18 from ldm.models.diffusion.ddpm import LatentDiffusion                  │
│    19 from typing import Optional                                            │
│    20                                                                        │
│    21 demo = None                                                            │
│                                                                              │
│ /content/microsoftexcel/repositories/stable-diffusion-stability-ai/ldm/model │
│ s/diffusion/ddpm.py:20 in <module>                                           │
│                                                                              │
│     17 import itertools                                                      │
│     18 from tqdm import tqdm                                                 │
│     19 from torchvision.utils import make_grid                               │
│ ❱   20 from pytorch_lightning.utilities.distributed import rank_zero_only    │
│     21 from omegaconf import ListConfig                                      │
│     22                                                                       │
│     23 from ldm.util import log_txt_as_img, exists, default, ismap, isimage, │
╰──────────────────────────────────────────────────────────────────────────────╯
ModuleNotFoundError: No module named 'pytorch_lightning.utilities.distributed'
```

### Additional information

_No response_


[Feature Request]:

Finer control of CFG Scale? now it goes by 0.5 steps. I'm trying to replicate work i did on other app which have CFG scale control by 0.1. i cannot get the same result, of course.


RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:0! (when checking argument for argument index in method wrapper__index_select)

how to pick the CUDA:0 ？


How to use .ckpt model on repo

Hello everyone!

I was able to train a custom model using Dreambooth and I now have a custom ckpt trained on myself. Where do I put this file to be able to use it in this repo?

I dropped in into models but not sure what to do next?

Appreciate any help


Feature request: GPU temperature control

**Is your feature request related to a problem? Please describe.**
I don't like 85 degrees (Celsius) on my GPU, especially if it lasts more than 30 minutes or even 1 hour

**Describe the solution you'd like**
If temp on a GPU is more than {maxTemp} and it lasts {accumulateTempTime} it will pause processing for {cooldownTime} or until it cools to {minTemp}, so my GPU won't end up with exploding

**Describe alternatives you've considered**
Not pausing, but lowering the activity to a few tens of seconds per step.

**Additional context**
Not lowering it in hard core, but smartly lowering activity (using sth similar to PID), so the temp will stay at {desiredTemp}


BPO | [832251](https://bugs.python.org/issue832251)
--- | :---
Nosy | @freddrake

*Note: these values reflect the state of the issue at the time it was migrated and might not reflect the current state.*

Show more details

GitHub fields:
```python
assignee = 'https://github.com/freddrake'
closed_at = <Date 2004-04-01.04:19:08.000>
created_at = <Date 2003-10-29.09:39:39.000>
labels = ['docs']
title = 'Wrong reference for specific minidom methods'
updated_at = <Date 2004-04-01.04:19:08.000>
user = 'https://bugs.python.org/nerby'
```

bugs.python.org fields:
```python
activity = <Date 2004-04-01.04:19:08.000>
actor = 'fdrake'
assignee = 'fdrake'
closed = True
closed_date = None
closer = None
components = ['Documentation']
creation = <Date 2003-10-29.09:39:39.000>
creator = 'nerby'
dependencies = []
files = []
hgrepos = []
issue_num = 832251
keywords = []
message_count = 3.0
messages = ['18799', '18800', '18801']
nosy_count = 2.0
nosy_names = ['fdrake', 'nerby']
pr_nums = []
priority = 'high'
resolution = 'fixed'
stage = None
status = 'closed'
superseder = None
type = None
url = 'https://bugs.python.org/issue832251'
versions = ['Python 2.3']
```


BPO | [210610](https://bugs.python.org/issue210610)
--- | :---
Nosy | @gvanrossum

*Note: these values reflect the state of the issue at the time it was migrated and might not reflect the current state.*

Show more details

GitHub fields:
```python
assignee = 'https://github.com/gvanrossum'
closed_at = <Date 2000-09-20.20:33:21.000>
created_at = <Date 2000-07-31.21:05:42.000>
labels = ['interpreter-core']
title = '.pyc writing/reading race condition (PR#189)'
updated_at = <Date 2000-09-20.20:33:21.000>
user = 'https://bugs.python.org/anonymous'
```

bugs.python.org fields:
```python
activity = <Date 2000-09-20.20:33:21.000>
actor = 'gvanrossum'
assignee = 'gvanrossum'
closed = True
closed_date = None
closer = None
components = ['Interpreter Core']
creation = <Date 2000-07-31.21:05:42.000>
creator = 'anonymous'
dependencies = []
files = []
hgrepos = []
issue_num = 210610
keywords = []
message_count = 4.0
messages = ['66', '67', '68', '69']
nosy_count = 2.0
nosy_names = ['gvanrossum', 'jhylton']
pr_nums = []
priority = 'low'
resolution = 'fixed'
stage = None
status = 'closed'
superseder = None
type = None
url = 'https://bugs.python.org/issue210610'
versions = []
```


BPO | [24432](https://bugs.python.org/issue24432)
--- | :---
Nosy | @pfmoore, @pitrou, @larryhastings, @giampaolo, @tiran, @tjguk, @benjaminp, @ned-deily, @alex, @bitdancer, @zware, @zooba, @dstufft

*Note: these values reflect the state of the issue at the time it was migrated and might not reflect the current state.*

Show more details

GitHub fields:
```python
assignee = 'https://github.com/zooba'
closed_at = <Date 2015-07-03.22:28:01.834>
created_at = <Date 2015-06-11.15:05:25.361>
labels = ['library', 'release-blocker']
title = 'Upgrade windows builds to use OpenSSL 1.0.2c'
updated_at = <Date 2015-07-04.06:47:41.096>
user = 'https://github.com/alex'
```

bugs.python.org fields:
```python
activity = <Date 2015-07-04.06:47:41.096>
actor = 'python-dev'
assignee = 'steve.dower'
closed = True
closed_date = <Date 2015-07-03.22:28:01.834>
closer = 'steve.dower'
components = ['Library (Lib)']
creation = <Date 2015-06-11.15:05:25.361>
creator = 'alex'
dependencies = []
files = []
hgrepos = []
issue_num = 24432
keywords = ['security_issue']
message_count = 29.0
messages = ['245173', '245178', '245283', '246116', '246133', '246136', '246143', '246172', '246182', '246185', '246189', '246190', '246195', '246205', '246209', '246210', '246211', '246212', '246213', '246214', '246215', '246216', '246221', '246222', '246224', '246225', '246227', '246228', '246240']
nosy_count = 15.0
nosy_names = ['paul.moore', 'janssen', 'pitrou', 'larry', 'giampaolo.rodola', 'christian.heimes', 'tim.golden', 'benjamin.peterson', 'ned.deily', 'alex', 'r.david.murray', 'python-dev', 'zach.ware', 'steve.dower', 'dstufft']
pr_nums = []
priority = 'release blocker'
resolution = 'fixed'
stage = 'resolved'
status = 'closed'
superseder = None
type = None
url = 'https://bugs.python.org/issue24432'
versions = ['Python 2.7', 'Python 3.4', 'Python 3.5', 'Python 3.6']
```


BPO | [4185](https://bugs.python.org/issue4185)
--- | :---
Nosy | @gvanrossum, @loewis, @akuchling, @birkenfeld, @ezio-melotti
Files |
[raw-strings-with-re.txt](https://bugs.python.org/file11868/raw-strings-with-re.txt "Uploaded as text/plain at 2008-10-23.03:55:27 by @ezio-melotti"): Interactive Python session with more examples

*Note: these values reflect the state of the issue at the time it was migrated and might not reflect the current state.*

Show more details

GitHub fields:
```python
assignee = 'https://github.com/akuchling'
closed_at = <Date 2009-01-01.12:00:35.699>
created_at = <Date 2008-10-23.03:55:28.615>
labels = ['expert-regex', 'type-bug', 'library']
title = 're module treats raw strings as normal strings'
updated_at = <Date 2009-01-01.12:00:35.697>
user = 'https://github.com/ezio-melotti'
```

bugs.python.org fields:
```python
activity = <Date 2009-01-01.12:00:35.697>
actor = 'georg.brandl'
assignee = 'akuchling'
closed = True
closed_date = <Date 2009-01-01.12:00:35.699>
closer = 'georg.brandl'
components = ['Library (Lib)', 'Regular Expressions']
creation = <Date 2008-10-23.03:55:28.615>
creator = 'ezio.melotti'
dependencies = []
files = ['11868']
hgrepos = []
issue_num = 4185
keywords = []
message_count = 8.0
messages = ['75133', '75134', '75135', '75760', '77502', '77562', '77575', '78699']
nosy_count = 5.0
nosy_names = ['gvanrossum', 'loewis', 'akuchling', 'georg.brandl', 'ezio.melotti']
pr_nums = []
priority = 'normal'
resolution = 'fixed'
stage = None
status = 'closed'
superseder = None
type = 'behavior'
url = 'https://bugs.python.org/issue4185'
versions = ['Python 2.6', 'Python 2.5', 'Python 2.4']
```


[BUG/Help] <title>使用ptuning微调时报错RuntimeError: Internal: src/sentencepiece_processor.cc(1101) [model_proto->ParseFromArray(serialized.data(), serialized.size())]

### Is there an existing issue for this?

- [X] I have searched the existing issues

### Current Behavior

Traceback (most recent call last):
  File "main.py", line 429, in <module>
    main()
  File "main.py", line 112, in main
    tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, trust_remote_code=True)
  File "/root/miniconda3/lib/python3.8/site-packages/transformers/models/auto/tokenization_auto.py", line 679, in from_pretrained
    return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
  File "/root/miniconda3/lib/python3.8/site-packages/transformers/tokenization_utils_base.py", line 1804, in from_pretrained
    return cls._from_pretrained(
  File "/root/miniconda3/lib/python3.8/site-packages/transformers/tokenization_utils_base.py", line 1958, in _from_pretrained
    tokenizer = cls(*init_inputs, **init_kwargs)
  File "/root/.cache/huggingface/modules/transformers_modules/chatglm-6b/tokenization_chatglm.py", line 205, in __init__
    self.sp_tokenizer = SPTokenizer(vocab_file, num_image_tokens=num_image_tokens)
  File "/root/.cache/huggingface/modules/transformers_modules/chatglm-6b/tokenization_chatglm.py", line 61, in __init__
    self.text_tokenizer = TextTokenizer(vocab_file)
  File "/root/.cache/huggingface/modules/transformers_modules/chatglm-6b/tokenization_chatglm.py", line 22, in __init__
    self.sp.Load(model_path)
  File "/root/miniconda3/lib/python3.8/site-packages/sentencepiece/__init__.py", line 905, in Load
    return self.LoadFromFile(model_file)
  File "/root/miniconda3/lib/python3.8/site-packages/sentencepiece/__init__.py", line 310, in LoadFromFile
    return _sentencepiece.SentencePieceProcessor_LoadFromFile(self, arg)
RuntimeError: Internal: src/sentencepiece_processor.cc(1101) [model_proto->ParseFromArray(serialized.data(), serialized.size())]

### Expected Behavior

_No response_

### Steps To Reproduce

使用ptuning微调时报错，已经是最新版的模型文件了

### Environment

```markdown
PyTorch  1.11.0
Python  3.8(ubuntu20.04)
Cuda  11.3
```

### Anything else?

_No response_


为什么不能用共享GPU内存呢[Feature] <title>

### Is your feature request related to a problem? Please describe.

为什么不能用共享GPU内存呢
专用6G都满了但是共享GPU内存一点都没动

### Solutions

emm

### Additional context

_No response_


### Is there an existing issue for this?

- [X] I have searched the existing issues

### Current Behavior

运行python cli_demo.py报错

root@4uot40mdrplpv-0:/yx/ChatGLM-6B# python mycli_demo.py
Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure no malicious code has been contributed in a newer revision.
Traceback (most recent call last):
  File "/yx/ChatGLM-6B/mycli_demo.py", line 6, in <module>
    tokenizer = AutoTokenizer.from_pretrained("/yx/ChatGLM-6B/THUDM/chatglm-6b", trust_remote_code=True)
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/transformers/models/auto/tokenization_auto.py", line 679, in from_pretrained
    return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/transformers/tokenization_utils_base.py", line 1804, in from_pretrained
    return cls._from_pretrained(
           ^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/transformers/tokenization_utils_base.py", line 1958, in _from_pretrained
    tokenizer = cls(*init_inputs, **init_kwargs)
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/.cache/huggingface/modules/transformers_modules/chatglm-6b/tokenization_chatglm.py", line 205, in __init__
    self.sp_tokenizer = SPTokenizer(vocab_file, num_image_tokens=num_image_tokens)
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/.cache/huggingface/modules/transformers_modules/chatglm-6b/tokenization_chatglm.py", line 61, in __init__
    self.text_tokenizer = TextTokenizer(vocab_file)
                          ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/.cache/huggingface/modules/transformers_modules/chatglm-6b/tokenization_chatglm.py", line 22, in __init__
    self.sp.Load(model_path)
  File "/usr/local/lib/python3.11/site-packages/sentencepiece/__init__.py", line 905, in Load
    return self.LoadFromFile(model_file)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/sentencepiece/__init__.py", line 310, in LoadFromFile
    return _sentencepiece.SentencePieceProcessor_LoadFromFile(self, arg)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: Internal: src/sentencepiece_processor.cc(1101) [model_proto->ParseFromArray(serialized.data(), serialized.size())]

我是在docker中运行的,  麻烦看看是怎么回事, 谢谢

### Expected Behavior

_No response_

### Steps To Reproduce

help

### Environment

```markdown
- OS:Red Hat 4.8.5-44
- Python:3.11
- Transformers:4.27.1
- PyTorch:2.0
- CUDA Support (`python -c "import torch; print(torch.cuda.is_available())"`) :False
```

### Anything else?

_No response_


How to fix error like this: torch.cuda.OutOfMemoryError: CUDA out of memory ?

OS: ubuntu 20.04
The error message said we need to change value of max_split_size_mb, but I search source code and cannot find any file contains max_split_size_mb, would you please provide some guidance about how to fix?
```
Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8/8 [00:16<00:00,  2.09s/it]
Traceback (most recent call last):
  File "/home/zhangclb/sandbox/ai_llm/ChatGLM-6B/cli_demo.py", line 6, in <module>
    model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
  File "/home/zhangclb/.local/env39/lib/python3.9/site-packages/torch/nn/modules/module.py", line 749, in cuda
    return self._apply(lambda t: t.cuda(device))
  File "/home/zhangclb/.local/env39/lib/python3.9/site-packages/torch/nn/modules/module.py", line 641, in _apply
    module._apply(fn)
  File "/home/zhangclb/.local/env39/lib/python3.9/site-packages/torch/nn/modules/module.py", line 641, in _apply
    module._apply(fn)
  File "/home/zhangclb/.local/env39/lib/python3.9/site-packages/torch/nn/modules/module.py", line 641, in _apply
    module._apply(fn)
  [Previous line repeated 2 more times]
  File "/home/zhangclb/.local/env39/lib/python3.9/site-packages/torch/nn/modules/module.py", line 664, in _apply
    param_applied = fn(param)
  File "/home/zhangclb/.local/env39/lib/python3.9/site-packages/torch/nn/modules/module.py", line 749, in <lambda>
    return self._apply(lambda t: t.cuda(device))
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB (GPU 0; 1.83 GiB total capacity; 1.27 GiB already allocated; 57.19 MiB free; 1.28 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

```


[Help] <title> Oracle Linux 7.9 运行int4模型出错，AttributeError: 'NoneType' object has no attribute 'int4WeightExtractionFloat'

### Is there an existing issue for this?

- [X] I have searched the existing issues

### Current Behavior

/x/home/chatglm_env/lib/python3.7/site-packages/requests/__init__.py:104: RequestsDependencyWarning: urllib3 (1.26.14) or chardet (5.1.0)/charset_normalizer (2.0.12) doesn't match a supported version!
  RequestsDependencyWarning)
Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure no malicious code has been contributed in a newer revision.
/x/home/chatglm_env/lib/python3.7/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: libc10_cuda.so: cannot open shared object file: No such file or directory
  warn(f"Failed to load image Python extension: {e}")
Explicitly passing a `revision` is encouraged when loading a configuration with custom code to ensure no malicious code has been contributed in a newer revision.
Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure no malicious code has been contributed in a newer revision.
No compiled kernel found.
Compiling kernels : /x/home/.cache/huggingface/modules/transformers_modules/local/quantization_kernels_parallel.c
Compiling gcc -O3 -fPIC -pthread -fopenmp -std=c99 /x/home/.cache/huggingface/modules/transformers_modules/local/quantization_kernels_parallel.c -shared -o /x/home/.cache/huggingface/modules/transformers_modules/local/quantization_kernels_parallel.so
sh: gcc: command not found
Compile failed, using default cpu kernel code.
Compiling gcc -O3 -fPIC -std=c99 /x/home/.cache/huggingface/modules/transformers_modules/local/quantization_kernels.c -shared -o /x/home/.cache/huggingface/modules/transformers_modules/local/quantization_kernels.so
Kernels compiled : /x/home/.cache/huggingface/modules/transformers_modules/local/quantization_kernels.so
Cannot load cpu kernel, don't use quantized model on cpu.
Using quantization cache
Applying quantization to glm layers
Traceback (most recent call last):
  File "chatglm-int4-demo.py", line 8, in <module>
    response, history = model.chat(tokenizer, '你好', history=[])
  File "/x/home/chatglm_env/lib/python3.7/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/x/home/.cache/huggingface/modules/transformers_modules/local/modeling_chatglm.py", line 1137, in chat
    outputs = self.generate(**input_ids, **gen_kwargs)
  File "/x/home/chatglm_env/lib/python3.7/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/x/home/chatglm_env/lib/python3.7/site-packages/transformers/generation/utils.py", line 1447, in generate
    **model_kwargs,
  File "/x/home/chatglm_env/lib/python3.7/site-packages/transformers/generation/utils.py", line 2447, in sample
    output_hidden_states=output_hidden_states,
  File "/x/home/chatglm_env/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "/x/home/.cache/huggingface/modules/transformers_modules/local/modeling_chatglm.py", line 1051, in forward
    return_dict=return_dict,
  File "/x/home/chatglm_env/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "/x/home/.cache/huggingface/modules/transformers_modules/local/modeling_chatglm.py", line 887, in forward
    output_attentions=output_attentions
  File "/x/home/chatglm_env/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "/x/home/.cache/huggingface/modules/transformers_modules/local/modeling_chatglm.py", line 588, in forward
    output_attentions=output_attentions
  File "/x/home/chatglm_env/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "/x/home/.cache/huggingface/modules/transformers_modules/local/modeling_chatglm.py", line 406, in forward
    mixed_raw_layer = self.query_key_value(hidden_states)
  File "/x/home/chatglm_env/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "/x/home/.cache/huggingface/modules/transformers_modules/local/quantization.py", line 334, in forward
    output = W8A16LinearCPU.apply(input, self.weight, self.weight_scale, self.weight_bit_width, self.quantization_cache)
  File "/x/home/.cache/huggingface/modules/transformers_modules/local/quantization.py", line 74, in forward
    weight = extract_weight_to_float(quant_w, scale_w, weight_bit_width, quantization_cache=quantization_cache)
  File "/x/home/.cache/huggingface/modules/transformers_modules/local/quantization.py", line 256, in extract_weight_to_float
    func = cpu_kernels.int4WeightExtractionFloat
AttributeError: 'NoneType' object has no attribute 'int4WeightExtractionFloat'

### Expected Behavior

_No response_

### Steps To Reproduce

from transformers import AutoTokenizer, AutoModel

model_path = '/x/home/chatglm-6b-int4'

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True).float()

response, history = model.chat(tokenizer, '你好', history=[])

### Environment

```markdown
- OS: Oracle 7.9
- Python: 3.7.13
- Transformers: 2.6.1
- PyTorch: 1.13.1
- CUDA Support (`python -c "import torch; print(torch.cuda.is_available())"`) : no cuda, use cpu
```

### Anything else?

_No response_


可以使用自己的数据微调吗


6GB显卡提示显存不足

显卡：3060laptop 6GB
报错：RuntimeError: CUDA out of memory. Tried to allocate 96.00 MiB (GPU 0; 6.00 GiB total capacity; 5.27 GiB already allocated; 0 bytes free; 5.28 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF


[BUG/Help] 这两天更新版本后，chat的微调好像用不了了

### Is there an existing issue for this?

- [X] I have searched the existing issues

### Current Behavior

前几天使用chat微调还是可以用的，那时候output文件是完整的包，而不是增量微调包。
这两天更新后，使用的还是项目自带的train_chat.sh，模型用的是int4。
output文件确实小了，但是却无法运行了，具体形式为运行以下代码
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("/content/ChatGLM-6B/ptuning/output/chattm/checkpoint-50", trust_remote_code=True)
model = AutoModel.from_pretrained("/content/ChatGLM-6B/ptuning/output/chattm/checkpoint-50", trust_remote_code=True).half().cuda()
model = model.eval()
response, history = model.chat(tokenizer, "你好", history=[])
print(response)
```
报以下内容后无反应，至少5分钟。期间显存一直在上升
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
The dtype of attention mask (torch.int64) is not bool
最终报错
2023-04-11 13:51:41.577016: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT

### Expected Behavior

_No response_

### Steps To Reproduce

-

### Environment

```markdown
colab pro 默认环境
```

### Anything else?

_No response_


centos上，2个12g显存的显卡如何配置可以同时使用

### Is there an existing issue for this?

- [X] I have searched the existing issues

### Current Behavior

centos上，2个12g显存的显卡，无论是训练还是web，都始终用0号显卡，如何配置可以同时使用

### Expected Behavior

_No response_

### Steps To Reproduce

Centos7
12G nvida *2

### Environment

```markdown
- OS:Centos7
- Python:3.8
- Transformers:4.26.1
- PyTorch: 1.12
- CUDA Support (`python -c "import torch; print(torch.cuda.is_available())"`) :True
```

### Anything else?

_No response_


[BUG/Help] ValueError: 150000 is not in list

### Is there an existing issue for this?

- [X] I have searched the existing issues

### Current Behavior

  0%|          | 19/30000 [31:30<828:54:23, 99.53s/it]
  0%|          | 20/30000 [33:09<828:37:17, 99.50s/it]
  0%|          | 21/30000 [34:48<828:09:42, 99.45s/it]Traceback (most recent call last):
  File "/root/projects/ChatGLM-6B/ptuning/main.py", line 393, in <module>
    main()
  File "/root/projects/ChatGLM-6B/ptuning/main.py", line 332, in main
    train_result = trainer.train(resume_from_checkpoint=checkpoint)
  File "/root/anaconda3/envs/torch10/lib/python3.9/site-packages/transformers/trainer.py", line 1633, in train
    return inner_training_loop(
  File "/root/anaconda3/envs/torch10/lib/python3.9/site-packages/transformers/trainer.py", line 1902, in _inner_training_loop
    tr_loss_step = self.training_step(model, inputs)
  File "/root/anaconda3/envs/torch10/lib/python3.9/site-packages/transformers/trainer.py", line 2645, in training_step
    loss = self.compute_loss(model, inputs)
  File "/root/anaconda3/envs/torch10/lib/python3.9/site-packages/transformers/trainer.py", line 2677, in compute_loss
    outputs = model(**inputs)
  File "/root/anaconda3/envs/torch10/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "/root/.cache/huggingface/modules/transformers_modules/modeling_chatglm.py", line 1160, in forward
    transformer_outputs = self.transformer(
  File "/root/anaconda3/envs/torch10/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "/root/.cache/huggingface/modules/transformers_modules/modeling_chatglm.py", line 928, in forward
    mask_positions = [seq.tolist().index(mask_token) for seq in input_ids]
  File "/root/.cache/huggingface/modules/transformers_modules/modeling_chatglm.py", line 928, in <listcomp>
    mask_positions = [seq.tolist().index(mask_token) for seq in input_ids]
ValueError: 150000 is not in list

### Expected Behavior

_No response_

### Steps To Reproduce

PRE_SEQ_LEN=8
LR=1e-2

CUDA_VISIBLE_DEVICES=0 python3 main.py \
    --do_train \
    --train_file ../data/train.json \
    --validation_file ../data/dev.json \
    --prompt_column instruction \
    --response_column output \
    --overwrite_cache \
    --model_name_or_path ~/projects/zero_nlp/simple_thu_chatglm6b/thuglm/ \
    --output_dir output/adgen-chatglm-6b-pt-$PRE_SEQ_LEN-$LR \
    --overwrite_output_dir \
    --max_source_length 64 \
    --max_target_length 64 \
    --per_device_train_batch_size 100 \
    --per_device_eval_batch_size 100 \
    --gradient_accumulation_steps 16 \
    --predict_with_generate \
    --max_steps 30000 \
    --logging_steps 100 \
    --save_steps 100 \
    --learning_rate $LR \
    --pre_seq_len $PRE_SEQ_LEN \
    # --quantization_bit 4

### Environment

```markdown
- OS: centos8
- Python: 3.9
- Transformers: 4.27.1
- PyTorch:2.0.0
- CUDA Support (`python -c "import torch; print(torch.cuda.is_available())"`) : True
```

### Anything else?

_No response_


[BUG/Help] ImportError: cannot import name 'GENERATION_CONFIG_NAME' from 'transformers.utils'

### Is there an existing issue for this?

- [X] I have searched the existing issues

### Current Behavior

>>> model = AutoModel.from_pretrained("THUDM/chatglm-6b-int4",trust_remote_code=True).float()
Explicitly passing a `revision` is encouraged when loading a configuration with custom code to ensure no malicious code has been contributed in a newer revision.
Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure no malicious code has been contributed in a newer revision.
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Users\mina_\Anaconda3\envs\ChatGLM-6B\lib\site-packages\transformers\models\auto\auto_factory.py", line 456, in from_pretrained
    logger.warning(
  File "C:\Users\mina_\Anaconda3\envs\ChatGLM-6B\lib\site-packages\transformers\dynamic_module_utils.py", line 374, in get_class_from_dynamic_module

  File "C:\Users\mina_\Anaconda3\envs\ChatGLM-6B\lib\site-packages\transformers\dynamic_module_utils.py", line 147, in get_class_in_module
    def get_class_in_module(class_name, module_path):
  File "C:\Users\mina_\Anaconda3\envs\ChatGLM-6B\lib\importlib\__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 1006, in _gcd_import
  File "<frozen importlib._bootstrap>", line 983, in _find_and_load
  File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 728, in exec_module
  File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
  File "C:\Users\mina_/.cache\huggingface\modules\transformers_modules\THUDM\chatglm-6b-int4\dac03c3ac833dab2845a569a9b7f6ac4e8c5dc9b\modeling_chatglm.py", line 30, in <module>
    from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig
  File "C:\Users\mina_\Anaconda3\envs\ChatGLM-6B\lib\site-packages\transformers\generation\utils.py", line 39, in <module>
    from .configuration_utils import GenerationConfig
  File "C:\Users\mina_\Anaconda3\envs\ChatGLM-6B\lib\site-packages\transformers\generation\configuration_utils.py", line 24, in <module>
    from ..utils import (
ImportError: cannot import name 'GENERATION_CONFIG_NAME' from 'transformers.utils' (C:\Users\mina_\Anaconda3\envs\ChatGLM-6B\lib\site-packages\transformers\utils\__init__.py)

### Expected Behavior

_No response_

### Steps To Reproduce

1. `conda activate chatglm-6b`
2. `from transformers import AutoTokenizer, AutoModel`
3. `tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)`
4. `model = AutoModel.from_pretrained("THUDM/chatglm-6b-int4",trust_remote_code=True).float()`
5. See this issue.

### Environment

```markdown
- OS: Windows 10
- Python: 3.7.5
- Transformers:
- PyTorch:
- CUDA Support (`python -c "import torch; print(torch.cuda.is_available())"`) : False
```

### Anything else?

_No response_


[BUG/Help] <title>KeyError: 'lm_head.weight'

### Is there an existing issue for this?

- [X] I have searched the existing issues

### Current Behavior

报错：KeyError: 'lm_head.weight'

### Expected Behavior

Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure no malicious code has been contributed in a newer revision.
Explicitly passing a `revision` is encouraged when loading a configuration with custom code to ensure no malicious code has been contributed in a newer revision.
Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure no malicious code has been contributed in a newer revision.

Loading checkpoint shards:   0%|          | 0/8 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/8 [00:00<?, ?it/s]
Traceback (most recent call last):
  File "C:\Users\Administrator\Downloads\ChatGLM-6B-main\cli_demo.py", line 7, in <module>
    model = AutoModel.from_pretrained(r"C:\Users\Administrator\Downloads\ChatGLM-6B-main\model",trust_remote_code=True,ignore_mismatched_sizes=True).half().quantize(4).cuda()
  File "C:\Program Files\Python310\lib\site-packages\transformers\models\auto\auto_factory.py", line 466, in from_pretrained
    return model_class.from_pretrained(
  File "C:\Program Files\Python310\lib\site-packages\transformers\modeling_utils.py", line 2646, in from_pretrained
    ) = cls._load_pretrained_model(
  File "C:\Program Files\Python310\lib\site-packages\transformers\modeling_utils.py", line 2959, in _load_pretrained_model
    mismatched_keys += _find_mismatched_keys(
  File "C:\Program Files\Python310\lib\site-packages\transformers\modeling_utils.py", line 2882, in _find_mismatched_keys
    and state_dict[checkpoint_key].shape != model_state_dict[model_key].shape
KeyError: 'lm_head.weight'

### Steps To Reproduce

报错：KeyError: 'lm_head.weight'

### Environment

```markdown
- OS:windows 10
- Python:3.10
- Transformers:4.27.1
- PyTorch:cu118
- CUDA Support True
```

### Anything else?

_No response_


[BUG/Help] ptuning时，指定PRE_SEQ_LEN=512，训练后，回答的问题仍旧有回答一百字左右就断了，该如何调整？

### Is there an existing issue for this?

- [X] I have searched the existing issues

### Current Behavior

训练参数如下：
PRE_SEQ_LEN=512
LR=2e-2

CUDA_VISIBLE_DEVICES=0 python3 main.py \
    --do_train \
    --train_file ./data/gwddc.json \
    --validation_file ./data/gwddc_test.json \
    --prompt_column instruction \
    --response_column output \
    --overwrite_cache \
    --model_name_or_path THUDM/chatglm-6b \
    --output_dir output/adgen-chatglm-6b-pt-gwddc-v3 \
    --overwrite_output_dir \
    --max_source_length 64 \
    --max_target_length 64 \
    --per_device_train_batch_size 4 \
    --per_device_eval_batch_size 1 \
    --gradient_accumulation_steps 16 \
    --predict_with_generate \
    --max_steps 3000 \
    --logging_steps 10 \
    --save_steps 1000 \
    --learning_rate $LR \
    --pre_seq_len $PRE_SEQ_LEN

训练成功，加载checkpoint模型也成功，输入prompts也能正常回答，可是，回答长度仍旧很短，还会出现回答半截断掉的情况，请问该如何调整训练参数？

### Expected Behavior

_No response_

### Steps To Reproduce

1. ./data/gwddc.json为自备的训练集，prompts只有不到2000条
2. 输入上述参数并运行，训练结果信息如下：
……
{'loss': 0.0371, 'learning_rate': 0.0, 'epoch': 96.77}
Saving PrefixEncoder
{'train_runtime': 21212.1807, 'train_samples_per_second': 9.051, 'train_steps_per_second': 0.141, 'train_loss': 0.2381483610868454, 'epoch': 96.77}
***** train metrics *****
  epoch                    =      96.77
  train_loss               =     0.2381
  train_runtime            = 5:53:32.18
  train_samples            =       1982
  train_samples_per_second =      9.051
  train_steps_per_second   =      0.141
帮看是不是train_loss不行？需要增加迭代次数？

### Environment

```markdown
- OS:centos 7.6
- Python:3.9
- Transformers:4.27.1
- PyTorch:2.0.0+cu117
- CUDA Support (`python -c "import torch; print(torch.cuda.is_available())"`) :True
```

### Anything else?

另外，特别训练了“你是谁”，部署后，也没生效。


[Help]  如何支持多显卡

### Is there an existing issue for this?

- [X] I have searched the existing issues

### Current Behavior

公司内部使用，装了2卡，发现默认配置只有1卡在跑，请问如何使用才可以使用多卡

### Expected Behavior

_No response_

### Steps To Reproduce

无

### Environment

```markdown
OS: Ubuntu 20.04
Python: 3.8
Transformers: 4.26.1
PyTorch: 1.12
CUDA Support: True
```

### Anything else?

_No response_


Bert output last hidden state

## ❓ Questions & Help

Hi,

Suppose we have an utterance of length 24 (considering special tokens) and we right-pad it with 0 to max length of 64.
If we use Bert pertained model to get the last hidden states, the output would be of size [1, 64, 768].
Can we use just the first 24 as the hidden states of the utterance? I mean is it right to say that the output[0, :24, :] has all the required information?
I realized that from index 24:64, the outputs has float values as well.


dataset download error in speech recognition examples

### System Info

- `transformers` version: 4.35.0.dev0
- Platform: Linux-5.15.0-43-generic-x86_64-with-glibc2.17
- Python version: 3.8.18
- Huggingface_hub version: 0.17.3
- Safetensors version: 0.4.0
- Accelerate version: 0.24.1
- Accelerate config:    not found
- PyTorch version (GPU?): 1.10.0+cu111 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>

### Who can help?

@stevhliu and @MKhalusova

### Information

- [x] The official example scripts
- [ ] My own modified scripts

### Tasks

- [X] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_ctc.py \
	--dataset_name="common_voice" \
	--model_name_or_path="facebook/wav2vec2-large-xlsr-53" \
	--dataset_config_name="tr" \
	--output_dir="./wav2vec2-common_voice-tr-demo" \
	--overwrite_output_dir \
	--num_train_epochs="15" \
	--per_device_train_batch_size="16" \
	--gradient_accumulation_steps="2" \
	--learning_rate="3e-4" \
	--warmup_steps="500" \
	--evaluation_strategy="steps" \
	--text_column_name="sentence" \
	--length_column_name="input_length" \
	--save_steps="400" \
	--eval_steps="100" \
	--layerdrop="0.0" \
	--save_total_limit="3" \
	--freeze_feature_encoder \
	--gradient_checkpointing \
	--chars_to_ignore , ? . ! - \; \: \" “ % ‘ ” � \
	--fp16 \
	--group_by_length \
	--push_to_hub \
	--do_train --do_eval

### Expected behavior

When I run the default command, which set `dataset_name` as "common_voice", and I got a warning:
```
/home/xintong/.cache/huggingface/modules/datasets_modules/datasets/common_voice/220833898d6a60c50f621126e51fb22eb2dfe5244392c70dccd8e6e2f055f4bf/common_voice.py:634: FutureWarning:
            This version of the Common Voice dataset is deprecated.
            You can download the latest one with
            >>> load_dataset("mozilla-foundation/common_voice_11_0", "en")

  warnings.warn(
Generating train split:   0%|                                                                                | 0/1831 [00:00<?, ? examples/s]
Traceback (most recent call last):
  File "/home/xintong/miniconda3/envs/test/lib/python3.8/tarfile.py", line 2578, in next
    tarinfo = self.tarinfo.fromtarfile(self)
  File "/home/xintong/miniconda3/envs/test/lib/python3.8/tarfile.py", line 1283, in fromtarfile
    obj = cls.frombuf(buf, tarfile.encoding, tarfile.errors)
  File "/home/xintong/miniconda3/envs/test/lib/python3.8/tarfile.py", line 1221, in frombuf
    raise TruncatedHeaderError("truncated header")
tarfile.TruncatedHeaderError: truncated header
```
I modified this into `mozilla-foundation/common_voice_11_0`, it passed.
```
Downloading builder script: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8.13k/8.13k [00:00<00:00, 30.3MB/s]
Downloading readme: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14.4k/14.4k [00:00<00:00, 19.2MB/s]
Downloading extra modules: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3.44k/3.44k [00:00<00:00, 19.9MB/s]
Downloading extra modules: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 60.9k/60.9k [00:00<00:00, 304kB/s]
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```


GPT2 Flax "TypeError: JAX only supports number and bool dtypes, got dtype object in array"

On GPU

```
>>> from transformers import AutoTokenizer, FlaxAutoModelForCausalLM

>>> tokenizer = AutoTokenizer.from_pretrained("gpt2-medium")
>>> model = FlaxAutoModelForCausalLM.from_pretrained("gpt2-medium")
>>> input_context = "The dog"
>>> # encode input context
>>> input_ids = tokenizer(input_context, return_tensors="jax").input_ids
>>> # generate candidates using sampling
>>> outputs = model.generate(input_ids=input_ids, max_length=20, top_k=30, do_sample=True)

TypeError: JAX only supports number and bool dtypes, got dtype object in array
```

@patrickvonplaten @patil-suraj


Unclear error "NotImplementedError:  "while saving tokenizer. How fix it?

Here is my tokenizer code and how I save it to a json file" /content/bert-datas7.json"

````
from tokenizers import normalizers
from tokenizers.normalizers import Lowercase, NFD, StripAccents

bert_tokenizer.pre_tokenizer = Whitespace()

from tokenizers.processors import TemplateProcessing

bert_tokenizer.post_processor = TemplateProcessing(
    single="[CLS] $A [SEP]",
    pair="[CLS] $A [SEP] $B:1 [SEP]:1",
    special_tokens=[
        ("[CLS]", 1),
        ("[SEP]", 2),
        ("[PAD]", 3),
    ],

)
from tokenizers.trainers import WordPieceTrainer

trainer = WordPieceTrainer(
    vocab_size=30522, special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"], pad_to_max_length=True
)
files = [f"/content/For_ITMO.txt" for split in ["test", "train", "valid"]]
bert_tokenizer.train(trainer, files)

model_files = bert_tokenizer.model.save("data", "/content/For_ITMO.txt")

bert_tokenizer.model = WordPiece.from_file(*model_files, unk_token="[UNK]",  pad_to_max_length=True)

bert_tokenizer.save("/content/bert-datas7.json")
````

When I output tokenizer name_or_path = nothing is displayed. This is normal?

````
tokenizer = PreTrainedTokenizerFast(tokenizer_file='/content/bert-datas7.json')
tokenizer.add_special_tokens({'pad_token': '[PAD]'})

print(tokenizer)
>>> PreTrainedTokenizerFast(name_or_path='', vocab_size=1435, model_max_len=1000000000000000019884624838656, is_fast=True, padding_side='right', special_tokens={'pad_token': '[PAD]'})
````
Also, when I try to save my tokenizer, I get an error without explanation. How can I rewrite the code so that all this???
#9658
#10039
[For_ITMO.txt-vocab (1) (1).txt](https://github.com/huggingface/transformers/files/5945659/For_ITMO.txt-vocab.1.1.txt)

````
tokenizer.save_pretrained("/content/tokennizerrrr")

NotImplementedError                       Traceback (most recent call last)
<ipython-input-11-efc48254a528> in <module>()
----> 1 tokenizer.save_pretrained("/content/tokennizerrrr")

2 frames
/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils_base.py in save_vocabulary(self, save_directory, filename_prefix)
   2042             :obj:`Tuple(str)`: Paths to the files saved.
   2043         """
-> 2044         raise NotImplementedError
   2045
   2046     def tokenize(self, text: str, pair: Optional[str] = None, add_special_tokens: bool = False, **kwargs) -> List[str]:

NotImplementedError:
````