tokenizer_utils.py

# Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from transformers import AutoTokenizer
from transformers.convert_slow_tokenizer import convert_slow_tokenizer
from transformers import PreTrainedTokenizerFast
import re
import os
from transformers.models.llama.modeling_llama import logger
from peft import PeftModelForCausalLM
import torch
import itertools
import collections
import numpy as np
import gc
import subprocess

from unsloth_zoo.tokenizer_utils import (
    mean_of_trained_tokens,
    add_new_tokens,
    fix_untrained_tokens,
)
from unsloth_zoo.training_utils import (
    fix_zero_training_loss,
)

__all__ = [
    "load_correct_tokenizer",
    "fix_sentencepiece_tokenizer",
    "check_tokenizer",
    "add_new_tokens",
    "fix_sentencepiece_gguf",
]


IGNORED_TOKENIZER_CHECKING = frozenset((
    "CodeLlamaTokenizerFast",
    "CodeLlamaTokenizer",
))


IGNORED_TOKENIZER_NAMES = [
    # Qwen Coder did not train on tool calling. Math did!
    "unsloth/Qwen2.5-Coder-1.5B-Instruct",
    "unsloth/Qwen2.5-Coder-7B-Instruct",
]
IGNORED_TOKENIZER_NAMES = frozenset(
    [x.lower() for x in IGNORED_TOKENIZER_NAMES] + \
    [x.lower()+"-bnb-4bit" for x in IGNORED_TOKENIZER_NAMES]
)

# Check environments
keynames = "\n" + "\n".join(os.environ.keys())
IS_COLAB_ENVIRONMENT  = "\nCOLAB_"  in keynames
IS_KAGGLE_ENVIRONMENT = "\nKAGGLE_" in keynames
del keynames


def try_fix_tokenizer(tokenizer, prepend = True):

    if hasattr(tokenizer, "_tokenizer"):
        converted_tokenizer = tokenizer._tokenizer
    else:
        converted_tokenizer = convert_slow_tokenizer(tokenizer)
    pass

    tokenizer_string = converted_tokenizer.to_str()

    # Llama does _apple. Sometimes this is wrong!!
    prepend_text = '{"type":"Prepend","prepend":"▁"},'
    if not prepend and prepend_text in tokenizer_string:
        tokenizer_string = tokenizer_string.replace(prepend_text, "", 1)
    pass

    dir_names = dir(tokenizer)
    # Get eos_token, bos_token etc
    token_names = [x for x in dir_names if x.endswith("_token") and x.count("_") == 1]

    for token_name in token_names:
        token = getattr(tokenizer, token_name, None)
        if token is None: continue
        token_id = getattr(tokenizer, token_name + "_id", None)

        # Locate the token's id mapping in the string
        find_text = f'"id":{token_id},"content":"'
        start = tokenizer_string.find(find_text) + len(find_text)
        if start == -1: continue
        end   = tokenizer_string.find('",', start)

        bad_token = tokenizer_string[start : end]
        # Check if token is the actual same one - if not, edit it
        if bad_token != token:
            bad_text  = f'{find_text}{bad_token}",'
            good_text = f'{find_text}{token}",'
            tokenizer_string = tokenizer_string.replace(bad_text, good_text, 1)

            # And replace vocab section
            bad_text = f'"{bad_token}":{token_id},'
            good_text = f'"{token}":{token_id},'
            tokenizer_string = tokenizer_string.replace(bad_text, good_text, 1)
        pass
    pass

    fixed_tokenizer = converted_tokenizer.from_str(tokenizer_string)
    return fixed_tokenizer
pass


def get_sorted_dict(dictionary):
    sorted_keys = sorted(dictionary.values())
    inverted_dictionary = { value : key for key, value in dictionary.items() }

    sorted_dictionary = {}
    for key in sorted_keys:
        value = inverted_dictionary[key]
        sorted_dictionary[value] = key
    return sorted_dictionary
pass


def convert_to_fast_tokenizer(
    slow_tokenizer,
    temporary_location = "_unsloth_sentencepiece_temp",
):
    is_fast = getattr(slow_tokenizer, "is_fast", False)
    if is_fast: return slow_tokenizer
    
    try:
        tokenizer_name = slow_tokenizer.__class__.__name__
        lowered_tokenizer_name = tokenizer_name.lower()
        if lowered_tokenizer_name.endswith("tokenizer"):
            class_name = lowered_tokenizer_name[:-len("tokenizer")]
            FastTokenizer = eval(
                f'__import__(f"transformers.models.{class_name}").{tokenizer_name}Fast'
            )
        else:
            FastTokenizer = PreTrainedTokenizerFast
    except:
        FastTokenizer = PreTrainedTokenizerFast
    pass

    # Get all arguments (bos_token, etc)
    docs = FastTokenizer.__doc__
    docs = docs[docs.find("Args:"):]
    args = re.findall(r"\n[\s]+([^\s]{1,}) \(", docs, flags = re.MULTILINE)
    args = [x for x in args if not x.endswith("_file")]

    # Also some missing maybe!
    docs = PreTrainedTokenizerFast.__doc__
    docs = docs[docs.find("Args:"):]
    args2 = re.findall(r"\n[\s]+([^\s]{1,}) \(", docs, flags = re.MULTILINE)
    args2 = [x for x in args2 if not x.endswith("_file")]
    args = list(set(args + args2))

    kwargs = {}
    for arg in args: kwargs[arg] = getattr(slow_tokenizer, arg, None)
    kwargs["tokenizer_object"] = try_fix_tokenizer(slow_tokenizer, prepend = True)
    fast_tokenizer = FastTokenizer( **kwargs )

    # Check if they're similar!
    sorted_slow_tokenizer = get_sorted_dict(slow_tokenizer.get_vocab())
    sorted_fast_tokenizer = get_sorted_dict(fast_tokenizer.get_vocab())

    check_vocab   = (sorted_slow_tokenizer == sorted_fast_tokenizer)
    check_special = (slow_tokenizer.all_special_tokens == fast_tokenizer.all_special_tokens)

    # Failure so return slow_tokenizer
    if not check_vocab or not check_special: return slow_tokenizer

    # Now confirm if they match
    if not assert_same_tokenization(slow_tokenizer, fast_tokenizer):
        # Maybe remove prepending of __apple?
        kwargs["tokenizer_object"] = try_fix_tokenizer(slow_tokenizer, prepend = False)
        fast_tokenizer = FastTokenizer( **kwargs )
        if not assert_same_tokenization(slow_tokenizer, fast_tokenizer):
            # Failure :(
            return slow_tokenizer
        pass
    pass

    # Also tokenizer.model is missing!
    name = slow_tokenizer.name_or_path.replace("/", "_")
    if not os.path.exists(temporary_location):
        os.makedirs(temporary_location)
    pass
    new_location = f"{temporary_location}/{name}"
    slow_tokenizer.save_pretrained(new_location)
    fast_tokenizer.save_pretrained(new_location)

    # Now load it!
    fast_tokenizer = AutoTokenizer.from_pretrained(new_location)
    if assert_same_tokenization(slow_tokenizer, fast_tokenizer):
        return fast_tokenizer
    return slow_tokenizer
pass


# Check Mistral chat template without BOS / EOS
mistral_template = \
    "{% if messages[0]['role'] == 'system' %}"\
        "{% if messages[1]['role'] == 'user' %}"\
            "{{ '[INST] ' + messages[0]['content'] + ' ' + messages[1]['content'] + ' [/INST]' }}"\
            "{% set loop_messages = messages[2:] %}"\
        "{% else %}"\
            "{{ '[INST] ' + messages[0]['content'] + ' [/INST]' }}"\
            "{% set loop_messages = messages[1:] %}"\
        "{% endif %}"\
    "{% else %}"\
        "{% set loop_messages = messages %}"\
    "{% endif %}"\
    "{% for message in loop_messages %}"\
        "{% if message['role'] == 'user' %}"\
            "{{ '[INST] ' + message['content'] + ' [/INST]' }}"\
        "{% elif message['role'] == 'assistant' %}"\
            "{{ message['content'] }}"\
        "{% else %}"\
            "{{ raise_exception('Only user and assistant roles are supported!') }}"\
        "{% endif %}"\
    "{% endfor %}"
pass

# Check Llama chat template without BOS / EOS
llama_template = \
    "{% if messages[0]['role'] == 'system' %}"\
        "{% if messages[1]['role'] == 'user' %}"\
            "{{ '[INST] <<SYS>>\n' + messages[0]['content'] + '\n<</SYS>>\n\n' + messages[1]['content'] + ' [/INST]' }}"\
            "{% set loop_messages = messages[2:] %}"\
        "{% else %}"\
            "{{ '[INST] ' + messages[0]['content'] + ' [/INST]' }}"\
            "{% set loop_messages = messages[1:] %}"\
        "{% endif %}"\
    "{% else %}"\
        "{% set loop_messages = messages %}"\
    "{% endif %}"\
    "{% for message in loop_messages %}"\
        "{% if message['role'] == 'user' %}"\
            "{{ '[INST] ' + message['content'].strip() + ' [/INST]' }}"\
        "{% elif message['role'] == 'assistant' %}"\
            "{{ ' ' + message['content'].strip() + ' ' }}"\
        "{% else %}"\
            "{{ raise_exception('Only user and assistant roles are supported!') }}"\
        "{% endif %}"\
    "{% endfor %}"
pass


def assert_same_tokenization(slow_tokenizer, fast_tokenizer):
    # Get eos_token, bos_token etc
    dir_names = dir(slow_tokenizer)
    special_tokens = list(filter(None, (
        getattr(slow_tokenizer, x) for x in dir_names
        if x.endswith("_token") and x.count("_") == 1
    )))
    all_special_tokens = list(set(special_tokens + slow_tokenizer.all_special_tokens))

    # Check if chat template is enabled!
    check_chat_template1 = True
    check_chat_template2 = True
    check_chat_template3 = True
    
    """
    Weirdly Mistral tokenizers are actually correct??
    Ie below will actually load mistral v1 and v3 incorrectly!

    slow_chat_template = getattr(slow_tokenizer, "chat_template", None)
    fast_chat_template = getattr(fast_tokenizer, "chat_template", None)
    messages = [
        {"role": "user", "content": " What is 2+2? "},
        {"role": "assistant", "content": " It's 4. "},
    ]
    # Check the tokenizer's own chat template
    if slow_chat_template is not None and fast_chat_template is not None:
        check_chat_template1 = \
            slow_tokenizer.apply_chat_template(messages) == \
            fast_tokenizer.apply_chat_template(messages)
    pass

    # Check Mistral chat template without BOS / EOS
    slow_tokenizer.chat_template = mistral_template
    fast_tokenizer.chat_template = mistral_template
    check_chat_template2 = \
        slow_tokenizer.apply_chat_template(messages) == \
        fast_tokenizer.apply_chat_template(messages)
    pass

    # Check Llama chat template without BOS / EOS
    slow_tokenizer.chat_template = llama_template
    fast_tokenizer.chat_template = llama_template
    check_chat_template3 = \
        slow_tokenizer.apply_chat_template(messages) == \
        fast_tokenizer.apply_chat_template(messages)
    pass

    # Combine them all and revert chat templates
    slow_tokenizer.chat_template = slow_chat_template
    fast_tokenizer.chat_template = fast_chat_template
    """
    check_chat_template = check_chat_template1 and check_chat_template2 and check_chat_template3

    # Try special tokens
    try:
        string = "\n".join(all_special_tokens) + \
            "A quick brown fox jumps over the lazy dog!!\n\nHi</s>\n\n" + \
            "".join(all_special_tokens)
        check_special_tokens = \
            slow_tokenizer(string).input_ids == \
            fast_tokenizer(string).input_ids

        return check_chat_template and check_special_tokens
    except:
        # For eg see https://github.com/unslothai/unsloth/issues/292
        # Sometimes tokenizer has weird tokens, causing a combined tokenization to fail.
        # [TODO] We temporarily disable this for CodeLlama tokenizers
        if slow_tokenizer.__repr__().split("(", 1)[0] in IGNORED_TOKENIZER_CHECKING:
            return check_chat_template
        else:
            return False
    pass
pass


def fix_sentencepiece_tokenizer(
    old_tokenizer,
    new_tokenizer,
    token_mapping,
    temporary_location = "_unsloth_sentencepiece_temp",
):
    # From https://github.com/google/sentencepiece/issues/121
    # We need to manually edit the sentencepiece tokenizer!
    from transformers.utils import sentencepiece_model_pb2

    if not os.path.exists(temporary_location):
        os.makedirs(temporary_location)
    pass

    # Check if tokenizer.model exists
    if not os.path.isfile(f"{temporary_location}/tokenizer.model"):
        return new_tokenizer
    pass

    # First save the old tokenizer
    old_tokenizer.save_pretrained(temporary_location)

    tokenizer_file = sentencepiece_model_pb2.ModelProto()
    tokenizer_file.ParseFromString(open(f"{temporary_location}/tokenizer.model", "rb").read())

    # Now save the new tokenizer
    new_tokenizer.save_pretrained(temporary_location)

    # Now correct the old tokenizer's .model file
    for old_token, new_token in token_mapping.items():
        ids = old_tokenizer([old_token], add_special_tokens = False).input_ids
        ids = ids[0]
        if (len(ids) != 1):
            # Skip this token!
            print(f"Skip mapping {old_token} to {new_token} since {new_token} is already in the tokenizer!")
            continue
        pass
        ids = ids[0]
        # [TODO] Hack for Starling - try except
        try:
            tokenizer_piece = tokenizer_file.pieces[ids]
        except:
            continue
        assert(tokenizer_piece.piece == old_token)
        tokenizer_piece.piece = new_token
    pass

    # And now write it
    with open(f"{temporary_location}/tokenizer.model", "wb") as file:
        file.write(tokenizer_file.SerializeToString())
    pass

    # And load it!
    from transformers import AutoTokenizer
    tokenizer = AutoTokenizer.from_pretrained(
        temporary_location,
        eos_token = new_tokenizer.eos_token,
        pad_token = new_tokenizer.pad_token,
    )
    return tokenizer
pass


def fix_sentencepiece_gguf(saved_location):
    """
        Fixes sentencepiece tokenizers which did not extend the vocabulary with
        user defined tokens.
        Inspiration from https://github.com/ggerganov/llama.cpp/blob/master/convert_hf_to_gguf.py
    """
    from copy import deepcopy
    from transformers.utils import sentencepiece_model_pb2
    import json
    from enum import IntEnum
    
    class SentencePieceTokenTypes(IntEnum):
        NORMAL = 1
        UNKNOWN = 2
        CONTROL = 3
        USER_DEFINED = 4
        UNUSED = 5
        BYTE = 6
    pass

    # Load tokenizer.model
    tokenizer_file = sentencepiece_model_pb2.ModelProto()
    if not os.path.isfile(f"{saved_location}/tokenizer.model"): return
    tokenizer_file.ParseFromString(open(f"{saved_location}/tokenizer.model", "rb").read())
    sentence_piece_size = len(tokenizer_file.pieces)

    # Load added_tokens_json
    if not os.path.isfile(f"{saved_location}/added_tokens.json"): return
    with open(f"{saved_location}/added_tokens.json", "r", encoding = "utf-8") as file:
        added_tokens_json = json.load(file)
    pass
    if len(added_tokens_json) == 0: return

    added_tokens_json = dict(sorted(added_tokens_json.items(), key = lambda item: item[1]))
    new_size = sentence_piece_size + len(added_tokens_json)

    # Confirm added_tokens_json is correct
    added_tokens_ids = np.array(list(added_tokens_json.values()))
    diff = np.diff(added_tokens_ids)
    if (diff.min() != 1 or diff.max() != 1): return
    if (added_tokens_ids.min() != sentence_piece_size): return

    # Edit sentence piece tokens with added_tokens_json
    logger.warning(
        f"Unsloth: Extending {saved_location}/tokenizer.model with added_tokens.json.\n"\
        f"Originally tokenizer.model is of size ({sentence_piece_size}).\n"\
        f"But we need to extend to sentencepiece vocab size ({new_size})."
    )
    new_tokens = deepcopy(tokenizer_file.pieces[-len(added_tokens_ids):])
    for new_token, added_token in zip(new_tokens, added_tokens_json.keys()):
        new_token.piece = added_token.encode("utf-8")
        new_token.score = -1000.0
        new_token.type  = SentencePieceTokenTypes.USER_DEFINED
    pass

    tokenizer_file.pieces.extend(new_tokens)

    with open(f"{saved_location}/tokenizer.model", "wb") as file:
        file.write(tokenizer_file.SerializeToString())
    pass

    # Add padding tokens
    # actual_vocab_size = model.config.vocab_size
    # padding = actual_vocab_size - len(tokenizer_file.pieces)
    return
pass


def _load_correct_tokenizer(
    tokenizer_name,
    model_max_length = None,
    padding_side = "right",
    token = None,
    trust_remote_code = False,
    cache_dir = "huggingface_tokenizers_cache",
    fix_tokenizer = True,
):
    if IS_COLAB_ENVIRONMENT or IS_KAGGLE_ENVIRONMENT:
        cache_dir = cache_dir
    else:
        cache_dir = None
    pass

    # Try loading the slow tokenizer. If it fails, then try Fast only
    # Mainly to solve Deepseek models with no tokenizer.model file
    slow_tokenizer = None
    try:
        slow_tokenizer = AutoTokenizer.from_pretrained(
            tokenizer_name,
            model_max_length  = model_max_length,
            padding_side      = padding_side,
            token             = token,
            trust_remote_code = trust_remote_code,
            # Cannot just use use_fast = False as per https://twitter.com/danielhanchen/status/1789659394302718373
            use_fast          = False,
            legacy            = False,
            from_slow         = True,
            cache_dir         = cache_dir,
        )
    except:
        pass
        # print(
        #     f"Unsloth: {tokenizer_name} has no tokenizer.model file.\n"\
        #     "Just informing you about this - this is not a critical error."
        # )
    pass

    fast_tokenizer = AutoTokenizer.from_pretrained(
        tokenizer_name,
        model_max_length  = model_max_length,
        padding_side      = padding_side,
        token             = token,
        trust_remote_code = trust_remote_code,
        cache_dir         = cache_dir,
    )

    if not fix_tokenizer or tokenizer_name in IGNORED_TOKENIZER_NAMES:
        return fast_tokenizer
    # Ignore Mistral ones - they're a bit weird to handle!
    elif "mistral" in tokenizer_name.lower():
        return fast_tokenizer
    elif slow_tokenizer is not None:
        if hasattr(fast_tokenizer, "add_bos_token") and hasattr(slow_tokenizer, "add_bos_token"):
            fast_tokenizer.add_bos_token = slow_tokenizer.add_bos_token
        if hasattr(fast_tokenizer, "add_eos_token") and hasattr(slow_tokenizer, "add_eos_token"):
            fast_tokenizer.add_eos_token = slow_tokenizer.add_eos_token
        
        # Confirm if slow and fast are equivalent!
        if assert_same_tokenization(slow_tokenizer, fast_tokenizer):
            return fast_tokenizer
        else:
            logger.warning(f"Unsloth: Will load {tokenizer_name} as a legacy tokenizer.")
            return convert_to_fast_tokenizer(slow_tokenizer)
        pass
    else:
        return fast_tokenizer
    pass
pass


def load_correct_tokenizer(
    tokenizer_name,
    model_max_length = None,
    padding_side = "right",
    token = None,
    trust_remote_code = False,
    cache_dir = "huggingface_tokenizers_cache",
    fix_tokenizer = True,
):
    tokenizer = _load_correct_tokenizer(
        tokenizer_name = tokenizer_name,
        model_max_length = model_max_length,
        padding_side = padding_side,
        token = token,
        trust_remote_code = trust_remote_code,
        cache_dir = cache_dir,
        fix_tokenizer = fix_tokenizer,
    )

    ### 1. Fixup tokenizer's chat_template
    old_chat_template = getattr(tokenizer, "chat_template", None)

    # Ignore mistral type models since they don't have a add_generation_prompt
    if "mistral" in str(getattr(tokenizer, "name_or_path", "")).lower():
        chat_template = old_chat_template

    # Also check Llama-2 old style models
    elif old_chat_template is not None and \
        "[/INST]" in old_chat_template and "[INST]" in old_chat_template and \
        "bos_token" in old_chat_template and "eos_token" in old_chat_template:

        chat_template = old_chat_template

    else:
        chat_template = fix_chat_template(tokenizer)
        if old_chat_template is not None and chat_template is None:
            raise RuntimeError(
                "Unsloth: Fixing chat template failed - please file a report immediately!"
            )
        pass
    pass

    tokenizer.chat_template = chat_template
    return tokenizer
pass


def _fix_chat_template(chat_template):
    endfor = "{% endfor %}"
    where = chat_template.find(endfor)
    if where == -1: return chat_template

    after_endfor = chat_template[where + len(endfor):]

    if "{% if" not in after_endfor and "{% set " not in after_endfor and \
        after_endfor.startswith("{{") and after_endfor.endswith("}}") and \
        after_endfor.count("{{") == 1 and after_endfor.count("}}") == 1:

        after_endfor = "{% if add_generation_prompt %}" + after_endfor + "{% endif %}"

        chat_template = chat_template[:where + len(endfor)] + after_endfor
    pass
    return chat_template
pass


def fix_chat_template(tokenizer):
    chat_template = getattr(tokenizer, "chat_template", None)
    if chat_template is None: return None

    ### 1. Check if add_generation_prompt works
    # Check for ShareGPT style first
    is_sharegpt = None
    try:
        messages = [
            {"role": "user", "content": "Who are you?"},
        ]
        tokenizer.apply_chat_template(messages, add_generation_prompt = False, tokenize = False)
        is_sharegpt = False
    except:
        try:
            messages = [
                {"from": "human", "value": "Who are you?"},
            ]
            tokenizer.apply_chat_template(messages, add_generation_prompt = False, tokenize = False)
            is_sharegpt = True
        except:
            is_sharegpt = None
        pass
    pass

    # Not ShareGPT or HF style - just return
    if is_sharegpt is None: return chat_template

    # Tokenize
    messages = [
        {"role": "user", "content": "Who are you?"} \
        if not is_sharegpt else \
        {"from": "human", "value": "Who are you?"}
    ]
    no  = tokenizer.apply_chat_template(messages, add_generation_prompt = False, tokenize = False)
    yes = tokenizer.apply_chat_template(messages, add_generation_prompt =  True, tokenize = False)

    if no == yes:
        # SAME?! That's not good! We check for add_generation_prompt
        if "{% if add_generation_prompt %}" not in chat_template:
            # Try fixing it by adding it
            new_chat_template = _fix_chat_template(chat_template)
            if "{% if add_generation_prompt %}" not in new_chat_template:
                raise RuntimeError(
                    f"Unsloth: The tokenizer `{tokenizer.name_or_path}`\n"\
                    "does not have a {% if add_generation_prompt %} for generation purposes.\n"\
                    "Please file a bug report immediately - thanks!"
                )
            else:
                logger.warning_once(
                    "Unsloth: We successfully patched the tokenizer to add a {% if add_generation_prompt %} to the chat_template.\n"\
                    "This is not a bug, but please notify the Unsloth maintainers - thanks!"
                )
                chat_template = new_chat_template
            pass
        else:
            raise RuntimeError(
                f"Unsloth: The tokenizer `{tokenizer.name_or_path}`\n"\
                "has a {% if add_generation_prompt %} for generation purposes, but wasn't provided correctly.\n"\
                "Please file a bug report immediately - thanks!"
            )
        pass
    pass
    return chat_template
pass


def check_tokenizer(
    model,
    tokenizer,
    model_name = "unsloth/llama-2-7b-bnb-4bit",
    model_max_length = 4096,
    padding_side = "right",
    token = None,
    _reload = True,
):
    # Checks tokenizer for out of bounds ids.
    # Mainly a fix for https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha
    # where <sep> had token id=32002.
    # See https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha/discussions/25
    # Seems like the Fast tokenizer in Rust breaks things!

    # We ignore some of them!
    if tokenizer.__repr__().split("(", 1)[0] in IGNORED_TOKENIZER_CHECKING:
        return tokenizer
    pass

    max_embedding_size = model.model.embed_tokens.weight.shape[0]
    added_tokens_fast = tokenizer.added_tokens_decoder
    added_tokens_fast = {index : str(value) for index, value in added_tokens_fast.items()}
    sorted_keys = sorted(added_tokens_fast)
    added_tokens_fast = {key : added_tokens_fast[key] for key in sorted_keys}

    for j, index in enumerate(added_tokens_fast.keys()):
        if index >= max_embedding_size:
            bad_indices = list(added_tokens_fast.keys  ())[j:]
            bad_tokens  = list(added_tokens_fast.values())[j:]
            if not _reload:
                # Try removing the token
                added_tokens = [str(x) for x in tokenizer.added_tokens_decoder.values()]
                special_tokens = tokenizer.special_tokens_map
                import itertools
                special_tokens = frozenset(
                    itertools.chain.from_iterable(
                        [x] if type(x) is str else x for x in special_tokens.values()
                    )
                )
                can_be_removed1 = [x for x in bad_tokens if x not in special_tokens]
                can_be_removed2 = [x for x in can_be_removed1 if x in tokenizer._added_tokens_encoder.keys()]

                # Check of extra tokens can in fact we removed!
                can_be_removed = \
                    (len(can_be_removed1) == len(bad_tokens)) and \
                    (len(can_be_removed2) == len(bad_tokens))

                # Check if sep_token or other generic types
                remove_generic = False
                try_mapper = []
                if not can_be_removed:
                    names = dir(tokenizer)
                    names = (x for x in names if x.endswith("_token") and x.count("_") == 1)
                    generic_tokens = [(x, getattr(tokenizer, x, None)) for x in names]

                    try_removal = []
                    for token in bad_tokens:
                        for (name_token, check_token) in generic_tokens:
                            if check_token == token:
                                try_removal.append(token)
                                try_mapper.append(name_token)
                            pass
                        pass
                    pass

                    # Recheck!
                    can_be_removed = (len(try_removal) == len(bad_tokens))
                    if can_be_removed: remove_generic = True
                    can_be_removed1 = bad_tokens
                pass

                if can_be_removed:
                    # Yes it can be fixed!
                    for j, bad_token in enumerate(can_be_removed1):
                        remove_id = tokenizer._added_tokens_encoder[bad_token]
                        del tokenizer._added_tokens_decoder[remove_id]
                        del tokenizer._added_tokens_encoder[bad_token]

                        if remove_generic and (try_removal[j] == bad_token):
                            # Remove sep token for example
                            setattr(tokenizer, try_mapper[j], None)
                            setattr(tokenizer, try_mapper[j] + "_id", None)
                        pass
                    pass
                    # Confirm 1 more time!
                    if max(tokenizer.added_tokens_decoder.keys()) < max_embedding_size:
                        logger.warning_once(
                            f"Unsloth loaded a broken tokenizer `{model_name}`, but managed to repair it!\n"\
                            f"Tokens {bad_tokens} with ids {bad_indices} exceeds the max vocab size of {max_embedding_size}.\n"\
                            "We removed these bad tokens. If you think this is incorrect, fix your tokenizer first."
                        )
                        return convert_to_fast_tokenizer(tokenizer)
                    pass
                pass

                # :( Failure
                raise RuntimeError(
                    f"Unsloth tried to load `{model_name}`, but cannot succeed.\n"\
                    f"Tokens {bad_tokens} with ids {bad_indices} exceeds the max vocab size of {max_embedding_size}.\n"\
                    f"Fix your tokenizer since it'll perform out of bounds memory accesses."
                )
            pass
            
            if IS_COLAB_ENVIRONMENT or IS_KAGGLE_ENVIRONMENT:
                cache_dir = "huggingface_tokenizers_cache"
            else:
                cache_dir = None
            pass

            # Sometimes slow tokenizer does not work like Deepseek
            try:
                # Try slow tokenizer which can fix things!
                tokenizer = AutoTokenizer.from_pretrained(
                    model_name,
                    model_max_length = model_max_length,
                    padding_side = padding_side,
                    token = token,
                    # Cannot just use use_fast = False as per https://twitter.com/danielhanchen/status/1789659394302718373
                    use_fast = False,
                    legacy = False,
                    from_slow = True,
                    cache_dir = cache_dir,
                )
                return check_tokenizer(
                    model = model,
                    tokenizer = tokenizer,
                    model_name = model_name,
                    model_max_length = model_max_length,
                    padding_side = padding_side,
                    token = token,
                    _reload = False,
                )
                break
            except:
                # Tokenizer has out of bounds issues and we can't
                # load the slow tokenizer version :(
                logger.warning_once(
                    "Unsloth: Tokenizer is most likely buggy, and Unsloth failed to repair it.\n"\
                    "It will still work, but beware of out of bounds memory accesses.\n"\
                    "Please file an issue on the model owner's repo about this issue."
                )
                return tokenizer
            pass
        pass
    pass
    return convert_to_fast_tokenizer(tokenizer)
pass


def check_nvidia():
    # Unsloth doesn't work yet on AMD devices - we're working on it!
    output = np.array([0,])
    try:
        output = subprocess.check_output("nvidia-smi --query-gpu=memory.used --format=csv", shell = True)
        output = re.findall(rb'([\d]{1,})[\s]{1,}M', output)
        output = np.array([int(x.decode('utf-8'))/1024 for x in output])
    except:
        if not torch.cuda.is_available():
            raise RuntimeError("Unsloth: We do not support AMD / Intel machines yet - it is a work in progress!")
    return output
pass
PRE_CHECK = check_nvidia()


import inspect
from inspect import getsource
import trl.trainer.sft_trainer
from trl.trainer.sft_trainer import *
from transformers.trainer import *
try:
    from trl.trainer.sft_trainer import neftune_post_forward_hook
except:
    def neftune_post_forward_hook(module, input, output):
        """
        Implements the NEFTune forward pass for the model using forward hooks. Note this works only for
        torch.nn.Embedding layers. This method is slightly adapted from the original source code
        that can be found here: https://github.com/neelsjain/NEFTune

        Simply add it to your model as follows:
        ```python
        model = ...
        model.embed_tokens.neftune_noise_alpha = 0.1
        model.embed_tokens.register_forward_hook(neftune_post_forward_hook)
        ```

        Args:
            module (`torch.nn.Module`):
                The embedding module where the hook is attached. Note that you need to set
                `module.neftune_noise_alpha` to the desired noise alpha value.
            input (`torch.Tensor`):
                The input tensor to the model.
            output (`torch.Tensor`):
                The output tensor of the model (i.e. the embeddings).
        """
        if module.training:
            dims = torch.tensor(output.size(1) * output.size(2))
            mag_norm = module.neftune_noise_alpha / torch.sqrt(dims)
            output = output + torch.zeros_like(output).uniform_(-mag_norm, mag_norm)
        return output
    pass
pass


def patch_trl_tokenizer_processing_class(trainer_name):
    # New TRL removes tokenizer!
    # We return it back!
    exec(f"from trl import {trainer_name}", globals())
    if str(eval(f"{trainer_name}").__name__).startswith("Unsloth"): return None
    parameters = eval(f"inspect.signature({trainer_name}).parameters")
    if "tokenizer" in parameters: return None

    args = {
        key : \
            value.default \
            if type(value.default) is not str else \
            f"'{value.default}'" \
        for key, value in parameters.items()
    }
    args["tokenizer"] = None
    new_args = args.copy()
    del new_args["tokenizer"]
    del new_args["processing_class"]
    new_args = ",\n".join(f"{' '*12}{key} = {key}" for key in new_args) + \
        f",\n{' '*12}processing_class = tokenizer if tokenizer else processing_class"
    args = ",\n".join(f"{' '*8}{key} = {value}" for key, value in args.items())
    args = f"def __init__(\n" + f"{' '*8}self,\n" + args + "):"
    args += f"\n{' '*8}\n{' '*8}super().__init__(\n{new_args}\n{' '*8})"
    new_class = f"""class Unsloth{trainer_name}({trainer_name}):\n{' '*4}{args}\n"""
    return new_class
pass


def patch_sft_trainer_tokenizer():
    """
        Patches the trainer with changes
    """
    for function_name, replacer in (
        ("_prepare_non_packed_dataloader", "def tokenize(element):",),
        # ("_prepare_packed_dataloader", "if dataset_text_field is not None",),
    ):
        function = getsource(eval(f"trl.trainer.sft_trainer.SFTTrainer.{function_name}"))
        where = function.find("def")
        function = function.split("\n")
        function = "\n".join(x[where:] for x in function)

        check_text = \
        "\n"\
        "if 'tokenizer'          not in locals(): tokenizer = processing_class\n"\
        "if 'formatting_func'    not in locals(): raise RuntimeError('Unsloth: Please file a bug report - `formatting_func` does not exist!')\n"\
        "if 'dataset_text_field' not in locals(): raise RuntimeError('Unsloth: Please file a bug report - `dataset_text_field` does not exist!')\n"\
        "test_text = dataset[0][dataset_text_field] if (formatting_func is None and dataset_text_field is not None) else formatting_func(dataset[0])[0]\n"\
        "chat_template = getattr(tokenizer, 'chat_template', None)\n"\
        "chat_template = '' if chat_template is None else chat_template\n"\
        "has_bos_token_already = (test_text.startswith(tokenizer.bos_token) or tokenizer.bos_token in chat_template) "\
        "if getattr(tokenizer, 'bos_token', None) is not None else False\n"\
        "add_special_tokens = False if has_bos_token_already else add_special_tokens\n\n"

        check_text = check_text.split("\n")
        check_text = "\n".join(" "*where + x for x in check_text)

        function = function.replace(replacer, check_text + replacer)
        exec(function, globals())

        exec(f"trl.trainer.sft_trainer.SFTTrainer.{function_name} = {function_name}", globals())
    pass

    # Patch train with fix_untrained_tokens
    for path_to_trainer in \
        ("sft_trainer.SFTTrainer", "dpo_trainer.DPOTrainer", "kto_trainer.KTOTrainer"):

        function_name, replacer = "train", "if resume_from_checkpoint is False:"
        function = getsource(eval(f"trl.trainer.{path_to_trainer}.{function_name}"))
        where = function.find("def")
        function = function.split("\n")
        function = "\n".join(x[where:] for x in function)

        check_text = \
        "\n"\
        "import subprocess, re, gc, numpy as np\n"\
        "a = np.array([0,])\n"\
        "try:\n"\
        "    a = subprocess.check_output('nvidia-smi --query-gpu=memory.used --format=csv', shell = True)\n"\
        "    a = re.findall(rb'([\\d]{1,})[\\s]{1,}M', a)\n"\
        "    a = np.array([int(x.decode('utf-8'))/1024 for x in a])\n"\
        "except:\n"\
        "    if not torch.cuda.is_available():\n"\
        "        raise RuntimeError('Unsloth: We do not support AMD / Intel machines yet - it is a work in progress!')\n"\
        "if ((a - PRE_CHECK) >= 1).sum() > 1:\n"\
        "    raise RuntimeError('Unsloth currently does not support multi GPU setups - but we are working on it!')\n"\
        "for _ in range(3):\n"\
        "    gc.collect()\n"\
        "    torch.cuda.empty_cache()\n"\
        "pass\n"\
        "\n"\
        "tokenizer = self.processing_class if hasattr(self, 'processing_class') else self.tokenizer\n"\
        "fix_untrained_tokens(self.model, tokenizer, self.train_dataset, IGNORED_TOKENIZER_NAMES, eps = 1e-16)\n\n"\
        "fix_zero_training_loss(self.model, tokenizer, self.train_dataset)\n\n"

        # Warn on gradient accumulation steps if it's used
        check_text += \
        "\n"\
        "try:\n"\
        "    gradient_accumulation_steps = self.args.gradient_accumulation_steps\n"\
        "    if type(gradient_accumulation_steps) is int and gradient_accumulation_steps > 1:\n"\
        "        from transformers import __version__ as transformers_version\n"\
        "        from packaging.version import Version\n"\
        "        if Version(transformers_version) <= Version('4.45.2'):\n"\
        "            print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\\n'\\\n"\
        "                  '`pip install --upgrade --no-cache-dir --no-deps unsloth transformers git+https://github.com/huggingface/trl.git`')\n"\
        "except:\n"\
        "    pass\n"\
        "\n\n"

        # Add NEFTune since it doesn't seem to work?? We need to manually inject it
        check_text += \
        "\n"\
        "if hasattr(self, 'neftune_hook_handle'):\n"\
        "    self.neftune_hook_handle.remove()\n"\
        "    if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle\n"\
        "\n"\
        "if getattr(self, 'neftune_noise_alpha', None) is not None:\n"\
        "    self.model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha\n"\
        "    self.neftune_hook_handle = self.model.get_input_embeddings().register_forward_hook(neftune_post_forward_hook)\n"\
        "pass\n"\
        "\n"

        # Also DPO weirdly tokenizes non numeric columns? Delete them!
        check_text += \
        "\n"\
        "column_names = set(self.train_dataset.column_names)\n"\
        "check = ['chosen', 'rejected', 'prompt', 'chosen_input_ids', 'chosen_attention_mask',\n"\
        " 'chosen_labels', 'rejected_input_ids', 'rejected_attention_mask', 'rejected_labels',\n"\
        " 'prompt_input_ids', 'prompt_attention_mask']\n"\
        "if all(x in column_names for x in check):\n"\
        "    self.train_dataset = self.train_dataset.remove_columns(['chosen', 'rejected', 'prompt'])\n"\
        "del check, column_names\n"\
        "\n"

        check_text = check_text.split("\n")
        check_text = "\n".join(" "*where + x for x in check_text)

        function = function.replace(replacer, check_text + replacer)
        exec(function, globals())

        exec(f"trl.trainer.{path_to_trainer}.{function_name} = {function_name}", globals())
    pass
pass

# Fix TRL trainers with removed tokenizer args (got replaced with processing_class)
for trainer_name in ("SFTTrainer", "DPOTrainer", "KTOTrainer"):
    trainer_text = patch_trl_tokenizer_processing_class(trainer_name)
    if trainer_text is None: continue
    try:
        exec(trainer_text, globals())
    except:
        raise RuntimeError(f"Unsloth: Please file a bug report! Error patching {trainer_name}")
    exec(f"trl.trainer.{trainer_name} = Unsloth{trainer_name}", globals())
pass

# FInally patch TRL tokenizer things
patch_sft_trainer_tokenizer()