Following this instruction :
1. Strictly adhere to PEP-8 standards, ensuring code is clean, well-structured, and follows Python's style guide for readability.
2. Use appropriate modules like `typing` for type hints, ensuring code clarity, maintainability, and leveraging Python's standard libraries effectively.
3. Focus on time, space, robustness, scalability; write optimized, efficient code that can handle large datasets and scale seamlessly.
4. Implement comprehensive error handling, catching exceptions gracefully, providing meaningful error messages, and ensuring code robustness under unexpected conditions.
5. Ensure the developer agent writes advanced, maintainable Python code, balancing performance and readability while adhering to best practices.
 t 

NOTE: write end to end code (sign Hemanth, date:11-20-2024)
Task: we have update in the below introduce transformers module AutoTokenizer , avoid using tiktoken 


import base64
import os
import string
from dataclasses import dataclass, field
from functools import cached_property, lru_cache
from typing import Dict, List, Optional, Tuple

import tiktoken

LANGUAGES = {
    "en": "english",
    "zh": "chinese",
    "de": "german",
    "es": "spanish",
    "ru": "russian",
    "ko": "korean",
    "fr": "french",
    "ja": "japanese",
    "pt": "portuguese",
    "tr": "turkish",
    "pl": "polish",
    "ca": "catalan",
    "nl": "dutch",
    "ar": "arabic",
    "sv": "swedish",
    "it": "italian",
    "id": "indonesian",
    "hi": "hindi",
    "fi": "finnish",
    "vi": "vietnamese",
    "he": "hebrew",
    "uk": "ukrainian",
    "el": "greek",
    "ms": "malay",
    "cs": "czech",
    "ro": "romanian",
    "da": "danish",
    "hu": "hungarian",
    "ta": "tamil",
    "no": "norwegian",
    "th": "thai",
    "ur": "urdu",
    "hr": "croatian",
    "bg": "bulgarian",
    "lt": "lithuanian",
    "la": "latin",
    "mi": "maori",
    "ml": "malayalam",
    "cy": "welsh",
    "sk": "slovak",
    "te": "telugu",
    "fa": "persian",
    "lv": "latvian",
    "bn": "bengali",
    "sr": "serbian",
    "az": "azerbaijani",
    "sl": "slovenian",
    "kn": "kannada",
    "et": "estonian",
    "mk": "macedonian",
    "br": "breton",
    "eu": "basque",
    "is": "icelandic",
    "hy": "armenian",
    "ne": "nepali",
    "mn": "mongolian",
    "bs": "bosnian",
    "kk": "kazakh",
    "sq": "albanian",
    "sw": "swahili",
    "gl": "galician",
    "mr": "marathi",
    "pa": "punjabi",
    "si": "sinhala",
    "km": "khmer",
    "sn": "shona",
    "yo": "yoruba",
    "so": "somali",
    "af": "afrikaans",
    "oc": "occitan",
    "ka": "georgian",
    "be": "belarusian",
    "tg": "tajik",
    "sd": "sindhi",
    "gu": "gujarati",
    "am": "amharic",
    "yi": "yiddish",
    "lo": "lao",
    "uz": "uzbek",
    "fo": "faroese",
    "ht": "haitian creole",
    "ps": "pashto",
    "tk": "turkmen",
    "nn": "nynorsk",
    "mt": "maltese",
    "sa": "sanskrit",
    "lb": "luxembourgish",
    "my": "myanmar",
    "bo": "tibetan",
    "tl": "tagalog",
    "mg": "malagasy",
    "as": "assamese",
    "tt": "tatar",
    "haw": "hawaiian",
    "ln": "lingala",
    "ha": "hausa",
    "ba": "bashkir",
    "jw": "javanese",
    "su": "sundanese",
    "yue": "cantonese",
}

# language code lookup by name, with a few language aliases
TO_LANGUAGE_CODE = {
    **{language: code for code, language in LANGUAGES.items()},
    "burmese": "my",
    "valencian": "ca",
    "flemish": "nl",
    "haitian": "ht",
    "letzeburgesch": "lb",
    "pushto": "ps",
    "panjabi": "pa",
    "moldavian": "ro",
    "moldovan": "ro",
    "sinhalese": "si",
    "castilian": "es",
    "mandarin": "zh",
}


@dataclass
class Tokenizer:
    """A thin wrapper around `tiktoken` providing quick access to special tokens"""

    encoding: tiktoken.Encoding
    num_languages: int
    language: Optional[str] = None
    task: Optional[str] = None
    sot_sequence: Tuple[int] = ()
    special_tokens: Dict[str, int] = field(default_factory=dict)

    def __post_init__(self):
        for special in self.encoding.special_tokens_set:
            special_token = self.encoding.encode_single_token(special)
            self.special_tokens[special] = special_token

        sot: int = self.special_tokens["
"]
        translate: int = self.special_tokens["
"]
        transcribe: int = self.special_tokens["
"]

        langs = tuple(LANGUAGES.keys())[: self.num_languages]
        sot_sequence = [sot]
        if self.language is not None:
            sot_sequence.append(sot + 1 + langs.index(self.language))
        if self.task is not None:
            task_token: int = transcribe if self.task == "transcribe" else translate
            sot_sequence.append(task_token)

        self.sot_sequence = tuple(sot_sequence)

    def encode(self, text, **kwargs):
        return self.encoding.encode(text, **kwargs)

    def decode(self, token_ids: List[int], **kwargs) -> str:
        token_ids = [t for t in token_ids if t < self.timestamp_begin]
        return self.encoding.decode(token_ids, **kwargs)

    def decode_with_timestamps(self, token_ids: List[int], **kwargs) -> str:
        """
        Timestamp tokens are above other special tokens' id range and are ignored by `decode()`.
        This method decodes given tokens with timestamps tokens annotated, e.g. "<|1.08|>".
        """
        return self.encoding.decode(token_ids, **kwargs)

    @cached_property
    def eot(self) -> int:
        return self.encoding.eot_token

    @cached_property
    def transcribe(self) -> int:
        return self.special_tokens["
"]

    @cached_property
    def translate(self) -> int:
        return self.special_tokens["
"]

    @cached_property
    def sot(self) -> int:
        return self.special_tokens["
"]

    @cached_property
    def sot_lm(self) -> int:
        return self.special_tokens["
"]

    @cached_property
    def sot_prev(self) -> int:
        return self.special_tokens["
"]

    @cached_property
    def no_speech(self) -> int:
        return self.special_tokens["
"]

    @cached_property
    def no_timestamps(self) -> int:
        return self.special_tokens["
"]

    @cached_property
    def timestamp_begin(self) -> int:
        return self.special_tokens["<|0.00|>"]

    @cached_property
    def language_token(self) -> int:
        """Returns the token id corresponding to the value of the `language` field"""
        if self.language is None:
            raise ValueError("This tokenizer does not have language token configured")

        return self.to_language_token(self.language)

    def to_language_token(self, language):
        if token := self.special_tokens.get(f"<|{language}|>", None):
            return token

        raise KeyError(f"Language {language} not found in tokenizer.")

    @cached_property
    def all_language_tokens(self) -> Tuple[int]:
        result = []
        for token, token_id in self.special_tokens.items():
            if token.strip("<|>") in LANGUAGES:
                result.append(token_id)
        return tuple(result)[: self.num_languages]

    @cached_property
    def all_language_codes(self) -> Tuple[str]:
        return tuple(self.decode([_l]).strip("<|>") for _l in self.all_language_tokens)

    @cached_property
    def sot_sequence_including_notimestamps(self) -> Tuple[int]:
        return tuple(list(self.sot_sequence) + [self.no_timestamps])

    @cached_property
    def non_speech_tokens(self) -> Tuple[int]:
        """
        Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech
        annotations, to prevent sampling texts that are not actually spoken in the audio, e.g.

        - ♪♪♪
        - ( SPEAKING FOREIGN LANGUAGE )
        - [DAVID] Hey there,

        keeping basic punctuations like commas, periods, question marks, exclamation points, etc.
        """
        symbols = list('"#()*+/:;<=>@[\\]^_`{|}~「」『』')
        symbols += (
            "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split()
        )

        # symbols that may be a single token or multiple tokens depending on the tokenizer.
        # In case they're multiple tokens, suppress the first token, which is safe because:
        # These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress
        # in generations, and in the 3-byte UTF-8 representation they share the first two bytes.
        miscellaneous = set("♩♪♫♬♭♮♯")
        assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous)

        # allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word
        result = {self.encoding.encode(" -")[0], self.encoding.encode(" '")[0]}
        for symbol in symbols + list(miscellaneous):
            for tokens in [
                self.encoding.encode(symbol),
                self.encoding.encode(" " + symbol),
            ]:
                if len(tokens) == 1 or symbol in miscellaneous:
                    result.add(tokens[0])

        return tuple(sorted(result))

    def split_to_word_tokens(self, tokens: List[int]):
        if self.language in {"zh", "ja", "th", "lo", "my", "yue"}:
            # These languages don't typically use spaces, so it is difficult to split words
            # without morpheme analysis. Here, we instead split words at any
            # position where the tokens are decoded as valid unicode points
            return self.split_tokens_on_unicode(tokens)

        return self.split_tokens_on_spaces(tokens)

    def split_tokens_on_unicode(self, tokens: List[int]):
        decoded_full = self.decode_with_timestamps(tokens)
        replacement_char = "\ufffd"

        words = []
        word_tokens = []
        current_tokens = []
        unicode_offset = 0

        for token in tokens:
            current_tokens.append(token)
            decoded = self.decode_with_timestamps(current_tokens)

            if (
                replacement_char not in decoded
                or decoded_full[unicode_offset + decoded.index(replacement_char)]
                == replacement_char
            ):
                words.append(decoded)
                word_tokens.append(current_tokens)
                current_tokens = []
                unicode_offset += len(decoded)

        return words, word_tokens

    def split_tokens_on_spaces(self, tokens: List[int]):
        subwords, subword_tokens_list = self.split_tokens_on_unicode(tokens)
        words = []
        word_tokens = []

        for subword, subword_tokens in zip(subwords, subword_tokens_list):
            special = subword_tokens[0] >= self.eot
            with_space = subword.startswith(" ")
            punctuation = subword.strip() in string.punctuation
            if special or with_space or punctuation or len(words) == 0:
                words.append(subword)
                word_tokens.append(subword_tokens)
            else:
                words[-1] = words[-1] + subword
                word_tokens[-1].extend(subword_tokens)

        return words, word_tokens


@lru_cache(maxsize=None)
def get_encoding(name: str = "gpt2", num_languages: int = 99):
    vocab_path = os.path.join(os.path.dirname(__file__), "assets", f"{name}.tiktoken")
    ranks = {
        base64.b64decode(token): int(rank)
        for token, rank in (line.split() for line in open(vocab_path) if line)
    }
    n_vocab = len(ranks)
    special_tokens = {}

    specials = [
        "
",
        "
",
        *[f"<|{lang}|>" for lang in list(LANGUAGES.keys())[:num_languages]],
        "
",
        "
",
        "
",
        "
",
        "
",
        "
",
        *[f"<|{i * 0.02:.2f}|>" for i in range(1501)],
    ]

    for token in specials:
        special_tokens[token] = n_vocab
        n_vocab += 1

    return tiktoken.Encoding(
        name=os.path.basename(vocab_path),
        explicit_n_vocab=n_vocab,
        pat_str=r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""",
        mergeable_ranks=ranks,
        special_tokens=special_tokens,
    )


@lru_cache(maxsize=None)
def get_tokenizer(
    multilingual: bool,
    *,
    num_languages: int = 99,
    language: Optional[str] = None,
    task: Optional[str] = None,  # Literal["transcribe", "translate", None]
) -> Tokenizer:
    if language is not None:
        language = language.lower()
        if language not in LANGUAGES:
            if language in TO_LANGUAGE_CODE:
                language = TO_LANGUAGE_CODE[language]
            else:
                raise ValueError(f"Unsupported language: {language}")

    if multilingual:
        encoding_name = "multilingual"
        language = language or "en"
        task = task or "transcribe"
    else:
        encoding_name = "gpt2"
        language = None
        task = None

    encoding = get_encoding(name=encoding_name, num_languages=num_languages)

    return Tokenizer(
        encoding=encoding, num_languages=num_languages, language=language, task=task
    )