import glob
from tokenizers import Tokenizer, normalizers, pre_tokenizers,  processors, decoders,Regex
from tokenizers.normalizers import NFC
from tokenizers.models import BPE
from tokenizers.trainers import BpeTrainer
from transformers import PreTrainedTokenizerFast
import pyarrow.dataset as ds

SPECIAL_TOKENS = [
    "<|endoftext|>",
    "<|im_start|>",
    "<|im_end|>",
    "<|object_ref_start|>",
    "<|object_ref_end|>",
    "<|box_start|>",
    "<|box_end|>",
    "<|quad_start|>",
    "<|quad_end|>",
    "<|vision_start|>",
    "<|vision_end|>",
    "<|vision_pad|>",
    "<|image_pad|>",
    "<|video_pad|>",
    "<tool_call>",
    "</tool_call>",
    "<|fim_prefix|>",
    "<|fim_middle|>",
    "<|fim_suffix|>",
    "<|fim_pad|>",
    "<|repo_name|>",
    "<|file_sep|>",
    "<tool_response>",
    "</tool_response>",
    "<think>",
    "</think>"
]

ADDITIONAL_SPECIAL_TOKENS = [
    "<|im_start|>",
    "<|im_end|>",
    "<|object_ref_start|>",
    "<|object_ref_end|>",
    "<|box_start|>",
    "<|box_end|>",
    "<|quad_start|>",
    "<|quad_end|>",
    "<|vision_start|>",
    "<|vision_end|>",
    "<|vision_pad|>",
    "<|image_pad|>",
    "<|video_pad|>"
]

def read_parquet_shards(file_pattern, batch_size=1000):
    dataset = ds.dataset(file_pattern, format='parquet')
    scanner = dataset.scanner(batch_size=batch_size)
    for batch in scanner.to_batches():
        yield batch.to_pandas()['text'].tolist()  

tokenizer = Tokenizer(BPE(
    unk_token=None,
    continuing_subword_prefix="",
    end_of_word_suffix="",
    fuse_unk=False,
    byte_fallback=False
))
tokenizer.normalizer = normalizers.Sequence([NFC()])
regex_pattern = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+"""
tokenizer.pre_tokenizer = pre_tokenizers.Sequence([
    pre_tokenizers.Split(
        pattern=Regex(regex_pattern),
        behavior="isolated",
        invert=False
    ),
    pre_tokenizers.ByteLevel(
        add_prefix_space=False,
        trim_offsets=False,
        use_regex=False
    )
])

tokenizer.post_processor = processors.ByteLevel(
    add_prefix_space=False,
    trim_offsets=False,
    use_regex=False
)

tokenizer.decoder = decoders.ByteLevel(
    add_prefix_space=False,
    trim_offsets=False,
    use_regex=False
)

trainer = BpeTrainer(
    vocab_size=151936,  
    min_frequency=1,    
    special_tokens=SPECIAL_TOKENS,
    show_progress=True,
    continuing_subword_prefix="",
    end_of_word_suffix=""
)

corpus_files = glob.glob('corpus/parquet/train-0000*-of-00399.parquet' )

tokenizer.train_from_iterator(
    iterator=read_parquet_shards(corpus_files),
    trainer=trainer,
    length=None, 
)

fast_tokenizer = PreTrainedTokenizerFast(
    tokenizer_object=tokenizer,
    eos_token="<|im_end|>",
    pad_token="<|endoftext|>",
    additional_special_tokens=ADDITIONAL_SPECIAL_TOKENS,
    add_bos_token=False,
    add_prefix_space=False,
    clean_up_tokenization_spaces=False,
    errors="replace",
    model_max_length=1024,
    split_special_tokens=False,
    unk_token=None,
    bos_token=None,
)

CHAT_TEMPLATE = """{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {{- messages[0].content + '\\n\\n' }}\n    {%- endif %}\n    {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n        {%- set ns.multi_step_tool = false %}\n        {%- set ns.last_query_index = index %}\n    {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n    {%- if message.content is string %}\n        {%- set content = message.content %}\n    {%- else %}\n        {%- set content = '' %}\n    {%- endif %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is string %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- else %}\n            {%- if '</think>' in content %}\n                {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n                {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n            {%- endif %}\n        {%- endif %}\n        {%- if loop.index0 > ns.last_query_index %}\n            {%- if loop.last or (not loop.last and reasoning_content) %}\n                {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n            {%- else %}\n                {{- '<|im_start|>' + message.role + '\\n' + content }}\n            {%- endif %}\n        {%- else %}\n            {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- endif %}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n    {%- if enable_thinking is defined and enable_thinking is false %}\n        {{- '<think>\\n\\n</think>\\n\\n' }}\n    {%- endif %}\n{%- endif %}"""
fast_tokenizer.chat_template = CHAT_TEMPLATE
save_path = "weights/weights_tokenizer/hf_tokenizer_BBPE"
fast_tokenizer.save_pretrained(save_path)
print(f"Tokenizer saved to {save_path}")