import datetime 
from huggingface_hub import HfApi 
import re 
import torch 
from torch import nn 
from transformers import AutoTokenizer 
from typing import List ,Dict ,Sequence 
import ast 
import json 


INCEPTION_FORMAT_V0 =(







)

SIMPLE_INCEPTION_FORMAT_V1 =(





















)

SIMPLE_INCEPTION_FORMAT_V1_NO_EMPTY2 =(






















)

SIMPLE_INCEPTION_FORMAT_V2 =(

























)

SIMPLE_INCEPTION_FORMAT_V2_NOEMPTY =(


























)

SIMPLE_INCEPTION_FORMAT_V1_1 =(





















)

SIMPLE_INCEPTION_FORMAT_V1_1_noempty =(



















)

ROUTING_QUESTION_FORMAT_V0_1 =(

































)



ROUTING_QUESTION_FORMAT_V0_c =(





































)


ROUTING_QUESTION_FORMAT_V1_7_jmrl =(






































)

ROUTING_QUESTION_FORMAT_V1_7 =(






































)

ROUTING_QUESTION_FORMAT_V1_7c =(














































)

ROUTING_QUESTION_FORMAT_V1_7c2_2_ood =(
















































)

ROUTING_QUESTION_FORMAT_V1_7c2 =(
















































)

ROUTING_QUESTION_FORMAT_V0_c_3closed =(





































)

ROUTING_QUESTION_FORMAT_V1_7c2_2_subtaskablation =(












































)

ROUTING_QUESTION_FORMAT_V1_c_3closed =(






































)

ROUTING_QUESTION_FORMAT_V2 =(













































)



ROUTING_QUESTION_FORMAT_V2_o =(













































)

ROUTING_QUESTION_FORMAT_V1_hybrid =(





































)

SMART_QUOTES =str .maketrans ("“”‘’",'""\'\'')

def get_hf_model_metadata (model_id ,token =None ):
    api =HfApi ()
    info =api .model_info (repo_id =model_id ,token =token )
    parts =model_id .split ("/")
    org =parts [0 ]if len (parts )>1 else None 
    commits =api .list_repo_commits (repo_id =model_id ,token =token )
    if commits :
        first =commits [-1 ]
        created =first .created_at 
    else :
        created =None 
    raw_up =info .lastModified 
    try :
        updated =datetime .datetime .fromisoformat (raw_up )
    except Exception :
        updated =None 
    tags =info .tags or []
    short_info ={
    "pipeline_tag":info .pipeline_tag ,
    "architectures":info .config .get ("architectures")
    if info .config else None ,
    }
    return {
    "organization":org ,
    "release_dates":{
    "created_at":created ,
    "last_modified":updated ,
    },
    "short_info":short_info ,
    "tags":tags ,
    }


def format_model_metadata (metadata =None ,model_id =None ,style ='short'):

    import datetime 
    if metadata is None :
        assert model_id is not None ,"Please, specify metadata or model_id"



        metadata =get_hf_model_metadata (model_id =model_id )
    org =metadata .get ("organization")or "N.A."
    rd =metadata .get ("release_dates",{})
    cr =rd .get ("created_at")
    up =rd .get ("last_modified")

    def format_date (dt ):
        if isinstance (dt ,datetime .datetime ):
            return f"{dt.day} {dt.strftime('%B %Y')}"
        return None 
    created_fmt =format_date (cr )or "N.A."
    updated_fmt =format_date (up )or "N.A."
    si =metadata .get ("short_info",{})
    pipeline =si .get ("pipeline_tag")or "N.A."
    archs =si .get ("architectures")
    if isinstance (archs ,list )and archs :
        archs_str =", ".join (archs )
    elif archs :
        archs_str =str (archs )
    else :
        archs_str ="N.A."
    tags =metadata .get ("tags")
    if isinstance (tags ,list )and tags :
        tags_str =", ".join (tags )
    else :
        tags_str ="N.A."
    if style =='short':
        return (
        f"Release date: {created_fmt}; "
        f"Architecture type: {archs_str}; "
        f"Model tags: {tags_str}"
        )
    else :
        return (
        f"org: {org}; created: {created_fmt}; updated: {updated_fmt}; "
        f"pipeline: {pipeline}; archs: {archs_str}; tags: {tags_str}"
        )

def _extract_model_size (model_name :str )->str :

    _SIZE_RE =re .compile (r"(\d+)(?=\s*[bB])")
    matches =_SIZE_RE .findall (model_name )
    return matches [-1 ]if matches else "unknown"

def _balanced_list (after :str )->str |None :
        depth =0 
        start_idx =None 
        in_quote =None 
        escape_next =False 

        for i ,ch in enumerate (after ):
            if escape_next :
                escape_next =False 
                continue 

            if ch =="\\":
                escape_next =True 
                continue 


            if in_quote :
                if ch ==in_quote :
                    in_quote =None 
                continue 
            elif ch in "\"'":
                in_quote =ch 
                continue 


            if ch =="[":
                if depth ==0 :
                    start_idx =i 
                depth +=1 
            elif ch =="]":
                depth -=1 
                if depth ==0 and start_idx is not None :
                    return after [start_idx :i +1 ]

        return None 


def _extract_any (text :str ,labels :Sequence [str ])->List :
    tag_regex ="|".join (re .escape (lbl )for lbl in labels )
    m =re .search (rf"({tag_regex})\s*[:=]\s*",text ,re .I )
    if not m :
        return []

    raw =_balanced_list (text [m .end ():])
    if not raw :
        return []

    raw =raw .translate (SMART_QUOTES ).strip ()


    try :
        return ast .literal_eval (raw )
    except Exception :
        pass 

    try :
        return json .loads (re .sub (r"'",'"',raw ))
    except Exception :
        pass 

    items =[x .strip (" \"'")for x in raw .strip ("[]").split (",")if x .strip ()]
    return [int (x )if x .isdigit ()else x for x in items ]

class ConductorEvalManager :


    @staticmethod 
    def _format_messages (messages :List [Dict [str ,str ]],tokenizer :AutoTokenizer )->str :


        if hasattr (tokenizer ,'apply_chat_template')and tokenizer .chat_template :
            try :
                return tokenizer .apply_chat_template (messages ,tokenize =False ,add_generation_prompt =False ,continue_final_message =True )
            except Exception :

                pass 


        formatted_parts =[]
        for msg in messages :
            role =msg ["role"]
            content =msg ["content"]

            if role =="system":
                formatted_parts .append (f"System: {content}")
            elif role =="user":
                formatted_parts .append (f"User: {content}")
            elif role =="assistant":
                formatted_parts .append (f"Assistant: {content}")

        return "\n".join (formatted_parts )+"\nAssistant:"

    @staticmethod 
    def generate (model :nn .Module ,tokenizer :AutoTokenizer ,messages :List [Dict [str ,str ]],max_tokens :int =1024 ,temperature :float =0.0 ,inference :bool =True ,**gen_kwargs )->str :



        formatted_text =ConductorEvalManager ._format_messages (messages ,tokenizer )


        input_ids =tokenizer (formatted_text ,return_tensors ="pt").input_ids .to (model .device )

        if inference :
            with torch .no_grad ():
                output_ids =model .generate (input_ids ,max_new_tokens =max_tokens ,temperature =temperature )
        else :
            output_ids =model .generate (input_ids ,max_new_tokens =max_tokens ,temperature =temperature )


        completion_ids =output_ids [0 ][input_ids .shape [1 ]:]
        completion =tokenizer .decode (completion_ids ,skip_special_tokens =True )
        return completion 
