import torch
import pandas as pd
import requests
import sys
import torchvision.models as models
import os

from transformers import AutoTokenizer, PreTrainedModel, AutoModelForSequenceClassification

import utils

# --------------------------------
# DATASET
# --------------------------------

"""
Dataset contents:

- 1000 subsets of text data, each subset stored under the key "subset_{i}" where i ranges from 0 to 999.
  Each subset is a dictionary with:
    -"prompts": List of 100 prompts in the subset
    -"labels": Tensor of true labels for the prompts in the subset, has shape (100)
    -"subset_id": Integer ID of the subset (from 0 to 999)
"""

# Load the dataset
dataset = torch.load("datasets/fulltuning.pt")

# Example: Acessing subsets
subset_0 = dataset["subset_0"]

print("Subset 0 keys:", subset_0.keys())
print("Subset ID:", subset_0["subset_id"])
print("Labels length:", len(subset_0["labels"])) 
print("First prompts:", subset_0["prompts"][:5])
print("First 5 labels:", subset_0["labels"][:5])

# --------------------------------
# QUERYING THE CLASSIFIER
# --------------------------------

# This Code can be used to load and query the fully fine-tuned models. You also need to the available utils.py file.

#|---------------------------------------------------------------------------------------------------|
#|    NOTE: "Missing or unexpected params" warnings are no reason for concern. They stem from the    | 
#|    fact that the model is first loaded without a classifier head, which is added afterwards.      |
#|---------------------------------------------------------------------------------------------------|

# Use this tokenizer for OLMO...

tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-1B-hf", trust_remote_code=True)

# ...and this one for Pythia

tokenizer = AutoTokenizer.from_pretrained("EleutherAI/pythia-410m", trust_remote_code=True)


tokenizer.padding_side = "left"

if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token


# Usage example (fulltuning):

model_path = "models/olmo-fulltuning"

model = utils.get_fulltuning_model(model_path, model_type="olmo") # model_type can be "olmo" or "pythia"

example_prompt = "I think, therefore I am.\n\nI am."

inputs = tokenizer(example_prompt, return_tensors="pt", truncation=True)
inputs = {k: v.to(model.device) for k, v in inputs.items()}
with torch.no_grad():
    outputs = model(**inputs)

logits = outputs.logits

print(f"Logits shape: {logits.shape}")
print(f"Logits: {logits}")



# Usage example (softprompt):

model_path = "models/olmo-softprompt"

model = utils.get_peft_model(model_path, model_type="olmo") # model_type can be "olmo" or "pythia"

example_prompt = "I think, but do I exist?\n\nSince you think, you exist."

inputs = tokenizer(example_prompt, return_tensors="pt", truncation=True)
inputs = {k: v.to(model.device) for k, v in inputs.items()}
with torch.no_grad():
    outputs = model(**inputs)

logits = outputs.logits

print(f"Logits shape: {logits.shape}")
print(f"Logits: {logits}")



# Usage example (lora):

model_path = "models/olmo-lora"

model = utils.get_peft_model(model_path, model_type="olmo") # model_type can be "olmo" or "pythia"

example_prompt = "Who am I?\n\nWhat am I?"

inputs = tokenizer(example_prompt, return_tensors="pt", truncation=True)
inputs = {k: v.to(model.device) for k, v in inputs.items()}
with torch.no_grad():
    outputs = model(**inputs)

logits = outputs.logits

print(f"Logits shape: {logits.shape}")
print(f"Logits: {logits}")



# Usage example (lastlayer):

model_path = "models/olmo-lastlayer"

model = utils.get_peft_model(model_path, model_type="olmo") # model_type can be "olmo" or "pythia"

example_prompt = "I love to exist!"

inputs = tokenizer(example_prompt, return_tensors="pt", truncation=True)
inputs = {k: v.to(model.device) for k, v in inputs.items()}
with torch.no_grad():
    outputs = model(**inputs)

logits = outputs.logits

print(f"Logits shape: {logits.shape}")
print(f"Logits: {logits}")



# Usage example (prefix):

model_path = "models/olmo-prefix"

model = utils.get_peft_model(model_path, model_type="olmo") # model_type can be "olmo" or "pythia"

example_prompt = "I will exist yesterday."

inputs = tokenizer(example_prompt, return_tensors="pt", truncation=True)
inputs = {k: v.to(model.device) for k, v in inputs.items()}
with torch.no_grad():
    outputs = utils.forward_peft_seqcls(model, **inputs)

logits = outputs.logits

print(f"Logits shape: {logits.shape}")
print(f"Logits: {logits}")

# --------------------------------
# SUBMISSION FORMAT
# --------------------------------

"""
The submission must be a .csv file with the following format:

-"type": Name of the model (e.g., "softprompt", "fulltuning", etc.)
-"subset_id": ID of the subset (from 0 to 999, per type)
-"membership": Membership score for each subset (float)
"""

# Example Submission:

types = ["softprompt", "fulltuning", "lora", "lastlayer", "prefix"]
type_list = []

for t in types:
    type_list.extend([t] * 1000)

subset_ids = []
for _ in types:
    subset_ids.extend(list(range(1000)))

membership_scores = torch.rand(5000).tolist()
submission_df = pd.DataFrame({
    "type": type_list,
    "subset_id": subset_ids,
    "membership": membership_scores
})
submission_df.to_csv("example_submission.csv", index=None)

# --------------------------------
# SUBMISSION PROCESS
# --------------------------------

"""
Example submission script for the LLM Dataset Membership Inference Task.

Submission Requirements (read carefully to avoid automatic rejection):

1. CSV FORMAT
----------------
- The file **must be a CSV** with extension `.csv`.
- It must contain **exactly three columns**, named:
      type, subset_id, membership
  → Column names must match exactly (lowercase, no extra spaces).
  → Column order does not matter, but all three must be present.

2. ROW COUNT AND IDENTIFIERS
-------------------------------
- Your file must contain **exactly 5000 rows**.
- Each row corresponds to one unique `subset_id`/`type` pair, with ids in the range **0–999** (inclusive).
- Every subset_id must appear **exactly once** for each type.
- Do **not** add, remove, or rename any IDs.
- Do **not** include duplicates or missing entries.
- The evaluator checks:
      subset_id.min() == 0
      subset_id.max() == 999
      subset_id.unique().size == 1000

3. MEMBERSHIP SCORES
----------------------
- The `membership` column must contain **numeric values** representing your model’s predicted confidence
  that the corresponding subset is a **member** of the training set.

  Examples of valid membership values:
    - Probabilities: values in [0.0, 1.0]
    - Raw model scores: any finite numeric values (will be ranked for TPR@FPR=0.05)

- Do **not** submit string labels like "yes"/"no" or "member"/"non-member".
- The evaluator converts your `membership` column to numeric using `pd.to_numeric()`.
  → Any non-numeric, NaN, or infinite entries will cause automatic rejection.

4. TECHNICAL LIMITS
----------------------
- Maximum file size: **20 MB**
- Encoding: UTF-8 recommended.
- Avoid extra columns, blank lines, or formulas.
- Ensure all values are numeric and finite.
- Supported data types: int, float (e.g., float32, float64)

5. VALIDATION SUMMARY
------------------------
Your submission will fail if:
- Columns don’t match exactly ("type", "subset_id", "membership")
- Row count differs from 5000
- Any type name is unexpected or not in the allowed set
- Any subset_id is missing, duplicated, or outside [0, 999] for any type
- Any membership value is NaN, Inf, or non-numeric
- File is too large or not a valid CSV

Two key metrics are computed:
  1. **ROC-AUC (Area Under the ROC Curve)** — measures overall discriminative ability.
  2. **TPR@FPR=0.05** — true positive rate when the false positive rate is at 5%.

"""

BASE_URL  = "http://35.192.205.84:80"
API_KEY   = "YOUR_API_KEY_HERE"  # replace with your actual API key

TASK_ID   = "14-llm-dataset-inference"
FILE_PATH = "Your-Submission-File.csv"  # replace with your actual file path

SUBMIT     = False  # Set to True to enable submission

def die(msg):
    print(f"{msg}", file=sys.stderr)
    sys.exit(1)

if SUBMIT:
    if not os.path.isfile(FILE_PATH):
        die(f"File not found: {FILE_PATH}")

    try:
        with open(FILE_PATH, "rb") as f:
            files = {
                # (fieldname) -> (filename, fileobj, content_type)
                "file": (os.path.basename(FILE_PATH), f, "csv"),
            }
            resp = requests.post(
                f"{BASE_URL}/submit/{TASK_ID}",
                headers={"X-API-Key": API_KEY},
                files=files,
                timeout=(10, 120),  # (connect timeout, read timeout)
            )
        # Helpful output even on non-2xx
        try:
            body = resp.json()
        except Exception:
            body = {"raw_text": resp.text}

        if resp.status_code == 413:
            die("Upload rejected: file too large (HTTP 413). Reduce size and try again.")

        resp.raise_for_status()

        submission_id = body.get("submission_id")
        print("Successfully submitted.")
        print("Server response:", body)
        if submission_id:
            print(f"Submission ID: {submission_id}")

    except requests.exceptions.RequestException as e:
        detail = getattr(e, "response", None)
        print(f"Submission error: {e}")
        if detail is not None:
            try:
                print("Server response:", detail.json())
            except Exception:
                print("Server response (text):", detail.text)
        sys.exit(1)