feat: Initial setup for AccessibilityEnv directory and placeholder files

environments/hack0/accessibility_env/accessibility_env.py

@@ -0,0 +1,260 @@
+# environments/hack0/accessibility_env/accessibility_env.py
+from typing import List, Optional, Tuple  # Common type hints
+
+from atroposlib.envs.base import APIServerConfig, BaseEnv, BaseEnvConfig
+from atroposlib.type_definitions import (  # Assuming you'll need these
+    Item,
+    ScoredDataGroup,
+)
+from atroposlib.utils.tokenize_for_trainer import tokenize_for_trainer
+
+
+class AccessibilityEnvConfig(BaseEnvConfig):
+    # Add any custom config fields specific to your env later
+    pass
+
+
+class AccessibilityEnv(BaseEnv):
+    name = "accessibility_env"  # A unique name for your environment
+
+    def __init__(
+        self,
+        config: AccessibilityEnvConfig,
+        server_configs: List[APIServerConfig],
+        slurm=True,
+        testing=False,
+    ):
+        super().__init__(config, server_configs, slurm, testing)
+        # Initialize any env-specific attributes here
+
+    @classmethod
+    def config_init(cls) -> Tuple[AccessibilityEnvConfig, List[APIServerConfig]]:
+        env_config = AccessibilityEnvConfig(
+            tokenizer_name="NousResearch/Llama-3-8B-Instruct- যেভাবে-তুমি-বাংলা-বলো",  # Placeholder, change later
+            group_size=4,  # Example, adjust as needed
+            use_wandb=True,  # Recommended for hackathon
+            rollout_server_url="http://localhost:8000",  # Standard Atropos default
+            total_steps=100,  # For process mode, this is more like num_items_to_process
+            batch_size=8,  # Example
+            steps_per_eval=20,  # Less relevant for process-only
+            max_token_length=2048,  # LLM context window
+            wandb_name="accessibility_env_hackathon",  # Your Wandb run name
+            # data_path_to_save_groups="accessibility_rollouts.jsonl" # Often set via CLI for process
+        )
+        server_configs = [
+            APIServerConfig(
+                model_name="gpt-3.5-turbo",  # Placeholder, use your desired model
+                # base_url="YOUR_LLM_PROVIDER_BASE_URL_IF_NOT_OPENAI_DEFAULT", # e.g., for vLLM
+                # api_key="YOUR_API_KEY_HERE_OR_USE_ENV_VAR", # Best to use os.environ.get("OPENAI_API_KEY")
+                num_requests_for_eval=32,  # Example
+            ),
+        ]
+        return env_config, server_configs
+
+    async def setup(self):
+        print(f"[{self.name}] Setting up environment...")
+        # Load dataset, initialize tools (e.g., HTML parser) here
+        self.dataset = []  # Placeholder for your HTML snippets
+        self.iter = 0
+        print(f"[{self.name}] Setup complete.")
+
+    async def get_next_item(self) -> Optional[Item]:
+        if self.iter >= len(self.dataset):
+            if (
+                self.iter >= self.config.total_steps
+            ):  # Stop after total_steps for 'process'
+                return None
+            # Potentially loop dataset or handle running out of unique items
+            # For hackathon, just stopping might be fine if dataset is small
+            # and total_steps is matched to dataset size.
+            # self.iter = 0 # To loop
+            print(f"[{self.name}] Reached end of dataset or total_steps.")
+            return None
+
+        item_data = self.dataset[self.iter]
+        self.iter += 1
+        # Format item_data into the 'Item' structure Atropos expects
+        # Typically (prompt_messages_tuple, gold_answer_or_metadata_tuple)
+        # Example:
+        # user_prompt = {"role": "user", "content": f"Make this HTML accessible: {item_data['html_snippet']}"}
+        # system_prompt_content = "You are an AI assistant specializing in web accessibility. Modify the given
+        # HTML to meet WCAG AA standards. Output only the modified HTML."
+        # system_prompt = {"role": "system", "content": system_prompt_content}
+        # prompt_messages = (system_prompt, user_prompt) # This needs to be a tuple of dicts
+        # messages_for_item = tuple(frozenset(p.items()) for p in prompt_messages) # Atropos often expects this format
+        # return (messages_for_item, item_data.get('expected_outcome_or_id')) # Second part is for scoring reference
+
+        # Simpler start for prompt:
+        # prompt = (
+        #     (
+        #         {
+        #             "role": "system",
+        #             "content": "You are an AI assistant. Given HTML, make it more accessible.",
+        #         },
+        #     ),
+        #     ({"role": "user", "content": f"Original HTML: {item_data['html']}"},),
+        # )
+        # This prompt structure might need adjustment based on how Atropos and the LLM API expect it.
+        # The gsm8k example has:
+        # user_message = {"role": "user", "content": item["question"]}
+        # chat_completions = await self.server.chat_completion(
+        #     messages=[{"role": "system", "content": system_prompt}, user_message], ...
+        # So a list of dicts is passed to chat_completion.
+        # The 'Item' type for get_next_item is often a tuple: ( (message_part_1, message_part_2, ...),
+        # metadata_for_scoring )
+        # where each message_part is often a frozenset of items from a dict. This is a bit complex.
+        # Let's start with a simple string prompt and adapt.
+        # For now, let's assume item is (prompt_string, metadata_for_scoring)
+        # The `collect_trajectories` in coding_server.py takes `item: Item`
+        # and then accesses `item[0][0]` which implies item is nested.
+        # `prompt = tuple([frozenset({"role": "user", "content": next_item["description"]}.items())])`
+        # `return (prompt, answer)`
+        # So, first element of item is a tuple of frozensets.
+
+        # Let's simplify for now and refine based on Atropos internals if needed.
+        # We'll construct the messages list directly in collect_trajectories.
+        # So get_next_item can return the raw data needed.
+        return item_data  # This will be like {"html": "...", "id": "..."}
+
+    async def collect_trajectories(
+        self, item_data: Item
+    ) -> Tuple[Optional[ScoredDataGroup], List[Item]]:
+        # 'item_data' here is what get_next_item returned.
+        original_html = item_data["html"]
+        system_message_content = (
+            "You are an expert web developer specializing in accessibility. "
+            "Given the following HTML snippet, please make the minimal necessary modifications "
+            "to ensure it meets WCAG 2.1 AA standards for the issues present. "
+            "Output only the complete, modified HTML snippet. Do not include explanations unless explicitly asked."
+        )
+        user_message_content = (
+            f"Original HTML:\n```html\n{original_html}\n```\nModified HTML:"
+        )
+
+        messages = [
+            {"role": "system", "content": system_message_content},
+            {"role": "user", "content": user_message_content},
+        ]
+
+        chat_completions = await self.server.chat_completion(
+            messages=messages,
+            n=self.config.group_size,
+            max_tokens=self.config.max_token_length,
+            # temperature=0.7, # Optional: adjust for creativity vs. determinism
+        )
+
+        to_score_inputs = []
+        for choice in chat_completions.choices:
+            llm_response_content = choice.message.content
+            # The 'messages' to store for scoring/tokenization should represent the full exchange
+            # that led to this specific llm_response_content.
+            # This includes the original system and user messages, and the assistant's response.
+            full_exchange_messages = messages + [
+                {"role": "assistant", "content": llm_response_content}
+            ]
+            to_score_inputs.append(
+                {
+                    "full_exchange_messages": full_exchange_messages,  # For tokenization
+                    "llm_modified_html": llm_response_content,  # For direct scoring
+                    "original_html_info": item_data,  # To know what to check against
+                }
+            )
+
+        # The `score` method in Atropos expects a list where each element typically is
+        # (messages_tuple_for_tokenization, original_item_metadata_for_scoring_logic)
+        # We need to adapt `to_score_inputs` to what `self.score` will expect.
+        # Let's define that `self.score` will take this list of dicts directly.
+        # The `collect_trajectories` from the blog post returns `to_postprocess, to_backlog`
+        # where `to_postprocess` is the output of `self.score`.
+
+        scored_data_group = await self.score(to_score_inputs)
+        return scored_data_group, []  # Assuming no backlog for now
+
+    async def score(self, rollout_group_data: List[dict]) -> Optional[ScoredDataGroup]:
+        # rollout_group_data is a list of dicts, each like:
+        # {
+        #     "full_exchange_messages": [...],
+        #     "llm_modified_html": "...",
+        #     "original_html_info": {"html": "...", "id": "...", "issues": [...]}
+        # }
+        print(f"[{self.name}] Scoring {len(rollout_group_data)} rollouts...")
+        scores_obj = ScoredDataGroup()  # Use the Atropos defined type
+        # Initialize lists within scores_obj as per ScoredDataGroup structure
+        # (typically 'tokens', 'masks', 'scores', maybe 'logprobs')
+        scores_obj["tokens"] = []
+        scores_obj["masks"] = []
+        scores_obj["scores"] = []
+        # scores_obj["infos"] = [] # Optional for extra debug info
+
+        for data_item in rollout_group_data:
+            llm_html = data_item["llm_modified_html"]
+            original_info = data_item["original_html_info"]
+
+            # Basic reward: 1.0 if fixed, -1.0 if not.
+            # This will be replaced with actual WCAG checks.
+            current_score = -1.0  # Default to failure
+            # ---- YOUR SCORING LOGIC HERE ----
+            # Example: (pseudo-code, requires BeautifulSoup and specific checks)
+            # violations_fixed = self.check_wcag_fixes(llm_html, original_info)
+            # if violations_fixed:
+            #    current_score = 1.0
+            # For now, a placeholder:
+            if "<img" in original_info["html"] and "alt=" in llm_html:
+                current_score = 1.0
+            elif "<label>" in original_info["html"] and "for=" in llm_html:
+                current_score = 1.0
+
+            # Tokenize the full exchange for the trainer
+            # The 'tokenize_for_trainer' util expects a tuple/list of message dicts
+            tokenized_output = tokenize_for_trainer(
+                self.tokenizer,
+                data_item["full_exchange_messages"],  # Pass the list of message dicts
+            )
+
+            # Ensure tokenized_output contains 'tokens' and 'masks'
+            if "tokens" not in tokenized_output or "masks" not in tokenized_output:
+                print(
+                    f"[{self.name}] Warning: Tokenization did not return tokens/masks for an item. Skipping."
+                )
+                continue
+
+            scores_obj["tokens"].append(tokenized_output["tokens"])
+            scores_obj["masks"].append(tokenized_output["masks"])
+            scores_obj["scores"].append(current_score)
+            # scores_obj["infos"].append({"original_id": original_info["id"], "llm_output_preview": llm_html[:100]})
+
+        # Handle case where no valid items were scored
+        if not scores_obj["scores"]:
+            print(f"[{self.name}] No valid items to score, returning None.")
+            return None
+
+        # Atropos convention: if all scores are identical, return None (no learning signal)
+        # This might be too strict for early testing. Consider enabling later.
+        # if len(set(scores_obj["scores"])) == 1 and len(scores_obj["scores"]) > 1 :
+        #     print(f"[{self.name}] All scores are identical ({scores_obj['scores'][0]}), returning None.")
+        #     return None
+
+        print(f"[{self.name}] Scoring complete. Scores: {scores_obj['scores']}")
+        return scores_obj
+
+    async def evaluate(
+        self,
+    ):  # Optional, might not be needed for hackathon 'process' focus
+        print(f"[{self.name}] Evaluate method called (placeholder).")
+        # Implement evaluation logic if you have a separate test set and metrics
+        pass
+
+    # --- Helper methods for scoring ---
+    # def check_wcag_fixes(self, modified_html: str, original_item_info: dict) -> bool:
+    #     # Placeholder for your actual WCAG checking logic
+    #     # e.g., using BeautifulSoup to parse modified_html
+    #     # and checking against `original_item_info['issues_to_fix']`
+    #     # from bs4 import BeautifulSoup
+    #     # soup = BeautifulSoup(modified_html, 'html.parser')
+    #     # ... logic ...
+    #     return False
+
+
+if __name__ == "__main__":
+    # This makes your environment runnable with `python accessibility_env.py process`
+    AccessibilityEnv.cli()