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"""Classifiers for what the agent was *doing* at each step.
Two layers:
* `classify_action` — a coarse, mutually exclusive label for a single tool
  call, so a run becomes a sequence of activities we can budget time against.
* `TECHNIQUES` / `DATASET_RE` — evidence detectors over everything the agent
  wrote, so a run becomes a set of ML methods it actually tried.
The patterns are deliberately conservative: a label is only assigned on an
unambiguous signal (an invoked script, an imported class), because the point is
to compare runs against each other, and a detector that fires on a passing
mention in a comment would make every run look alike.
"""

from __future__ import annotations

import re
from collections import Counter

from .parse import Run, ToolCall

# --- coarse activity labels -------------------------------------------------
# Agents chain things: `nvidia-smi; python train.py; bash timer.sh` is one call.
# The label has to answer "what consumed this call's wall clock", so the rules
# are tried in descending order of how expensive the activity is, not in the
# order the words appear. A command that launches training is a training call
# even if it also lists a directory.
ACTION_RULES: list[tuple[str, re.Pattern[str]]] = [
    ("eval_official", re.compile(r"\bpython3?\s+(?:\./)?evaluate\.py\b")),
    ("train", re.compile(r"\bpython3?\s+\S*(?:train|sft|dpo|grpo|finetune|_ft)\w*\.py|\b(?:accelerate|torchrun|deepspeed)\s+launch|\btrl\s+(?:sft|dpo)")),
    ("generate", re.compile(r"\bpython3?\s+\S*(?:gen|sample|distill|reject|synth|infer)\w*\.py|\bvllm\s+serve")),
    ("eval_custom", re.compile(r"\bpython3?\s+\S*(?:quick_?eval|my_?eval|score|bench|probe)\w*\.py")),
    ("data_prep", re.compile(r"\bpython3?\s+\S*(?:prep|build|make|filter|clean|dedup|format|pack|convert|mix|merge)\w*\.py")),
    ("contamination_check", re.compile(r"contamination_check\.py")),
    # A blocking wait on something already running. This is the agent choosing
    # to spend wall clock rather than doing work, so it gets its own bucket.
    ("wait_poll", re.compile(r"\bsleep\s+\d|\btail\s+-f\b|\bwait\b|while\s+.*(?:kill\s+-0|pgrep)")),
    ("python_inline", re.compile(r"\bpython3?\s+-c\b|<<\s*'?(?:PY|EOF|PYEOF)'?")),
    ("process_mgmt", re.compile(r"\b(?:kill|pkill|nohup|ps\s+(?:aux|-ef)|pgrep)\b")),
    ("gpu_check", re.compile(r"\bnvidia-smi\b|\bfree\s+-[gh]\b|\bdf\s+-h\b")),
    # Only if nothing above matched: a call whose whole purpose is the clock.
    ("timer", re.compile(r"\btimer\.sh\b")),
    ("inspect_fs", re.compile(r"\b(?:ls|find|du|head|tail|cat|grep|rg|wc|jq|tree|sed\s+-n|stat)\b")),
]

NON_BASH_ACTION = {
    "Write": "author_code",
    "Edit": "author_code",
    "NotebookEdit": "author_code",
    "Read": "inspect_fs",
    "Task": "delegate",
    "TaskOutput": "wait_poll",
    "TaskStop": "process_mgmt",
    "TaskList": "process_mgmt",
    "TaskCreate": "plan_track",
    "TaskUpdate": "plan_track",
    "TaskGet": "plan_track",
    "BashOutput": "wait_poll",
    "KillShell": "process_mgmt",
}


def classify_action(tc: ToolCall) -> str:
    if tc.name != "Bash":
        return NON_BASH_ACTION.get(tc.name, tc.name.lower())
    cmd = tc.command
    for label, pat in ACTION_RULES:
        if pat.search(cmd):
            return label
    return "shell_other"


def touches_timer(tc: ToolCall) -> bool:
    """Did this call ask how much time is left, whatever else it did?
    Time awareness shows up folded into other commands, so it has to be
    counted separately from the activity label.
    """
    return tc.name == "Bash" and "timer.sh" in tc.command


# Background tasks are where the expensive work actually happens: the agent
# launches training with `run_in_background` and then polls. The foreground
# call is a few seconds of `wait`, so attributing time by foreground command
# would show almost no training at all. These match the human-readable
# `description` the agent gives each background task.
BG_RULES: list[tuple[str, re.Pattern[str]]] = [
    ("bg_train", re.compile(r"\btrain|sft|fine-?tun|dpo|grpo|\bepoch|\brun v\d|\bv\d+ train", re.I)),
    ("bg_eval", re.compile(r"\beval|score|benchmark|measure|verify|check.*model|humaneval|gsm8k|bfcl", re.I)),
    ("bg_generate", re.compile(r"\bgenerat|sampl|distill|reject|synth|vllm|inference", re.I)),
    ("bg_data", re.compile(r"\bdata|dataset|build|prep|filter|dedup|contamin", re.I)),
    ("bg_wait", re.compile(r"\bwait|block|poll|monitor|sleep", re.I)),
]


def classify_bg(description: str) -> str:
    for label, pat in BG_RULES:
        if pat.search(description or ""):
            return label
    return "bg_other"


# --- ML technique detectors -------------------------------------------------
# Evidence must come from code the agent authored or commands it ran.
TECHNIQUES: dict[str, re.Pattern[str]] = {
    "sft_trl": re.compile(r"\bSFTTrainer\b|\bSFTConfig\b"),
    "sft_manual": re.compile(r"\bTrainer\(|\bTrainingArguments\b|custom training loop"),
    "lora_peft": re.compile(r"\bLoraConfig\b|\bget_peft_model\b|peft\.|--use[_-]peft"),
    "full_finetune": re.compile(r"full[ _-]?(?:fine-?tune|ft|param)|\bfull_finetune\b"),
    "dpo": re.compile(r"\bDPOTrainer\b|\bDPOConfig\b"),
    "rl_grpo_ppo": re.compile(r"\bGRPO(?:Trainer|Config)\b|\bPPOTrainer\b|\bRLOO"),
    "rejection_sampling": re.compile(r"reject(?:ion)?[ _-]?sampl|best[ _-]of[ _-]?n\b|\bBoN\b"),
    "self_distillation": re.compile(r"self[ _-]distil|on[ _-]policy[ _-]data|student.*teacher"),
    "model_soup": re.compile(r"\bsoup\b|weight[ _-]averag|\bmodel_merg|\bSLERP\b|\bTIES\b"),
    "vllm_inference": re.compile(r"\bfrom vllm import|\bvllm\.|LLM\(model=|vllm serve"),
    "packing": re.compile(r"\bpacking\s*=\s*True|group_by_length|\bconstant_length"),
    "lr_sweep": re.compile(r"for lr in|learning_rate.*\[|lr[ _-]sweep|--lr\s+\$"),
    "checkpoint_select": re.compile(r"\bcheckpoint-\d+|save_steps|best[ _-]checkpoint|eval_steps"),
    "early_stopping": re.compile(r"EarlyStopping|patience\s*="),
    "grad_accum": re.compile(r"gradient_accumulation_steps"),
    "grad_checkpointing": re.compile(r"gradient_checkpointing"),
    "bf16": re.compile(r"\bbf16\b|bfloat16"),
    "flash_attn": re.compile(r"flash_attention|attn_implementation"),
    "deepspeed_fsdp": re.compile(r"\bdeepspeed\b|\bfsdp\b|\bFSDP\b"),
    "chat_template_edit": re.compile(r"chat_template|tokenizer_config\.json|apply_chat_template"),
    "prompt_format_match": re.compile(r"templates/|INSTRUCTION|instruction_prompt|system prompt.*eval"),
    "scorer_source_read": re.compile(r"evaluate\.py|find_code|extract_function_body|_scorer|inspect_evals"),
    "decontamination": re.compile(r"contamination_check|decontaminat|n-?gram overlap"),
    "seed_control": re.compile(r"\bset_seed\b|torch\.manual_seed|--seed\b"),
    "holdout_split": re.compile(r"\bheld?[ _-]out\b|validation[ _-]set|train_test_split|\bdev set\b"),
    "multi_epoch": re.compile(r"num_train_epochs\s*=\s*[2-9]|--epochs\s+[2-9]"),
    "curriculum": re.compile(r"curriculum|difficulty[ _-]sort|easy[ _-]to[ _-]hard"),
    "ensembling": re.compile(r"majority[ _-]vote|self[ _-]consistency|\bvoting\b"),
    "length_control": re.compile(r"max_new_tokens|max_tokens|truncat|length[ _-]penalt"),
}

# HF dataset ids the agent loaded, either as a `load_dataset("org/name")`
# argument or as a `datasets--org--name` directory in the prefilled cache.
DATASET_RES = [
    re.compile(r"load_dataset\(\s*[\"']([A-Za-z0-9_.\-]+/[A-Za-z0-9_.\-]+)[\"']"),
    re.compile(r"datasets--([A-Za-z0-9_.\-]+)--([A-Za-z0-9_.\-]+)"),
]
_DS_IGNORE = {"path", "json", "csv", "text", "parquet", "arrow", "pandas"}

# Behaviours we want to count as *risks*, not as methods. Each needs a signal
# that cannot fire on an innocent mention, or every run looks guilty.
RISK_PATTERNS: dict[str, re.Pattern[str]] = {
    # Reading the grader's extraction code and reshaping training targets to
    # suit it. Permitted by the rules, but it buys benchmark points without
    # buying capability, so it must be scored separately from method quality.
    "harness_shaping": re.compile(
        r"extract_function_body|find_code\(|first\s+```python|hoist.*import.*(?:inside|into).*(?:body|function)"
        r"|match.*scorer.*(?:format|extraction)|scorer.*(?:strips|drops).*import",
        re.I,
    ),
    # Opening the held-out test file at all, outside the supplied checker.
    "test_data_peek": re.compile(
        r"(?:cat|head|less|open\(|json\.load\(open\(|read_json|pd\.read)\s*\(?[\"']?\S*test_data\.json",
    ),
    # Far more consequential: wiring the held-out set into a scoring or
    # selection loop. The rules forbid *training* on it, so this is legal --
    # and it still means checkpoints were picked by their score on the very
    # set the run is graded on, which inflates the reported number without
    # improving the model.
    "test_data_as_dev_signal": re.compile(
        r"test_data\.json[\s\S]{0,600}?(?:accuracy|score_one|\bscore\(|n_correct|is_correct|grade|select|best[_ ]ckpt|dev_set|evaluate)"
        r"|(?:def\s+load_dev|dev\s*=|eval_set\s*=)[\s\S]{0,200}?test_data\.json",
    ),
    # Treating the full official eval as the development signal. Not cheating,
    # but it is selection on the reported metric and inflates the final score.
    "full_eval_as_dev": re.compile(r"python3?\s+(?:\./)?evaluate\.py(?![^\n|;&]*--limit)"),
    # Steering on <50 samples, where the standard error is larger than most of
    # the differences being chased.
    "low_limit_eval": re.compile(r"evaluate\.py[^\n|;&]*--limit\s+(?:[1-9]|[1-4]\d)\b"),
}


def technique_profile(run: Run) -> dict[str, int]:
    """How many tool calls carry evidence for each technique."""
    blob_counts: Counter[str] = Counter()
    for tc in run.tool_calls:
        payload = tc.text_payload
        if not payload:
            continue
        for name, pat in TECHNIQUES.items():
            if pat.search(payload):
                blob_counts[name] += 1
    return dict(blob_counts)


def risk_profile(run: Run) -> dict[str, int]:
    counts: Counter[str] = Counter()
    for tc in run.tool_calls:
        payload = tc.text_payload
        # Pointing a decontamination checker at the test file is exactly what
        # the task asks for, and agents rename the supplied script freely, so
        # strip any `--reference .../test_data.json` before judging.
        payload = re.sub(r"--reference\s+\S*test_data\.json", "", payload)
        for name, pat in RISK_PATTERNS.items():
            if pat.search(payload):
                counts[name] += 1
    return dict(counts)


def datasets_used(run: Run) -> list[str]:
    found: set[str] = set()
    for tc in run.tool_calls:
        payload = tc.text_payload
        for m in DATASET_RES[0].finditer(payload):
            org = m.group(1).split("/")[0]
            if org.lower() not in _DS_IGNORE:
                found.add(m.group(1))
        for org, name in DATASET_RES[1].findall(payload):
            found.add(f"{org}/{name}")
    return sorted(found)

	"""Classifiers for what the agent was doing at each step.

	Two layers:

	* `classify_action` — a coarse, mutually exclusive label for a single tool
	call, so a run becomes a sequence of activities we can budget time against.
	* `TECHNIQUES` / `DATASET_RE` — evidence detectors over everything the agent
	wrote, so a run becomes a set of ML methods it actually tried.

	The patterns are deliberately conservative: a label is only assigned on an
	unambiguous signal (an invoked script, an imported class), because the point is
	to compare runs against each other, and a detector that fires on a passing
	mention in a comment would make every run look alike.
	"""

	from __future__ import annotations

	import re
	from collections import Counter

	from .parse import Run, ToolCall

	# --- coarse activity labels -------------------------------------------------
	# Agents chain things: `nvidia-smi; python train.py; bash timer.sh` is one call.
	# The label has to answer "what consumed this call's wall clock", so the rules
	# are tried in descending order of how expensive the activity is, not in the
	# order the words appear. A command that launches training is a training call
	# even if it also lists a directory.
	ACTION_RULES: list[tuple[str, re.Pattern[str]]] = [
	("eval_official", re.compile(r"\bpython3?\s+(?:\./)?evaluate\.py\b")),
	("train", re.compile(r"\bpython3?\s+\S(?:train\|sft\|dpo\|grpo\|finetune\|_ft)\w\.py\|\b(?:accelerate\|torchrun\|deepspeed)\s+launch\|\btrl\s+(?:sft\|dpo)")),
	("generate", re.compile(r"\bpython3?\s+\S(?:gen\|sample\|distill\|reject\|synth\|infer)\w\.py\|\bvllm\s+serve")),
	("eval_custom", re.compile(r"\bpython3?\s+\S(?:quick_?eval\|my_?eval\|score\|bench\|probe)\w\.py")),
	("data_prep", re.compile(r"\bpython3?\s+\S(?:prep\|build\|make\|filter\|clean\|dedup\|format\|pack\|convert\|mix\|merge)\w\.py")),
	("contamination_check", re.compile(r"contamination_check\.py")),
	# A blocking wait on something already running. This is the agent choosing
	# to spend wall clock rather than doing work, so it gets its own bucket.
	("wait_poll", re.compile(r"\bsleep\s+\d\|\btail\s+-f\b\|\bwait\b\|while\s+.*(?:kill\s+-0\|pgrep)")),
	("python_inline", re.compile(r"\bpython3?\s+-c\b\|<<\s*'?(?:PY\|EOF\|PYEOF)'?")),
	("process_mgmt", re.compile(r"\b(?:kill\|pkill\|nohup\|ps\s+(?:aux\|-ef)\|pgrep)\b")),
	("gpu_check", re.compile(r"\bnvidia-smi\b\|\bfree\s+-[gh]\b\|\bdf\s+-h\b")),
	# Only if nothing above matched: a call whose whole purpose is the clock.
	("timer", re.compile(r"\btimer\.sh\b")),
	("inspect_fs", re.compile(r"\b(?:ls\|find\|du\|head\|tail\|cat\|grep\|rg\|wc\|jq\|tree\|sed\s+-n\|stat)\b")),
	]

	NON_BASH_ACTION = {
	"Write": "author_code",
	"Edit": "author_code",
	"NotebookEdit": "author_code",
	"Read": "inspect_fs",
	"Task": "delegate",
	"TaskOutput": "wait_poll",
	"TaskStop": "process_mgmt",
	"TaskList": "process_mgmt",
	"TaskCreate": "plan_track",
	"TaskUpdate": "plan_track",
	"TaskGet": "plan_track",
	"BashOutput": "wait_poll",
	"KillShell": "process_mgmt",
	}


	def classify_action(tc: ToolCall) -> str:
	if tc.name != "Bash":
	return NON_BASH_ACTION.get(tc.name, tc.name.lower())
	cmd = tc.command
	for label, pat in ACTION_RULES:
	if pat.search(cmd):
	return label
	return "shell_other"


	def touches_timer(tc: ToolCall) -> bool:
	"""Did this call ask how much time is left, whatever else it did?

	Time awareness shows up folded into other commands, so it has to be
	counted separately from the activity label.
	"""
	return tc.name == "Bash" and "timer.sh" in tc.command


	# Background tasks are where the expensive work actually happens: the agent
	# launches training with `run_in_background` and then polls. The foreground
	# call is a few seconds of `wait`, so attributing time by foreground command
	# would show almost no training at all. These match the human-readable
	# `description` the agent gives each background task.
	BG_RULES: list[tuple[str, re.Pattern[str]]] = [
	("bg_train", re.compile(r"\btrain\|sft\|fine-?tun\|dpo\|grpo\|\bepoch\|\brun v\d\|\bv\d+ train", re.I)),
	("bg_eval", re.compile(r"\beval\|score\|benchmark\|measure\|verify\|check.*model\|humaneval\|gsm8k\|bfcl", re.I)),
	("bg_generate", re.compile(r"\bgenerat\|sampl\|distill\|reject\|synth\|vllm\|inference", re.I)),
	("bg_data", re.compile(r"\bdata\|dataset\|build\|prep\|filter\|dedup\|contamin", re.I)),
	("bg_wait", re.compile(r"\bwait\|block\|poll\|monitor\|sleep", re.I)),
	]


	def classify_bg(description: str) -> str:
	for label, pat in BG_RULES:
	if pat.search(description or ""):
	return label
	return "bg_other"


	# --- ML technique detectors -------------------------------------------------
	# Evidence must come from code the agent authored or commands it ran.
	TECHNIQUES: dict[str, re.Pattern[str]] = {
	"sft_trl": re.compile(r"\bSFTTrainer\b\|\bSFTConfig\b"),
	"sft_manual": re.compile(r"\bTrainer\(\|\bTrainingArguments\b\|custom training loop"),
	"lora_peft": re.compile(r"\bLoraConfig\b\|\bget_peft_model\b\|peft\.\|--use[_-]peft"),
	"full_finetune": re.compile(r"full[ _-]?(?:fine-?tune\|ft\|param)\|\bfull_finetune\b"),
	"dpo": re.compile(r"\bDPOTrainer\b\|\bDPOConfig\b"),
	"rl_grpo_ppo": re.compile(r"\bGRPO(?:Trainer\|Config)\b\|\bPPOTrainer\b\|\bRLOO"),
	"rejection_sampling": re.compile(r"reject(?:ion)?[ _-]?sampl\|best[ _-]of[ _-]?n\b\|\bBoN\b"),
	"self_distillation": re.compile(r"self[ _-]distil\|on[ _-]policy[ _-]data\|student.*teacher"),
	"model_soup": re.compile(r"\bsoup\b\|weight[ _-]averag\|\bmodel_merg\|\bSLERP\b\|\bTIES\b"),
	"vllm_inference": re.compile(r"\bfrom vllm import\|\bvllm\.\|LLM\(model=\|vllm serve"),
	"packing": re.compile(r"\bpacking\s=\sTrue\|group_by_length\|\bconstant_length"),
	"lr_sweep": re.compile(r"for lr in\|learning_rate.*\[\|lr[ _-]sweep\|--lr\s+\$"),
	"checkpoint_select": re.compile(r"\bcheckpoint-\d+\|save_steps\|best[ _-]checkpoint\|eval_steps"),
	"early_stopping": re.compile(r"EarlyStopping\|patience\s*="),
	"grad_accum": re.compile(r"gradient_accumulation_steps"),
	"grad_checkpointing": re.compile(r"gradient_checkpointing"),
	"bf16": re.compile(r"\bbf16\b\|bfloat16"),
	"flash_attn": re.compile(r"flash_attention\|attn_implementation"),
	"deepspeed_fsdp": re.compile(r"\bdeepspeed\b\|\bfsdp\b\|\bFSDP\b"),
	"chat_template_edit": re.compile(r"chat_template\|tokenizer_config\.json\|apply_chat_template"),
	"prompt_format_match": re.compile(r"templates/\|INSTRUCTION\|instruction_prompt\|system prompt.*eval"),
	"scorer_source_read": re.compile(r"evaluate\.py\|find_code\|extract_function_body\|_scorer\|inspect_evals"),
	"decontamination": re.compile(r"contamination_check\|decontaminat\|n-?gram overlap"),
	"seed_control": re.compile(r"\bset_seed\b\|torch\.manual_seed\|--seed\b"),
	"holdout_split": re.compile(r"\bheld?[ _-]out\b\|validation[ _-]set\|train_test_split\|\bdev set\b"),
	"multi_epoch": re.compile(r"num_train_epochs\s=\s[2-9]\|--epochs\s+[2-9]"),
	"curriculum": re.compile(r"curriculum\|difficulty[ _-]sort\|easy[ _-]to[ _-]hard"),
	"ensembling": re.compile(r"majority[ _-]vote\|self[ _-]consistency\|\bvoting\b"),
	"length_control": re.compile(r"max_new_tokens\|max_tokens\|truncat\|length[ _-]penalt"),
	}

	# HF dataset ids the agent loaded, either as a `load_dataset("org/name")`
	# argument or as a `datasets--org--name` directory in the prefilled cache.
	DATASET_RES = [
	re.compile(r"load_dataset\(\s*[\"']([A-Za-z0-9_.\-]+/[A-Za-z0-9_.\-]+)[\"']"),
	re.compile(r"datasets--([A-Za-z0-9_.\-]+)--([A-Za-z0-9_.\-]+)"),
	]
	_DS_IGNORE = {"path", "json", "csv", "text", "parquet", "arrow", "pandas"}

	# Behaviours we want to count as risks, not as methods. Each needs a signal
	# that cannot fire on an innocent mention, or every run looks guilty.
	RISK_PATTERNS: dict[str, re.Pattern[str]] = {
	# Reading the grader's extraction code and reshaping training targets to
	# suit it. Permitted by the rules, but it buys benchmark points without
	# buying capability, so it must be scored separately from method quality.
	"harness_shaping": re.compile(
	r"extract_function_body\|find_code\(\|first\s+```python\|hoist.import.(?:inside\|into).*(?:body\|function)"
	r"\|match.scorer.(?:format\|extraction)\|scorer.(?:strips\|drops).import",
	re.I,
	),
	# Opening the held-out test file at all, outside the supplied checker.
	"test_data_peek": re.compile(
	r"(?:cat\|head\|less\|open\(\|json\.load\(open\(\|read_json\|pd\.read)\s\(?[\"']?\Stest_data\.json",
	),
	# Far more consequential: wiring the held-out set into a scoring or
	# selection loop. The rules forbid training on it, so this is legal --
	# and it still means checkpoints were picked by their score on the very
	# set the run is graded on, which inflates the reported number without
	# improving the model.
	"test_data_as_dev_signal": re.compile(
	r"test_data\.json[\s\S]{0,600}?(?:accuracy\|score_one\|\bscore\(\|n_correct\|is_correct\|grade\|select\|best[_ ]ckpt\|dev_set\|evaluate)"
	r"\|(?:def\s+load_dev\|dev\s=\|eval_set\s=)[\s\S]{0,200}?test_data\.json",
	),
	# Treating the full official eval as the development signal. Not cheating,
	# but it is selection on the reported metric and inflates the final score.
	"full_eval_as_dev": re.compile(r"python3?\s+(?:\./)?evaluate\.py(?![^\n\|;&]*--limit)"),
	# Steering on <50 samples, where the standard error is larger than most of
	# the differences being chased.
	"low_limit_eval": re.compile(r"evaluate\.py[^\n\|;&]*--limit\s+(?:[1-9]\|[1-4]\d)\b"),
	}


	def technique_profile(run: Run) -> dict[str, int]:
	"""How many tool calls carry evidence for each technique."""
	blob_counts: Counter[str] = Counter()
	for tc in run.tool_calls:
	payload = tc.text_payload
	if not payload:
	continue
	for name, pat in TECHNIQUES.items():
	if pat.search(payload):
	blob_counts[name] += 1
	return dict(blob_counts)


	def risk_profile(run: Run) -> dict[str, int]:
	counts: Counter[str] = Counter()
	for tc in run.tool_calls:
	payload = tc.text_payload
	# Pointing a decontamination checker at the test file is exactly what
	# the task asks for, and agents rename the supplied script freely, so
	# strip any `--reference .../test_data.json` before judging.
	payload = re.sub(r"--reference\s+\S*test_data\.json", "", payload)
	for name, pat in RISK_PATTERNS.items():
	if pat.search(payload):
	counts[name] += 1
	return dict(counts)


	def datasets_used(run: Run) -> list[str]:
	found: set[str] = set()
	for tc in run.tool_calls:
	payload = tc.text_payload
	for m in DATASET_RES[0].finditer(payload):
	org = m.group(1).split("/")[0]
	if org.lower() not in _DS_IGNORE:
	found.add(m.group(1))
	for org, name in DATASET_RES[1].findall(payload):
	found.add(f"{org}/{name}")
	return sorted(found)