"""CPU/FP64 identity check for shared/untied complete Transformer Jacobians.

No scientific training, dataset, checkpoint, or active experiment is accessed.
Two fixed token-logit scalars provide a cross-input Jacobian kernel. Actual
parameter identity determines aliases, including MLP-only sharing; the tied
input/output embedding is differentiated once with both computational paths.
Small plain-SGD steps below are finite-difference checks on disposable copies,
not claims about AdamW, generalization, or learning efficiency.
"""

from __future__ import annotations

import argparse
import copy
import hashlib
import json
import platform
import sys
from datetime import datetime, timezone
from pathlib import Path

ROOT = Path(__file__).resolve().parents[3]
sys.path.insert(0, str(ROOT / "src"))

import torch  # noqa: E402
import transformers  # noqa: E402

from llm_memory_editability.loop_learning import construct  # noqa: E402

SEED = 20261007
TOLERANCE = 1e-10
SGD_STEPS = (1e-4, 5e-5, 2.5e-5)
SPEC = {
    "initialization": SEED,
    "base_unique_blocks": 1,
    "repeats": 2,
    "initialization_reference_layers": 8,
    "model": {
        "vocab_size": 11,
        "positions": 16,
        "hidden_size": 8,
        "attention_heads": 2,
        "dropout": 0.0,
    },
}
OUTPUTS = {
    "query": {"tokens": [[1, 4, 2]], "position": 2, "token_logit": 7},
    "train": {"tokens": [[3, 1, 5]], "position": 2, "token_logit": 4},
}


def scalar_output(model, name):
    setting = OUTPUTS[name]
    tokens = torch.tensor(setting["tokens"], dtype=torch.long)
    return model(tokens)[0, setting["position"], setting["token_logit"]]


def scalar_jacobian(model, name):
    parameters = dict(model.named_parameters())
    output = scalar_output(model, name)
    gradients = torch.autograd.grad(output, tuple(parameters.values()))
    return float(output.detach()), {
        key: gradient.detach().clone() for key, gradient in zip(parameters, gradients, strict=True)
    }


def dot(left, right):
    return sum(float((left[key] * right[key]).sum()) for key in left)


def max_abs(value):
    return float(value.abs().max())


def parameter_aliases(model, untied):
    """Map each actual unique parameter to its independent execution copies."""
    unique = dict(model.named_parameters())
    names = {id(parameter): name for name, parameter in unique.items()}
    aliases = {name: [] for name in unique}
    for name, parameter in unique.items():
        if not name.startswith("transformer.h."):
            aliases[names[id(parameter)]].append(name)
    occurrence = 0
    for _ in range(model.repeats):
        for block in model.transformer.h:
            for name, parameter in block.named_parameters():
                aliases[names[id(parameter)]].append(f"transformer.h.{occurrence}.{name}")
            occurrence += 1
    independent = dict(untied.named_parameters())
    flattened = [alias for group in aliases.values() for alias in group]
    if len(flattened) != len(set(flattened)) or set(flattened) != set(independent):
        raise AssertionError("Alias groups must partition all independent parameters")
    for name, group in aliases.items():
        if not group or any(not torch.equal(unique[name], independent[alias]) for alias in group):
            raise AssertionError(f"Alias weights differ at common forward point: {name}")
    return aliases


def check_kernel(model, untied, reference, arm):
    aliases = parameter_aliases(model, untied)
    scalar_values, gradients = {}, {}
    gradient_errors = {}
    for output in OUTPUTS:
        scalar_values[output], gradients[output] = scalar_jacobian(model, output)
        errors = {}
        for name, group in aliases.items():
            expected = torch.stack([reference[output][alias] for alias in group]).sum(0)
            errors[name] = max_abs(gradients[output][name] - expected)
        gradient_errors[output] = errors

    common, diagonal, cross = 0.0, 0.0, 0.0
    for group in aliases.values():
        for left in group:
            for right in group:
                value = float((reference["query"][left] * reference["train"][right]).sum())
                if not left.startswith("transformer.h."):
                    if left != right:
                        raise AssertionError("Common parameters must occur exactly once")
                    common += value
                elif left == right:
                    diagonal += value
                else:
                    cross += value
                    if arm == "mlp_shared" and not (".mlp." in left and ".mlp." in right):
                        raise AssertionError("MLP-only sharing introduced a non-MLP cross term")

    actual = dot(gradients["query"], gradients["train"])
    independent_actual = dot(reference["query"], reference["train"])
    errors = {
        "scalar_jacobian_vs_alias_sum_max_abs": max(
            max(values.values()) for values in gradient_errors.values()
        ),
        "kernel_vs_common_plus_diagonal_plus_cross_abs": abs(actual - common - diagonal - cross),
        "untied_kernel_vs_common_plus_diagonal_abs": abs(independent_actual - common - diagonal),
        "kernel_difference_vs_cross_terms_abs": abs(actual - independent_actual - cross),
    }
    if not all(error < TOLERANCE for error in errors.values()):
        raise AssertionError(errors)
    embedding = "transformer.wte.weight"
    return {
        "arm": arm,
        "scalar_outputs": scalar_values,
        "unique_parameters": sum(parameter.numel() for parameter in model.parameters()),
        "parameter_aliases": aliases,
        "kernel": actual,
        "untied_kernel": independent_actual,
        "common_parameter_kernel": common,
        "common_tied_embedding_kernel": float(
            (reference["query"][embedding] * reference["train"][embedding]).sum()
        ),
        "block_diagonal_kernel": diagonal,
        "block_cross_call_kernel": cross,
        "per_parameter_jacobian_errors": gradient_errors,
        "errors": errors,
        "passed": True,
    }, gradients


def check_plain_sgd(model, gradients, kernel):
    """Check Eq. 26 for 1/2 * (one train logit - 0.5)**2 loss."""
    before = float(scalar_output(model, "query").detach())
    derivative = float(scalar_output(model, "train").detach()) - 0.5
    parameters = dict(model.named_parameters())
    direction = {name: -derivative * gradient for name, gradient in gradients["train"].items()}
    direct_derivative = dot(gradients["query"], direction)
    derivative_error = abs(direct_derivative + derivative * kernel)
    if derivative_error >= TOLERANCE:
        raise AssertionError({"SGD_first_derivative_error": derivative_error})
    records = []
    for step in SGD_STEPS:
        disposable = copy.deepcopy(model)
        with torch.no_grad():
            for name, parameter in disposable.named_parameters():
                parameter.copy_(parameters[name] + step * direction[name])
            change = float(scalar_output(disposable, "query")) - before
        prediction = step * direct_derivative
        residual = abs(change - prediction)
        records.append(
            {
                "step_size": step,
                "actual_query_logit_change": change,
                "kernel_first_order_change": prediction,
                "absolute_taylor_remainder": residual,
                "absolute_remainder_divided_by_step_squared": residual / step**2,
            }
        )
    ratios = [
        records[index]["absolute_taylor_remainder"]
        / records[index + 1]["absolute_taylor_remainder"]
        for index in range(len(records) - 1)
    ]
    # A halved step has quarter-sized quadratic error. These bounds only assess
    # the fixed, nondegenerate tiny numerical example; they are not a proof of
    # a global remainder bound or a rule for selecting a training learning rate.
    passed = all(3.0 < ratio < 5.0 for ratio in ratios)
    if not passed:
        raise AssertionError({"finite_difference_records": records, "ratios": ratios})
    return {
        "loss": "0.5 * (selected train token logit - 0.5)**2",
        "optimizer": "plain SGD, no momentum, clipping, decay or optimizer state",
        "kernel_directional_derivative_error": derivative_error,
        "steps": records,
        "remainder_ratio_when_step_halved": ratios,
        "accepted_ratio_interval": [3.0, 5.0],
        "passed": passed,
    }


def main():
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--output", type=Path, default=Path(__file__).with_suffix(".json"))
    args = parser.parse_args()
    torch.set_num_threads(1)
    torch.manual_seed(SEED)
    models = {
        arm: construct({**SPEC, "arm": arm}, "cpu").double().eval()
        for arm in ("shared", "untied", "mlp_shared")
    }
    untied = models["untied"]
    reference = {name: scalar_jacobian(untied, name)[1] for name in OUTPUTS}
    with torch.no_grad():
        tokens = torch.tensor([OUTPUTS[name]["tokens"][0] for name in OUTPUTS])
        baseline = untied(tokens)
        forward_errors = {arm: max_abs(model(tokens) - baseline) for arm, model in models.items()}
    if not all(error < TOLERANCE for error in forward_errors.values()):
        raise AssertionError(forward_errors)
    checks = {}
    for arm, model in models.items():
        check, gradients = check_kernel(model, untied, reference, arm)
        check["plain_sgd_taylor_check"] = check_plain_sgd(model, gradients, check["kernel"])
        checks[arm] = check
    files = [
        Path(__file__),
        ROOT / "src/llm_memory_editability/loop_learning.py",
        ROOT / "src/llm_memory_editability/grokking_reproduction.py",
    ]
    result = {
        "created_utc": datetime.now(timezone.utc).isoformat(),
        "purpose": "Independent numerical verification of theory Eq. 24-26; no science run changes",
        "device": "cpu",
        "dtype": "torch.float64",
        "python": platform.python_version(),
        "torch": torch.__version__,
        "transformers": transformers.__version__,
        "spec": SPEC,
        "scalar_selections": OUTPUTS,
        "absolute_error_tolerance": TOLERANCE,
        "initial_full_logits_max_abs_errors": forward_errors,
        "checks": checks,
        "source_sha256": {
            str(path.relative_to(ROOT)): hashlib.sha256(path.read_bytes()).hexdigest()
            for path in files
        },
        "passed": True,
        "limits": (
            "Tiny randomly initialized complete ReproductionGPT with causal attention, "
            "GELU MLP, residual paths, LayerNorm and tied input/output embedding. "
            "Kernel identities hold at the shared common-function point. Finite-difference "
            "steps use a fixed scalar squared loss and disposable CPU copies. No claim "
            "about AdamW dynamics, knowledge quality, generalization, sample efficiency, "
            "grokking, scientific training completion or an active GPU run is tested."
        ),
    }
    args.output.write_text(json.dumps(result, indent=2) + "\n")
    print(
        json.dumps(
            {
                "passed": True,
                "output": str(args.output),
                "absolute_error_tolerance": TOLERANCE,
                "initial_forward_errors": forward_errors,
                "kernel_errors": {arm: check["errors"] for arm, check in checks.items()},
                "quadratic_remainder_ratios": {
                    arm: check["plain_sgd_taylor_check"]["remainder_ratio_when_step_halved"]
                    for arm, check in checks.items()
                },
            },
            indent=2,
        )
    )


if __name__ == "__main__":
    main()