# Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.

"""Transformer Engine bindings for pyTorch"""

# pylint: disable=wrong-import-position

import functools

import torch

from transformer_engine.common import load_framework_extension
from transformer_engine.pytorch.torch_version import torch_version

assert torch_version() >= (2, 1), f"Minimum torch version 2.1 required. Found {torch_version()}."

load_framework_extension("torch")
from transformer_engine.pytorch.module import LayerNormLinear
from transformer_engine.pytorch.module import Linear
from transformer_engine.pytorch.module import LayerNormMLP
from transformer_engine.pytorch.module import LayerNorm
from transformer_engine.pytorch.module import RMSNorm
from transformer_engine.pytorch.module import GroupedLinear, BatchedLinear
from transformer_engine.pytorch.module import Fp8Padding, Fp8Unpadding
from transformer_engine.pytorch.module import initialize_ub
from transformer_engine.pytorch.module import destroy_ub
from transformer_engine.pytorch.module import UserBufferQuantizationMode
from transformer_engine.pytorch.attention import DotProductAttention
from transformer_engine.pytorch.attention import MultiheadAttention
from transformer_engine.pytorch.attention import InferenceParams
from transformer_engine.pytorch.attention import RotaryPositionEmbedding
from transformer_engine.pytorch.transformer import TransformerLayer
from transformer_engine.pytorch.permutation import (
    moe_permute,
    moe_permute_with_probs,
    moe_permute_and_pad_with_probs,
    moe_unpermute,
    moe_sort_chunks_by_index,
    moe_sort_chunks_by_index_with_probs,
)
from transformer_engine.pytorch.quantization import fp8_autocast
from transformer_engine.pytorch.quantization import fp8_model_init
from transformer_engine.pytorch.quantization import autocast
from transformer_engine.pytorch.quantization import quantized_model_init
from transformer_engine.pytorch.quantization import is_fp8_available
from transformer_engine.pytorch.quantization import is_mxfp8_available
from transformer_engine.pytorch.quantization import is_fp8_block_scaling_available
from transformer_engine.pytorch.quantization import is_nvfp4_available
from transformer_engine.pytorch.quantization import get_default_recipe
from transformer_engine.pytorch.utils import get_cudnn_version
from transformer_engine.pytorch.utils import get_device_compute_capability
from transformer_engine.pytorch.utils import is_bf16_available
from transformer_engine.pytorch.graph import make_graphed_callables
from transformer_engine.pytorch.distributed import checkpoint
from transformer_engine.pytorch.distributed import CudaRNGStatesTracker
from transformer_engine.pytorch.cpu_offload import get_cpu_offload_context
from transformer_engine.pytorch import ops
from transformer_engine.pytorch import optimizers
from transformer_engine.pytorch.export import onnx_export
from transformer_engine.pytorch.cross_entropy import parallel_cross_entropy
from transformer_engine.pytorch.quantized_tensor import QuantizedTensorStorage
from transformer_engine.pytorch.quantized_tensor import QuantizedTensor
from transformer_engine.pytorch.quantized_tensor import Quantizer
from transformer_engine.pytorch.quantized_tensor import prepare_for_saving
from transformer_engine.pytorch.quantized_tensor import restore_from_saved
from transformer_engine.pytorch.tensor import Float8Quantizer
from transformer_engine.pytorch.tensor import Float8CurrentScalingQuantizer
from transformer_engine.pytorch.tensor import MXFP8Quantizer
from transformer_engine.pytorch.tensor import Float8BlockQuantizer
from transformer_engine.pytorch.tensor import NVFP4Quantizer
from transformer_engine.pytorch.tensor import Float8TensorStorage
from transformer_engine.pytorch.tensor import MXFP8TensorStorage
from transformer_engine.pytorch.tensor import Float8BlockwiseQTensorStorage
from transformer_engine.pytorch.tensor import NVFP4TensorStorage
from transformer_engine.pytorch.tensor import Float8Tensor
from transformer_engine.pytorch.tensor import MXFP8Tensor
from transformer_engine.pytorch.tensor import Float8BlockwiseQTensor
from transformer_engine.pytorch.tensor import NVFP4Tensor

try:
    torch._dynamo.config.error_on_nested_jit_trace = False
except AttributeError:
    pass  # error_on_nested_jit_trace was added in PyTorch 2.2.0
