# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/ae/caesewssr72tnfzylftwhqzythnvjdgdne5dcv6beyedinmq6yuo.py
# Topologically Sorted Source Nodes: [seq_logits, loss], Original ATen: [aten.index, aten._log_softmax]
# Source node to ATen node mapping:
#   loss => amax, exp, log, sub, sum_1
#   seq_logits => index
# Graph fragment:
#   %index : [num_users=2] = call_function[target=torch.ops.aten.index.Tensor](args = (%view_1, [%primals_4]), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%index, [1], True), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%index, %amax), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_1,), kwargs = {})
triton_poi_fused__log_softmax_index_0 = async_compile.triton('triton_poi_fused__log_softmax_index_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__log_softmax_index_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__log_softmax_index_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16)
    x0 = xindex % 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert(((0 <= tmp4) & (tmp4 < 4)) | ~(xmask), "index out of bounds: 0 <= tmp4 < 4")
    tmp6 = tl.load(in_ptr1 + (x0 + (64*tmp4)), xmask)
    tmp7 = tl.load(in_ptr1 + (16 + x0 + (64*tmp4)), xmask)
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = tl.load(in_ptr1 + (32 + x0 + (64*tmp4)), xmask)
    tmp10 = triton_helpers.maximum(tmp8, tmp9)
    tmp11 = tl.load(in_ptr1 + (48 + x0 + (64*tmp4)), xmask)
    tmp12 = triton_helpers.maximum(tmp10, tmp11)
    tmp13 = tmp6 - tmp12
    tmp14 = tl_math.exp(tmp13)
    tmp15 = tmp7 - tmp12
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp14 + tmp16
    tmp18 = tmp9 - tmp12
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp17 + tmp19
    tmp21 = tmp11 - tmp12
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp20 + tmp22
    tmp24 = tl_math.log(tmp23)
    tl.store(out_ptr0 + (x2), tmp12, xmask)
    tl.store(out_ptr1 + (x2), tmp24, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ko/ckohrstaa3faloe7li73sfu47oooq46wq2rhtsvewwxg7axvq7vp.py
# Topologically Sorted Source Nodes: [seq_logits, seq_labels, loss], Original ATen: [aten.index, aten._log_softmax, aten.mul, aten.sum, aten.neg, aten.div]
# Source node to ATen node mapping:
#   loss => amax, div, exp, log, mul, neg, sub, sub_1, sum_1, sum_2
#   seq_labels => index_1
#   seq_logits => index
# Graph fragment:
#   %index : [num_users=2] = call_function[target=torch.ops.aten.index.Tensor](args = (%view_1, [%primals_4]), kwargs = {})
#   %index_1 : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%primals_5, [%primals_4]), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%index, [1], True), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%index, %amax), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_1,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %log), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %index_1), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul,), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%sum_2,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Scalar](args = (%neg, 64), kwargs = {})
triton_per_fused__log_softmax_div_index_mul_neg_sum_1 = async_compile.triton('triton_per_fused__log_softmax_div_index_mul_neg_sum_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.persistent_reduction(
    size_hints=[1, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i64', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {6: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 7), equal_to_1=(6,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__log_softmax_div_index_mul_neg_sum_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 3, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__log_softmax_div_index_mul_neg_sum_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r2 = (rindex // 64)
    r3 = rindex % 64
    r0 = rindex % 16
    tmp0 = tl.load(in_ptr0 + (r2), None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr2 + (r0 + (16*r2)), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr3 + (r0 + (16*r2)), None, eviction_policy='evict_last')
    tmp1 = tl.full([RBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 4), "index out of bounds: 0 <= tmp4 < 4")
    tmp6 = tl.load(in_ptr1 + (r3 + (64*tmp4)), None)
    tmp8 = tmp6 - tmp7
    tmp10 = tmp8 - tmp9
    tmp11 = tl.load(in_ptr4 + (r3 + (64*tmp4)), None)
    tmp12 = tmp10 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = -tmp15
    tmp17 = 0.015625
    tmp18 = tmp16 * tmp17
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp18, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, ), (1, ))
    assert_size_stride(primals_5, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [logits], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [seq_logits, loss], Original ATen: [aten.index, aten._log_softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__log_softmax_index_0.run(primals_4, buf0, buf1, buf2, 64, grid=grid(64), stream=stream0)
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [seq_logits, seq_labels, loss], Original ATen: [aten.index, aten._log_softmax, aten.mul, aten.sum, aten.neg, aten.div]
        triton_per_fused__log_softmax_div_index_mul_neg_sum_1.run(buf4, primals_4, buf0, buf1, buf2, primals_5, 1, 256, grid=grid(1), stream=stream0)
        del buf1
        del buf2
    return (buf4, primals_4, primals_5, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf0, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.int64)
    primals_5 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import math
import torch
from torch import nn
import torch.nn.functional as F


def get_mask_subset_with_prob(mask, prob):
    batch, seq_len, device = *mask.shape, mask.device
    max_masked = math.ceil(prob * seq_len)
    num_tokens = mask.sum(dim=-1, keepdim=True)
    mask_excess = mask.cumsum(dim=-1) > (num_tokens * prob).ceil()
    mask_excess = mask_excess[:, :max_masked]
    rand = torch.rand((batch, seq_len), device=device).masked_fill(~mask, -
        1000000000.0)
    _, sampled_indices = rand.topk(max_masked, dim=-1)
    sampled_indices = (sampled_indices + 1).masked_fill_(mask_excess, 0)
    new_mask = torch.zeros((batch, seq_len + 1), device=device)
    new_mask.scatter_(-1, sampled_indices, 1)
    return new_mask[:, 1:].bool()


class MLM(nn.Module):

    def __init__(self, dim, num_tokens, mask_id, mask_prob=0.15,
        random_replace_token_prob=0.1, keep_token_same_prob=0.1,
        exclude_token_ids=(0,)):
        super().__init__()
        self.to_logits = nn.Linear(dim, num_tokens)
        self.mask_id = mask_id
        self.mask_prob = mask_prob
        self.exclude_token_ids = exclude_token_ids
        self.keep_token_same_prob = keep_token_same_prob
        self.random_replace_token_prob = random_replace_token_prob

    def noise(self, seq, mask):
        num_msa = seq.shape[1]
        seq = rearrange(seq, 'b n ... -> (b n) ...')
        mask = rearrange(mask, 'b n ... -> (b n) ...')
        excluded_tokens_mask = mask
        for token_id in self.exclude_token_ids:
            excluded_tokens_mask = excluded_tokens_mask & (seq != token_id)
        mlm_mask = get_mask_subset_with_prob(excluded_tokens_mask, self.
            mask_prob)
        get_mask_subset_with_prob(mlm_mask, 1.0 - self.keep_token_same_prob)
        seq = seq.masked_fill(mlm_mask, self.mask_id)
        random_replace_token_prob_mask = get_mask_subset_with_prob(mlm_mask,
            (1 - self.keep_token_same_prob) * self.random_replace_token_prob)
        random_tokens = torch.randint(1, constants.NUM_AMINO_ACIDS, seq.shape)
        for token_id in self.exclude_token_ids:
            random_replace_token_prob_mask = random_replace_token_prob_mask & (
                random_tokens != token_id)
        noised_seq = torch.where(random_replace_token_prob_mask,
            random_tokens, seq)
        noised_seq = rearrange(noised_seq, '(b n) ... -> b n ...', n=num_msa)
        mlm_mask = rearrange(mlm_mask, '(b n) ... -> b n ...', n=num_msa)
        return noised_seq, mlm_mask

    def forward(self, seq_embed, original_seq, mask):
        logits = self.to_logits(seq_embed)
        seq_logits = logits[mask]
        seq_labels = original_seq[mask]
        loss = F.cross_entropy(seq_logits, seq_labels, reduction='mean')
        return loss


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.ones(
        [4], dtype=torch.int64)]


def get_init_inputs():
    return [[], {'dim': 4, 'num_tokens': 4, 'mask_id': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused__log_softmax_index_0(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16
    x0 = xindex % 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 4) | ~xmask,
        'index out of bounds: 0 <= tmp4 < 4')
    tmp6 = tl.load(in_ptr1 + (x0 + 64 * tmp4), xmask)
    tmp7 = tl.load(in_ptr1 + (16 + x0 + 64 * tmp4), xmask)
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = tl.load(in_ptr1 + (32 + x0 + 64 * tmp4), xmask)
    tmp10 = triton_helpers.maximum(tmp8, tmp9)
    tmp11 = tl.load(in_ptr1 + (48 + x0 + 64 * tmp4), xmask)
    tmp12 = triton_helpers.maximum(tmp10, tmp11)
    tmp13 = tmp6 - tmp12
    tmp14 = tl_math.exp(tmp13)
    tmp15 = tmp7 - tmp12
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp14 + tmp16
    tmp18 = tmp9 - tmp12
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp17 + tmp19
    tmp21 = tmp11 - tmp12
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp20 + tmp22
    tmp24 = tl_math.log(tmp23)
    tl.store(out_ptr0 + x2, tmp12, xmask)
    tl.store(out_ptr1 + x2, tmp24, xmask)


@triton.jit
def triton_per_fused__log_softmax_div_index_mul_neg_sum_1(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r2 = rindex // 64
    r3 = rindex % 64
    r0 = rindex % 16
    tmp0 = tl.load(in_ptr0 + r2, None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr2 + (r0 + 16 * r2), None, eviction_policy='evict_last'
        )
    tmp9 = tl.load(in_ptr3 + (r0 + 16 * r2), None, eviction_policy='evict_last'
        )
    tmp1 = tl.full([RBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 4),
        'index out of bounds: 0 <= tmp4 < 4')
    tmp6 = tl.load(in_ptr1 + (r3 + 64 * tmp4), None)
    tmp8 = tmp6 - tmp7
    tmp10 = tmp8 - tmp9
    tmp11 = tl.load(in_ptr4 + (r3 + 64 * tmp4), None)
    tmp12 = tmp10 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = -tmp15
    tmp17 = 0.015625
    tmp18 = tmp16 * tmp17
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp18, None)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax_index_0[grid(64)](primals_4, buf0,
            buf1, buf2, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3
        del buf3
        triton_per_fused__log_softmax_div_index_mul_neg_sum_1[grid(1)](buf4,
            primals_4, buf0, buf1, buf2, primals_5, 1, 256, num_warps=2,
            num_stages=1)
        del buf1
        del buf2
    return buf4, primals_4, primals_5, reinterpret_tensor(primals_3, (64, 4
        ), (4, 1), 0), buf0


def get_mask_subset_with_prob(mask, prob):
    batch, seq_len, device = *mask.shape, mask.device
    max_masked = math.ceil(prob * seq_len)
    num_tokens = mask.sum(dim=-1, keepdim=True)
    mask_excess = mask.cumsum(dim=-1) > (num_tokens * prob).ceil()
    mask_excess = mask_excess[:, :max_masked]
    rand = torch.rand((batch, seq_len), device=device).masked_fill(~mask, -
        1000000000.0)
    _, sampled_indices = rand.topk(max_masked, dim=-1)
    sampled_indices = (sampled_indices + 1).masked_fill_(mask_excess, 0)
    new_mask = torch.zeros((batch, seq_len + 1), device=device)
    new_mask.scatter_(-1, sampled_indices, 1)
    return new_mask[:, 1:].bool()


class MLMNew(nn.Module):

    def __init__(self, dim, num_tokens, mask_id, mask_prob=0.15,
        random_replace_token_prob=0.1, keep_token_same_prob=0.1,
        exclude_token_ids=(0,)):
        super().__init__()
        self.to_logits = nn.Linear(dim, num_tokens)
        self.mask_id = mask_id
        self.mask_prob = mask_prob
        self.exclude_token_ids = exclude_token_ids
        self.keep_token_same_prob = keep_token_same_prob
        self.random_replace_token_prob = random_replace_token_prob

    def noise(self, seq, mask):
        num_msa = seq.shape[1]
        seq = rearrange(seq, 'b n ... -> (b n) ...')
        mask = rearrange(mask, 'b n ... -> (b n) ...')
        excluded_tokens_mask = mask
        for token_id in self.exclude_token_ids:
            excluded_tokens_mask = excluded_tokens_mask & (seq != token_id)
        mlm_mask = get_mask_subset_with_prob(excluded_tokens_mask, self.
            mask_prob)
        get_mask_subset_with_prob(mlm_mask, 1.0 - self.keep_token_same_prob)
        seq = seq.masked_fill(mlm_mask, self.mask_id)
        random_replace_token_prob_mask = get_mask_subset_with_prob(mlm_mask,
            (1 - self.keep_token_same_prob) * self.random_replace_token_prob)
        random_tokens = torch.randint(1, constants.NUM_AMINO_ACIDS, seq.shape)
        for token_id in self.exclude_token_ids:
            random_replace_token_prob_mask = random_replace_token_prob_mask & (
                random_tokens != token_id)
        noised_seq = torch.where(random_replace_token_prob_mask,
            random_tokens, seq)
        noised_seq = rearrange(noised_seq, '(b n) ... -> b n ...', n=num_msa)
        mlm_mask = rearrange(mlm_mask, '(b n) ... -> b n ...', n=num_msa)
        return noised_seq, mlm_mask

    def forward(self, input_0, input_1, input_2):
        primals_1 = self.to_logits.weight
        primals_2 = self.to_logits.bias
        primals_3 = input_0
        primals_5 = input_1
        primals_4 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
