# AOT ID: ['0_inference']
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/ob/cobmpcdvzzwgkde2zq2sq22d4cwqk3fg7cfsx34h6d2vcadz4yty.py
# Topologically Sorted Source Nodes: [eq, int_1, argmax], Original ATen: [aten.eq, aten._to_copy, aten.argmax]
# Source node to ATen node mapping:
#   argmax => argmax
#   eq => eq
#   int_1 => convert_element_type
# Graph fragment:
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Tensor](args = (%view, %arg1_1), kwargs = {})
#   %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%eq, torch.int32), kwargs = {})
#   %argmax : [num_users=1] = call_function[target=torch.ops.aten.argmax.default](args = (%convert_element_type, 1), kwargs = {})
triton_poi_fused__to_copy_argmax_eq_0 = async_compile.triton('triton_poi_fused__to_copy_argmax_eq_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i32', 2: '*i64', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_argmax_eq_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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__to_copy_argmax_eq_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr1 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp6 = tl.load(in_ptr1 + (1))
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp20 = tl.load(in_ptr1 + (2))
    tmp21 = tl.broadcast_to(tmp20, [XBLOCK])
    tmp33 = tl.load(in_ptr1 + (3))
    tmp34 = tl.broadcast_to(tmp33, [XBLOCK])
    tmp3 = tmp2.to(tl.float32)
    tmp4 = tmp0 == tmp3
    tmp5 = tmp4.to(tl.int32)
    tmp8 = tmp7.to(tl.float32)
    tmp9 = tmp0 == tmp8
    tmp10 = tmp9.to(tl.int32)
    tmp11 = tmp5 > tmp10
    tmp12 = tmp5 == tmp10
    tmp13 = tl.full([1], 0, tl.int64)
    tmp14 = tl.full([1], 1, tl.int64)
    tmp15 = tmp13 < tmp14
    tmp16 = tmp12 & tmp15
    tmp17 = tmp11 | tmp16
    tmp18 = tl.where(tmp17, tmp5, tmp10)
    tmp19 = tl.where(tmp17, tmp13, tmp14)
    tmp22 = tmp21.to(tl.float32)
    tmp23 = tmp0 == tmp22
    tmp24 = tmp23.to(tl.int32)
    tmp25 = tmp18 > tmp24
    tmp26 = tmp18 == tmp24
    tmp27 = tl.full([1], 2, tl.int64)
    tmp28 = tmp19 < tmp27
    tmp29 = tmp26 & tmp28
    tmp30 = tmp25 | tmp29
    tmp31 = tl.where(tmp30, tmp18, tmp24)
    tmp32 = tl.where(tmp30, tmp19, tmp27)
    tmp35 = tmp34.to(tl.float32)
    tmp36 = tmp0 == tmp35
    tmp37 = tmp36.to(tl.int32)
    tmp38 = tmp31 > tmp37
    tmp39 = tmp31 == tmp37
    tmp40 = tl.full([1], 3, tl.int64)
    tmp41 = tmp32 < tmp40
    tmp42 = tmp39 & tmp41
    tmp43 = tmp38 | tmp42
    tmp44 = tl.where(tmp43, tmp31, tmp37)
    tmp45 = tl.where(tmp43, tmp32, tmp40)
    tl.store(out_ptr0 + (x0), tmp45, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((256, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [eq, int_1, argmax], Original ATen: [aten.eq, aten._to_copy, aten.argmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__to_copy_argmax_eq_0.run(arg0_1, arg1_1, buf0, 256, grid=grid(256), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf0, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.int32)
    fn = lambda: call([arg0_1, arg1_1])
    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 torch
from abc import ABC


class BaseOperator(ABC):
    """
    Abstract class defining the basic structure for operator implementations in Hummingbird.
    """

    def __init__(self, regression=False, classification=False, transformer=
        False, anomaly_detection=False, **kwargs):
        """
        Args:
            regression: Whether operator is a regression model.
            classification: Whether the operator is a classification model.
            transformer: Whether the operator is a feature transformer.
            anomaly_detection: Whether the operator is an anomaly detection model.
            kwargs: Other keyword arguments.
        """
        super().__init__()
        self.regression = regression
        self.classification = classification
        self.transformer = transformer
        self.anomaly_detection = anomaly_detection


class NumericLabelEncoder(BaseOperator, torch.nn.Module):

    def __init__(self, classes, device):
        super(NumericLabelEncoder, self).__init__(transformer=True)
        self.regression = False
        self.check_tensor = torch.nn.Parameter(torch.IntTensor(classes),
            requires_grad=False)

    def forward(self, x):
        x = x.view(-1, 1)
        return torch.argmax(torch.eq(x, self.check_tensor).int(), dim=1)


def get_inputs():
    return [torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'classes': 4, 'device': 0}]

import torch
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 abc import ABC
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused__to_copy_argmax_eq_0(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp6 = tl.load(in_ptr1 + 1)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp20 = tl.load(in_ptr1 + 2)
    tmp21 = tl.broadcast_to(tmp20, [XBLOCK])
    tmp33 = tl.load(in_ptr1 + 3)
    tmp34 = tl.broadcast_to(tmp33, [XBLOCK])
    tmp3 = tmp2.to(tl.float32)
    tmp4 = tmp0 == tmp3
    tmp5 = tmp4.to(tl.int32)
    tmp8 = tmp7.to(tl.float32)
    tmp9 = tmp0 == tmp8
    tmp10 = tmp9.to(tl.int32)
    tmp11 = tmp5 > tmp10
    tmp12 = tmp5 == tmp10
    tmp13 = tl.full([1], 0, tl.int64)
    tmp14 = tl.full([1], 1, tl.int64)
    tmp15 = tmp13 < tmp14
    tmp16 = tmp12 & tmp15
    tmp17 = tmp11 | tmp16
    tmp18 = tl.where(tmp17, tmp5, tmp10)
    tmp19 = tl.where(tmp17, tmp13, tmp14)
    tmp22 = tmp21.to(tl.float32)
    tmp23 = tmp0 == tmp22
    tmp24 = tmp23.to(tl.int32)
    tmp25 = tmp18 > tmp24
    tmp26 = tmp18 == tmp24
    tmp27 = tl.full([1], 2, tl.int64)
    tmp28 = tmp19 < tmp27
    tmp29 = tmp26 & tmp28
    tmp30 = tmp25 | tmp29
    tmp31 = tl.where(tmp30, tmp18, tmp24)
    tmp32 = tl.where(tmp30, tmp19, tmp27)
    tmp35 = tmp34.to(tl.float32)
    tmp36 = tmp0 == tmp35
    tmp37 = tmp36.to(tl.int32)
    tmp38 = tmp31 > tmp37
    tmp39 = tmp31 == tmp37
    tmp40 = tl.full([1], 3, tl.int64)
    tmp41 = tmp32 < tmp40
    tmp42 = tmp39 & tmp41
    tmp43 = tmp38 | tmp42
    tl.where(tmp43, tmp31, tmp37)
    tmp45 = tl.where(tmp43, tmp32, tmp40)
    tl.store(out_ptr0 + x0, tmp45, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((256,), (1,), torch.int64)
        get_raw_stream(0)
        triton_poi_fused__to_copy_argmax_eq_0[grid(256)](arg0_1, arg1_1,
            buf0, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


class BaseOperator(ABC):
    """
    Abstract class defining the basic structure for operator implementations in Hummingbird.
    """

    def __init__(self, regression=False, classification=False, transformer=
        False, anomaly_detection=False, **kwargs):
        """
        Args:
            regression: Whether operator is a regression model.
            classification: Whether the operator is a classification model.
            transformer: Whether the operator is a feature transformer.
            anomaly_detection: Whether the operator is an anomaly detection model.
            kwargs: Other keyword arguments.
        """
        super().__init__()
        self.regression = regression
        self.classification = classification
        self.transformer = transformer
        self.anomaly_detection = anomaly_detection


class NumericLabelEncoderNew(BaseOperator, torch.nn.Module):

    def __init__(self, classes, device):
        super(NumericLabelEncoderNew, self).__init__(transformer=True)
        self.regression = False
        self.check_tensor = torch.nn.Parameter(torch.IntTensor(classes),
            requires_grad=False)

    def forward(self, input_0):
        arg1_1 = self.check_tensor
        arg0_1 = input_0
        output = call([arg0_1, arg1_1])
        return output[0]
