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# SPDX-License-Identifier: Apache-2.0
# Adapted from vllm: https://github.com/vllm-project/vllm/blob/v0.7.3/vllm/platforms/cpu.py

import platform

import torch

from trainer.platforms.interface import CpuArchEnum, Platform, PlatformEnum


class CpuPlatform(Platform):
    _enum = PlatformEnum.CPU
    device_name = "CPU"
    device_type = "cpu"
    dispatch_key = "CPU"

    @classmethod
    def get_cpu_architecture(cls) -> CpuArchEnum:
        """Get the CPU architecture."""
        machine = platform.machine().lower()
        if machine in ("x86_64", "amd64", "i386", "i686"):
            return CpuArchEnum.X86
        elif machine in ("arm64", "aarch64"):
            return CpuArchEnum.ARM
        else:
            return CpuArchEnum.UNSPECIFIED

    @classmethod
    def get_device_name(cls, device_id: int = 0) -> str:
        return platform.processor()

    @classmethod
    def get_device_uuid(cls, device_id: int = 0) -> str:
        return platform.machine()

    @classmethod
    def get_device_total_memory(cls, device_id: int = 0) -> int:
        # This is a rough estimate for CPU memory
        # In practice, you might want to use psutil or similar
        return 0

    @classmethod
    def is_async_output_supported(cls, enforce_eager: bool | None) -> bool:
        return True

    @classmethod
    def get_current_memory_usage(cls,
                                 device: torch.types.Device | None = None
                                 ) -> float:
        # For CPU, we can't easily get memory usage without additional libraries
        return 0.0

    @classmethod
    def get_device_communicator_cls(cls) -> str:
        return "trainer.distributed.device_communicators.cpu_communicator.CpuCommunicator"

	# SPDX-License-Identifier: Apache-2.0
	# Adapted from vllm: https://github.com/vllm-project/vllm/blob/v0.7.3/vllm/platforms/cpu.py

	import platform

	import torch

	from trainer.platforms.interface import CpuArchEnum, Platform, PlatformEnum


	class CpuPlatform(Platform):
	_enum = PlatformEnum.CPU
	device_name = "CPU"
	device_type = "cpu"
	dispatch_key = "CPU"

	@classmethod
	def get_cpu_architecture(cls) -> CpuArchEnum:
	"""Get the CPU architecture."""
	machine = platform.machine().lower()
	if machine in ("x86_64", "amd64", "i386", "i686"):
	return CpuArchEnum.X86
	elif machine in ("arm64", "aarch64"):
	return CpuArchEnum.ARM
	else:
	return CpuArchEnum.UNSPECIFIED

	@classmethod
	def get_device_name(cls, device_id: int = 0) -> str:
	return platform.processor()

	@classmethod
	def get_device_uuid(cls, device_id: int = 0) -> str:
	return platform.machine()

	@classmethod
	def get_device_total_memory(cls, device_id: int = 0) -> int:
	# This is a rough estimate for CPU memory
	# In practice, you might want to use psutil or similar
	return 0

	@classmethod
	def is_async_output_supported(cls, enforce_eager: bool \| None) -> bool:
	return True

	@classmethod
	def get_current_memory_usage(cls,
	device: torch.types.Device \| None = None
	) -> float:
	# For CPU, we can't easily get memory usage without additional libraries
	return 0.0

	@classmethod
	def get_device_communicator_cls(cls) -> str:
	return "trainer.distributed.device_communicators.cpu_communicator.CpuCommunicator"