# SPDX-License-Identifier: Apache-2.0

# ruff: noqa
# code borrowed from https://github.com/pytorch/pytorch/blob/main/torch/utils/collect_env.py

import datetime
import locale
import os
import re
import subprocess
import sys
# Unlike the rest of the PyTorch this file must be python2 compliant.
# This script outputs relevant system environment info
# Run it with `python collect_env.py` or `python -m torch.utils.collect_env`
from collections import namedtuple

from vllm.envs import environment_variables

try:
    import torch
    TORCH_AVAILABLE = True
except (ImportError, NameError, AttributeError, OSError):
    TORCH_AVAILABLE = False

# System Environment Information
SystemEnv = namedtuple(
    'SystemEnv',
    [
        'torch_version',
        'is_debug_build',
        'cuda_compiled_version',
        'gcc_version',
        'clang_version',
        'cmake_version',
        'os',
        'libc_version',
        'python_version',
        'python_platform',
        'is_cuda_available',
        'cuda_runtime_version',
        'cuda_module_loading',
        'nvidia_driver_version',
        'nvidia_gpu_models',
        'cudnn_version',
        'pip_version',  # 'pip' or 'pip3'
        'pip_packages',
        'conda_packages',
        'hip_compiled_version',
        'hip_runtime_version',
        'miopen_runtime_version',
        'caching_allocator_config',
        'is_xnnpack_available',
        'cpu_info',
        'rocm_version',  # vllm specific field
        'neuron_sdk_version',  # vllm specific field
        'vllm_version',  # vllm specific field
        'vllm_build_flags',  # vllm specific field
        'gpu_topo',  # vllm specific field
        'env_vars',
    ])

DEFAULT_CONDA_PATTERNS = {
    "torch",
    "numpy",
    "cudatoolkit",
    "soumith",
    "mkl",
    "magma",
    "triton",
    "optree",
    "nccl",
    "transformers",
    "zmq",
    "nvidia",
    "pynvml",
}

DEFAULT_PIP_PATTERNS = {
    "torch",
    "numpy",
    "mypy",
    "flake8",
    "triton",
    "optree",
    "onnx",
    "nccl",
    "transformers",
    "zmq",
    "nvidia",
    "pynvml",
}


def run(command):
    """Return (return-code, stdout, stderr)."""
    shell = True if type(command) is str else False
    p = subprocess.Popen(command,
                         stdout=subprocess.PIPE,
                         stderr=subprocess.PIPE,
                         shell=shell)
    raw_output, raw_err = p.communicate()
    rc = p.returncode
    if get_platform() == 'win32':
        enc = 'oem'
    else:
        enc = locale.getpreferredencoding()
    output = raw_output.decode(enc)
    err = raw_err.decode(enc)
    return rc, output.strip(), err.strip()


def run_and_read_all(run_lambda, command):
    """Run command using run_lambda; reads and returns entire output if rc is 0."""
    rc, out, _ = run_lambda(command)
    if rc != 0:
        return None
    return out


def run_and_parse_first_match(run_lambda, command, regex):
    """Run command using run_lambda, returns the first regex match if it exists."""
    rc, out, _ = run_lambda(command)
    if rc != 0:
        return None
    match = re.search(regex, out)
    if match is None:
        return None
    return match.group(1)


def run_and_return_first_line(run_lambda, command):
    """Run command using run_lambda and returns first line if output is not empty."""
    rc, out, _ = run_lambda(command)
    if rc != 0:
        return None
    return out.split('\n')[0]


def get_conda_packages(run_lambda, patterns=None):
    if patterns is None:
        patterns = DEFAULT_CONDA_PATTERNS
    conda = os.environ.get('CONDA_EXE', 'conda')
    out = run_and_read_all(run_lambda, "{} list".format(conda))
    if out is None:
        return out

    return "\n".join(line for line in out.splitlines()
                     if not line.startswith("#") and any(name in line
                                                         for name in patterns))


def get_gcc_version(run_lambda):
    return run_and_parse_first_match(run_lambda, 'gcc --version', r'gcc (.*)')


def get_clang_version(run_lambda):
    return run_and_parse_first_match(run_lambda, 'clang --version',
                                     r'clang version (.*)')


def get_cmake_version(run_lambda):
    return run_and_parse_first_match(run_lambda, 'cmake --version',
                                     r'cmake (.*)')


def get_nvidia_driver_version(run_lambda):
    if get_platform() == 'darwin':
        cmd = 'kextstat | grep -i cuda'
        return run_and_parse_first_match(run_lambda, cmd,
                                         r'com[.]nvidia[.]CUDA [(](.*?)[)]')
    smi = get_nvidia_smi()
    return run_and_parse_first_match(run_lambda, smi,
                                     r'Driver Version: (.*?) ')


def get_gpu_info(run_lambda):
    if get_platform() == 'darwin' or (TORCH_AVAILABLE and hasattr(
            torch.version, 'hip') and torch.version.hip is not None):
        if TORCH_AVAILABLE and torch.cuda.is_available():
            if torch.version.hip is not None:
                prop = torch.cuda.get_device_properties(0)
                if hasattr(prop, "gcnArchName"):
                    gcnArch = " ({})".format(prop.gcnArchName)
                else:
                    gcnArch = "NoGCNArchNameOnOldPyTorch"
            else:
                gcnArch = ""
            return torch.cuda.get_device_name(None) + gcnArch
        return None
    smi = get_nvidia_smi()
    uuid_regex = re.compile(r' \(UUID: .+?\)')
    rc, out, _ = run_lambda(smi + ' -L')
    if rc != 0:
        return None
    # Anonymize GPUs by removing their UUID
    return re.sub(uuid_regex, '', out)


def get_running_cuda_version(run_lambda):
    return run_and_parse_first_match(run_lambda, 'nvcc --version',
                                     r'release .+ V(.*)')


def get_cudnn_version(run_lambda):
    """Return a list of libcudnn.so; it's hard to tell which one is being used."""
    if get_platform() == 'win32':
        system_root = os.environ.get('SYSTEMROOT', 'C:\\Windows')
        cuda_path = os.environ.get('CUDA_PATH', "%CUDA_PATH%")
        where_cmd = os.path.join(system_root, 'System32', 'where')
        cudnn_cmd = '{} /R "{}\\bin" cudnn*.dll'.format(where_cmd, cuda_path)
    elif get_platform() == 'darwin':
        # CUDA libraries and drivers can be found in /usr/local/cuda/. See
        # https://docs.nvidia.com/cuda/cuda-installation-guide-mac-os-x/index.html#install
        # https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installmac
        # Use CUDNN_LIBRARY when cudnn library is installed elsewhere.
        cudnn_cmd = 'ls /usr/local/cuda/lib/libcudnn*'
    else:
        cudnn_cmd = 'ldconfig -p | grep libcudnn | rev | cut -d" " -f1 | rev'
    rc, out, _ = run_lambda(cudnn_cmd)
    # find will return 1 if there are permission errors or if not found
    if len(out) == 0 or (rc != 1 and rc != 0):
        l = os.environ.get('CUDNN_LIBRARY')
        if l is not None and os.path.isfile(l):
            return os.path.realpath(l)
        return None
    files_set = set()
    for fn in out.split('\n'):
        fn = os.path.realpath(fn)  # eliminate symbolic links
        if os.path.isfile(fn):
            files_set.add(fn)
    if not files_set:
        return None
    # Alphabetize the result because the order is non-deterministic otherwise
    files = sorted(files_set)
    if len(files) == 1:
        return files[0]
    result = '\n'.join(files)
    return 'Probably one of the following:\n{}'.format(result)


def get_nvidia_smi():
    # Note: nvidia-smi is currently available only on Windows and Linux
    smi = 'nvidia-smi'
    if get_platform() == 'win32':
        system_root = os.environ.get('SYSTEMROOT', 'C:\\Windows')
        program_files_root = os.environ.get('PROGRAMFILES',
                                            'C:\\Program Files')
        legacy_path = os.path.join(program_files_root, 'NVIDIA Corporation',
                                   'NVSMI', smi)
        new_path = os.path.join(system_root, 'System32', smi)
        smis = [new_path, legacy_path]
        for candidate_smi in smis:
            if os.path.exists(candidate_smi):
                smi = '"{}"'.format(candidate_smi)
                break
    return smi


def get_rocm_version(run_lambda):
    """Returns the ROCm version if available, otherwise 'N/A'."""
    return run_and_parse_first_match(run_lambda, 'hipcc --version',
                                     r'HIP version: (\S+)')


def get_neuron_sdk_version(run_lambda):
    # Adapted from your install script
    try:
        result = run_lambda(["neuron-ls"])
        return result if result[0] == 0 else 'N/A'
    except Exception:
        return 'N/A'


def get_vllm_version():
    from vllm import __version__, __version_tuple__

    if __version__ == "dev":
        return "N/A (dev)"

    if len(__version_tuple__) == 4: # dev build
        git_sha = __version_tuple__[-1][1:] # type: ignore
        return f"{__version__} (git sha: {git_sha}"

    return __version__

def summarize_vllm_build_flags():
    # This could be a static method if the flags are constant, or dynamic if you need to check environment variables, etc.
    return 'CUDA Archs: {}; ROCm: {}; Neuron: {}'.format(
        os.environ.get('TORCH_CUDA_ARCH_LIST', 'Not Set'),
        'Enabled' if os.environ.get('ROCM_HOME') else 'Disabled',
        'Enabled' if os.environ.get('NEURON_CORES') else 'Disabled',
    )


def get_gpu_topo(run_lambda):
    output = None

    if get_platform() == 'linux':
        output = run_and_read_all(run_lambda, 'nvidia-smi topo -m')
        if output is None:
            output = run_and_read_all(run_lambda, 'rocm-smi --showtopo')

    return output


# example outputs of CPU infos
#  * linux
#    Architecture:            x86_64
#      CPU op-mode(s):        32-bit, 64-bit
#      Address sizes:         46 bits physical, 48 bits virtual
#      Byte Order:            Little Endian
#    CPU(s):                  128
#      On-line CPU(s) list:   0-127
#    Vendor ID:               GenuineIntel
#      Model name:            Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz
#        CPU family:          6
#        Model:               106
#        Thread(s) per core:  2
#        Core(s) per socket:  32
#        Socket(s):           2
#        Stepping:            6
#        BogoMIPS:            5799.78
#        Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr
#                             sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl
#                             xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16
#                             pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand
#                             hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced
#                             fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap
#                             avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1
#                             xsaves wbnoinvd ida arat avx512vbmi pku ospke avx512_vbmi2 gfni vaes vpclmulqdq
#                             avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid md_clear flush_l1d arch_capabilities
#    Virtualization features:
#      Hypervisor vendor:     KVM
#      Virtualization type:   full
#    Caches (sum of all):
#      L1d:                   3 MiB (64 instances)
#      L1i:                   2 MiB (64 instances)
#      L2:                    80 MiB (64 instances)
#      L3:                    108 MiB (2 instances)
#    NUMA:
#      NUMA node(s):          2
#      NUMA node0 CPU(s):     0-31,64-95
#      NUMA node1 CPU(s):     32-63,96-127
#    Vulnerabilities:
#      Itlb multihit:         Not affected
#      L1tf:                  Not affected
#      Mds:                   Not affected
#      Meltdown:              Not affected
#      Mmio stale data:       Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
#      Retbleed:              Not affected
#      Spec store bypass:     Mitigation; Speculative Store Bypass disabled via prctl and seccomp
#      Spectre v1:            Mitigation; usercopy/swapgs barriers and __user pointer sanitization
#      Spectre v2:            Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
#      Srbds:                 Not affected
#      Tsx async abort:       Not affected
#  * win32
#    Architecture=9
#    CurrentClockSpeed=2900
#    DeviceID=CPU0
#    Family=179
#    L2CacheSize=40960
#    L2CacheSpeed=
#    Manufacturer=GenuineIntel
#    MaxClockSpeed=2900
#    Name=Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz
#    ProcessorType=3
#    Revision=27142
#
#    Architecture=9
#    CurrentClockSpeed=2900
#    DeviceID=CPU1
#    Family=179
#    L2CacheSize=40960
#    L2CacheSpeed=
#    Manufacturer=GenuineIntel
#    MaxClockSpeed=2900
#    Name=Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz
#    ProcessorType=3
#    Revision=27142


def get_cpu_info(run_lambda):
    rc, out, err = 0, '', ''
    if get_platform() == 'linux':
        rc, out, err = run_lambda('lscpu')
    elif get_platform() == 'win32':
        rc, out, err = run_lambda(
            'wmic cpu get Name,Manufacturer,Family,Architecture,ProcessorType,DeviceID, \
        CurrentClockSpeed,MaxClockSpeed,L2CacheSize,L2CacheSpeed,Revision /VALUE'
        )
    elif get_platform() == 'darwin':
        rc, out, err = run_lambda("sysctl -n machdep.cpu.brand_string")
    cpu_info = 'None'
    if rc == 0:
        cpu_info = out
    else:
        cpu_info = err
    return cpu_info


def get_platform():
    if sys.platform.startswith('linux'):
        return 'linux'
    elif sys.platform.startswith('win32'):
        return 'win32'
    elif sys.platform.startswith('cygwin'):
        return 'cygwin'
    elif sys.platform.startswith('darwin'):
        return 'darwin'
    else:
        return sys.platform


def get_mac_version(run_lambda):
    return run_and_parse_first_match(run_lambda, 'sw_vers -productVersion',
                                     r'(.*)')


def get_windows_version(run_lambda):
    system_root = os.environ.get('SYSTEMROOT', 'C:\\Windows')
    wmic_cmd = os.path.join(system_root, 'System32', 'Wbem', 'wmic')
    findstr_cmd = os.path.join(system_root, 'System32', 'findstr')
    return run_and_read_all(
        run_lambda,
        '{} os get Caption | {} /v Caption'.format(wmic_cmd, findstr_cmd))


def get_lsb_version(run_lambda):
    return run_and_parse_first_match(run_lambda, 'lsb_release -a',
                                     r'Description:\t(.*)')


def check_release_file(run_lambda):
    return run_and_parse_first_match(run_lambda, 'cat /etc/*-release',
                                     r'PRETTY_NAME="(.*)"')


def get_os(run_lambda):
    from platform import machine
    platform = get_platform()

    if platform == 'win32' or platform == 'cygwin':
        return get_windows_version(run_lambda)

    if platform == 'darwin':
        version = get_mac_version(run_lambda)
        if version is None:
            return None
        return 'macOS {} ({})'.format(version, machine())

    if platform == 'linux':
        # Ubuntu/Debian based
        desc = get_lsb_version(run_lambda)
        if desc is not None:
            return '{} ({})'.format(desc, machine())

        # Try reading /etc/*-release
        desc = check_release_file(run_lambda)
        if desc is not None:
            return '{} ({})'.format(desc, machine())

        return '{} ({})'.format(platform, machine())

    # Unknown platform
    return platform


def get_python_platform():
    import platform
    return platform.platform()


def get_libc_version():
    import platform
    if get_platform() != 'linux':
        return 'N/A'
    return '-'.join(platform.libc_ver())


def get_pip_packages(run_lambda, patterns=None):
    """Return `pip list` output. Note: will also find conda-installed pytorch and numpy packages."""
    if patterns is None:
        patterns = DEFAULT_PIP_PATTERNS

    def run_with_pip():
        try:
            import importlib.util
            pip_spec = importlib.util.find_spec('pip')
            pip_available = pip_spec is not None
        except ImportError:
            pip_available = False

        if pip_available:
            cmd = [sys.executable, '-mpip', 'list', '--format=freeze']
        elif os.environ.get("UV") is not None:
            print("uv is set")
            cmd = ["uv", "pip", "list", "--format=freeze"]
        else:
            raise RuntimeError("Could not collect pip list output (pip or uv module not available)")

        out = run_and_read_all(run_lambda, cmd)
        return "\n".join(line for line in out.splitlines()
                         if any(name in line for name in patterns))

    pip_version = 'pip3' if sys.version[0] == '3' else 'pip'
    out = run_with_pip()
    return pip_version, out


def get_cachingallocator_config():
    ca_config = os.environ.get('PYTORCH_CUDA_ALLOC_CONF', '')
    return ca_config


def get_cuda_module_loading_config():
    if TORCH_AVAILABLE and torch.cuda.is_available():
        torch.cuda.init()
        config = os.environ.get('CUDA_MODULE_LOADING', '')
        return config
    else:
        return "N/A"


def is_xnnpack_available():
    if TORCH_AVAILABLE:
        import torch.backends.xnnpack
        return str(
            torch.backends.xnnpack.enabled)  # type: ignore[attr-defined]
    else:
        return "N/A"

def get_env_vars():
    env_vars = ''
    secret_terms=('secret', 'token', 'api', 'access', 'password')
    report_prefix = ("TORCH", "NCCL", "PYTORCH",
                     "CUDA", "CUBLAS", "CUDNN",
                     "OMP_", "MKL_",
                     "NVIDIA")
    for k, v in os.environ.items():
        if any(term in k.lower() for term in secret_terms):
            continue
        if k in environment_variables:
            env_vars = env_vars + "{}={}".format(k, v) + "\n"
        if k.startswith(report_prefix):
            env_vars = env_vars + "{}={}".format(k, v) + "\n"

    return env_vars

def get_env_info():
    run_lambda = run
    pip_version, pip_list_output = get_pip_packages(run_lambda)

    if TORCH_AVAILABLE:
        version_str = torch.__version__
        debug_mode_str = str(torch.version.debug)
        cuda_available_str = str(torch.cuda.is_available())
        cuda_version_str = torch.version.cuda
        if not hasattr(torch.version,
                       'hip') or torch.version.hip is None:  # cuda version
            hip_compiled_version = hip_runtime_version = miopen_runtime_version = 'N/A'
        else:  # HIP version

            def get_version_or_na(cfg, prefix):
                _lst = [s.rsplit(None, 1)[-1] for s in cfg if prefix in s]
                return _lst[0] if _lst else 'N/A'

            cfg = torch._C._show_config().split('\n')
            hip_runtime_version = get_version_or_na(cfg, 'HIP Runtime')
            miopen_runtime_version = get_version_or_na(cfg, 'MIOpen')
            cuda_version_str = 'N/A'
            hip_compiled_version = torch.version.hip
    else:
        version_str = debug_mode_str = cuda_available_str = cuda_version_str = 'N/A'
        hip_compiled_version = hip_runtime_version = miopen_runtime_version = 'N/A'

    sys_version = sys.version.replace("\n", " ")

    conda_packages = get_conda_packages(run_lambda)

    rocm_version = get_rocm_version(run_lambda)
    neuron_sdk_version = get_neuron_sdk_version(run_lambda)
    vllm_version = get_vllm_version()
    vllm_build_flags = summarize_vllm_build_flags()
    gpu_topo = get_gpu_topo(run_lambda)

    return SystemEnv(
        torch_version=version_str,
        is_debug_build=debug_mode_str,
        python_version='{} ({}-bit runtime)'.format(
            sys_version,
            sys.maxsize.bit_length() + 1),
        python_platform=get_python_platform(),
        is_cuda_available=cuda_available_str,
        cuda_compiled_version=cuda_version_str,
        cuda_runtime_version=get_running_cuda_version(run_lambda),
        cuda_module_loading=get_cuda_module_loading_config(),
        nvidia_gpu_models=get_gpu_info(run_lambda),
        nvidia_driver_version=get_nvidia_driver_version(run_lambda),
        cudnn_version=get_cudnn_version(run_lambda),
        hip_compiled_version=hip_compiled_version,
        hip_runtime_version=hip_runtime_version,
        miopen_runtime_version=miopen_runtime_version,
        pip_version=pip_version,
        pip_packages=pip_list_output,
        conda_packages=conda_packages,
        os=get_os(run_lambda),
        libc_version=get_libc_version(),
        gcc_version=get_gcc_version(run_lambda),
        clang_version=get_clang_version(run_lambda),
        cmake_version=get_cmake_version(run_lambda),
        caching_allocator_config=get_cachingallocator_config(),
        is_xnnpack_available=is_xnnpack_available(),
        cpu_info=get_cpu_info(run_lambda),
        rocm_version=rocm_version,
        neuron_sdk_version=neuron_sdk_version,
        vllm_version=vllm_version,
        vllm_build_flags=vllm_build_flags,
        gpu_topo=gpu_topo,
        env_vars=get_env_vars(),
    )


env_info_fmt = """
PyTorch version: {torch_version}
Is debug build: {is_debug_build}
CUDA used to build PyTorch: {cuda_compiled_version}
ROCM used to build PyTorch: {hip_compiled_version}

OS: {os}
GCC version: {gcc_version}
Clang version: {clang_version}
CMake version: {cmake_version}
Libc version: {libc_version}

Python version: {python_version}
Python platform: {python_platform}
Is CUDA available: {is_cuda_available}
CUDA runtime version: {cuda_runtime_version}
CUDA_MODULE_LOADING set to: {cuda_module_loading}
GPU models and configuration: {nvidia_gpu_models}
Nvidia driver version: {nvidia_driver_version}
cuDNN version: {cudnn_version}
HIP runtime version: {hip_runtime_version}
MIOpen runtime version: {miopen_runtime_version}
Is XNNPACK available: {is_xnnpack_available}

CPU:
{cpu_info}

Versions of relevant libraries:
{pip_packages}
{conda_packages}
""".strip()

# both the above code and the following code use `strip()` to
# remove leading/trailing whitespaces, so we need to add a newline
# in between to separate the two sections
env_info_fmt += "\n"

env_info_fmt += """
ROCM Version: {rocm_version}
Neuron SDK Version: {neuron_sdk_version}
vLLM Version: {vllm_version}
vLLM Build Flags:
{vllm_build_flags}
GPU Topology:
{gpu_topo}

{env_vars}
""".strip()


def pretty_str(envinfo):

    def replace_nones(dct, replacement='Could not collect'):
        for key in dct.keys():
            if dct[key] is not None:
                continue
            dct[key] = replacement
        return dct

    def replace_bools(dct, true='Yes', false='No'):
        for key in dct.keys():
            if dct[key] is True:
                dct[key] = true
            elif dct[key] is False:
                dct[key] = false
        return dct

    def prepend(text, tag='[prepend]'):
        lines = text.split('\n')
        updated_lines = [tag + line for line in lines]
        return '\n'.join(updated_lines)

    def replace_if_empty(text, replacement='No relevant packages'):
        if text is not None and len(text) == 0:
            return replacement
        return text

    def maybe_start_on_next_line(string):
        # If `string` is multiline, prepend a \n to it.
        if string is not None and len(string.split('\n')) > 1:
            return '\n{}\n'.format(string)
        return string

    mutable_dict = envinfo._asdict()

    # If nvidia_gpu_models is multiline, start on the next line
    mutable_dict['nvidia_gpu_models'] = \
        maybe_start_on_next_line(envinfo.nvidia_gpu_models)

    # If the machine doesn't have CUDA, report some fields as 'No CUDA'
    dynamic_cuda_fields = [
        'cuda_runtime_version',
        'nvidia_gpu_models',
        'nvidia_driver_version',
    ]
    all_cuda_fields = dynamic_cuda_fields + ['cudnn_version']
    all_dynamic_cuda_fields_missing = all(mutable_dict[field] is None
                                          for field in dynamic_cuda_fields)
    if TORCH_AVAILABLE and not torch.cuda.is_available(
    ) and all_dynamic_cuda_fields_missing:
        for field in all_cuda_fields:
            mutable_dict[field] = 'No CUDA'
        if envinfo.cuda_compiled_version is None:
            mutable_dict['cuda_compiled_version'] = 'None'

    # Replace True with Yes, False with No
    mutable_dict = replace_bools(mutable_dict)

    # Replace all None objects with 'Could not collect'
    mutable_dict = replace_nones(mutable_dict)

    # If either of these are '', replace with 'No relevant packages'
    mutable_dict['pip_packages'] = replace_if_empty(
        mutable_dict['pip_packages'])
    mutable_dict['conda_packages'] = replace_if_empty(
        mutable_dict['conda_packages'])

    # Tag conda and pip packages with a prefix
    # If they were previously None, they'll show up as ie '[conda] Could not collect'
    if mutable_dict['pip_packages']:
        mutable_dict['pip_packages'] = prepend(
            mutable_dict['pip_packages'], '[{}] '.format(envinfo.pip_version))
    if mutable_dict['conda_packages']:
        mutable_dict['conda_packages'] = prepend(
            mutable_dict['conda_packages'], '[conda] ')
    mutable_dict['cpu_info'] = envinfo.cpu_info
    return env_info_fmt.format(**mutable_dict)


def get_pretty_env_info():
    return pretty_str(get_env_info())


def main():
    print("Collecting environment information...")
    output = get_pretty_env_info()
    print(output)

    if TORCH_AVAILABLE and hasattr(torch, 'utils') and hasattr(
            torch.utils, '_crash_handler'):
        minidump_dir = torch.utils._crash_handler.DEFAULT_MINIDUMP_DIR
        if sys.platform == "linux" and os.path.exists(minidump_dir):
            dumps = [
                os.path.join(minidump_dir, dump)
                for dump in os.listdir(minidump_dir)
            ]
            latest = max(dumps, key=os.path.getctime)
            ctime = os.path.getctime(latest)
            creation_time = datetime.datetime.fromtimestamp(ctime).strftime(
                '%Y-%m-%d %H:%M:%S')
            msg = "\n*** Detected a minidump at {} created on {}, ".format(latest, creation_time) + \
                  "if this is related to your bug please include it when you file a report ***"
            print(msg, file=sys.stderr)


if __name__ == '__main__':
    main()
