"""
# Copyright (c) 2025  PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""

# This file is modified from https://github.com/vllm-project/vllm/collect_env.py

import datetime
import locale
import os
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

import regex as re

from fastdeploy.envs import environment_variables

try:
    import torch

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

try:
    import paddle

    PADDLE_AVAILABLE = True
except (ImportError, NameError, AttributeError, OSError):
    PADDLE_AVAILABLE = False

# System Environment Information
SystemEnv = namedtuple(
    "SystemEnv",
    [
        "torch_version",
        "is_debug_build",
        "cuda_compiled_version",
        "paddle_version",
        "cuda_compiled_version_paddle",
        "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",
        "is_xnnpack_available",
        "cpu_info",
        "fastdeploy_version",  # fastdploy specific field
        "fastdeploy_build_flags",  # fastdploy specific field
        "gpu_topo",  # fastdploy 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
    try:
        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)
        if command == "nvidia-smi topo -m":
            # don't remove the leading whitespace of `nvidia-smi topo -m`
            #   because they are meaningful
            output = output.rstrip()
        else:
            output = output.strip()
        err = raw_err.decode(enc)
        return rc, output, err.strip()

    except FileNotFoundError:
        cmd_str = command if isinstance(command, str) else command[0]
        return 127, "", f"Command not found: {cmd_str}"


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, [conda, "list"])
    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":
        if TORCH_AVAILABLE and torch.cuda.is_available():
            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_fastdeploy_version():
    import pkg_resources

    version = os.environ.get("FASTDEPLOY_VERSION")
    if version:
        return version

    try:
        return pkg_resources.get_distribution("fastdeploy").version
    except:
        pass

    try:
        result = subprocess.run(["pip", "show", "fastdeploy"], capture_output=True, text=True)
        if result.returncode == 0:
            for line in result.stdout.split("\n"):
                if line.startswith("Version:"):
                    return line.split(":")[1].strip()
    except:
        pass

    return "unknown (could not determine version)"


def summarize_fastdeploy_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: {};".format(os.getenv("FD_BUILDING_ARCS", "[]"))


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_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:
        try:
            import torch.backends.xnnpack

            return str(torch.backends.xnnpack.enabled)  # type: ignore[attr-defined]
        except:
            return "N/A"
    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)
        torch_cuda_available_str = str(torch.cuda.is_available())
        cuda_version_str = torch.version.cuda
    else:
        version_str = debug_mode_str = torch_cuda_available_str = cuda_version_str = "N/A"

    if PADDLE_AVAILABLE:
        paddle_version_str = paddle.__version__
        paddle_cuda_available_str = str(torch.cuda.is_available())
        paddle_cuda_version_str = str(paddle.version.cuda())
    else:
        version_str = paddle_cuda_available_str = cuda_version_str = "N/A"

    if torch_cuda_available_str == "True" or paddle_cuda_available_str == "True":
        cuda_available_str = "True"
    else:
        cuda_available_str = "False"

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

    conda_packages = get_conda_packages(run_lambda)

    fastdeploy_version = get_fastdeploy_version()
    fastdeploy_build_flags = summarize_fastdeploy_build_flags()
    gpu_topo = get_gpu_topo(run_lambda)

    return SystemEnv(
        torch_version=version_str,
        is_debug_build=debug_mode_str,
        paddle_version=paddle_version_str,
        cuda_compiled_version_paddle=paddle_cuda_version_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),
        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),
        is_xnnpack_available=is_xnnpack_available(),
        cpu_info=get_cpu_info(run_lambda),
        fastdeploy_version=fastdeploy_version,
        fastdeploy_build_flags=fastdeploy_build_flags,
        gpu_topo=gpu_topo,
        env_vars=get_env_vars(),
    )


env_info_fmt = """
==============================
        System Info
==============================
OS                           : {os}
GCC version                  : {gcc_version}
Clang version                : {clang_version}
CMake version                : {cmake_version}
Libc version                 : {libc_version}

==============================
       PyTorch Info
==============================
PyTorch version              : {torch_version}
Is debug build               : {is_debug_build}
CUDA used to build PyTorch   : {cuda_compiled_version}

==============================
       Paddle Info
==============================
Paddle version              : {paddle_version}
CUDA used to build paddle   : {cuda_compiled_version_paddle}

==============================
      Python Environment
==============================
Python version               : {python_version}
Python platform              : {python_platform}

==============================
       CUDA / GPU Info
==============================
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}
Is XNNPACK available         : {is_xnnpack_available}

==============================
          CPU Info
==============================
{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\n"

env_info_fmt += """
==============================
         FastDeploy Info
==============================
FastDeply Version                 : {fastdeploy_version}
FastDeply Build Flags:
  {fastdeploy_build_flags}
GPU Topology:
  {gpu_topo}

==============================
     Environment Variables
==============================
{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()