from __future__ import annotations
from functools import partial
import os
import re
import sys
import logging
import warnings
import urllib3
from modules import timer, errors


initialized = False
errors.install()
logging.getLogger("DeepSpeed").disabled = True
timer.startup.record("loader")
errors.log.debug('Initializing: libraries')

np = None
try:
    os.environ.setdefault('NEP50_DISABLE_WARNING', '1')
    import numpy as np # pylint: disable=W0611,C0411
    import numpy.random # pylint: disable=W0611,C0411 # this causes failure if numpy version changed
    def obj2sctype(obj):
        return np.dtype(obj).type
    if np.__version__.startswith('2.'): # monkeypatch for np==1.2 compatibility
        np.obj2sctype = obj2sctype # noqa: NPY201
        np.bool8 = np.bool
        np.float_ = np.float64 # noqa: NPY201
        def dummy_npwarn_decorator_factory():
            def npwarn_decorator(x):
                return x
            return npwarn_decorator
        np._no_nep50_warning = getattr(np, '_no_nep50_warning', dummy_npwarn_decorator_factory) # pylint: disable=protected-access
except Exception as e:
    errors.log.error(f'Loader: numpy=={np.__version__ if np is not None else None} {e}')
    errors.log.error('Please restart the app to fix this issue')
    sys.exit(1)
timer.startup.record("numpy")

scipy = None
try:
    import scipy # pylint: disable=W0611,C0411
except Exception as e:
    errors.log.error(f'Loader: scipy=={scipy.__version__ if scipy is not None else None} {e}')
    errors.log.error('Please restart the app to fix this issue')
    sys.exit(1)
timer.startup.record("scipy")

import torch # pylint: disable=C0411
if torch.__version__.startswith('2.5.0'):
    errors.log.warning(f'Disabling cuDNN for SDP on torch={torch.__version__}')
    torch.backends.cuda.enable_cudnn_sdp(False)
try:
    import intel_extension_for_pytorch as ipex # pylint: disable=import-error,unused-import
    errors.log.debug(f'Load IPEX=={ipex.__version__}')
except Exception:
    pass
try:
    import torch.distributed.distributed_c10d as _c10d # pylint: disable=unused-import,ungrouped-imports
except Exception:
    errors.log.warning('Loader: torch is not built with distributed support')


urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
warnings.filterwarnings(action="ignore", category=UserWarning, module="torchvision")
torchvision = None
try:
    import torchvision # pylint: disable=W0611,C0411
    import pytorch_lightning # pytorch_lightning should be imported after torch, but it re-enables warnings on import so import once to disable them # pylint: disable=W0611,C0411
except Exception as e:
    errors.log.error(f'Loader: torchvision=={torchvision.__version__ if "torchvision" in sys.modules else None} {e}')
    if '_no_nep' in str(e):
        errors.log.error('Loaded versions of packaged are not compatible')
        errors.log.error('Please restart the app to fix this issue')
logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
logging.getLogger("pytorch_lightning").disabled = True
warnings.filterwarnings(action="ignore", category=DeprecationWarning)
warnings.filterwarnings(action="ignore", category=FutureWarning)
warnings.filterwarnings(action="ignore", category=UserWarning, module="torchvision")
warnings.filterwarnings(action="ignore", message="numpy.dtype size changed")
try:
    import torch._logging # pylint: disable=ungrouped-imports
    torch._logging._internal.DEFAULT_LOG_LEVEL = logging.ERROR # pylint: disable=protected-access
    torch._logging.set_logs(all=logging.ERROR, bytecode=False, aot_graphs=False, aot_joint_graph=False, ddp_graphs=False, graph=False, graph_code=False, graph_breaks=False, graph_sizes=False, guards=False, recompiles=False, recompiles_verbose=False, trace_source=False, trace_call=False, trace_bytecode=False, output_code=False, kernel_code=False, schedule=False, perf_hints=False, post_grad_graphs=False, onnx_diagnostics=False, fusion=False, overlap=False, export=None, modules=None, cudagraphs=False, sym_node=False, compiled_autograd_verbose=False) # pylint: disable=protected-access
    import torch._dynamo
    torch._dynamo.config.verbose = False # pylint: disable=protected-access
    torch._dynamo.config.suppress_errors = True # pylint: disable=protected-access
except Exception as e:
    errors.log.warning(f'Torch logging: {e}')
if ".dev" in torch.__version__ or "+git" in torch.__version__:
    torch.__long_version__ = torch.__version__
    torch.__version__ = re.search(r'[\d.]+[\d]', torch.__version__).group(0)
timer.startup.record("torch")

try:
    import bitsandbytes # pylint: disable=W0611,C0411
    _bnb = True
except Exception:
    _bnb = False
timer.startup.record("bnb")

import huggingface_hub # pylint: disable=W0611,C0411
logging.getLogger("huggingface_hub.file_download").setLevel(logging.ERROR)
if huggingface_hub.__version__.startswith('0.'):
    huggingface_hub.is_offline_mode = lambda: False
timer.startup.record("hfhub")

import accelerate # pylint: disable=W0611,C0411
timer.startup.record("accelerate")

import pydantic # pylint: disable=W0611,C0411
timer.startup.record("pydantic")

import transformers # pylint: disable=W0611,C0411
from transformers import logging as transformers_logging # pylint: disable=W0611,C0411
transformers_logging.set_verbosity_error()
timer.startup.record("transformers")

try:
    import onnxruntime # pylint: disable=W0611,C0411
    onnxruntime.set_default_logger_severity(4)
    onnxruntime.set_default_logger_verbosity(1)
    onnxruntime.disable_telemetry_events()
except Exception as e:
    errors.log.warning(f'Torch onnxruntime: {e}')
timer.startup.record("onnx")

from fastapi import FastAPI # pylint: disable=W0611,C0411
import gradio # pylint: disable=W0611,C0411
timer.startup.record("gradio")
errors.install([gradio])

# patch different progress bars
import tqdm as tqdm_lib # pylint: disable=C0411
from tqdm.rich import tqdm # pylint: disable=W0611,C0411

try:
    logging.getLogger("diffusers.guiders").setLevel(logging.ERROR)
    logging.getLogger("diffusers.loaders.single_file").setLevel(logging.ERROR)
    import diffusers.utils.import_utils # pylint: disable=W0611,C0411
    diffusers.utils.import_utils._k_diffusion_available = True # pylint: disable=protected-access # monkey-patch since we use k-diffusion from git
    diffusers.utils.import_utils._k_diffusion_version = '0.0.12' # pylint: disable=protected-access
    diffusers.utils.import_utils._bitsandbytes_available = _bnb # pylint: disable=protected-access

    import diffusers # pylint: disable=W0611,C0411
    import diffusers.loaders.single_file # pylint: disable=W0611,C0411
    diffusers.loaders.single_file.logging.tqdm = partial(tqdm, unit='C')
    timer.startup.record("diffusers")
except Exception as e:
    errors.log.error(f'Loader: diffusers=={diffusers.__version__ if "diffusers" in sys.modules else None} {e}')
    errors.log.error('Please restart re-run the installer')
    sys.exit(1)

try:
    import pillow_jxl # pylint: disable=W0611,C0411
except Exception:
    pass
from PIL import Image # pylint: disable=W0611,C0411
timer.startup.record("pillow")


import cv2 # pylint: disable=W0611,C0411
timer.startup.record("cv2")

class _tqdm_cls():
    def __call__(self, *args, **kwargs):
        bar_format = 'Progress {rate_fmt}{postfix} {bar} {percentage:3.0f}% {n_fmt}/{total_fmt} {elapsed} {remaining} ' + '\x1b[38;5;71m' + '{desc}' + '\x1b[0m'
        return tqdm_lib.tqdm(*args, bar_format=bar_format, ncols=80, colour='#327fba', **kwargs)

class _tqdm_old(tqdm_lib.tqdm):
    def __init__(self, *args, **kwargs):
        kwargs.pop("name", None)
        kwargs['bar_format'] = 'Progress {rate_fmt}{postfix} {bar} {percentage:3.0f}% {n_fmt}/{total_fmt} {elapsed} {remaining} ' + '\x1b[38;5;71m' + '{desc}' + '\x1b[0m'
        kwargs['ncols'] = 80
        super().__init__(*args, **kwargs)


transformers.utils.logging.tqdm = _tqdm_cls()
diffusers.pipelines.pipeline_utils.logging.tqdm = _tqdm_cls()
huggingface_hub._snapshot_download.hf_tqdm = _tqdm_old # pylint: disable=protected-access


def get_packages():
    return {
        "torch": getattr(torch, "__long_version__", torch.__version__),
        "diffusers": diffusers.__version__,
        "gradio": gradio.__version__,
        "transformers": transformers.__version__,
        "accelerate": accelerate.__version__,
        "hub": huggingface_hub.__version__,
    }

try:
    import math
    cores = os.cpu_count()
    affinity = len(os.sched_getaffinity(0)) # pylint: disable=no-member
    threads = torch.get_num_threads()
    if threads < (affinity / 2):
        torch.set_num_threads(math.floor(affinity / 2))
        threads = torch.get_num_threads()
    errors.log.debug(f'System: cores={cores} affinity={affinity} threads={threads}')
except Exception:
    pass

try:
    import torchvision.transforms.functional_tensor # pylint: disable=unused-import, ungrouped-imports
except ImportError:
    try:
        import torchvision.transforms.functional as functional
        sys.modules["torchvision.transforms.functional_tensor"] = functional
    except ImportError:
        pass  # shrug...


deprecate_diffusers = diffusers.utils.deprecation_utils.deprecate
def deprecate_warn(*args, **kwargs):
    try:
        deprecate_diffusers(*args, **kwargs)
    except Exception as e:
        errors.log.warning(f'Deprecation: {e}')
diffusers.utils.deprecation_utils.deprecate = deprecate_warn
diffusers.utils.deprecate = deprecate_warn


class VersionString(str): # support both string and tuple for version check
    def __ge__(self, version):
        if isinstance(version, tuple):
            version_tuple = re.findall(r'\d+', torch.__version__.split('+')[0])
            version_tuple = tuple(int(x) for x in version_tuple[:3])
            return version_tuple >= version
        return super().__ge__(version)


torch.__version__ = VersionString(torch.__version__)
errors.log.info(f'Torch: torch=={torch.__version__} torchvision=={torchvision.__version__}')
errors.log.info(f'Packages: diffusers=={diffusers.__version__} transformers=={transformers.__version__} accelerate=={accelerate.__version__} gradio=={gradio.__version__} pydantic=={pydantic.__version__} numpy=={np.__version__} cv2=={cv2.__version__}')