import os
import glob
import tempfile
import shutil
from functools import wraps, reduce
from string import Template
import torch
import torch.cuda
from torch._utils import _accumulate

try:
	import cffi
except ImportError:
	raise ImportError("torch.utils.ffi requires the cffi package")


if cffi.__version_info__ < (1, 4, 0):
	raise ImportError("torch.utils.ffi requires cffi version >= 1.4, but "
					  "got " + '.'.join(map(str, cffi.__version_info__)))


def _generate_typedefs():
	typedefs = []
	for t in ['Double', 'Float', 'Long', 'Int', 'Short', 'Char', 'Byte']:
		for lib in ['TH', 'THCuda']:
			for kind in ['Tensor', 'Storage']:
				python_name = t + kind
				if t == 'Float' and lib == 'THCuda':
					th_name = 'THCuda' + kind
				else:
					th_name = lib + t + kind
				th_struct = 'struct ' + th_name

				typedefs += ['typedef {} {};'.format(th_struct, th_name)]
				# We have to assemble a string here, because we're going to
				# do this lookup based on tensor.type(), which returns a
				# string (not a type object, as this code was before)
				python_module = 'torch.cuda' if lib == 'THCuda' else 'torch'
				python_class = python_module + '.' + python_name
				_cffi_to_torch[th_struct] = python_class
				_torch_to_cffi[python_class] = th_struct
	return '\n'.join(typedefs) + '\n'
_cffi_to_torch = {}
_torch_to_cffi = {}
_typedefs = _generate_typedefs()


PY_MODULE_TEMPLATE = Template("""
from torch.utils.ffi import _wrap_function
from .$cffi_wrapper_name import lib as _lib, ffi as _ffi

__all__ = []
def _import_symbols(locals):
	for symbol in dir(_lib):
		fn = getattr(_lib, symbol)
		if callable(fn):
			locals[symbol] = _wrap_function(fn, _ffi)
		else:
			locals[symbol] = fn
		__all__.append(symbol)

_import_symbols(locals())
""")


def _setup_wrapper(with_cuda):
	here = os.path.abspath(os.path.dirname(__file__))
	lib_dir = os.path.join(here, '..', '..', 'lib')
	include_dirs = [
		os.path.join(lib_dir, 'include'),
		os.path.join(lib_dir, 'include', 'TH'),
	]

	wrapper_source = '#include <TH/TH.h>\n'
	if with_cuda:
		import torch.cuda
		wrapper_source += '#include <THC/THC.h>\n'
		if os.sys.platform == 'win32':
			cuda_include_dirs = glob.glob(os.getenv('CUDA_PATH', '') + '/include')
			cuda_include_dirs += glob.glob(os.getenv('NVTOOLSEXT_PATH', '') + '/include')
		else:
			cuda_include_dirs = glob.glob('/usr/local/cuda/include')
			cuda_include_dirs += glob.glob('/Developer/NVIDIA/CUDA-*/include')
		include_dirs.append(os.path.join(lib_dir, 'include', 'THC'))
		include_dirs.extend(cuda_include_dirs)
	return wrapper_source, include_dirs


def _create_module_dir(base_path, fullname):
	module, _, name = fullname.rpartition('.')
	if not module:
		target_dir = name
	else:
		target_dir = reduce(os.path.join, fullname.split('.'))
	target_dir = os.path.join(base_path, target_dir)
	try:
		os.makedirs(target_dir)
	except os.error:
		pass
	for dirname in _accumulate(fullname.split('.'), os.path.join):
		init_file = os.path.join(base_path, dirname, '__init__.py')
		open(init_file, 'a').close()  # Create file if it doesn't exist yet
	return name, target_dir


def _build_extension(ffi, cffi_wrapper_name, target_dir, verbose):
	try:
		tmpdir = tempfile.mkdtemp()
		ext_suf = '.pyd' if os.sys.platform == 'win32' else '.so'
		libname = cffi_wrapper_name + ext_suf
		outfile = ffi.compile(tmpdir=tmpdir, verbose=verbose, target=libname)
		shutil.copy(outfile, os.path.join(target_dir, libname))
	finally:
		shutil.rmtree(tmpdir)


def _make_python_wrapper(name, cffi_wrapper_name, target_dir):
	py_source = PY_MODULE_TEMPLATE.substitute(name=name,
											  cffi_wrapper_name=cffi_wrapper_name)
	with open(os.path.join(target_dir, '__init__.py'), 'w') as f:
		f.write(py_source)


def create_extension(name, headers, sources, verbose=True, with_cuda=False,
					 package=False, relative_to='.', **kwargs):
	base_path = os.path.abspath(os.path.dirname(relative_to))
	name_suffix, target_dir = _create_module_dir(base_path, name)
	if not package:
		cffi_wrapper_name = '_' + name_suffix
	else:
		cffi_wrapper_name = (name.rpartition('.')[0] +
							 '.{0}._{0}'.format(name_suffix))

	wrapper_source, include_dirs = _setup_wrapper(with_cuda)
	include_dirs.extend(kwargs.pop('include_dirs', []))

	if os.sys.platform == 'win32':
		library_dirs = glob.glob(os.getenv('CUDA_PATH', '') + '/lib/x64')
		library_dirs += glob.glob(os.getenv('NVTOOLSEXT_PATH', '') + '/lib/x64')

		here = os.path.abspath(os.path.dirname(__file__))
		lib_dir = os.path.join(here, '..', '..', 'lib')

		library_dirs.append(os.path.join(lib_dir))
	else:
		library_dirs = []
	library_dirs.extend(kwargs.pop('library_dirs', []))

	if isinstance(headers, str):
		headers = [headers]
	all_headers_source = ''
	for header in headers:
		with open(os.path.join(base_path, header), 'r') as f:
			all_headers_source += f.read() + '\n\n'

	ffi = cffi.FFI()
	sources = [os.path.join(base_path, src) for src in sources]
	# NB: TH headers are C99 now
	kwargs['extra_compile_args'] = ['-std=c99'] + kwargs.get('extra_compile_args', [])
	ffi.set_source(cffi_wrapper_name, wrapper_source + all_headers_source,
				   sources=sources,
				   include_dirs=include_dirs,
				   library_dirs=library_dirs, **kwargs)
	ffi.cdef(_typedefs + all_headers_source)

	_make_python_wrapper(name_suffix, '_' + name_suffix, target_dir)

	def build():
		_build_extension(ffi, cffi_wrapper_name, target_dir, verbose)
	ffi.build = build
	return ffi

def _wrap_function(function, ffi):
	@wraps(function)
	def safe_call(*args, **kwargs):
		args = tuple(ffi.cast(_torch_to_cffi.get(arg.type(), 'void') + '*', arg._cdata)
					 if isinstance(arg, torch.Tensor) or torch.is_storage(arg)
					 else arg
					 for arg in args)
		args = (function,) + args
		result = torch._C._safe_call(*args, **kwargs)
		if isinstance(result, ffi.CData):
			typeof = ffi.typeof(result)
			if typeof.kind == 'pointer':
				cdata = int(ffi.cast('uintptr_t', result))
				cname = typeof.item.cname
				if cname in _cffi_to_torch:
					# TODO: Maybe there is a less janky way to eval
					# off of this
					return eval(_cffi_to_torch[cname])(cdata=cdata)
		return result
	return safe_call
