"""
Utilities for working with the local dataset cache.
This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
Copyright by the AllenNLP authors.
"""

import fnmatch
import json
import logging
import os
import shutil
import sys
import tarfile
import tempfile
from contextlib import contextmanager
from functools import partial, wraps
from hashlib import sha256
from typing import Optional
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile

import requests
from filelock import FileLock
from tqdm.auto import tqdm

# from . import __version__
__version__ = "2.8.0"

logger = logging.getLogger(__name__)  # pylint: disable=invalid-name

try:
	USE_TF = os.environ.get("USE_TF", "AUTO").upper()
	USE_TORCH = os.environ.get("USE_TORCH", "AUTO").upper()
	if USE_TORCH in ("1", "ON", "YES", "AUTO") and USE_TF not in ("1", "ON", "YES"):
		import torch

		_torch_available = True  # pylint: disable=invalid-name
		logger.info("PyTorch version {} available.".format(torch.__version__))
	else:
		logger.info("Disabling PyTorch because USE_TF is set")
		_torch_available = False
except ImportError:
	_torch_available = False  # pylint: disable=invalid-name

try:
	USE_TF = os.environ.get("USE_TF", "AUTO").upper()
	USE_TORCH = os.environ.get("USE_TORCH", "AUTO").upper()

	if USE_TF in ("1", "ON", "YES", "AUTO") and USE_TORCH not in ("1", "ON", "YES"):
		import tensorflow as tf

		assert hasattr(tf, "__version__") and int(tf.__version__[0]) >= 2
		_tf_available = True  # pylint: disable=invalid-name
		logger.info("TensorFlow version {} available.".format(tf.__version__))
	else:
		logger.info("Disabling Tensorflow because USE_TORCH is set")
		_tf_available = False
except (ImportError, AssertionError):
	_tf_available = False  # pylint: disable=invalid-name

try:
	from torch.hub import _get_torch_home

	torch_cache_home = _get_torch_home()
except ImportError:
	torch_cache_home = os.path.expanduser(
		os.getenv("TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch"))
	)
default_cache_path = os.path.join(torch_cache_home, "transformers")

try:
	from pathlib import Path

	PYTORCH_PRETRAINED_BERT_CACHE = Path(
		os.getenv("PYTORCH_TRANSFORMERS_CACHE", os.getenv("PYTORCH_PRETRAINED_BERT_CACHE", default_cache_path))
	)
except (AttributeError, ImportError):
	PYTORCH_PRETRAINED_BERT_CACHE = os.getenv(
		"PYTORCH_TRANSFORMERS_CACHE", os.getenv("PYTORCH_PRETRAINED_BERT_CACHE", default_cache_path)
	)

PYTORCH_TRANSFORMERS_CACHE = PYTORCH_PRETRAINED_BERT_CACHE  # Kept for backward compatibility
TRANSFORMERS_CACHE = PYTORCH_PRETRAINED_BERT_CACHE  # Kept for backward compatibility

WEIGHTS_NAME = "pytorch_model.bin"
TF2_WEIGHTS_NAME = "tf_model.h5"
TF_WEIGHTS_NAME = "model.ckpt"
CONFIG_NAME = "config.json"
MODEL_CARD_NAME = "modelcard.json"


MULTIPLE_CHOICE_DUMMY_INPUTS = [[[0], [1]], [[0], [1]]]
DUMMY_INPUTS = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
DUMMY_MASK = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]

S3_BUCKET_PREFIX = "https://s3.amazonaws.com/models.huggingface.co/bert"
CLOUDFRONT_DISTRIB_PREFIX = "https://d2ws9o8vfrpkyk.cloudfront.net"


def is_torch_available():
	return _torch_available


def is_tf_available():
	return _tf_available


def add_start_docstrings(*docstr):
	def docstring_decorator(fn):
		fn.__doc__ = "".join(docstr) + (fn.__doc__ if fn.__doc__ is not None else "")
		return fn

	return docstring_decorator


def add_start_docstrings_to_callable(*docstr):
	def docstring_decorator(fn):
		class_name = ":class:`~transformers.{}`".format(fn.__qualname__.split(".")[0])
		intro = "   The {} forward method, overrides the :func:`__call__` special method.".format(class_name)
		note = r"""

	.. note::
		Although the recipe for forward pass needs to be defined within
		this function, one should call the :class:`Module` instance afterwards
		instead of this since the former takes care of running the
		pre and post processing steps while the latter silently ignores them.
		"""
		fn.__doc__ = intro + note + "".join(docstr) + (fn.__doc__ if fn.__doc__ is not None else "")
		return fn

	return docstring_decorator


def add_end_docstrings(*docstr):
	def docstring_decorator(fn):
		fn.__doc__ = fn.__doc__ + "".join(docstr)
		return fn

	return docstring_decorator


def is_remote_url(url_or_filename):
	parsed = urlparse(url_or_filename)
	return parsed.scheme in ("http", "https")


def hf_bucket_url(identifier, postfix=None, cdn=False) -> str:
	endpoint = CLOUDFRONT_DISTRIB_PREFIX if cdn else S3_BUCKET_PREFIX
	if postfix is None:
		return "/".join((endpoint, identifier))
	else:
		return "/".join((endpoint, identifier, postfix))


def url_to_filename(url, etag=None):
	"""
	Convert `url` into a hashed filename in a repeatable way.
	If `etag` is specified, append its hash to the url's, delimited
	by a period.
	If the url ends with .h5 (Keras HDF5 weights) adds '.h5' to the name
	so that TF 2.0 can identify it as a HDF5 file
	(see https://github.com/tensorflow/tensorflow/blob/00fad90125b18b80fe054de1055770cfb8fe4ba3/tensorflow/python/keras/engine/network.py#L1380)
	"""
	url_bytes = url.encode("utf-8")
	url_hash = sha256(url_bytes)
	filename = url_hash.hexdigest()

	if etag:
		etag_bytes = etag.encode("utf-8")
		etag_hash = sha256(etag_bytes)
		filename += "." + etag_hash.hexdigest()

	if url.endswith(".h5"):
		filename += ".h5"

	return filename


def filename_to_url(filename, cache_dir=None):
	"""
	Return the url and etag (which may be ``None``) stored for `filename`.
	Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist.
	"""
	if cache_dir is None:
		cache_dir = TRANSFORMERS_CACHE
	if isinstance(cache_dir, Path):
		cache_dir = str(cache_dir)

	cache_path = os.path.join(cache_dir, filename)
	if not os.path.exists(cache_path):
		raise EnvironmentError("file {} not found".format(cache_path))

	meta_path = cache_path + ".json"
	if not os.path.exists(meta_path):
		raise EnvironmentError("file {} not found".format(meta_path))

	with open(meta_path, encoding="utf-8") as meta_file:
		metadata = json.load(meta_file)
	url = metadata["url"]
	etag = metadata["etag"]

	return url, etag


def cached_path(
	url_or_filename,
	cache_dir=None,
	force_download=False,
	proxies=None,
	resume_download=False,
	user_agent=None,
	extract_compressed_file=False,
	force_extract=False,
	local_files_only=False,
) -> Optional[str]:
	"""
	Given something that might be a URL (or might be a local path),
	determine which. If it's a URL, download the file and cache it, and
	return the path to the cached file. If it's already a local path,
	make sure the file exists and then return the path.
	Args:
		cache_dir: specify a cache directory to save the file to (overwrite the default cache dir).
		force_download: if True, re-dowload the file even if it's already cached in the cache dir.
		resume_download: if True, resume the download if incompletly recieved file is found.
		user_agent: Optional string or dict that will be appended to the user-agent on remote requests.
		extract_compressed_file: if True and the path point to a zip or tar file, extract the compressed
			file in a folder along the archive.
		force_extract: if True when extract_compressed_file is True and the archive was already extracted,
			re-extract the archive and overide the folder where it was extracted.

	Return:
		None in case of non-recoverable file (non-existent or inaccessible url + no cache on disk).
		Local path (string) otherwise
	"""
	if cache_dir is None:
		cache_dir = TRANSFORMERS_CACHE
	if isinstance(url_or_filename, Path):
		url_or_filename = str(url_or_filename)
	if isinstance(cache_dir, Path):
		cache_dir = str(cache_dir)

	if is_remote_url(url_or_filename):
		# URL, so get it from the cache (downloading if necessary)
		output_path = get_from_cache(
			url_or_filename,
			cache_dir=cache_dir,
			force_download=force_download,
			proxies=proxies,
			resume_download=resume_download,
			user_agent=user_agent,
			local_files_only=local_files_only,
		)
	elif os.path.exists(url_or_filename):
		# File, and it exists.
		output_path = url_or_filename
	elif urlparse(url_or_filename).scheme == "":
		# File, but it doesn't exist.
		raise EnvironmentError("file {} not found".format(url_or_filename))
	else:
		# Something unknown
		raise ValueError("unable to parse {} as a URL or as a local path".format(url_or_filename))

	if extract_compressed_file:
		if not is_zipfile(output_path) and not tarfile.is_tarfile(output_path):
			return output_path

		# Path where we extract compressed archives
		# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
		output_dir, output_file = os.path.split(output_path)
		output_extract_dir_name = output_file.replace(".", "-") + "-extracted"
		output_path_extracted = os.path.join(output_dir, output_extract_dir_name)

		if os.path.isdir(output_path_extracted) and os.listdir(output_path_extracted) and not force_extract:
			return output_path_extracted

		# Prevent parallel extractions
		lock_path = output_path + ".lock"
		with FileLock(lock_path):
			shutil.rmtree(output_path_extracted, ignore_errors=True)
			os.makedirs(output_path_extracted)
			if is_zipfile(output_path):
				with ZipFile(output_path, "r") as zip_file:
					zip_file.extractall(output_path_extracted)
					zip_file.close()
			elif tarfile.is_tarfile(output_path):
				tar_file = tarfile.open(output_path)
				tar_file.extractall(output_path_extracted)
				tar_file.close()
			else:
				raise EnvironmentError("Archive format of {} could not be identified".format(output_path))

		return output_path_extracted

	return output_path


def http_get(url, temp_file, proxies=None, resume_size=0, user_agent=None):
	ua = "transformers/{}; python/{}".format(__version__, sys.version.split()[0])
	if is_torch_available():
		ua += "; torch/{}".format(torch.__version__)
	if is_tf_available():
		ua += "; tensorflow/{}".format(tf.__version__)
	if isinstance(user_agent, dict):
		ua += "; " + "; ".join("{}/{}".format(k, v) for k, v in user_agent.items())
	elif isinstance(user_agent, str):
		ua += "; " + user_agent
	headers = {"user-agent": ua}
	if resume_size > 0:
		headers["Range"] = "bytes=%d-" % (resume_size,)
	response = requests.get(url, stream=True, proxies=proxies, headers=headers)
	if response.status_code == 416:  # Range not satisfiable
		return
	content_length = response.headers.get("Content-Length")
	total = resume_size + int(content_length) if content_length is not None else None
	progress = tqdm(
		unit="B",
		unit_scale=True,
		total=total,
		initial=resume_size,
		desc="Downloading",
		disable=bool(logger.getEffectiveLevel() == logging.NOTSET),
	)
	for chunk in response.iter_content(chunk_size=1024):
		if chunk:  # filter out keep-alive new chunks
			progress.update(len(chunk))
			temp_file.write(chunk)
	progress.close()


def get_from_cache(
	url,
	cache_dir=None,
	force_download=False,
	proxies=None,
	etag_timeout=10,
	resume_download=False,
	user_agent=None,
	local_files_only=False,
) -> Optional[str]:
	"""
	Given a URL, look for the corresponding file in the local cache.
	If it's not there, download it. Then return the path to the cached file.

	Return:
		None in case of non-recoverable file (non-existent or inaccessible url + no cache on disk).
		Local path (string) otherwise
	"""
	if cache_dir is None:
		cache_dir = TRANSFORMERS_CACHE
	if isinstance(cache_dir, Path):
		cache_dir = str(cache_dir)

	os.makedirs(cache_dir, exist_ok=True)

	etag = None
	if not local_files_only:
		try:
			response = requests.head(url, allow_redirects=True, proxies=proxies, timeout=etag_timeout)
			if response.status_code == 200:
				etag = response.headers.get("ETag")
		except (EnvironmentError, requests.exceptions.Timeout):
			# etag is already None
			pass

	filename = url_to_filename(url, etag)

	# get cache path to put the file
	cache_path = os.path.join(cache_dir, filename)

	# etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
	# try to get the last downloaded one
	if etag is None:
		if os.path.exists(cache_path):
			return cache_path
		else:
			matching_files = [
				file
				for file in fnmatch.filter(os.listdir(cache_dir), filename + ".*")
				if not file.endswith(".json") and not file.endswith(".lock")
			]
			if len(matching_files) > 0:
				return os.path.join(cache_dir, matching_files[-1])
			else:
				# If files cannot be found and local_files_only=True,
				# the models might've been found if local_files_only=False
				# Notify the user about that
				if local_files_only:
					raise ValueError(
						"Cannot find the requested files in the cached path and outgoing traffic has been"
						" disabled. To enable model look-ups and downloads online, set 'local_files_only'"
						" to False."
					)
				return None

	# From now on, etag is not None.
	if os.path.exists(cache_path) and not force_download:
		return cache_path

	# Prevent parallel downloads of the same file with a lock.
	lock_path = cache_path + ".lock"
	with FileLock(lock_path):

		# If the download just completed while the lock was activated.
		if os.path.exists(cache_path) and not force_download:
			# Even if returning early like here, the lock will be released.
			return cache_path

		if resume_download:
			incomplete_path = cache_path + ".incomplete"

			@contextmanager
			def _resumable_file_manager():
				with open(incomplete_path, "a+b") as f:
					yield f

			temp_file_manager = _resumable_file_manager
			if os.path.exists(incomplete_path):
				resume_size = os.stat(incomplete_path).st_size
			else:
				resume_size = 0
		else:
			temp_file_manager = partial(tempfile.NamedTemporaryFile, dir=cache_dir, delete=False)
			resume_size = 0

		# Download to temporary file, then copy to cache dir once finished.
		# Otherwise you get corrupt cache entries if the download gets interrupted.
		with temp_file_manager() as temp_file:
			logger.info("%s not found in cache or force_download set to True, downloading to %s", url, temp_file.name)

			http_get(url, temp_file, proxies=proxies, resume_size=resume_size, user_agent=user_agent)

		logger.info("storing %s in cache at %s", url, cache_path)
		os.replace(temp_file.name, cache_path)

		logger.info("creating metadata file for %s", cache_path)
		meta = {"url": url, "etag": etag}
		meta_path = cache_path + ".json"
		with open(meta_path, "w") as meta_file:
			json.dump(meta, meta_file)

	return cache_path


class cached_property(property):
	"""
	Descriptor that mimics @property but caches output in member variable.

	From tensorflow_datasets

	Built-in in functools from Python 3.8.
	"""

	def __get__(self, obj, objtype=None):
		# See docs.python.org/3/howto/descriptor.html#properties
		if obj is None:
			return self
		if self.fget is None:
			raise AttributeError("unreadable attribute")
		attr = "__cached_" + self.fget.__name__
		cached = getattr(obj, attr, None)
		if cached is None:
			cached = self.fget(obj)
			setattr(obj, attr, cached)
		return cached


def torch_required(func):
	# Chose a different decorator name than in tests so it's clear they are not the same.
	@wraps(func)
	def wrapper(*args, **kwargs):
		if is_torch_available():
			return func(*args, **kwargs)
		else:
			raise ImportError(f"Method `{func.__name__}` requires PyTorch.")

	return wrapper


def tf_required(func):
	# Chose a different decorator name than in tests so it's clear they are not the same.
	@wraps(func)
	def wrapper(*args, **kwargs):
		if is_tf_available():
			return func(*args, **kwargs)
		else:
			raise ImportError(f"Method `{func.__name__}` requires TF.")

	return wrapper