import contextlib
import os
import pathlib
import shutil
import tempfile
import zipfile
from collections import namedtuple
from typing import Optional

import yaml

from .types import Model, ModelState

ZIP_EXT = ".zip"

NNModel = namedtuple("NNModel", ["model", "state"])


class ModelError(Exception):
    pass


def _read_bytes(filename):
    with open(filename, "rb") as file:
        return file.read()


@contextlib.contextmanager
def tempdir():
    dirpath = tempfile.mkdtemp()
    yield dirpath
    shutil.rmtree(dirpath)


def _tuple_to_list(dct):
    for key, val in dct.items():
        if isinstance(val, dict):
            _tuple_to_list(val)
        elif isinstance(val, tuple):
            dct[key] = list(val)


def _guess_model_path(path: str) -> Optional[str]:
    path = pathlib.Path(path)
    possible_paths = path.glob("**/tiktorch_config.yml")
    config_path = next(possible_paths, None)

    if not config_path:
        return None

    return config_path.parent


def read_model(path: str) -> NNModel:
    if path.endswith(ZIP_EXT):
        with tempdir() as tmppath:
            with zipfile.ZipFile(path, "r") as model_zip:
                model_zip.extractall(tmppath)
                path = _guess_model_path(tmppath)
                if not path:
                    raise ModelError(f"Couldn't find config in {path}")

                return read_model(str(path))

    try:
        with open(os.path.join(path, "tiktorch_config.yml")) as file:
            conf = yaml.load(file)
            if "name" not in conf:
                conf["name"] = os.path.basename(os.path.normpath(path))
            _tuple_to_list(conf)

        code = _read_bytes(os.path.join(path, "model.py"))
        state = _read_bytes(os.path.join(path, "state.nn"))
    except Exception as e:
        raise ModelError(f"Failed to load model {path}") from e

    return NNModel(Model(code=code, config=conf), ModelState(model_state=state))

764477176c5f255535759f8792298755814c62e2
<mask token>


class ModelError(Exception):
    pass


def _read_bytes(filename):
    with open(filename, 'rb') as file:
        return file.read()


@contextlib.contextmanager
def tempdir():
    dirpath = tempfile.mkdtemp()
    yield dirpath
    shutil.rmtree(dirpath)


def _tuple_to_list(dct):
    for key, val in dct.items():
        if isinstance(val, dict):
            _tuple_to_list(val)
        elif isinstance(val, tuple):
            dct[key] = list(val)


def _guess_model_path(path: str) ->Optional[str]:
    path = pathlib.Path(path)
    possible_paths = path.glob('**/tiktorch_config.yml')
    config_path = next(possible_paths, None)
    if not config_path:
        return None
    return config_path.parent


<mask token>

<mask token>


class ModelError(Exception):
    pass


def _read_bytes(filename):
    with open(filename, 'rb') as file:
        return file.read()


@contextlib.contextmanager
def tempdir():
    dirpath = tempfile.mkdtemp()
    yield dirpath
    shutil.rmtree(dirpath)


def _tuple_to_list(dct):
    for key, val in dct.items():
        if isinstance(val, dict):
            _tuple_to_list(val)
        elif isinstance(val, tuple):
            dct[key] = list(val)


def _guess_model_path(path: str) ->Optional[str]:
    path = pathlib.Path(path)
    possible_paths = path.glob('**/tiktorch_config.yml')
    config_path = next(possible_paths, None)
    if not config_path:
        return None
    return config_path.parent


def read_model(path: str) ->NNModel:
    if path.endswith(ZIP_EXT):
        with tempdir() as tmppath:
            with zipfile.ZipFile(path, 'r') as model_zip:
                model_zip.extractall(tmppath)
                path = _guess_model_path(tmppath)
                if not path:
                    raise ModelError(f"Couldn't find config in {path}")
                return read_model(str(path))
    try:
        with open(os.path.join(path, 'tiktorch_config.yml')) as file:
            conf = yaml.load(file)
            if 'name' not in conf:
                conf['name'] = os.path.basename(os.path.normpath(path))
            _tuple_to_list(conf)
        code = _read_bytes(os.path.join(path, 'model.py'))
        state = _read_bytes(os.path.join(path, 'state.nn'))
    except Exception as e:
        raise ModelError(f'Failed to load model {path}') from e
    return NNModel(Model(code=code, config=conf), ModelState(model_state=state)
        )

<mask token>
ZIP_EXT = '.zip'
NNModel = namedtuple('NNModel', ['model', 'state'])


class ModelError(Exception):
    pass


def _read_bytes(filename):
    with open(filename, 'rb') as file:
        return file.read()


@contextlib.contextmanager
def tempdir():
    dirpath = tempfile.mkdtemp()
    yield dirpath
    shutil.rmtree(dirpath)


def _tuple_to_list(dct):
    for key, val in dct.items():
        if isinstance(val, dict):
            _tuple_to_list(val)
        elif isinstance(val, tuple):
            dct[key] = list(val)


def _guess_model_path(path: str) ->Optional[str]:
    path = pathlib.Path(path)
    possible_paths = path.glob('**/tiktorch_config.yml')
    config_path = next(possible_paths, None)
    if not config_path:
        return None
    return config_path.parent


def read_model(path: str) ->NNModel:
    if path.endswith(ZIP_EXT):
        with tempdir() as tmppath:
            with zipfile.ZipFile(path, 'r') as model_zip:
                model_zip.extractall(tmppath)
                path = _guess_model_path(tmppath)
                if not path:
                    raise ModelError(f"Couldn't find config in {path}")
                return read_model(str(path))
    try:
        with open(os.path.join(path, 'tiktorch_config.yml')) as file:
            conf = yaml.load(file)
            if 'name' not in conf:
                conf['name'] = os.path.basename(os.path.normpath(path))
            _tuple_to_list(conf)
        code = _read_bytes(os.path.join(path, 'model.py'))
        state = _read_bytes(os.path.join(path, 'state.nn'))
    except Exception as e:
        raise ModelError(f'Failed to load model {path}') from e
    return NNModel(Model(code=code, config=conf), ModelState(model_state=state)
        )

import contextlib
import os
import pathlib
import shutil
import tempfile
import zipfile
from collections import namedtuple
from typing import Optional
import yaml
from .types import Model, ModelState
ZIP_EXT = '.zip'
NNModel = namedtuple('NNModel', ['model', 'state'])


class ModelError(Exception):
    pass


def _read_bytes(filename):
    with open(filename, 'rb') as file:
        return file.read()


@contextlib.contextmanager
def tempdir():
    dirpath = tempfile.mkdtemp()
    yield dirpath
    shutil.rmtree(dirpath)


def _tuple_to_list(dct):
    for key, val in dct.items():
        if isinstance(val, dict):
            _tuple_to_list(val)
        elif isinstance(val, tuple):
            dct[key] = list(val)


def _guess_model_path(path: str) ->Optional[str]:
    path = pathlib.Path(path)
    possible_paths = path.glob('**/tiktorch_config.yml')
    config_path = next(possible_paths, None)
    if not config_path:
        return None
    return config_path.parent


def read_model(path: str) ->NNModel:
    if path.endswith(ZIP_EXT):
        with tempdir() as tmppath:
            with zipfile.ZipFile(path, 'r') as model_zip:
                model_zip.extractall(tmppath)
                path = _guess_model_path(tmppath)
                if not path:
                    raise ModelError(f"Couldn't find config in {path}")
                return read_model(str(path))
    try:
        with open(os.path.join(path, 'tiktorch_config.yml')) as file:
            conf = yaml.load(file)
            if 'name' not in conf:
                conf['name'] = os.path.basename(os.path.normpath(path))
            _tuple_to_list(conf)
        code = _read_bytes(os.path.join(path, 'model.py'))
        state = _read_bytes(os.path.join(path, 'state.nn'))
    except Exception as e:
        raise ModelError(f'Failed to load model {path}') from e
    return NNModel(Model(code=code, config=conf), ModelState(model_state=state)
        )

import contextlib
import os
import pathlib
import shutil
import tempfile
import zipfile
from collections import namedtuple
from typing import Optional

import yaml

from .types import Model, ModelState

ZIP_EXT = ".zip"

NNModel = namedtuple("NNModel", ["model", "state"])


class ModelError(Exception):
    pass


def _read_bytes(filename):
    with open(filename, "rb") as file:
        return file.read()


@contextlib.contextmanager
def tempdir():
    dirpath = tempfile.mkdtemp()
    yield dirpath
    shutil.rmtree(dirpath)


def _tuple_to_list(dct):
    for key, val in dct.items():
        if isinstance(val, dict):
            _tuple_to_list(val)
        elif isinstance(val, tuple):
            dct[key] = list(val)


def _guess_model_path(path: str) -> Optional[str]:
    path = pathlib.Path(path)
    possible_paths = path.glob("**/tiktorch_config.yml")
    config_path = next(possible_paths, None)

    if not config_path:
        return None

    return config_path.parent


def read_model(path: str) -> NNModel:
    if path.endswith(ZIP_EXT):
        with tempdir() as tmppath:
            with zipfile.ZipFile(path, "r") as model_zip:
                model_zip.extractall(tmppath)
                path = _guess_model_path(tmppath)
                if not path:
                    raise ModelError(f"Couldn't find config in {path}")

                return read_model(str(path))

    try:
        with open(os.path.join(path, "tiktorch_config.yml")) as file:
            conf = yaml.load(file)
            if "name" not in conf:
                conf["name"] = os.path.basename(os.path.normpath(path))
            _tuple_to_list(conf)

        code = _read_bytes(os.path.join(path, "model.py"))
        state = _read_bytes(os.path.join(path, "state.nn"))
    except Exception as e:
        raise ModelError(f"Failed to load model {path}") from e

    return NNModel(Model(code=code, config=conf), ModelState(model_state=state))
