'''
Created on 05.01.2014

@author: root
'''
from org.askalon.jlibcloud.compute.wrapperInterfaces.base import StorageVolume as JStorageVolume
from javaimpl.compute.utils import none_check

class StorageVolumeImpl(JStorageVolume):
    '''
    classdocs
    '''
    def __init__(self, volume):
        '''
        Constructor
        '''
        #keep a reference to access in jython
        self.volume = volume
        self.obj = volume
        if hasattr(volume, 'uuid'):
            self.uuidp = none_check(volume.uuid, "")
        else:
            self.uuidp = ""
        if hasattr(volume, 'id'):
            self.idp = none_check(volume.id, "")
        else:
            self.idp = ""
        if hasattr(volume, 'name'):
            self.namep = none_check(volume.name, "")
        else:
            self.namep = ""
        if hasattr(volume, 'size'):
            self.sizep = none_check(volume.size, -1)
        else:
            self.sizep = -1
        if hasattr(volume, 'extra'):
            self.extrap = volume.extra
        else:
            self.extrap = {}
        if hasattr(volume, '__repr__()'):
            self.reprp = volume.__repr__()
        else:
            self.reprp = str(volume)

    def getUUID(self):
        return self.uuidp

    def getId(self):
        return self.idp

    def getName(self):
        return self.namep

    def getSizeGB(self):
        return self.sizep

    def getExtra(self):
        return self.extrap

    def attach(self, node, device=None):
        return self.volume.attach(node.node, device)

    def detach(self):
        return self.volume.detach()

    def destroy(self):
        return self.volume.destroy()

    def listSnapshots(self):
        return self.volume.list_snapshots()

    def createSnapshot(self, name):
        return self.volume.snapshot(name)

    def toString(self):
        return self.reprp


from __future__ import annotations

import copy as _copy
import enum
import logging as _logging
import os
import pathlib
import typing
from dataclasses import dataclass
from datetime import datetime
from inspect import getfullargspec as _getargspec

import six as _six

from flytekit.common import constants as _constants
from flytekit.common import interface as _interface
from flytekit.common import sdk_bases as _sdk_bases
from flytekit.common import utils as _common_utils
from flytekit.common.core.identifier import WorkflowExecutionIdentifier
from flytekit.common.exceptions import scopes as _exception_scopes
from flytekit.common.exceptions import user as _user_exceptions
from flytekit.common.tasks import output as _task_output
from flytekit.common.tasks import task as _base_task
from flytekit.common.types import helpers as _type_helpers
from flytekit.configuration import internal as _internal_config
from flytekit.configuration import resources as _resource_config
from flytekit.configuration import sdk as _sdk_config
from flytekit.configuration import secrets
from flytekit.engines import loader as _engine_loader
from flytekit.interfaces.stats import taggable
from flytekit.models import literals as _literal_models
from flytekit.models import task as _task_models

class SecretsManager(object):
    """
    This provides a secrets resolution logic at runtime.
    The resolution order is
      - Try env var first. The env var should have the configuration.SECRETS_ENV_PREFIX. The env var will be all upper
         cased
      - If not then try the file where the name matches lower case
        ``configuration.SECRETS_DEFAULT_DIR/<group>/configuration.SECRETS_FILE_PREFIX<key>``

    All configuration values can always be overridden by injecting an environment variable
    """

    def __init__(self):
        self._base_dir = str(secrets.SECRETS_DEFAULT_DIR.get()).strip()
        self._file_prefix = str(secrets.SECRETS_FILE_PREFIX.get()).strip()
        self._env_prefix = str(secrets.SECRETS_ENV_PREFIX.get()).strip()

    def get(self, group: str, key: str) -> str:
        """
        Retrieves a secret using the resolution order -> Env followed by file. If not found raises a ValueError
        """
        self.check_group_key(group, key)
        env_var = self.get_secrets_env_var(group, key)
        fpath = self.get_secrets_file(group, key)
        v = os.environ.get(env_var)
        if v is not None:
            return v
        if os.path.exists(fpath):
            with open(fpath, "r") as f:
                return f.read().strip()
        raise ValueError(
            f"Unable to find secret for key {key} in group {group} " f"in Env Var:{env_var} and FilePath: {fpath}"
        )

    def get_secrets_env_var(self, group: str, key: str) -> str:
        """
        Returns a string that matches the ENV Variable to look for the secrets
        """
        self.check_group_key(group, key)
        return f"{self._env_prefix}{group.upper()}_{key.upper()}"

    def get_secrets_file(self, group: str, key: str) -> str:
        """
        Returns a path that matches the file to look for the secrets
        """
        self.check_group_key(group, key)
        return os.path.join(self._base_dir, group.lower(), f"{self._file_prefix}{key.lower()}")

    @staticmethod
    def check_group_key(group: str, key: str):
        if group is None or group == "":
            raise ValueError("secrets group is a mandatory field.")
        if key is None or key == "":
            raise ValueError("secrets key is a mandatory field.")

# TODO: Clean up working dir name
class ExecutionParameters(object):
    """
    This is a run-time user-centric context object that is accessible to every @task method. It can be accessed using

    .. code-block:: python

        flytekit.current_context()

    This object provides the following
    * a statsd handler
    * a logging handler
    * the execution ID as an :py:class:`flytekit.models.core.identifier.WorkflowExecutionIdentifier` object
    * a working directory for the user to write arbitrary files to

    Please do not confuse this object with the :py:class:`flytekit.FlyteContext` object.
    """

    @dataclass(init=False)
    class Builder(object):
        stats: taggable.TaggableStats
        execution_date: datetime
        logging: _logging
        execution_id: str
        attrs: typing.Dict[str, typing.Any]
        working_dir: typing.Union[os.PathLike, _common_utils.AutoDeletingTempDir]

        def __init__(self, current: typing.Optional[ExecutionParameters] = None):
            self.stats = current.stats if current else None
            self.execution_date = current.execution_date if current else None
            self.working_dir = current.working_directory if current else None
            self.execution_id = current.execution_id if current else None
            self.logging = current.logging if current else None
            self.attrs = current._attrs if current else {}

        def add_attr(self, key: str, v: typing.Any) -> ExecutionParameters.Builder:
            self.attrs[key] = v
            return self

        def build(self) -> ExecutionParameters:
            if not isinstance(self.working_dir, _common_utils.AutoDeletingTempDir):
                pathlib.Path(self.working_dir).mkdir(parents=True, exist_ok=True)
            return ExecutionParameters(
                execution_date=self.execution_date,
                stats=self.stats,
                tmp_dir=self.working_dir,
                execution_id=self.execution_id,
                logging=self.logging,
                **self.attrs,
            )

    @staticmethod
    def new_builder(current: ExecutionParameters = None) -> Builder:
        return ExecutionParameters.Builder(current=current)

    def builder(self) -> Builder:
        return ExecutionParameters.Builder(current=self)

    def __init__(self, execution_date, tmp_dir, stats, execution_id, logging, **kwargs):
        """
        Args:
            execution_date: Date when the execution is running
            tmp_dir: temporary directory for the execution
            stats: handle to emit stats
            execution_id: Identifier for the xecution
            logging: handle to logging
        """
        self._stats = stats
        self._execution_date = execution_date
        self._working_directory = tmp_dir
        self._execution_id = execution_id
        self._logging = logging
        # AutoDeletingTempDir's should be used with a with block, which creates upon entry
        self._attrs = kwargs
        # It is safe to recreate the Secrets Manager
        self._secrets_manager = SecretsManager()

    @property
    def stats(self) -> taggable.TaggableStats:
        """
        A handle to a special statsd object that provides usefully tagged stats.
        TODO: Usage examples and better comments
        """
        return self._stats

    @property
    def logging(self) -> _logging:
        """
        A handle to a useful logging object.
        TODO: Usage examples
        """
        return self._logging

    @property
    def working_directory(self) -> _common_utils.AutoDeletingTempDir:
        """
        A handle to a special working directory for easily producing temporary files.

        TODO: Usage examples
        TODO: This does not always return a AutoDeletingTempDir
        """
        return self._working_directory

    @property
    def execution_date(self) -> datetime:
        """
        This is a datetime representing the time at which a workflow was started.  This is consistent across all tasks
        executed in a workflow or sub-workflow.

        .. note::

            Do NOT use this execution_date to drive any production logic.  It might be useful as a tag for data to help
            in debugging.
        """
        return self._execution_date

    @property
    def execution_id(self) -> str:
        """
        This is the identifier of the workflow execution within the underlying engine.  It will be consistent across all
        task executions in a workflow or sub-workflow execution.

        .. note::

            Do NOT use this execution_id to drive any production logic.  This execution ID should only be used as a tag
            on output data to link back to the workflow run that created it.
        """
        return self._execution_id

    @property
    def secrets(self) -> SecretsManager:
        return self._secrets_manager

    def __getattr__(self, attr_name: str) -> typing.Any:
        """
        This houses certain task specific context. For example in Spark, it houses the SparkSession, etc
        """
        attr_name = attr_name.upper()
        if self._attrs and attr_name in self._attrs:
            return self._attrs[attr_name]
        raise AssertionError(f"{attr_name} not available as a parameter in Flyte context - are you in right task-type?")

    def has_attr(self, attr_name: str) -> bool:
        attr_name = attr_name.upper()
        if self._attrs and attr_name in self._attrs:
            return True
        return False

    def get(self, key: str) -> typing.Any:
        """
        Returns task specific context if present else raise an error. The returned context will match the key
        """
        return self.__getattr__(attr_name=key)

class SdkRunnableContainer(_task_models.Container, metaclass=_sdk_bases.ExtendedSdkType):
    """
    This is not necessarily a local-only Container object. So long as configuration is present, you can use this object
    """

    def __init__(
        self,
        command,
        args,
        resources,
        env,
        config,
    ):
        super(SdkRunnableContainer, self).__init__("", command, args, resources, env or {}, config)

    @property
    def args(self):
        """
        :rtype: list[Text]
        """
        return _sdk_config.SDK_PYTHON_VENV.get() + self._args

    @property
    def image(self):
        """
        :rtype: Text
        """
        return _internal_config.IMAGE.get()

    @property
    def env(self):
        """
        :rtype: dict[Text,Text]
        """
        env = super(SdkRunnableContainer, self).env.copy()
        env.update(
            {
                _internal_config.CONFIGURATION_PATH.env_var: _internal_config.CONFIGURATION_PATH.get(),
                _internal_config.IMAGE.env_var: _internal_config.IMAGE.get(),
                # TODO: Phase out the below.  Propeller will set these and these are not SDK specific
                _internal_config.PROJECT.env_var: _internal_config.PROJECT.get(),
                _internal_config.DOMAIN.env_var: _internal_config.DOMAIN.get(),
                _internal_config.NAME.env_var: _internal_config.NAME.get(),
                _internal_config.VERSION.env_var: _internal_config.VERSION.get(),
            }
        )
        return env

    @classmethod
    def get_resources(
        cls,
        storage_request=None,
        cpu_request=None,
        gpu_request=None,
        memory_request=None,
        storage_limit=None,
        cpu_limit=None,
        gpu_limit=None,
        memory_limit=None,
    ):
        """
        :param Text storage_request:
        :param Text cpu_request:
        :param Text gpu_request:
        :param Text memory_request:
        :param Text storage_limit:
        :param Text cpu_limit:
        :param Text gpu_limit:
        :param Text memory_limit:
        """
        requests = []
        if storage_request:
            requests.append(
                _task_models.Resources.ResourceEntry(_task_models.Resources.ResourceName.STORAGE, storage_request)
            )
        if cpu_request:
            requests.append(_task_models.Resources.ResourceEntry(_task_models.Resources.ResourceName.CPU, cpu_request))
        if gpu_request:
            requests.append(_task_models.Resources.ResourceEntry(_task_models.Resources.ResourceName.GPU, gpu_request))
        if memory_request:
            requests.append(
                _task_models.Resources.ResourceEntry(_task_models.Resources.ResourceName.MEMORY, memory_request)
            )

        limits = []
        if storage_limit:
            limits.append(
                _task_models.Resources.ResourceEntry(_task_models.Resources.ResourceName.STORAGE, storage_limit)
            )
        if cpu_limit:
            limits.append(_task_models.Resources.ResourceEntry(_task_models.Resources.ResourceName.CPU, cpu_limit))
        if gpu_limit:
            limits.append(_task_models.Resources.ResourceEntry(_task_models.Resources.ResourceName.GPU, gpu_limit))
        if memory_limit:
            limits.append(
                _task_models.Resources.ResourceEntry(_task_models.Resources.ResourceName.MEMORY, memory_limit)
            )

        return _task_models.Resources(limits=limits, requests=requests)

class SdkRunnableTaskStyle(enum.Enum):
    V0 = 0
    V1 = 1

class SdkRunnableTask(_base_task.SdkTask, metaclass=_sdk_bases.ExtendedSdkType):
    """
    This class includes the additional logic for building a task that executes in Python code.  It has even more
    validation checks to ensure proper behavior than it's superclasses.

    Since an SdkRunnableTask is assumed to run by hooking into Python code, we will provide additional shortcuts and
    methods on this object.
    """

    def __init__(
        self,
        task_function,
        task_type,
        discovery_version,
        retries,
        interruptible,
        deprecated,
        storage_request,
        cpu_request,
        gpu_request,
        memory_request,
        storage_limit,
        cpu_limit,
        gpu_limit,
        memory_limit,
        discoverable,
        timeout,
        environment,
        custom,
    ):
        """
        :param task_function: Function container user code.  This will be executed via the SDK's engine.
        :param Text task_type: string describing the task type
        :param Text discovery_version: string describing the version for task discovery purposes
        :param int retries: Number of retries to attempt
        :param bool interruptible: Specify whether task is interruptible
        :param Text deprecated:
        :param Text storage_request:
        :param Text cpu_request:
        :param Text gpu_request:
        :param Text memory_request:
        :param Text storage_limit:
        :param Text cpu_limit:
        :param Text gpu_limit:
        :param Text memory_limit:
        :param bool discoverable:
        :param datetime.timedelta timeout:
        :param dict[Text, Text] environment:
        :param dict[Text, T] custom:
        """
        # Circular dependency
        from flytekit import __version__

        self._task_function = task_function
        super(SdkRunnableTask, self).__init__(
            task_type,
            _task_models.TaskMetadata(
                discoverable,
                _task_models.RuntimeMetadata(
                    _task_models.RuntimeMetadata.RuntimeType.FLYTE_SDK,
                    __version__,
                    "python",
                ),
                timeout,
                _literal_models.RetryStrategy(retries),
                interruptible,
                discovery_version,
                deprecated,
            ),
            # TODO: If we end up using SdkRunnableTask for the new code, make sure this is set correctly.
            _interface.TypedInterface({}, {}),
            custom,
            container=self._get_container_definition(
                storage_request=storage_request,
                cpu_request=cpu_request,
                gpu_request=gpu_request,
                memory_request=memory_request,
                storage_limit=storage_limit,
                cpu_limit=cpu_limit,
                gpu_limit=gpu_limit,
                memory_limit=memory_limit,
                environment=environment,
            ),
        )
        self.id._name = "{}.{}".format(self.task_module, self.task_function_name)
        self._has_fast_registered = False

        # TODO: Remove this in the future, I don't think we'll be using this.
        self._task_style = SdkRunnableTaskStyle.V0

    _banned_inputs = {}
    _banned_outputs = {}

    @_exception_scopes.system_entry_point
    def add_inputs(self, inputs):
        """
        Adds the inputs to this task.  This can be called multiple times, but it will fail if an input with a given
        name is added more than once, a name collides with an output, or if the name doesn't exist as an arg name in
        the wrapped function.
        :param dict[Text, flytekit.models.interface.Variable] inputs: names and variables
        """
        self._validate_inputs(inputs)
        self.interface.inputs.update(inputs)

    @classmethod
    def promote_from_model(cls, base_model):
        # TODO: If the task exists in this container, we should be able to retrieve it.
        raise _user_exceptions.FlyteAssertion("Cannot promote a base object to a runnable task.")

    @property
    def task_style(self):
        return self._task_style

    @property
    def task_function(self):
        return self._task_function

    @property
    def task_function_name(self):
        """
        :rtype: Text
        """
        return self.task_function.__name__

    @property
    def task_module(self):
        """
        :rtype: Text
        """
        return self._task_function.__module__

    def validate(self):
        super(SdkRunnableTask, self).validate()
        missing_args = self._missing_mapped_inputs_outputs()
        if len(missing_args) > 0:
            raise _user_exceptions.FlyteAssertion(
                "The task {} is invalid because not all inputs and outputs in the "
                "task function definition were specified in @outputs and @inputs. "
                "We are missing definitions for {}.".format(self, missing_args)
            )

    @_exception_scopes.system_entry_point
    def unit_test(self, **input_map):
        """
        :param dict[Text, T] input_map: Python Std input from users.  We will cast these to the appropriate Flyte
            literals.
        :returns: Depends on the behavior of the specific task in the unit engine.
        """
        return (
            _engine_loader.get_engine("unit")
            .get_task(self)
            .execute(
                _type_helpers.pack_python_std_map_to_literal_map(
                    input_map,
                    {
                        k: _type_helpers.get_sdk_type_from_literal_type(v.type)
                        for k, v in _six.iteritems(self.interface.inputs)
                    },
                )
            )
        )

    @_exception_scopes.system_entry_point
    def local_execute(self, **input_map):
        """
        :param dict[Text, T] input_map: Python Std input from users.  We will cast these to the appropriate Flyte
            literals.
        :rtype: dict[Text, T]
        :returns: The output produced by this task in Python standard format.
        """
        return (
            _engine_loader.get_engine("local")
            .get_task(self)
            .execute(
                _type_helpers.pack_python_std_map_to_literal_map(
                    input_map,
                    {
                        k: _type_helpers.get_sdk_type_from_literal_type(v.type)
                        for k, v in _six.iteritems(self.interface.inputs)
                    },
                )
            )
        )

    def _execute_user_code(self, context, inputs):
        """
        :param flytekit.engines.common.EngineContext context:
        :param dict[Text, T] inputs: This variable is a bit of a misnomer, since it's both inputs and outputs. The
            dictionary passed here will be passed to the user-defined function, and will have values that are a
            variety of types.  The T's here are Python std values for inputs.  If there isn't a native Python type for
            something (like Schema or Blob), they are the Flyte classes.  For outputs they are OutputReferences.
            (Note that these are not the same OutputReferences as in BindingData's)
        :rtype: Any: the returned object from user code.
        :returns: This function must return a dictionary mapping 'filenames' to Flyte Interface Entities.  These
            entities will be used by the engine to pass data from node to node, populate metadata, etc. etc..  Each
            engine will have different behavior.  For instance, the Flyte engine will upload the entities to a remote
            working directory (with the names provided), which will in turn allow Flyte Propeller to push along the
            workflow.  Where as local engine will merely feed the outputs directly into the next node.
        """
        if self.task_style == SdkRunnableTaskStyle.V0:
            return _exception_scopes.user_entry_point(self.task_function)(
                ExecutionParameters(
                    execution_date=context.execution_date,
                    # TODO: it might be better to consider passing the full struct
                    execution_id=_six.text_type(WorkflowExecutionIdentifier.promote_from_model(context.execution_id)),
                    stats=context.stats,
                    logging=context.logging,
                    tmp_dir=context.working_directory,
                ),
                **inputs,
            )

    @_exception_scopes.system_entry_point
    def execute(self, context, inputs):
        """
        :param flytekit.engines.common.EngineContext context:
        :param flytekit.models.literals.LiteralMap inputs:
        :rtype: dict[Text, flytekit.models.common.FlyteIdlEntity]
        :returns: This function must return a dictionary mapping 'filenames' to Flyte Interface Entities.  These
            entities will be used by the engine to pass data from node to node, populate metadata, etc. etc..  Each
            engine will have different behavior.  For instance, the Flyte engine will upload the entities to a remote
            working directory (with the names provided), which will in turn allow Flyte Propeller to push along the
            workflow.  Where as local engine will merely feed the outputs directly into the next node.
        """
        inputs_dict = _type_helpers.unpack_literal_map_to_sdk_python_std(
            inputs, {k: _type_helpers.get_sdk_type_from_literal_type(v.type) for k, v in self.interface.inputs.items()}
        )
        outputs_dict = {
            name: _task_output.OutputReference(_type_helpers.get_sdk_type_from_literal_type(variable.type))
            for name, variable in _six.iteritems(self.interface.outputs)
        }

        # Old style - V0: If annotations are used to define outputs, do not append outputs to the inputs dict
        if not self.task_function.__annotations__ or "return" not in self.task_function.__annotations__:
            inputs_dict.update(outputs_dict)
            self._execute_user_code(context, inputs_dict)
            return {
                _constants.OUTPUT_FILE_NAME: _literal_models.LiteralMap(
                    literals={k: v.sdk_value for k, v in _six.iteritems(outputs_dict)}
                )
            }

    @_exception_scopes.system_entry_point
    def fast_register(self, project, domain, name, digest, additional_distribution, dest_dir) -> str:
        """
        The fast register call essentially hijacks the task container commandline.
        Say an existing task container definition had a commandline like so:
            flyte_venv pyflyte-execute --task-module app.workflows.my_workflow --task-name my_task

        The fast register command introduces a wrapper call to fast-execute the original commandline like so:
            flyte_venv pyflyte-fast-execute --additional-distribution s3://my-s3-bucket/foo/bar/12345.tar.gz --
                flyte_venv pyflyte-execute --task-module app.workflows.my_workflow --task-name my_task

        At execution time pyflyte-fast-execute will ensure the additional distribution (i.e. the fast-registered code)
        exists before calling the original task commandline.

        :param Text project: The project in which to register this task.
        :param Text domain: The domain in which to register this task.
        :param Text name: The name to give this task.
        :param Text digest: The version in which to register this task.
        :param Text additional_distribution: User-specified location for remote source code distribution.
        :param Text The optional location for where to install the additional distribution at runtime
        :rtype: Text: Registered identifier.
        """

        original_container = self.container
        container = _copy.deepcopy(original_container)
        args = ["pyflyte-fast-execute", "--additional-distribution", additional_distribution]
        if dest_dir:
            args += ["--dest-dir", dest_dir]
        args += ["--"] + container.args
        container._args = args
        self._container = container

        try:
            registered_id = self.register(project, domain, name, digest)
        except Exception:
            self._container = original_container
            raise
        self._has_fast_registered = True
        self._container = original_container
        return str(registered_id)

    @property
    def has_fast_registered(self) -> bool:
        return self._has_fast_registered

    def _get_container_definition(
        self,
        storage_request=None,
        cpu_request=None,
        gpu_request=None,
        memory_request=None,
        storage_limit=None,
        cpu_limit=None,
        gpu_limit=None,
        memory_limit=None,
        environment=None,
        cls=None,
    ):
        """
        :param Text storage_request:
        :param Text cpu_request:
        :param Text gpu_request:
        :param Text memory_request:
        :param Text storage_limit:
        :param Text cpu_limit:
        :param Text gpu_limit:
        :param Text memory_limit:
        :param dict[Text,Text] environment:
        :param cls Optional[type]: Type of container to instantiate. Generally should subclass SdkRunnableContainer.
        :rtype: flytekit.models.task.Container
        """
        storage_limit = storage_limit or _resource_config.DEFAULT_STORAGE_LIMIT.get()
        storage_request = storage_request or _resource_config.DEFAULT_STORAGE_REQUEST.get()
        cpu_limit = cpu_limit or _resource_config.DEFAULT_CPU_LIMIT.get()
        cpu_request = cpu_request or _resource_config.DEFAULT_CPU_REQUEST.get()
        gpu_limit = gpu_limit or _resource_config.DEFAULT_GPU_LIMIT.get()
        gpu_request = gpu_request or _resource_config.DEFAULT_GPU_REQUEST.get()
        memory_limit = memory_limit or _resource_config.DEFAULT_MEMORY_LIMIT.get()
        memory_request = memory_request or _resource_config.DEFAULT_MEMORY_REQUEST.get()

        resources = SdkRunnableContainer.get_resources(
            storage_request, cpu_request, gpu_request, memory_request, storage_limit, cpu_limit, gpu_limit, memory_limit
        )

        return (cls or SdkRunnableContainer)(
            command=[],
            args=[
                "pyflyte-execute",
                "--task-module",
                self.task_module,
                "--task-name",
                self.task_function_name,
                "--inputs",
                "{{.input}}",
                "--output-prefix",
                "{{.outputPrefix}}",
                "--raw-output-data-prefix",
                "{{.rawOutputDataPrefix}}",
            ],
            resources=resources,
            env=environment,
            config={},
        )

    def _validate_inputs(self, inputs):
        """
        This method should be overridden in sub-classes that intend to do additional checks on inputs.  If validation
        fails, this function should raise an informative exception.
        :param dict[Text, flytekit.models.interface.Variable] inputs:  Input variables to validate
        :raises: flytekit.common.exceptions.user.FlyteValidationException
        """
        super(SdkRunnableTask, self)._validate_inputs(inputs)
        for k, v in _six.iteritems(inputs):
            if not self._is_argname_in_function_definition(k):
                raise _user_exceptions.FlyteValidationException(
                    "The input named '{}' was not specified in the task function.  Therefore, this input cannot be "
                    "provided to the task.".format(k)
                )
            if _type_helpers.get_sdk_type_from_literal_type(v.type) in type(self)._banned_inputs:
                raise _user_exceptions.FlyteValidationException(
                    "The input '{}' is not an accepted input type.".format(v)
                )

    def _validate_outputs(self, outputs):
        """
        This method should be overridden in sub-classes that intend to do additional checks on outputs.  If validation
        fails, this function should raise an informative exception.
        :param dict[Text, flytekit.models.interface.Variable] outputs:  Output variables to validate
        :raises: flytekit.common.exceptions.user.FlyteValidationException
        """
        super(SdkRunnableTask, self)._validate_outputs(outputs)
        for k, v in _six.iteritems(outputs):
            if not self._is_argname_in_function_definition(k):
                raise _user_exceptions.FlyteValidationException(
                    "The output named '{}' was not specified in the task function.  Therefore, this output cannot be "
                    "provided to the task.".format(k)
                )
            if _type_helpers.get_sdk_type_from_literal_type(v.type) in type(self)._banned_outputs:
                raise _user_exceptions.FlyteValidationException(
                    "The output '{}' is not an accepted output type.".format(v)
                )

    def _get_kwarg_inputs(self):
        # Trim off first parameter as it is reserved for workflow_parameters
        return set(_getargspec(self.task_function).args[1:])

    def _is_argname_in_function_definition(self, key):
        return key in self._get_kwarg_inputs()

    def _missing_mapped_inputs_outputs(self):
        # Trim off first parameter as it is reserved for workflow_parameters
        args = self._get_kwarg_inputs()
        inputs_and_outputs = set(self.interface.outputs.keys()) | set(self.interface.inputs.keys())
        return args ^ inputs_and_outputs


# A sample recursive neural network for text classification
# @Time: 8/13/2020
# @Author: lnblanke
# @Email: fjh314.84@gmail.com
# @File: cnn.py

import numpy as np
import tensorflow as tf
from blocks import RNN, Dense
from model import Model
import os

path = os.path.join("glove.6B.100d.txt")

embedding_indices = {}

with open(path) as f:
    for line in f:
        word, coef = line.split(maxsplit = 1)
        coef = np.fromstring(coef, "f", sep = " ")

        embedding_indices[word] = coef

def embedding(x):
    word_idx = tf.keras.datasets.imdb.get_word_index()
    embedding_dim = 100

    l, w = x.shape
    embed = np.zeros((l, w, embedding_dim))

    vec_to_word = {vec + 3: ww for ww, vec in word_idx.items()}
    vec_to_word[0] = "<pad>"
    vec_to_word[1] = "<sos>"
    vec_to_word[2] = "<unk>"

    for i in range(l):
        for j in range(w):
            embedding_vec = embedding_indices.get(vec_to_word[x[i][j]])

            if embedding_vec is not None:
                embed[i][j] = embedding_vec

    return embed

word_size = 15000

(train_x, train_y), (test_x, test_y) = tf.keras.datasets.imdb.load_data(num_words = word_size)

max_len = 300
train_x = tf.keras.preprocessing.sequence.pad_sequences(train_x, max_len)[:1000]
train_y = train_y[:1000]
test_x = tf.keras.preprocessing.sequence.pad_sequences(test_x, max_len)[:200]
test_y = test_y[:200]

train_x_embed = embedding(train_x)
test_x_embed = embedding(test_x)

rate = 1e-2  # Learning rate
epoch = 100  # Learning epochs
patience = 10  # Early stop patience

model = Model("RNN")
model.add(RNN(input_size = 100, output_size = 64, units = 128))
model.add(Dense(64, 2, activation = "softmax"))

if __name__ == '__main__':
    model.fit(train_x_embed, train_y, loss_func = "cross entropy loss", epochs = epoch, learning_rate = rate,
              patience = patience)

    pred = model.predict(test_x_embed)

    print("Accuracy: %.2f" % (np.sum(pred == test_y) / len(test_y) * 100) + "%")


# -*- coding: utf-8 -*-
# Copyright 2022 Google LLC
#
# 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.
#
# Generated code. DO NOT EDIT!
#
# Snippet for CreateDocument
# NOTE: This snippet has been automatically generated for illustrative purposes only.
# It may require modifications to work in your environment.

# To install the latest published package dependency, execute the following:
#   python3 -m pip install google-cloud-dialogflow

# [START dialogflow_v2_generated_Documents_CreateDocument_sync]
from google.cloud import dialogflow_v2

def sample_create_document():
    # Create a client
    client = dialogflow_v2.DocumentsClient()

    # Initialize request argument(s)
    document = dialogflow_v2.Document()
    document.content_uri = "content_uri_value"
    document.display_name = "display_name_value"
    document.mime_type = "mime_type_value"
    document.knowledge_types = "AGENT_FACING_SMART_REPLY"

    request = dialogflow_v2.CreateDocumentRequest(
        parent="parent_value",
        document=document,
    )

    # Make the request
    operation = client.create_document(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)

# [END dialogflow_v2_generated_Documents_CreateDocument_sync]


#Download Data and Save Data

import urllib

print "downloading with urllib"
dl_url = http://s3.amazonaws.com/open_data/*
urllib.urlretrieve(dl_url)

# file1 = opendata_giftcards000.gz
# file2 = opendata_giving_page_projects000.gz
# file3 = opendata_giving_pages000.gz
# file4 = opendata_essays000.gz
# file5 = opendata_resources000.gz
# file6 = opendata_donations000.gz
# file7 = opendata_projects000.gz

# http://s3.amazonaws.com/open_data/opendata_giftcards000.gz
# http://s3.amazonaws.com/open_data/opendata_giving_page_projects000.gz
# http://s3.amazonaws.com/open_data/opendata_giving_pages000.gz
# http://s3.amazonaws.com/open_data/opendata_essays000.gz
# http://s3.amazonaws.com/open_data/opendata_resources000.gz
# http://s3.amazonaws.com/open_data/opendata_donations000.gz
# http://s3.amazonaws.com/open_data/opendata_projects000.gz


"""Initial migration

Revision ID: e4ef83148109
Revises:
Create Date: 2021-11-25 12:45:30.514576

"""
from alembic import op
import sqlalchemy as sa

# revision identifiers, used by Alembic.
revision = 'e4ef83148109'
down_revision = None
branch_labels = None
depends_on = None

def upgrade():
    # ### commands auto generated by Alembic - please adjust! ###
    pass
    # ### end Alembic commands ###

def downgrade():
    # ### commands auto generated by Alembic - please adjust! ###
    pass
    # ### end Alembic commands ###


import os
from setuptools import setup, find_packages

with open(os.path.join(os.path.dirname(__file__), 'README.rst')) as readme:
    README = readme.read()

# allow setup.py to be run from any path
os.chdir(os.path.normpath(os.path.join(os.path.abspath(__file__), os.pardir)))

setup(
    name='django_admin_monitoring',
    version='0.1.3',
    packages=find_packages(),
    include_package_data=True,
    license='MIT License',
    description='A simple Django app that provides ability to monitor such things as user feedback in admin',
    long_description=README,
    url='https://github.com/eternalfame/django_admin_monitoring',
    author='Vyacheslav Sukhenko',
    author_email='eternalfame@mail.ru',
    classifiers=[
        'Environment :: Web Environment',
        'Framework :: Django',
        'Intended Audience :: Developers',
        'License :: OSI Approved :: MIT License',
        'Operating System :: OS Independent',
        'Programming Language :: Python',
        'Topic :: Internet :: WWW/HTTP',
        'Topic :: Internet :: WWW/HTTP :: Dynamic Content',
    ],
)