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   # to you 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
   # 
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hunk.file_path: airflow/contrib/utils/sendgrid.py
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file: # -*- coding: utf-8 -*-
  #
  # Licensed to the Apache Software Foundation (ASF) under one
  # or more contributor license agreements.  See the NOTICE file
  # distributed with this work for additional information
  # regarding copyright ownership.  The ASF licenses this file
  # to you 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.
  
  from builtins import str
  import dill
  import inspect
  import os
  import pickle
  import subprocess
  import sys
  import types
  
  from airflow.exceptions import AirflowException
  from airflow.models import BaseOperator, SkipMixin
  from airflow.utils.decorators import apply_defaults
  from airflow.utils.file import TemporaryDirectory
  
  from textwrap import dedent
  
  
  class PythonOperator(BaseOperator):
      """
      Executes a Python callable
  
      :param python_callable: A reference to an object that is callable
      :type python_callable: python callable
      :param op_kwargs: a dictionary of keyword arguments that will get unpacked
          in your function
      :type op_kwargs: dict
      :param op_args: a list of positional arguments that will get unpacked when
          calling your callable
      :type op_args: list
      :param provide_context: if set to true, Airflow will pass a set of
          keyword arguments that can be used in your function. This set of
          kwargs correspond exactly to what you can use in your jinja
          templates. For this to work, you need to define `**kwargs` in your
          function header.
      :type provide_context: bool
      :param templates_dict: a dictionary where the values are templates that
          will get templated by the Airflow engine sometime between
          ``__init__`` and ``execute`` takes place and are made available
          in your callable's context after the template has been applied
      :type templates_dict: dict of str
      :param templates_exts: a list of file extensions to resolve while
          processing templated fields, for examples ``['.sql', '.hql']``
      :type templates_exts: list(str)
      """
      template_fields = ('templates_dict',)
      template_ext = tuple()
      ui_color = '#ffefeb'
  
      @apply_defaults
      def __init__(
              self,
              python_callable,
              op_args=None,
              op_kwargs=None,
              provide_context=False,
              templates_dict=None,
              templates_exts=None,
              *args, **kwargs):
          super(PythonOperator, self).__init__(*args, **kwargs)
          if not callable(python_callable):
              raise AirflowException('`python_callable` param must be callable')
          self.python_callable = python_callable
          self.op_args = op_args or []
          self.op_kwargs = op_kwargs or {}
          self.provide_context = provide_context
          self.templates_dict = templates_dict
          if templates_exts:
              self.template_ext = templates_exts
  
      def execute(self, context):
          if self.provide_context:
              context.update(self.op_kwargs)
              context['templates_dict'] = self.templates_dict
              self.op_kwargs = context
  
          return_value = self.execute_callable()
          self.log.info("Done. Returned value was: %s", return_value)
          return return_value
  
      def execute_callable(self):
          return self.python_callable(*self.op_args, **self.op_kwargs)
  
  
  class BranchPythonOperator(PythonOperator, SkipMixin):
      """
      Allows a workflow to "branch" or follow a single path following the
      execution of this task.
  
      It derives the PythonOperator and expects a Python function that returns
      the task_id to follow. The task_id returned should point to a task
      directly downstream from {self}. All other "branches" or
      directly downstream tasks are marked with a state of ``skipped`` so that
      these paths can't move forward. The ``skipped`` states are propageted
      downstream to allow for the DAG state to fill up and the DAG run's state
      to be inferred.
  
      Note that using tasks with ``depends_on_past=True`` downstream from
      ``BranchPythonOperator`` is logically unsound as ``skipped`` status
      will invariably lead to block tasks that depend on their past successes.
      ``skipped`` states propagates where all directly upstream tasks are
      ``skipped``.
      """
      def execute(self, context):
          branch = super(BranchPythonOperator, self).execute(context)
          self.log.info("Following branch %s", branch)
          self.log.info("Marking other directly downstream tasks as skipped")
  
          downstream_tasks = context['task'].downstream_list
          self.log.debug("Downstream task_ids %s", downstream_tasks)
  
          skip_tasks = [t for t in downstream_tasks if t.task_id != branch]
          if downstream_tasks:
              self.skip(context['dag_run'], context['ti'].execution_date, skip_tasks)
  
          self.log.info("Done.")
  
  
  class ShortCircuitOperator(PythonOperator, SkipMixin):
      """
      Allows a workflow to continue only if a condition is met. Otherwise, the
      workflow "short-circuits" and downstream tasks are skipped.
  
      The ShortCircuitOperator is derived from the PythonOperator. It evaluates a
      condition and short-circuits the workflow if the condition is False. Any
      downstream tasks are marked with a state of "skipped". If the condition is
      True, downstream tasks proceed as normal.
  
      The condition is determined by the result of `python_callable`.
      """
      def execute(self, context):
          condition = super(ShortCircuitOperator, self).execute(context)
          self.log.info("Condition result is %s", condition)
  
          if condition:
              self.log.info('Proceeding with downstream tasks...')
              return
  
          self.log.info('Skipping downstream tasks...')
  
          downstream_tasks = context['task'].get_flat_relatives(upstream=False)
          self.log.debug("Downstream task_ids %s", downstream_tasks)
  
          if downstream_tasks:
              self.skip(context['dag_run'], context['ti'].execution_date, downstream_tasks)
  
          self.log.info("Done.")
  
  class PythonVirtualenvOperator(PythonOperator):
      """
      Allows one to run a function in a virtualenv that is created and destroyed
      automatically (with certain caveats).
  
      The function must be defined using def, and not be part of a class. All imports
      must happen inside the function and no variables outside of the scope may be referenced.
      A global scope variable named virtualenv_string_args will be available (populated by
      string_args). In addition, one can pass stuff through op_args and op_kwargs, and one
      can use a return value.
  
      Note that if your virtualenv runs in a different Python major version than Airflow,
      you cannot use return values, op_args, or op_kwargs. You can use string_args though.
  
      :param python_callable: A python function with no references to outside variables,
          defined with def, which will be run in a virtualenv
      :type python_callable: function
      :param requirements: A list of requirements as specified in a pip install command
      :type requirements: list(str)
      :param python_version: The Python version to run the virtualenv with. Note that
          both 2 and 2.7 are acceptable forms.
      :type python_version: str
      :param use_dill: Whether to use dill to serialize the args and result (pickle is default).
          This allow more complex types but requires you to include dill in your requirements.
      :type use_dill: bool
      :param system_site_packages: Whether to include system_site_packages in your virtualenv.
          See virtualenv documentation for more information.
      :type system_site_packages: bool
      :param op_args: A list of positional arguments to pass to python_callable.
      :type op_kwargs: list
      :param op_kwargs: A dict of keyword arguments to pass to python_callable.
      :type op_kwargs: dict
      :param string_args: Strings that are present in the global var virtualenv_string_args,
          available to python_callable at runtime as a list(str). Note that args are split
          by newline.
      :type string_args: list(str)
      :param templates_dict: a dictionary where the values are templates that
          will get templated by the Airflow engine sometime between
          ``__init__`` and ``execute`` takes place and are made available
          in your callable's context after the template has been applied
      :type templates_dict: dict of str
      :param templates_exts: a list of file extensions to resolve while
          processing templated fields, for examples ``['.sql', '.hql']``
      :type templates_exts: list(str)
      """
      def __init__(self, python_callable, requirements=None, python_version=None, use_dill=False,
                   system_site_packages=True, op_args=None, op_kwargs=None, string_args=None,
                   templates_dict=None, templates_exts=None, *args, **kwargs):
          super(PythonVirtualenvOperator, self).__init__(
              python_callable=python_callable,
              op_args=op_args,
              op_kwargs=op_kwargs,
              templates_dict=templates_dict,
              templates_exts=templates_exts,
              provide_context=False,
              *args,
              **kwargs)
          self.requirements = requirements or []
          self.string_args = string_args or []
          self.python_version = python_version
          self.use_dill = use_dill
          self.system_site_packages = system_site_packages
          # check that dill is present if needed
          dill_in_requirements = map(lambda x: x.lower().startswith('dill'), self.requirements)
          if (not system_site_packages) and use_dill and not any(dill_in_requirements):
              raise AirflowException('If using dill, dill must be in the environment ' +
                                     'either via system_site_packages or requirements')
          # check that a function is passed, and that it is not a lambda
          if (not isinstance(self.python_callable, types.FunctionType)
                  or self.python_callable.__name__ == (lambda x: 0).__name__):
              raise AirflowException('{} only supports functions for python_callable arg',
                                     self.__class__.__name__)
          # check that args are passed iff python major version matches
          if (python_version is not None
                  and str(python_version)[0] != str(sys.version_info[0])
                  and self._pass_op_args()):
              raise AirflowException("Passing op_args or op_kwargs is not supported across "
                                     "different Python major versions "
                                     "for PythonVirtualenvOperator. Please use string_args.")
  
      def execute_callable(self):
          with TemporaryDirectory(prefix='venv') as tmp_dir:
              if self.templates_dict:
                  self.op_kwargs['templates_dict'] = self.templates_dict
              # generate filenames
              input_filename = os.path.join(tmp_dir, 'script.in')
              output_filename = os.path.join(tmp_dir, 'script.out')
              string_args_filename = os.path.join(tmp_dir, 'string_args.txt')
              script_filename = os.path.join(tmp_dir, 'script.py')
  
              # set up virtualenv
              self._execute_in_subprocess(self._generate_virtualenv_cmd(tmp_dir))
              cmd = self._generate_pip_install_cmd(tmp_dir)
              if cmd:
                  self._execute_in_subprocess(cmd)
  
              self._write_args(input_filename)
              self._write_script(script_filename)
              self._write_string_args(string_args_filename)
  
              # execute command in virtualenv
              self._execute_in_subprocess(
                  self._generate_python_cmd(tmp_dir,
                                            script_filename,
                                            input_filename,
                                            output_filename,
                                            string_args_filename))
              return self._read_result(output_filename)
  
      def _pass_op_args(self):
          # we should only pass op_args if any are given to us
          return len(self.op_args) + len(self.op_kwargs) > 0
  
      def _execute_in_subprocess(self, cmd):
          try:
              self.log.info("Executing cmd\n{}".format(cmd))
              output = subprocess.check_output(cmd,
                                               stderr=subprocess.STDOUT,
                                               close_fds=True)
              if output:
                  self.log.info("Got output\n{}".format(output))
          except subprocess.CalledProcessError as e:
              self.log.info("Got error output\n{}".format(e.output))
              raise
  
      def _write_string_args(self, filename):
          # writes string_args to a file, which are read line by line
          with open(filename, 'w') as f:
              f.write('\n'.join(map(str, self.string_args)))
  
      def _write_args(self, input_filename):
          # serialize args to file
          if self._pass_op_args():
              with open(input_filename, 'wb') as f:
                  arg_dict = ({'args': self.op_args, 'kwargs': self.op_kwargs})
                  if self.use_dill:
                      dill.dump(arg_dict, f)
                  else:
                      pickle.dump(arg_dict, f)
  
      def _read_result(self, output_filename):
          if os.stat(output_filename).st_size == 0:
              return None
          with open(output_filename, 'rb') as f:
              try:
                  if self.use_dill:
                      return dill.load(f)
                  else:
                      return pickle.load(f)
              except ValueError:
                  self.log.error("Error deserializing result. Note that result deserialization "
                                "is not supported across major Python versions.")
                  raise
  
      def _write_script(self, script_filename):
          with open(script_filename, 'w') as f:
              python_code = self._generate_python_code()
              self.log.debug('Writing code to file\n{}'.format(python_code))
              f.write(python_code)
  
      def _generate_virtualenv_cmd(self, tmp_dir):
          cmd = ['virtualenv', tmp_dir]
          if self.system_site_packages:
              cmd.append('--system-site-packages')
          if self.python_version is not None:
              cmd.append('--python=python{}'.format(self.python_version))
          return cmd
  
      def _generate_pip_install_cmd(self, tmp_dir):
          if len(self.requirements) == 0:
              return []
          else:
              # direct path alleviates need to activate
              cmd = ['{}/bin/pip'.format(tmp_dir), 'install']
              return cmd + self.requirements
  
      def _generate_python_cmd(self, tmp_dir, script_filename, input_filename, output_filename, string_args_filename):
          # direct path alleviates need to activate
          return ['{}/bin/python'.format(tmp_dir), script_filename, input_filename, output_filename, string_args_filename]
  
      def _generate_python_code(self):
          if self.use_dill:
              pickling_library = 'dill'
          else:
              pickling_library = 'pickle'
          fn = self.python_callable
          # dont try to read pickle if we didnt pass anything
          if self._pass_op_args():
              load_args_line = 'with open(sys.argv[1], "rb") as f: arg_dict = {}.load(f)'.format(pickling_library)
          else:
              load_args_line = 'arg_dict = {"args": [], "kwargs": {}}'
  
          # no indents in original code so we can accept any type of indents in the original function
          # we deserialize args, call function, serialize result if necessary
          return dedent("""\
          import {pickling_library}
          import sys
          {load_args_code}
          args = arg_dict["args"]
          kwargs = arg_dict["kwargs"]
          with open(sys.argv[3], 'r') as f: virtualenv_string_args = list(map(lambda x: x.strip(), list(f)))
          {python_callable_lines}
          res = {python_callable_name}(*args, **kwargs)
          with open(sys.argv[2], 'wb') as f: res is not None and {pickling_library}.dump(res, f)
          """).format(
                  load_args_code=load_args_line,
                  python_callable_lines=dedent(inspect.getsource(fn)),
                  python_callable_name=fn.__name__,
                  pickling_library=pickling_library)
  
          self.log.info("Done.")
  
  