text: # Copyright 2019 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.
  
  import re
  import warnings
  import yaml
  from collections import OrderedDict
  from typing import Union, List, Any, Callable, TypeVar, Dict
  
  from ._k8s_helper import K8sHelper
  from .. import dsl
  from ..dsl._container_op import BaseOp
  from ..dsl._artifact_location import ArtifactLocation
  
  # generics
  T = TypeVar('T')
  
  
  def _process_obj(obj: Any, map_to_tmpl_var: dict):
      """Recursively sanitize and replace any PipelineParam (instances and serialized strings)
      in the object with the corresponding template variables
      (i.e. '{{inputs.parameters.<PipelineParam.full_name>}}').
      
      Args:
        obj: any obj that may have PipelineParam
        map_to_tmpl_var: a dict that maps an unsanitized pipeline
                         params signature into a template var
      """
      # serialized str might be unsanitized
      if isinstance(obj, str):
          # get signature
          param_tuples = dsl.match_serialized_pipelineparam(obj)
          if not param_tuples:
              return obj
          # replace all unsanitized signature with template var
          for param_tuple in param_tuples:
              obj = re.sub(param_tuple.pattern, map_to_tmpl_var[param_tuple.pattern], obj)
  
      # list
      if isinstance(obj, list):
          return [_process_obj(item, map_to_tmpl_var) for item in obj]
  
      # tuple
      if isinstance(obj, tuple):
          return tuple((_process_obj(item, map_to_tmpl_var) for item in obj))
  
      # dict
      if isinstance(obj, dict):
          return {
              key: _process_obj(value, map_to_tmpl_var)
              for key, value in obj.items()
          }
  
      # pipelineparam
      if isinstance(obj, dsl.PipelineParam):
          # if not found in unsanitized map, then likely to be sanitized
          return map_to_tmpl_var.get(
              str(obj), '{{inputs.parameters.%s}}' % obj.full_name)
  
      # k8s objects (generated from swaggercodegen)
      if hasattr(obj, 'swagger_types') and isinstance(obj.swagger_types, dict):
          # process everything inside recursively
          for key in obj.swagger_types.keys():
              setattr(obj, key, _process_obj(getattr(obj, key), map_to_tmpl_var))
          # return json representation of the k8s obj
          return K8sHelper.convert_k8s_obj_to_json(obj)
  
      # k8s objects (generated from openapi)
      if hasattr(obj, 'openapi_types') and isinstance(obj.openapi_types, dict):
          # process everything inside recursively
          for key in obj.openapi_types.keys():
              setattr(obj, key, _process_obj(getattr(obj, key), map_to_tmpl_var))
          # return json representation of the k8s obj
          return K8sHelper.convert_k8s_obj_to_json(obj)
  
      # do nothing
      return obj
  
  
  def _process_base_ops(op: BaseOp):
      """Recursively go through the attrs listed in `attrs_with_pipelineparams`
      and sanitize and replace pipeline params with template var string.
  
      Returns a processed `BaseOp`.
  
      NOTE this is an in-place update to `BaseOp`'s attributes (i.e. the ones
      specified in `attrs_with_pipelineparams`, all `PipelineParam` are replaced
      with the corresponding template variable strings).
  
      Args:
          op {BaseOp}: class that inherits from BaseOp
  
      Returns:
          BaseOp
      """
  
      # map param's (unsanitized pattern or serialized str pattern) -> input param var str
      map_to_tmpl_var = {
          (param.pattern or str(param)): '{{inputs.parameters.%s}}' % param.full_name
          for param in op.inputs
      }
  
      # process all attr with pipelineParams except inputs and outputs parameters
      for key in op.attrs_with_pipelineparams:
          setattr(op, key, _process_obj(getattr(op, key), map_to_tmpl_var))
  
      return op
  
  
  def _parameters_to_json(params: List[dsl.PipelineParam]):
      """Converts a list of PipelineParam into an argo `parameter` JSON obj."""
      _to_json = (lambda param: dict(name=param.full_name, value=param.value)
                  if param.value else dict(name=param.full_name))
      params = [_to_json(param) for param in params]
      # Sort to make the results deterministic.
      params.sort(key=lambda x: x['name'])
      return params
  
  
  # TODO: artifacts?
  def _inputs_to_json(inputs_params: List[dsl.PipelineParam], _artifacts=None):
      """Converts a list of PipelineParam into an argo `inputs` JSON obj."""
      parameters = _parameters_to_json(inputs_params)
      return {'parameters': parameters} if parameters else None
  
  
  def _outputs_to_json(op: BaseOp,
                       outputs: Dict[str, dsl.PipelineParam],
                       param_outputs: Dict[str, str],
                       output_artifacts: List[dict]):
      """Creates an argo `outputs` JSON obj."""
      if isinstance(op, dsl.ResourceOp):
          value_from_key = "jsonPath"
      else:
          value_from_key = "path"
      output_parameters = []
      for param in outputs.values():
          output_parameters.append({
              'name': param.full_name,
              'valueFrom': {
                  value_from_key: param_outputs[param.name]
              }
          })
      output_parameters.sort(key=lambda x: x['name'])
      ret = {}
      if output_parameters:
          ret['parameters'] = output_parameters
      if output_artifacts:
          ret['artifacts'] = output_artifacts
  
      return ret
  
  
  # TODO: generate argo python classes from swagger and use convert_k8s_obj_to_json??
  def _op_to_template(op: BaseOp):
      """Generate template given an operator inherited from BaseOp."""
  
      # NOTE in-place update to BaseOp
      # replace all PipelineParams with template var strings
      processed_op = _process_base_ops(op)
  
      if isinstance(op, dsl.ContainerOp):
          # default output artifacts
          output_artifact_paths = OrderedDict(op.output_artifact_paths)
          output_artifact_paths.setdefault('mlpipeline-ui-metadata', '/mlpipeline-ui-metadata.json')
          output_artifact_paths.setdefault('mlpipeline-metrics', '/mlpipeline-metrics.json')
  
          output_artifacts = [
               K8sHelper.convert_k8s_obj_to_json(
                   ArtifactLocation.create_artifact_for_s3(
                       op.artifact_location, 
                       name=name, 
                       path=path, 
                       key='runs/{{workflow.uid}}/{{pod.name}}/' + name + '.tgz'))
              for name, path in output_artifact_paths.items()
          ]
  
          for output_artifact in output_artifacts:
              if output_artifact['name'] in ['mlpipeline-ui-metadata', 'mlpipeline-metrics']:
                  output_artifact['optional'] = True
  
          # workflow template
          template = {
              'name': processed_op.name,
              'container': K8sHelper.convert_k8s_obj_to_json(
                  processed_op.container
              )
          }
      elif isinstance(op, dsl.ResourceOp):
          # no output artifacts
          output_artifacts = []
  
          # workflow template
          processed_op.resource["manifest"] = yaml.dump(
              K8sHelper.convert_k8s_obj_to_json(processed_op.k8s_resource),
              default_flow_style=False
          )
          template = {
              'name': processed_op.name,
              'resource': K8sHelper.convert_k8s_obj_to_json(
                  processed_op.resource
              )
          }
  
      # inputs
      inputs = _inputs_to_json(processed_op.inputs)
      if inputs:
          template['inputs'] = inputs
  
      # outputs
      if isinstance(op, dsl.ContainerOp):
          param_outputs = processed_op.file_outputs
      elif isinstance(op, dsl.ResourceOp):
          param_outputs = processed_op.attribute_outputs
      template['outputs'] = _outputs_to_json(op, processed_op.outputs,
                                             param_outputs, output_artifacts)
  
      # node selector
      if processed_op.node_selector:
          template['nodeSelector'] = processed_op.node_selector
  
      # tolerations
      if processed_op.tolerations:
          template['tolerations'] = processed_op.tolerations
  
      # metadata
      if processed_op.pod_annotations or processed_op.pod_labels:
          template['metadata'] = {}
          if processed_op.pod_annotations:
              template['metadata']['annotations'] = processed_op.pod_annotations
          if processed_op.pod_labels:
              template['metadata']['labels'] = processed_op.pod_labels
      # retries
      if processed_op.num_retries:
          template['retryStrategy'] = {'limit': processed_op.num_retries}
  
      # timeout
      if processed_op.timeout:
          template['activeDeadlineSeconds'] = processed_op.timeout
  
      # sidecars
      if processed_op.sidecars:
          template['sidecars'] = processed_op.sidecars
  
      # Display name
      if processed_op.display_name:
          template.setdefault('metadata', {}).setdefault('annotations', {})['pipelines.kubeflow.org/task_display_name'] = processed_op.display_name
  
      return template
  