# Copyright 2019-2020 kubeflow.org.
#
# 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 inspect
import json
import tarfile
import yaml
import copy
import itertools
import zipfile
import re
import textwrap

from typing import Callable, List, Text, Dict, Any
from os import environ as env
from distutils.util import strtobool

# Kubeflow Pipeline imports
from kfp import dsl
from kfp.compiler._default_transformers import add_pod_env  # , add_pod_labels, get_default_telemetry_labels
from kfp.compiler.compiler import Compiler
# from kfp.components._yaml_utils import dump_yaml
from kfp.components.structures import InputSpec
from kfp.dsl._for_loop import LoopArguments, LoopArgumentVariable
from kfp.dsl._metadata import _extract_pipeline_metadata

# KFP-Tekton imports
from kfp_tekton.compiler import __tekton_api_version__ as tekton_api_version
from kfp_tekton.compiler._data_passing_rewriter import fix_big_data_passing
from kfp_tekton.compiler._k8s_helper import convert_k8s_obj_to_json, sanitize_k8s_name, sanitize_k8s_object
from kfp_tekton.compiler._op_to_template import _op_to_template


DEFAULT_ARTIFACT_BUCKET = env.get('DEFAULT_ARTIFACT_BUCKET', 'mlpipeline')
DEFAULT_ARTIFACT_ENDPOINT = env.get('DEFAULT_ARTIFACT_ENDPOINT', 'minio-service.kubeflow:9000')
DEFAULT_ARTIFACT_ENDPOINT_SCHEME = env.get('DEFAULT_ARTIFACT_ENDPOINT_SCHEME', 'http://')
TEKTON_GLOBAL_DEFAULT_TIMEOUT = strtobool(env.get('TEKTON_GLOBAL_DEFAULT_TIMEOUT', 'false'))


def _get_super_condition_template():

  python_script = textwrap.dedent('''\
    'import sys
    input1=str.rstrip(sys.argv[1])
    input2=str.rstrip(sys.argv[2])
    try:
      input1=int(input1)
      input2=int(input2)
    except:
      input1=str(input1)
    status="true" if (input1 $(inputs.params.operator) input2) else "false"
    f = open("/tekton/results/status", "w")
    f.write(status)
    f.close()' ''')

  template = {
    'results': [
      {'name': 'status',
       'description': 'Conditional task status'
       }
    ],
    'params': [
      {'name': 'operand1'},
      {'name': 'operand2'},
      {'name': 'operator'}
    ],
    'steps': [{
      'script': 'python -c ' + python_script + "'$(inputs.params.operand1)' '$(inputs.params.operand2)'",
      'image': 'python:alpine3.6',
    }]
  }

  return template


class TektonCompiler(Compiler):
  """DSL Compiler to generate Tekton YAML.

  It compiles DSL pipeline functions into workflow yaml. Example usage:
  ```python
  @dsl.pipeline(
    name='name',
    description='description'
  )
  def my_pipeline(a: int = 1, b: str = "default value"):
    ...

  TektonCompiler().compile(my_pipeline, 'path/to/workflow.yaml')
  ```
  """

  def __init__(self, **kwargs):
    # Input and output artifacts are hash maps for metadata tracking.
    # artifact_items is the artifact dependency map
    self.input_artifacts = {}
    self.output_artifacts = {}
    self.artifact_items = {}
    super().__init__(**kwargs)

  def _get_loop_task(self, task: Dict, op_name_to_for_loop_op):
    """Get the list of task references which will flatten the loop parameters defined in pipeline.

    Args:
      task: ops template in pipeline.
      op_name_to_for_loop_op: a dictionary of ospgroup
    """
    # Get all the params in the task
    task_params_list = []
    for tp in task.get('params', []):
      task_params_list.append(tp)
    # Get the loop values for each param
    for tp in task_params_list:
      for loop_param in op_name_to_for_loop_op.values():
        loop_args = loop_param.loop_args
        if loop_args.name in tp['name']:
          lpn = tp['name'].replace(loop_args.name, '').replace(LoopArgumentVariable.SUBVAR_NAME_DELIMITER, '')
          if lpn:
            tp['loop-value'] = [value[lpn] for value in loop_args.items_or_pipeline_param]
          else:
            tp['loop-value'] = loop_args.items_or_pipeline_param
    # Get the task params list
    # 1. Get the task_params list without loop first
    loop_value = [p['loop-value'] for p in task_params_list if p.get('loop-value')]
    task_params_without_loop = [p for p in task_params_list if not p.get('loop-value')]
    # 2. Get the task_params list with loop
    loop_params = [p for p in task_params_list if p.get('loop-value')]
    for param in loop_params:
      del param['loop-value']
      del param['value']

    value_iter = list(itertools.product(*loop_value))
    value_iter_list = []
    for values in value_iter:
      opt = []
      for value in values:
        opt.append({"value": str(value)})
      value_iter_list.append(opt)
    {value[i].update(loop_params[i]) for i in range(len(loop_params)) for value in value_iter_list}
    task_params_with_loop = value_iter_list
    # 3. combine task params
    list(a.extend(task_params_without_loop) for a in task_params_with_loop)
    task_params_all = task_params_with_loop
    # Get the task list based on params list
    task_list = []
    del task['params']
    task_name_suffix_length = len(LoopArguments.LOOP_ITEM_NAME_BASE) + LoopArguments.NUM_CODE_CHARS + 2
    task_old_name = sanitize_k8s_name(task['name'], suffix_space=task_name_suffix_length)
    for i in range(len(task_params_all)):
      task['params'] = task_params_all[i]
      task['name'] = '%s-%s-%d' % (task_old_name, LoopArguments.LOOP_ITEM_NAME_BASE, i)
      task_list.append(copy.deepcopy(task))
      del task['params']
    return task_list

  def _resolve_value_or_reference(self, value_or_reference, potential_references):
    """_resolve_value_or_reference resolves values and PipelineParams, which could be task parameters or input parameters.
    Args:
      value_or_reference: value or reference to be resolved. It could be basic python types or PipelineParam
      potential_references(dict{str->str}): a dictionary of parameter names to task names
      """
    if isinstance(value_or_reference, dsl.PipelineParam):
      parameter_name = self._pipelineparam_full_name(value_or_reference)
      task_names = [task_name for param_name, task_name in potential_references if param_name == parameter_name]
      if task_names:
        task_name = task_names[0]
        # When the task_name is None, the parameter comes directly from ancient ancesters
        # instead of parents. Thus, it is resolved as the input parameter in the current group.
        if task_name is None:
          return '$(params.%s)' % parameter_name
        else:
          return '$(params.%s)' % task_name
      else:
        return '$(params.%s)' % parameter_name
    else:
      return str(value_or_reference)

  def _group_to_dag_template(self, group, inputs, outputs, dependencies):
    """Generate template given an OpsGroup.
    inputs, outputs, dependencies are all helper dicts.
    """

    # Generate GroupOp template
    sub_group = group
    template = {
      'metadata': {
        'name': sanitize_k8s_name(sub_group.name),
      },
      'spec': {}
    }

    # Generates a pseudo-template unique to conditions due to the catalog condition approach
    # where every condition is an extension of one super-condition
    if isinstance(sub_group, dsl.OpsGroup) and sub_group.type == 'condition':
      subgroup_inputs = inputs.get(sub_group.name, [])
      condition = sub_group.condition

      operand1_value = self._resolve_value_or_reference(condition.operand1, subgroup_inputs)
      operand2_value = self._resolve_value_or_reference(condition.operand2, subgroup_inputs)

      template['kind'] = 'Condition'
      template['spec']['params'] = [
        {'name': 'operand1', 'value': operand1_value},
        {'name': 'operand2', 'value': operand2_value},
        {'name': 'operator', 'value': str(condition.operator)}
      ]

    return template

  def _create_dag_templates(self, pipeline, op_transformers=None, params=None, op_to_templates_handler=None):
    """Create all groups and ops templates in the pipeline.

    Args:
      pipeline: Pipeline context object to get all the pipeline data from.
      op_transformers: A list of functions that are applied to all ContainerOp instances that are being processed.
      op_to_templates_handler: Handler which converts a base op into a list of argo templates.
    """

    op_to_steps_handler = op_to_templates_handler or (lambda op: [_op_to_template(op,
                                                                  self.output_artifacts,
                                                                  self.artifact_items)])
    root_group = pipeline.groups[0]

    # Call the transformation functions before determining the inputs/outputs, otherwise
    # the user would not be able to use pipeline parameters in the container definition
    # (for example as pod labels) - the generated template is invalid.
    for op in pipeline.ops.values():
      for transformer in op_transformers or []:
        transformer(op)

    # Generate core data structures to prepare for argo yaml generation
    #   op_name_to_parent_groups: op name -> list of ancestor groups including the current op
    #   opsgroups: a dictionary of ospgroup.name -> opsgroup
    #   inputs, outputs: group/op names -> list of tuples (full_param_name, producing_op_name)
    #   condition_params: recursive_group/op names -> list of pipelineparam
    #   dependencies: group/op name -> list of dependent groups/ops.
    # Special Handling for the recursive opsgroup
    #   op_name_to_parent_groups also contains the recursive opsgroups
    #   condition_params from _get_condition_params_for_ops also contains the recursive opsgroups
    #   groups does not include the recursive opsgroups
    opsgroups = self._get_groups(root_group)
    op_name_to_parent_groups = self._get_groups_for_ops(root_group)
    opgroup_name_to_parent_groups = self._get_groups_for_opsgroups(root_group)
    condition_params = self._get_condition_params_for_ops(root_group)
    inputs, outputs = self._get_inputs_outputs(
      pipeline,
      root_group,
      op_name_to_parent_groups,
      opgroup_name_to_parent_groups,
      condition_params,
      {}
    )
    dependencies = self._get_dependencies(
      pipeline,
      root_group,
      op_name_to_parent_groups,
      opgroup_name_to_parent_groups,
      opsgroups,
      condition_params,
    )

    templates = []
    for opsgroup in opsgroups.keys():
      # Only Conditions get templates in Tekton
      if opsgroups[opsgroup].type == 'condition':
        template = self._group_to_dag_template(opsgroups[opsgroup], inputs, outputs, dependencies)
        templates.append(template)

    for op in pipeline.ops.values():
      templates.extend(op_to_steps_handler(op))

    return templates

  def _process_resourceOp(self, task_refs, pipeline):
    """ handle resourceOp cases in pipeline """
    for task in task_refs:
      op = pipeline.ops.get(task['name'])
      if isinstance(op, dsl.ResourceOp):
        action = op.resource.get('action')
        merge_strategy = op.resource.get('merge_strategy')
        success_condition = op.resource.get('successCondition')
        failure_condition = op.resource.get('failureCondition')
        task['params'] = [tp for tp in task.get('params', []) if tp.get('name') != "image"]
        if not merge_strategy:
          task['params'] = [tp for tp in task.get('params', []) if tp.get('name') != 'merge-strategy']
        if not success_condition:
          task['params'] = [tp for tp in task.get('params', []) if tp.get('name') != 'success-condition']
        if not failure_condition:
          task['params'] = [tp for tp in task.get('params', []) if tp.get('name') != "failure-condition"]
        for tp in task.get('params', []):
          if tp.get('name') == "action" and action:
            tp['value'] = action
          if tp.get('name') == "merge-strategy" and merge_strategy:
            tp['value'] = merge_strategy
          if tp.get('name') == "success-condition" and success_condition:
            tp['value'] = success_condition
          if tp.get('name') == "failure-condition" and failure_condition:
            tp['value'] = failure_condition
          if tp.get('name') == "output":
            output_values = ''
            for value in sorted(list(op.attribute_outputs.items()), key=lambda x: x[0]):
              output_value = textwrap.dedent("""\
                    - name: %s
                      valueFrom: '%s'
              """ % (value[0], value[1]))
              output_values += output_value
            tp['value'] = output_values

  def _create_pipeline_workflow(self, args, pipeline, op_transformers=None, pipeline_conf=None) \
          -> Dict[Text, Any]:
    """Create workflow for the pipeline."""
    # Input Parameters
    params = []
    for arg in args:
      param = {'name': arg.name}
      if arg.value is not None:
        if isinstance(arg.value, (list, tuple, dict)):
          param['default'] = json.dumps(arg.value, sort_keys=True)
        else:
          param['default'] = str(arg.value)
      params.append(param)

    # TODO: task templates?

    # generate Tekton tasks from pipeline ops
    raw_templates = self._create_dag_templates(pipeline, op_transformers, params)

    # generate task and condition reference list for the Tekton Pipeline
    condition_refs = {}

    # TODO
    task_refs = []
    templates = []
    condition_task_refs = {}
    for template in raw_templates:
      # TODO Allow an opt-out for the condition_template
      if template['kind'] == 'Condition':
        condition_task_ref = [{
            'name': template['metadata']['name'],
            'params': [{
                'name': p['name'],
                'value': p.get('value', '')
              } for p in template['spec'].get('params', [])
            ],
            'taskSpec': _get_super_condition_template(),
        }]
        condition_refs[template['metadata']['name']] = [
            {
              'input': '$(tasks.%s.results.status)' % template['metadata']['name'],
              'operator': 'in',
              'values': ['true']
            }
          ]
        condition_task_refs[template['metadata']['name']] = condition_task_ref
      else:
        templates.append(template)
        task_ref = {
            'name': template['metadata']['name'],
            'params': [{
                'name': p['name'],
                'value': p.get('default', '')
              } for p in template['spec'].get('params', [])
            ],
            'taskSpec': template['spec'],
          }

        if template['metadata'].get('labels', None):
          task_ref['taskSpec']['metadata'] = task_ref['taskSpec'].get('metadata', {})
          task_ref['taskSpec']['metadata']['labels'] = template['metadata']['labels']
        if template['metadata'].get('annotations', None):
          task_ref['taskSpec']['metadata'] = task_ref['taskSpec'].get('metadata', {})
          task_ref['taskSpec']['metadata']['annotations'] = template['metadata']['annotations']
        task_refs.append(task_ref)

    # process input parameters from upstream tasks for conditions and pair conditions with their ancestor conditions
    opsgroup_stack = [pipeline.groups[0]]
    condition_stack = [None]
    while opsgroup_stack:
      cur_opsgroup = opsgroup_stack.pop()
      most_recent_condition = condition_stack.pop()

      if cur_opsgroup.type == 'condition':
        condition_task_ref = condition_task_refs[cur_opsgroup.name][0]
        condition = cur_opsgroup.condition
        input_params = []

        # Process input parameters if needed
        if isinstance(condition.operand1, dsl.PipelineParam):
          if condition.operand1.op_name:
            operand_value = '$(tasks.' + condition.operand1.op_name + '.results.' + sanitize_k8s_name(condition.operand1.name) + ')'
          else:
            operand_value = '$(params.' + condition.operand1.name + ')'
          input_params.append(operand_value)
        if isinstance(condition.operand2, dsl.PipelineParam):
          if condition.operand2.op_name:
            operand_value = '$(tasks.' + condition.operand2.op_name + '.results.' + sanitize_k8s_name(condition.operand2.name) + ')'
          else:
            operand_value = '$(params.' + condition.operand2.name + ')'
          input_params.append(operand_value)
        for param_iter in range(len(input_params)):
          condition_task_ref['params'][param_iter]['value'] = input_params[param_iter]

      opsgroup_stack.extend(cur_opsgroup.groups)
      condition_stack.extend([most_recent_condition for x in range(len(cur_opsgroup.groups))])
    # add task dependencies and add condition refs to the task ref that depends on the condition
    op_name_to_parent_groups = self._get_groups_for_ops(pipeline.groups[0])
    for task in task_refs:
      op = pipeline.ops.get(task['name'])
      parent_group = op_name_to_parent_groups.get(task['name'], [])
      if parent_group:
        if condition_refs.get(parent_group[-2], []):
          task['when'] = condition_refs.get(op_name_to_parent_groups[task['name']][-2], [])
      if op.dependent_names:
        task['runAfter'] = op.dependent_names

    # process input parameters from upstream tasks
    pipeline_param_names = [p['name'] for p in params]
    for task in task_refs:
      op = pipeline.ops.get(task['name'])
      for tp in task.get('params', []):
        if tp['name'] in pipeline_param_names:
          tp['value'] = '$(params.%s)' % tp['name']
        else:
          for pp in op.inputs:
            if tp['name'] == pp.full_name:
              tp['value'] = '$(tasks.%s.results.%s)' % (pp.op_name, pp.name)
              # Create input artifact tracking annotation
              input_annotation = self.input_artifacts.get(task['name'], [])
              input_annotation.append(
                  {
                      'name': tp['name'],
                      'parent_task': pp.op_name
                  }
              )
              self.input_artifacts[task['name']] = input_annotation
              break

    # add retries params
    for task in task_refs:
      op = pipeline.ops.get(task['name'])
      if op.num_retries:
        task['retries'] = op.num_retries

    # add timeout params to task_refs, instead of task.
    for task in task_refs:
      op = pipeline.ops.get(task['name'])
      if not TEKTON_GLOBAL_DEFAULT_TIMEOUT or op.timeout:
        task['timeout'] = '%ds' % op.timeout

    # handle resourceOp cases in pipeline
    self._process_resourceOp(task_refs, pipeline)

    # handle exit handler in pipeline
    finally_tasks = []
    for task in task_refs:
      op = pipeline.ops.get(task['name'])
      if op.is_exit_handler:
        finally_tasks.append(task)
    task_refs = [task for task in task_refs if not pipeline.ops.get(task['name']).is_exit_handler]

    # process loop parameters, keep this section in the behind of other processes, ahead of gen pipeline
    root_group = pipeline.groups[0]
    op_name_to_for_loop_op = self._get_for_loop_ops(root_group)
    if op_name_to_for_loop_op:
      for loop_param in op_name_to_for_loop_op.values():
        if loop_param.items_is_pipeline_param is True:
          raise NotImplementedError("dynamic params are not yet implemented")
      include_loop_task_refs = []
      for task in task_refs:
        with_loop_task = self._get_loop_task(task, op_name_to_for_loop_op)
        include_loop_task_refs.extend(with_loop_task)
      task_refs = include_loop_task_refs

    # Flatten condition task
    condition_task_refs_temp = []
    for condition_task_ref in condition_task_refs.values():
      for ref in condition_task_ref:
        condition_task_refs_temp.append(ref)
    condition_task_refs = condition_task_refs_temp
    # TODO: generate the PipelineRun template
    pipeline_run = {
      'apiVersion': tekton_api_version,
      'kind': 'PipelineRun',
      'metadata': {
        'name': sanitize_k8s_name(pipeline.name or 'Pipeline', suffix_space=4),
        # 'labels': get_default_telemetry_labels(),
        'annotations': {
          'tekton.dev/output_artifacts': json.dumps(self.output_artifacts, sort_keys=True),
          'tekton.dev/input_artifacts': json.dumps(self.input_artifacts, sort_keys=True),
          'tekton.dev/artifact_bucket': DEFAULT_ARTIFACT_BUCKET,
          'tekton.dev/artifact_endpoint': DEFAULT_ARTIFACT_ENDPOINT,
          'tekton.dev/artifact_endpoint_scheme': DEFAULT_ARTIFACT_ENDPOINT_SCHEME,
          'tekton.dev/artifact_items': json.dumps(self.artifact_items, sort_keys=True),
          'sidecar.istio.io/inject': 'false'  # disable Istio inject since Tekton cannot run with Istio sidecar
        }
      },
      'spec': {
        'params': [{
            'name': p['name'],
            'value': p.get('default', '')
          } for p in params],
        'pipelineSpec': {
          'params': params,
          'tasks': task_refs + condition_task_refs,
          'finally': finally_tasks
        }
      }
    }

    # TODO: pipelineRun additions

    # Generate TaskRunSpec PodTemplate:s
    task_run_spec = []
    for task in task_refs:

      # TODO: should loop-item tasks be included here?
      if LoopArguments.LOOP_ITEM_NAME_BASE in task['name']:
        task_name = re.sub(r'-%s-.+$' % LoopArguments.LOOP_ITEM_NAME_BASE, '', task['name'])
      else:
        task_name = task['name']
      op = pipeline.ops.get(task_name)
      if not op:
        raise RuntimeError("unable to find op with name '%s'" % task["name"])

      task_spec = {"pipelineTaskName": task['name'],
                   "taskPodTemplate": {}}
      if op.affinity:
        task_spec["taskPodTemplate"]["affinity"] = convert_k8s_obj_to_json(op.affinity)
      if op.tolerations:
        task_spec["taskPodTemplate"]['tolerations'] = op.tolerations
      if op.node_selector:
        task_spec["taskPodTemplate"]['nodeSelector'] = op.node_selector
      if bool(task_spec["taskPodTemplate"]):
        task_run_spec.append(task_spec)
    if len(task_run_spec) > 0:
      pipeline_run['spec']['taskRunSpecs'] = task_run_spec

    # add workflow level timeout to pipeline run
    if not TEKTON_GLOBAL_DEFAULT_TIMEOUT or pipeline.conf.timeout:
      pipeline_run['spec']['timeout'] = '%ds' % pipeline.conf.timeout

    # generate the Tekton podTemplate for image pull secret
    if len(pipeline.conf.image_pull_secrets) > 0:
      pipeline_run['spec']['podTemplate'] = pipeline_run['spec'].get('podTemplate', {})
      pipeline_run['spec']['podTemplate']['imagePullSecrets'] = [
        {"name": s.name} for s in pipeline.conf.image_pull_secrets]

    workflow = pipeline_run

    return workflow

  def _sanitize_and_inject_artifact(self, pipeline: dsl.Pipeline, pipeline_conf=None):
    """Sanitize operator/param names and inject pipeline artifact location."""

    # Sanitize operator names and param names
    sanitized_ops = {}

    for op in pipeline.ops.values():
      sanitized_name = sanitize_k8s_name(op.name)
      op.name = sanitized_name
      for param in op.outputs.values():
        param.name = sanitize_k8s_name(param.name, True)
        if param.op_name:
          param.op_name = sanitize_k8s_name(param.op_name)
      if op.output is not None and not isinstance(op.output, dsl._container_op._MultipleOutputsError):
        op.output.name = sanitize_k8s_name(op.output.name, True)
        op.output.op_name = sanitize_k8s_name(op.output.op_name)
      if op.dependent_names:
        op.dependent_names = [sanitize_k8s_name(name) for name in op.dependent_names]
      if isinstance(op, dsl.ContainerOp) and op.file_outputs is not None:
        sanitized_file_outputs = {}
        for key in op.file_outputs.keys():
          sanitized_file_outputs[sanitize_k8s_name(key, True)] = op.file_outputs[key]
        op.file_outputs = sanitized_file_outputs
      elif isinstance(op, dsl.ResourceOp) and op.attribute_outputs is not None:
        sanitized_attribute_outputs = {}
        for key in op.attribute_outputs.keys():
          sanitized_attribute_outputs[sanitize_k8s_name(key, True)] = \
            op.attribute_outputs[key]
        op.attribute_outputs = sanitized_attribute_outputs
      if isinstance(op, dsl.ContainerOp) and op.container is not None:
        sanitize_k8s_object(op.container)
      sanitized_ops[sanitized_name] = op
    pipeline.ops = sanitized_ops

  # NOTE: the methods below are "copied" from KFP with changes in the method signatures (only)
  #       to accommodate multiple documents in the YAML output file:
  #         KFP Argo -> Dict[Text, Any]
  #         KFP Tekton -> List[Dict[Text, Any]]

  def _create_workflow(self,
                       pipeline_func: Callable,
                       pipeline_name: Text = None,
                       pipeline_description: Text = None,
                       params_list: List[dsl.PipelineParam] = None,
                       pipeline_conf: dsl.PipelineConf = None,
                       ) -> Dict[Text, Any]:
    """ Internal implementation of create_workflow."""
    params_list = params_list or []
    argspec = inspect.getfullargspec(pipeline_func)

    # Create the arg list with no default values and call pipeline function.
    # Assign type information to the PipelineParam
    pipeline_meta = _extract_pipeline_metadata(pipeline_func)
    pipeline_meta.name = pipeline_name or pipeline_meta.name
    pipeline_meta.description = pipeline_description or pipeline_meta.description
    pipeline_name = sanitize_k8s_name(pipeline_meta.name)

    # Need to first clear the default value of dsl.PipelineParams. Otherwise, it
    # will be resolved immediately in place when being to each component.
    default_param_values = {}
    for param in params_list:
      default_param_values[param.name] = param.value
      param.value = None

    # Currently only allow specifying pipeline params at one place.
    if params_list and pipeline_meta.inputs:
      raise ValueError('Either specify pipeline params in the pipeline function, or in "params_list", but not both.')

    args_list = []
    for arg_name in argspec.args:
      arg_type = None
      for input in pipeline_meta.inputs or []:
        if arg_name == input.name:
          arg_type = input.type
          break
      args_list.append(dsl.PipelineParam(sanitize_k8s_name(arg_name, True), param_type=arg_type))

    with dsl.Pipeline(pipeline_name) as dsl_pipeline:
      pipeline_func(*args_list)

    # Configuration passed to the compiler is overriding. Unfortunately, it is
    # not trivial to detect whether the dsl_pipeline.conf was ever modified.
    pipeline_conf = pipeline_conf or dsl_pipeline.conf

    self._validate_exit_handler(dsl_pipeline)
    self._sanitize_and_inject_artifact(dsl_pipeline, pipeline_conf)

    # Fill in the default values.
    args_list_with_defaults = []
    if pipeline_meta.inputs:
      args_list_with_defaults = [dsl.PipelineParam(sanitize_k8s_name(arg_name, True))
                                 for arg_name in argspec.args]
      if argspec.defaults:
        for arg, default in zip(reversed(args_list_with_defaults), reversed(argspec.defaults)):
          arg.value = default.value if isinstance(default, dsl.PipelineParam) else default
    elif params_list:
      # Or, if args are provided by params_list, fill in pipeline_meta.
      for param in params_list:
        param.value = default_param_values[param.name]

      args_list_with_defaults = params_list
      pipeline_meta.inputs = [
        InputSpec(
            name=param.name,
            type=param.param_type,
            default=param.value) for param in params_list]

    op_transformers = [add_pod_env]

    # # By default adds telemetry instruments. Users can opt out toggling
    # # allow_telemetry.
    # # Also, TFX pipelines will be bypassed for pipeline compiled by tfx>0.21.4.
    # if allow_telemetry:
    #   pod_labels = get_default_telemetry_labels()
    #   op_transformers.append(add_pod_labels(pod_labels))

    op_transformers.extend(pipeline_conf.op_transformers)

    workflow = self._create_pipeline_workflow(
        args_list_with_defaults,
        dsl_pipeline,
        op_transformers,
        pipeline_conf,
    )

    workflow = fix_big_data_passing(workflow)

    workflow.setdefault('metadata', {}).setdefault('annotations', {})['pipelines.kubeflow.org/pipeline_spec'] = \
      json.dumps(pipeline_meta.to_dict(), sort_keys=True)

    # recursively strip empty structures, DANGER: this may remove necessary empty elements ?!
    def remove_empty_elements(obj) -> dict:
      if not isinstance(obj, (dict, list)):
        return obj
      if isinstance(obj, list):
        return [remove_empty_elements(o) for o in obj if o != []]
      return {k: remove_empty_elements(v) for k, v in obj.items()
              if v != []}

    workflow = remove_empty_elements(workflow)

    return workflow

  def compile(self,
              pipeline_func,
              package_path,
              type_check=True,
              pipeline_conf: dsl.PipelineConf = None):
    """Compile the given pipeline function into workflow yaml.
    Args:
      pipeline_func: pipeline functions with @dsl.pipeline decorator.
      package_path: the output workflow tar.gz file path. for example, "~/a.tar.gz"
      type_check: whether to enable the type check or not, default: False.
      pipeline_conf: PipelineConf instance. Can specify op transforms,
                     image pull secrets and other pipeline-level configuration options.
                     Overrides any configuration that may be set by the pipeline.
    """
    super().compile(pipeline_func, package_path, type_check, pipeline_conf=pipeline_conf)

  @staticmethod
  def _write_workflow(workflow: Dict[Text, Any],
                      package_path: Text = None):
    """Dump pipeline workflow into yaml spec and write out in the format specified by the user.

    Args:
      workflow: Workflow spec of the pipline, dict.
      package_path: file path to be written. If not specified, a yaml_text string
        will be returned.
    """
    # yaml_text = dump_yaml(workflow)
    yaml.Dumper.ignore_aliases = lambda *args: True
    yaml_text = yaml.dump(workflow, default_flow_style=False)  # Tekton change

    # Use regex to replace all the Argo variables to Tekton variables. For variables that are unique to Argo,
    # we raise an Error to alert users about the unsupported variables. Here is the list of Argo variables.
    # https://github.com/argoproj/argo/blob/master/docs/variables.md
    # Since Argo variables can be used in anywhere in the yaml, we need to dump and then parse the whole yaml
    # using regular expression.
    tekton_var_regex_rules = [
        {
          'argo_rule': '{{inputs.parameters.([^ \t\n.:,;{}]+)}}',
          'tekton_rule': '$(inputs.params.\g<1>)'
        },
        {
          'argo_rule': '{{outputs.parameters.([^ \t\n.:,;{}]+).path}}',
          'tekton_rule': '$(results.\g<1>.path)'
        },
        {
          'argo_rule': '{{workflow.uid}}',
          'tekton_rule': '$(context.pipelineRun.uid)'
        },
        {
          'argo_rule': '{{workflow.name}}',
          'tekton_rule': '$(context.pipelineRun.name)'
        },
        {
          'argo_rule': '{{workflow.namespace}}',
          'tekton_rule': '$(context.pipelineRun.namespace)'
        },
        {
          'argo_rule': '{{workflow.parameters.([^ \t\n.:,;{}]+)}}',
          'tekton_rule': '$(params.\g<1>)'
        }
    ]
    for regex_rule in tekton_var_regex_rules:
      yaml_text = re.sub(regex_rule['argo_rule'], regex_rule['tekton_rule'], yaml_text)

    unsupported_vars = re.findall(r"{{[^ \t\n.:,;{}]+\.[^ \t\n:,;{}]+}}", yaml_text)
    if unsupported_vars:
      raise ValueError('These Argo variables are not supported in Tekton Pipeline: %s' % ", ".join(str(v) for v in set(unsupported_vars)))

    if '{{pipelineparam' in yaml_text:
      raise RuntimeError(
          'Internal compiler error: Found unresolved PipelineParam. '
          'Please create a new issue at https://github.com/kubeflow/kfp-tekton/issues '
          'attaching the pipeline DSL code and the pipeline YAML.')

    if package_path is None:
      return yaml_text

    if package_path.endswith('.tar.gz') or package_path.endswith('.tgz'):
      from contextlib import closing
      from io import BytesIO
      with tarfile.open(package_path, "w:gz") as tar:
          with closing(BytesIO(yaml_text.encode())) as yaml_file:
            tarinfo = tarfile.TarInfo('pipeline.yaml')
            tarinfo.size = len(yaml_file.getvalue())
            tar.addfile(tarinfo, fileobj=yaml_file)
    elif package_path.endswith('.zip'):
      with zipfile.ZipFile(package_path, "w") as zip:
        zipinfo = zipfile.ZipInfo('pipeline.yaml')
        zipinfo.compress_type = zipfile.ZIP_DEFLATED
        zip.writestr(zipinfo, yaml_text)
    elif package_path.endswith('.yaml') or package_path.endswith('.yml'):
      with open(package_path, 'w') as yaml_file:
        yaml_file.write(yaml_text)
    else:
      raise ValueError(
          'The output path %s should end with one of the following formats: '
          '[.tar.gz, .tgz, .zip, .yaml, .yml]' % package_path)

  def _create_and_write_workflow(self,
                                 pipeline_func: Callable,
                                 pipeline_name: Text = None,
                                 pipeline_description: Text = None,
                                 params_list: List[dsl.PipelineParam] = None,
                                 pipeline_conf: dsl.PipelineConf = None,
                                 package_path: Text = None,
                                 ) -> None:
    """Compile the given pipeline function and dump it to specified file format."""
    workflow = self._create_workflow(
        pipeline_func,
        pipeline_name,
        pipeline_description,
        params_list,
        pipeline_conf)
    TektonCompiler._write_workflow(workflow=workflow, package_path=package_path)   # Tekton change
    _validate_workflow(workflow)


def _validate_workflow(workflow: Dict[Text, Any]):

  # verify that all names and labels conform to kubernetes naming standards
  #   https://kubernetes.io/docs/concepts/overview/working-with-objects/names/
  #   https://kubernetes.io/docs/concepts/overview/working-with-objects/labels/

  def _find_items(obj, search_key, current_path="", results_dict=dict()) -> dict:
    if isinstance(obj, dict):
      if search_key in obj:
        results_dict.update({"%s.%s" % (current_path, search_key): obj[search_key]})
      for k, v in obj.items():
        _find_items(v, search_key, "%s.%s" % (current_path, k), results_dict)
    elif isinstance(obj, list):
      for i, list_item in enumerate(obj):
        _find_items(list_item, search_key, "%s[%i]" % (current_path, i), results_dict)
    return {k.lstrip("."): v for k, v in results_dict.items()}

  non_k8s_names = {path: name for path, name in _find_items(workflow, "name").items()
                   if "metadata" in path and name != sanitize_k8s_name(name)
                   or "param" in path and name != sanitize_k8s_name(name, allow_capital_underscore=True)}

  non_k8s_labels = {path: k_v_dict for path, k_v_dict in _find_items(workflow, "labels", "", {}).items()
                    if "metadata" in path and
                    any([k != sanitize_k8s_name(k, allow_capital_underscore=True, allow_dot=True, allow_slash=True, max_length=253) or
                         v != sanitize_k8s_name(v, allow_capital_underscore=True, allow_dot=True)
                         for k, v in k_v_dict.items()])}

  non_k8s_annotations = {path: k_v_dict for path, k_v_dict in _find_items(workflow, "annotations", "", {}).items()
                         if "metadata" in path and
                         any([k != sanitize_k8s_name(k, allow_capital_underscore=True, allow_dot=True, allow_slash=True, max_length=253)
                              for k in k_v_dict.keys()])}

  error_msg_tmplt = textwrap.dedent("""\
    Internal compiler error: Found non-compliant Kubernetes %s:
    %s
    Please create a new issue at https://github.com/kubeflow/kfp-tekton/issues
    attaching the pipeline DSL code and the pipeline YAML.""")

  if non_k8s_names:
    raise RuntimeError(error_msg_tmplt % ("names", json.dumps(non_k8s_names, sort_keys=False, indent=2)))

  if non_k8s_labels:
    raise RuntimeError(error_msg_tmplt % ("labels", json.dumps(non_k8s_labels, sort_keys=False, indent=2)))

  if non_k8s_annotations:
    raise RuntimeError(error_msg_tmplt % ("annotations", json.dumps(non_k8s_annotations, sort_keys=False, indent=2)))

  # TODO: Tekton pipeline parameter validation
  #   workflow = workflow.copy()
  #   # Working around Argo lint issue
  #   for argument in workflow['spec'].get('arguments', {}).get('parameters', []):
  #     if 'value' not in argument:
  #       argument['value'] = ''
  #   yaml_text = dump_yaml(workflow)
  #   if '{{pipelineparam' in yaml_text:
  #     raise RuntimeError(
  #         '''Internal compiler error: Found unresolved PipelineParam.
  # Please create a new issue at https://github.com/kubeflow/kfp-tekton/issues
  # attaching the pipeline code and the pipeline package.'''
  #     )

  # TODO: Tekton lint, if a tool exists for it
  #   # Running Argo lint if available
  #   import shutil
  #   import subprocess
  #   argo_path = shutil.which('argo')
  #   if argo_path:
  #     result = subprocess.run([argo_path, 'lint', '/dev/stdin'], input=yaml_text.encode('utf-8'),
  #                             stdout=subprocess.PIPE, stderr=subprocess.PIPE)
  #     if result.returncode:
  #       raise RuntimeError(
  #         '''Internal compiler error: Compiler has produced Argo-incompatible workflow.
  # Please create a new issue at https://github.com/kubeflow/kfp-tekton/issues
  # attaching the pipeline code and the pipeline package.
  # Error: {}'''.format(result.stderr.decode('utf-8'))
  #       )
  pass
