# -*- coding: utf-8 -*- #
# Copyright 2020 Google LLC. All Rights Reserved.
#
# 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.
"""Utilities for validating parameters."""

from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals

from googlecloudsdk.calliope import exceptions
from googlecloudsdk.command_lib.ai import constants


def ValidateDisplayName(display_name):
  """Validates the display name."""
  if display_name is not None and not display_name:
    raise exceptions.InvalidArgumentException(
        '--display-name', 'Display name can not be empty.'
    )


def ValidateRegion(region, available_regions=constants.SUPPORTED_REGION):
  """Validates whether a given region is among the available ones."""
  if region not in available_regions:
    raise exceptions.InvalidArgumentException(
        'region',
        'Available values are [{}], but found [{}].'.format(
            ', '.join(available_regions), region
        ),
    )


def GetAndValidateKmsKey(args):
  """Parse CMEK resource arg, and check if the arg was partially specified."""
  if hasattr(args.CONCEPTS, 'kms_key'):
    kms_ref = args.CONCEPTS.kms_key.Parse()
    if kms_ref:
      return kms_ref.RelativeName()
    else:
      for keyword in ['kms_key', 'kms_keyring', 'kms_location', 'kms_project']:
        if getattr(args, keyword, None):
          raise exceptions.InvalidArgumentException(
              '--kms-key', 'Encryption key not fully specified.'
          )


def ValidateAutoscalingMetricSpecs(specs):
  """Value validation for autoscaling metric specs target name and value."""
  if specs is None:
    return

  for key, value in specs.items():
    if key not in constants.OP_AUTOSCALING_METRIC_NAME_MAPPER:
      raise exceptions.InvalidArgumentException(
          '--autoscaling-metric-specs',
          """Autoscaling metric name can only be one of the following: {}.""".format(
              ', '.join([
                  "'{}'".format(c)
                  for c in sorted(
                      constants.OP_AUTOSCALING_METRIC_NAME_MAPPER.keys()
                  )
              ])
          ),
      )

    if key == 'request-counts-per-minute':
      if value <= 0:
        raise exceptions.InvalidArgumentException(
            '--autoscaling-metric-specs',
            'Metric target for request-counts-per-minute must be a positive'
            ' value.',
        )
    elif value <= 0 or value > 100:
      raise exceptions.InvalidArgumentException(
          '--autoscaling-metric-specs',
          'Metric target value {} for {} is not between 0 and 100.'.format(
              value, key
          ),
      )


def ValidateRequiredReplicaCount(required_replica_count, min_replica_count):
  """Value validation for required replica count."""
  if required_replica_count is not None:
    min_replica_count = min_replica_count or 1
    if required_replica_count > min_replica_count:
      raise exceptions.InvalidArgumentException(
          '--required-replica-count',
          'Value must be less than or equal to min-replica-count.'
      )


def ValidateScaleToZeroArgs(
    min_replica_count=None,
    initial_replica_count=None,
    max_replica_count=None,
    min_scaleup_period=None,
    idle_scaledown_period=None,
):
  """Value validation for scale-to-zero args."""
  # Validation for initial replica count.
  if initial_replica_count is not None:
    if min_replica_count is None:
      raise exceptions.InvalidArgumentException(
          '--initial-replica-count',
          """Cannot set initial-replica-count without explicitly setting
          min-replica-count to 0 to enable scale-to-zero.""",
      )
    if min_replica_count > 0:
      raise exceptions.InvalidArgumentException(
          '--initial-replica-count',
          """Cannot set initial-replica-count when min-replica-count > 0 as
          scale-to-zero will not be enabled.""",
      )
    if (
        max_replica_count is not None
        and max_replica_count < initial_replica_count
    ):
      raise exceptions.InvalidArgumentException(
          '--initial-replica-count',
          """Initial-replica-count must be smaller than max replica count.""",
      )

  # Validation for STZConfig args with min replica count > 0.
  if min_scaleup_period is not None:
    if min_replica_count is None:
      raise exceptions.InvalidArgumentException(
          '--min-scaleup-period',
          """Cannot set min-scaleup-period without explicitly setting
          min-replica-count to 0 to enable scale-to-zero.""",
      )
    if min_replica_count > 0:
      raise exceptions.InvalidArgumentException(
          '--min-scaleup-period',
          """Cannot set min-scaleup-period when min-replica-count > 0 as
          scale-to-zero will not be enabled.""",
      )
  if idle_scaledown_period is not None:
    if min_replica_count is None:
      raise exceptions.InvalidArgumentException(
          '--idle-scaledown-period',
          """Cannot set idle-scaledown-period without explicitly setting
          min-replica-count to 0 to enable scale-to-zero.""",
      )
    if min_replica_count > 0:
      raise exceptions.InvalidArgumentException(
          '--idle-scaledown-period',
          """Cannot set idle-scaledown-period when min-replica-count > 0 as
          scale-to-zero will not be enabled.""",
      )


def ValidateSharedResourceArgs(
    shared_resources_ref=None,
    machine_type=None,
    accelerator_dict=None,
    min_replica_count=None,
    max_replica_count=None,
    required_replica_count=None,
    autoscaling_metric_specs=None,
):
  """Value validation for dedicated resource args while making a shared resource command call.

  Args:
      shared_resources_ref: str or None, the shared deployment resource pool
        full name the model should use, formatted as the full URI
      machine_type: str or None, the type of the machine to serve the model.
      accelerator_dict: dict or None, the accelerator attached to the deployed
        model from args.
      min_replica_count: int or None, the minimum number of replicas the
        deployed model will be always deployed on.
      max_replica_count: int or None, the maximum number of replicas the
        deployed model may be deployed on.
      required_replica_count: int or None, the required number of replicas the
        deployed model will be considered successfully deployed.
      autoscaling_metric_specs: dict or None, the metric specification that
        defines the target resource utilization for calculating the desired
        replica count.
  """
  if shared_resources_ref is None:
    return

  if machine_type is not None:
    raise exceptions.InvalidArgumentException(
        '--machine-type',
        """Cannot use
    machine type and shared resources in the same command.""",
    )
  if accelerator_dict is not None:
    raise exceptions.InvalidArgumentException(
        '--accelerator',
        """Cannot
    use accelerator and shared resources in the same command.""",
    )
  if min_replica_count is not None:
    raise exceptions.InvalidArgumentException(
        '--min-replica-count',
        """Cannot
    use min replica count and shared resources in the same command.""",
    )
  if max_replica_count is not None:
    raise exceptions.InvalidArgumentException(
        '--max-replica-count',
        """Cannot
    use max replica count and shared resources in the same command.""",
    )
  if required_replica_count is not None:
    raise exceptions.InvalidArgumentException(
        '--required-replica-count',
        """Cannot
    use required replica count and shared resources in the same command.""",
    )
  if autoscaling_metric_specs is not None:
    raise exceptions.InvalidArgumentException(
        '--autoscaling-metric-specs',
        """Cannot use autoscaling metric specs
        and shared resources in the same command.""",
    )


def ValidateEndpointArgs(network=None, public_endpoint_enabled=None):
  """Validates the network and public_endpoint_enabled."""
  if network is not None and public_endpoint_enabled:
    raise exceptions.InvalidArgumentException(
        'Please either set --network for private endpoint, or set'
        ' --public-endpoint-enabled',
        'for public enpdoint.',
    )


def ValidateModelGardenModelArgs(args):
  """Validates the model garden model args."""
  if args.model is not None and not args.model:
    raise exceptions.InvalidArgumentException(
        '--model',
        'Model name should not be empty.',
    )

  if (
      len(args.model.split('/')) != 2
      or len(args.model.split('/')[1].split('@')) > 2
  ):
    raise exceptions.InvalidArgumentException(
        '--model',
        'Model name should be in the format of Model Garden, e.g.'
        ' `{publisher_name}/{model_name}@{model_version_name}, e.g.'
        ' `google/gemma2@gemma-2-2b` or in the format of Hugging Face'
        ' convention, e.g. `meta-llama/Meta-Llama-3-8B`. You can use the'
        ' `gcloud ai model-garden models list` command to find supported'
        ' models.',
    )