from datetime import datetime, timedelta
from typing import List

from pydantic import Field

from .base import BaseModel


class RunHardwareMetric(BaseModel):
    resource_type: str
    total_percentage: float
    run_count: int
    average_runtime: int


class ProjectHardwareMetric(BaseModel):
    project_id: str
    project_name: str
    project_usage: List[RunHardwareMetric]


class HardwareMetric(BaseModel):
    start: datetime
    end: datetime
    projects: List[ProjectHardwareMetric]
    account_usage: List[RunHardwareMetric]


class RunMetricsBucket(BaseModel):
    """
    Type of metadata of a specific bucket.

    start: start timestamp of the time range
    end: end timestamp of the time range
    run_count: count of runs part of this bucket
    success_rate: percentage of successful runs as float between 0 and 1
    average_runtime: average runtime of all runs in this bucket
    total_runtime: total runtime of all runs in this bucket
    """

    start: datetime
    end: datetime
    run_count: int
    success_rate: float
    average_runtime: int
    total_runtime: int


class RunMetricsGet(BaseModel):
    """
    Return type of the metrics endpoint on run related requests.

    start: start timestamp of the time range
    end: end timestamp of the time range
    bucket_count: number of buckets time range was divided into
    metrics_buckets: list of metadata about each bucket
    overall_bucket: metadata for the whole time range
    preceding_bucket: metadata for the bucket directly preceding the time range
    """

    start: datetime
    end: datetime
    bucket_count: int
    metrics_buckets: List[RunMetricsBucket]
    overall_bucket: RunMetricsBucket
    preceding_bucket: RunMetricsBucket


class MetricsQuery(BaseModel):
    """
    Query parameters for generic metrics requests.

    start: start timestamp of the time range
    end: end timestamp of the time range
    """

    start: datetime = Field(
        default_factory=lambda: datetime.utcnow() - timedelta(hours=24)
    )
    end: datetime = Field(default_factory=datetime.utcnow)


class RunMetricsQuery(MetricsQuery):
    """
    Query parameters for run metrics requests.

    start: start timestamp of the time range
    end: end timestamp of the time range
    bucket_count: number of buckets to divide the time range into
    """

    bucket_count: int = 100

from datetime import datetime, timedelta
from typing import List

from pydantic import Field

from .base import BaseModel


class RunHardwareMetric(BaseModel):
    resource_type: str
    total_percentage: float
    run_count: int
    average_runtime: int


class ProjectHardwareMetric(BaseModel):
    project_id: str
    project_name: str
    project_usage: List[RunHardwareMetric]


class HardwareMetric(BaseModel):
    start: datetime
    end: datetime
    projects: List[ProjectHardwareMetric]
    account_usage: List[RunHardwareMetric]


class RunMetricsBucket(BaseModel):
    """
    Type of metadata of a specific bucket.

    start: start timestamp of the time range
    end: end timestamp of the time range
    run_count: count of runs part of this bucket
    success_rate: percentage of successful runs as float between 0 and 1
    average_runtime: average runtime of all runs in this bucket
    total_runtime: total runtime of all runs in this bucket
    """

    start: datetime
    end: datetime
    run_count: int
    success_rate: float
    average_runtime: int
    total_runtime: int


class RunMetricsGet(BaseModel):
    """
    Return type of the metrics endpoint on run related requests.

    start: start timestamp of the time range
    end: end timestamp of the time range
    bucket_count: number of buckets time range was divided into
    metrics_buckets: list of metadata about each bucket
    overall_bucket: metadata for the whole time range
    preceding_bucket: metadata for the bucket directly preceding the time range
    """

    start: datetime
    end: datetime
    bucket_count: int
    metrics_buckets: List[RunMetricsBucket]
    overall_bucket: RunMetricsBucket
    preceding_bucket: RunMetricsBucket


class MetricsQuery(BaseModel):
    """
    Query parameters for generic metrics requests.

    start: start timestamp of the time range
    end: end timestamp of the time range
    """

    start: datetime = Field(
        default_factory=lambda: datetime.utcnow() - timedelta(hours=24)
    )
    end: datetime = Field(default_factory=datetime.utcnow)


class RunMetricsQuery(MetricsQuery):
    """
    Query parameters for run metrics requests.

    start: start timestamp of the time range
    end: end timestamp of the time range
    bucket_count: number of buckets to divide the time range into
    """

    bucket_count: int = 100

Type of metadata of a specific bucket.      start: start timestamp of the time range     end: end timestamp of the time range     run_count: count of runs part of this bucket     success_rate: percentage of successful runs as float between 0 and 1     average_runtime: average runtime of all runs in this bucket     total_runtime: total runtime of all runs in this bucket Return type of the metrics endpoint on run related requests.      start: start timestamp of the time range     end: end timestamp of the time range     bucket_count: number of buckets time range was divided into     metrics_buckets: list of metadata about each bucket     overall_bucket: metadata for the whole time range     preceding_bucket: metadata for the bucket directly preceding the time range Query parameters for generic metrics requests.      start: start timestamp of the time range     end: end timestamp of the time range Query parameters for run metrics requests.      start: start timestamp of the time range     end: end timestamp of the time range     bucket_count: number of buckets to divide the time range into