#
# Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved.
#

from __future__ import annotations  # pylint: disable=unused-variable

import json
import logging
from pathlib import Path
import time
from typing import Dict, List, NewType, Iterable
import requests

from aistore.sdk.ais_source import AISSource
from aistore.sdk.etl_const import DEFAULT_ETL_TIMEOUT
from aistore.sdk.object_iterator import ObjectIterator
from aistore.sdk.const import (
    ACT_COPY_BCK,
    ACT_CREATE_BCK,
    ACT_DESTROY_BCK,
    ACT_ETL_BCK,
    ACT_EVICT_REMOTE_BCK,
    ACT_LIST,
    ACT_MOVE_BCK,
    ACT_SUMMARY_BCK,
    HEADER_ACCEPT,
    HEADER_BUCKET_PROPS,
    HEADER_BUCKET_SUMM,
    HTTP_METHOD_DELETE,
    HTTP_METHOD_GET,
    HTTP_METHOD_HEAD,
    HTTP_METHOD_POST,
    MSGPACK_CONTENT_TYPE,
    PROVIDER_AIS,
    QPARAM_BCK_TO,
    QPARAM_COUNT_REMOTE_OBJS,
    QPARAM_FLT_PRESENCE,
    QPARAM_KEEP_REMOTE,
    QPARAM_NAMESPACE,
    QPARAM_PROVIDER,
    URL_PATH_BUCKETS,
    STATUS_ACCEPTED,
    STATUS_OK,
)

from aistore.sdk.errors import (
    InvalidBckProvider,
    ErrBckAlreadyExists,
    ErrBckNotFound,
    UnexpectedHTTPStatusCode,
)
from aistore.sdk.multiobj import ObjectGroup, ObjectRange
from aistore.sdk.request_client import RequestClient
from aistore.sdk.object import Object
from aistore.sdk.types import (
    ActionMsg,
    BucketEntry,
    BucketList,
    BucketModel,
    BsummCtrlMsg,
    Namespace,
    CopyBckMsg,
    TransformBckMsg,
    TCBckMsg,
    ListObjectsMsg,
)
from aistore.sdk.list_object_flag import ListObjectFlag
from aistore.sdk.utils import validate_directory, get_file_size

Header = NewType("Header", requests.structures.CaseInsensitiveDict)


# pylint: disable=unused-variable,too-many-public-methods
class Bucket(AISSource):
    """
    A class representing a bucket that contains user data.

    Args:
        client (RequestClient): Client for interfacing with AIS cluster
        name (str): name of bucket
        provider (str, optional): Provider of bucket (one of "ais", "aws", "gcp", ...), defaults to "ais"
        namespace (Namespace, optional): Namespace of bucket, defaults to None
    """

    def __init__(
        self,
        name: str,
        client: RequestClient = None,
        provider: str = PROVIDER_AIS,
        namespace: Namespace = None,
    ):
        self._client = client
        self._name = name
        self._provider = provider
        self._namespace = namespace
        self._qparam = {QPARAM_PROVIDER: provider}
        if self.namespace:
            self._qparam[QPARAM_NAMESPACE] = namespace.get_path()

    @property
    def client(self) -> RequestClient:
        """The client bound to this bucket."""
        return self._client

    @property
    def qparam(self) -> Dict:
        """Default query parameters to use with API calls from this bucket."""
        return self._qparam

    @property
    def provider(self) -> str:
        """The provider for this bucket."""
        return self._provider

    @property
    def name(self) -> str:
        """The name of this bucket."""
        return self._name

    @property
    def namespace(self) -> Namespace:
        """The namespace for this bucket."""
        return self._namespace

    def list_urls(self, prefix: str = "", etl_name: str = None) -> Iterable[str]:
        """
            Get an iterator of full URLs to every object in this bucket matching the prefix
        Args:
            prefix (str, optional): Limit objects selected by a given string prefix
            etl_name (str, optional): ETL to include in URLs

        Returns:
            Iterator of all object URLs matching the prefix
        """
        for entry in self.list_objects_iter(prefix=prefix, props="name"):
            yield self.object(entry.name).get_url(etl_name=etl_name)

    def create(self, exist_ok=False):
        """
        Creates a bucket in AIStore cluster.
        Can only create a bucket for AIS provider on localized cluster. Remote cloud buckets do not support creation.

        Args:
            exist_ok (bool, optional): Ignore error if the cluster already contains this bucket

        Raises:
            aistore.sdk.errors.AISError: All other types of errors with AIStore
            aistore.sdk.errors.InvalidBckProvider: Invalid bucket provider for requested operation
            requests.ConnectionError: Connection error
            requests.ConnectionTimeout: Timed out connecting to AIStore
            requests.exceptions.HTTPError: Service unavailable
            requests.RequestException: "There was an ambiguous exception that occurred while handling..."
            requests.ReadTimeout: Timed out receiving response from AIStore
        """
        self._verify_ais_bucket()
        try:
            self.make_request(HTTP_METHOD_POST, ACT_CREATE_BCK)
        except ErrBckAlreadyExists as err:
            if not exist_ok:
                raise err
        return self

    def delete(self, missing_ok=False):
        """
        Destroys bucket in AIStore cluster.
        In all cases removes both the bucket's content _and_ the bucket's metadata from the cluster.
        Note: AIS will _not_ call the remote backend provider to delete the corresponding Cloud bucket
        (iff the bucket in question is, in fact, a Cloud bucket).

        Args:
            missing_ok (bool, optional): Ignore error if bucket does not exist

        Raises:
            aistore.sdk.errors.AISError: All other types of errors with AIStore
            aistore.sdk.errors.InvalidBckProvider: Invalid bucket provider for requested operation
            requests.ConnectionError: Connection error
            requests.ConnectionTimeout: Timed out connecting to AIStore
            requests.exceptions.HTTPError: Service unavailable
            requests.RequestException: "There was an ambiguous exception that occurred while handling..."
            requests.ReadTimeout: Timed out receiving response from AIStore
        """
        self._verify_ais_bucket()
        try:
            self.make_request(HTTP_METHOD_DELETE, ACT_DESTROY_BCK)
        except ErrBckNotFound as err:
            if not missing_ok:
                raise err

    def rename(self, to_bck_name: str) -> str:
        """
        Renames bucket in AIStore cluster.
        Only works on AIS buckets. Returns job ID that can be used later to check the status of the asynchronous
            operation.

        Args:
            to_bck_name (str): New bucket name for bucket to be renamed as

        Returns:
            Job ID (as str) that can be used to check the status of the operation

        Raises:
            aistore.sdk.errors.AISError: All other types of errors with AIStore
            aistore.sdk.errors.InvalidBckProvider: Invalid bucket provider for requested operation
            requests.ConnectionError: Connection error
            requests.ConnectionTimeout: Timed out connecting to AIStore
            requests.exceptions.HTTPError: Service unavailable
            requests.RequestException: "There was an ambiguous exception that occurred while handling..."
            requests.ReadTimeout: Timed out receiving response from AIStore
        """
        self._verify_ais_bucket()
        params = self.qparam.copy()
        params[QPARAM_BCK_TO] = Bucket(
            name=to_bck_name, namespace=self.namespace
        ).get_path()
        resp = self.make_request(HTTP_METHOD_POST, ACT_MOVE_BCK, params=params)
        self._name = to_bck_name
        return resp.text

    def evict(self, keep_md: bool = False):
        """
        Evicts bucket in AIStore cluster.
        NOTE: only Cloud buckets can be evicted.

        Args:
            keep_md (bool, optional): If true, evicts objects but keeps the bucket's metadata (i.e., the bucket's name
                and its properties)

        Raises:
            aistore.sdk.errors.AISError: All other types of errors with AIStore
            aistore.sdk.errors.InvalidBckProvider: Invalid bucket provider for requested operation
            requests.ConnectionError: Connection error
            requests.ConnectionTimeout: Timed out connecting to AIStore
            requests.exceptions.HTTPError: Service unavailable
            requests.RequestException: "There was an ambiguous exception that occurred while handling..."
            requests.ReadTimeout: Timed out receiving response from AIStore
        """
        self.verify_cloud_bucket()
        params = self.qparam.copy()
        params[QPARAM_KEEP_REMOTE] = str(keep_md)
        self.make_request(HTTP_METHOD_DELETE, ACT_EVICT_REMOTE_BCK, params=params)

    def head(self) -> Header:
        """
        Requests bucket properties.

        Returns:
            Response header with the bucket properties

        Raises:
            aistore.sdk.errors.AISError: All other types of errors with AIStore
            requests.ConnectionError: Connection error
            requests.ConnectionTimeout: Timed out connecting to AIStore
            requests.exceptions.HTTPError: Service unavailable
            requests.RequestException: "There was an ambiguous exception that occurred while handling..."
            requests.ReadTimeout: Timed out receiving response from AIStore
        """
        return self.client.request(
            HTTP_METHOD_HEAD,
            path=f"{URL_PATH_BUCKETS}/{self.name}",
            params=self.qparam,
        ).headers

    # pylint: disable=too-many-arguments
    def summary(
        self,
        uuid: str = "",
        prefix: str = "",
        fast: bool = True,
        cached: bool = True,
        present: bool = True,
    ):
        """
        Returns bucket summary (starts xaction job and polls for results).

        Args:
            uuid (str): Identifier for the bucket summary. Defaults to an empty string.
            prefix (str): Prefix for objects to be included in the bucket summary.
                          Defaults to an empty string (all objects).
            fast (bool): If True, performs and returns a quick summary. Defaults to True.
            cached (bool): If True, summary entails cached entities. Defaults to True.
            present (bool): If True, summary entails present entities. Defaults to True.

        Raises:
            requests.ConnectionError: Connection error
            requests.ConnectionTimeout: Timed out connecting to AIStore
            requests.exceptions.HTTPError: Service unavailable
            requests.RequestException: "There was an ambiguous exception that occurred while handling..."
            requests.ReadTimeout: Timed out receiving response from AIStore
            aistore.sdk.errors.AISError: All other types of errors with AIStore
        """
        bsumm_ctrl_msg = BsummCtrlMsg(
            uuid=uuid, prefix=prefix, fast=fast, cached=cached, present=present
        )

        # Start the job and get the job ID
        resp = self.make_request(
            HTTP_METHOD_GET,
            ACT_SUMMARY_BCK,
            params=self.qparam,
            value=bsumm_ctrl_msg.dict(),
        )

        # Initial response status code should be 202
        if resp.status_code == STATUS_OK:
            raise UnexpectedHTTPStatusCode([STATUS_ACCEPTED], resp.status_code)

        job_id = resp.text.strip('"')

        # Update the uuid in the control message
        bsumm_ctrl_msg.uuid = job_id

        # Sleep and request frequency in sec (starts at 200 ms)
        sleep_time = 0.2

        # Poll async task for http.StatusOK completion
        while True:
            resp = self.make_request(
                HTTP_METHOD_GET,
                ACT_SUMMARY_BCK,
                params=self.qparam,
                value=bsumm_ctrl_msg.dict(),
            )

            # If task completed successfully, break the loop
            if resp.status_code == STATUS_OK:
                break
            # If task is still running, wait for some time and try again
            if resp.status_code == STATUS_ACCEPTED:
                time.sleep(sleep_time)
                sleep_time = min(
                    10, sleep_time * 1.5
                )  # Increase sleep_time by 50%, but don't exceed 10 seconds
            # Otherwise, if status code received is neither STATUS_OK or STATUS_ACCEPTED, raise an exception
            else:
                raise UnexpectedHTTPStatusCode(
                    [STATUS_OK, STATUS_ACCEPTED], resp.status_code
                )

        return json.loads(resp.content.decode("utf-8"))[0]

    def info(self, flt_presence: int = 0, count_remote_objs: bool = True):
        """
        Returns bucket summary and information/properties.

        Args:
            count_remote_objs (bool): If True, returned bucket info will entail remote objects as well
            flt_presence (int): Describes the presence of buckets and objects with respect to their existence
                                or non-existence in the AIS cluster. Defaults to 0.

                                Expected values are:
                                0 - (object | bucket) exists inside and/or outside cluster
                                1 - same as 0 but no need to return summary
                                2 - bucket: is present | object: present and properly located
                                3 - same as 2 but no need to return summary
                                4 - objects: present anywhere/anyhow _in_ the cluster as: replica, ec-slices, misplaced
                                5 - not present - exists _outside_ cluster

        Raises:
            requests.ConnectionError: Connection error
            requests.ConnectionTimeout: Timed out connecting to AIStore
            requests.exceptions.HTTPError: Service unavailable
            requests.RequestException: "There was an ambiguous exception that occurred while handling..."
            requests.ReadTimeout: Timed out receiving response from AIStore
            ValueError: `flt_presence` is not one of the expected values
            aistore.sdk.errors.AISError: All other types of errors with AIStore
        """
        if flt_presence not in range(6):
            raise ValueError("`flt_presence` must be one of 0, 1, 2, 3, 4, or 5.")

        params = self.qparam.copy()
        params.update({QPARAM_FLT_PRESENCE: flt_presence})
        if count_remote_objs:
            params.update({QPARAM_COUNT_REMOTE_OBJS: count_remote_objs})

        response = self.client.request(
            HTTP_METHOD_HEAD,
            path=f"{URL_PATH_BUCKETS}/{self.name}",
            params=params,
        )

        bucket_props = json.loads(response.headers.get(HEADER_BUCKET_PROPS, "{}"))
        bucket_summ = json.loads(response.headers.get(HEADER_BUCKET_SUMM, "{}"))

        return bucket_props, bucket_summ

    # pylint: disable=too-many-arguments
    def copy(
        self,
        to_bck: Bucket,
        prefix_filter: str = "",
        prepend: str = "",
        dry_run: bool = False,
        force: bool = False,
    ) -> str:
        """
        Returns job ID that can be used later to check the status of the asynchronous operation.

        Args:
            to_bck (Bucket): Destination bucket
            prefix_filter (str, optional): Only copy objects with names starting with this prefix
            prepend (str, optional): Value to prepend to the name of copied objects
            dry_run (bool, optional): Determines if the copy should actually
                happen or not
            force (bool, optional): Override existing destination bucket

        Returns:
            Job ID (as str) that can be used to check the status of the operation

        Raises:
            aistore.sdk.errors.AISError: All other types of errors with AIStore
            requests.ConnectionError: Connection error
            requests.ConnectionTimeout: Timed out connecting to AIStore
            requests.exceptions.HTTPError: Service unavailable
            requests.RequestException: "There was an ambiguous exception that occurred while handling..."
            requests.ReadTimeout: Timed out receiving response from AIStore
        """
        value = CopyBckMsg(
            prefix=prefix_filter, prepend=prepend, dry_run=dry_run, force=force
        ).as_dict()
        params = self.qparam.copy()
        params[QPARAM_BCK_TO] = to_bck.get_path()
        return self.make_request(
            HTTP_METHOD_POST, ACT_COPY_BCK, value=value, params=params
        ).text

    # pylint: disable=too-many-arguments
    def list_objects(
        self,
        prefix: str = "",
        props: str = "",
        page_size: int = 0,
        uuid: str = "",
        continuation_token: str = "",
        flags: List[ListObjectFlag] = None,
        target: str = "",
    ) -> BucketList:
        """
        Returns a structure that contains a page of objects, job ID, and continuation token (to read the next page, if
            available).

        Args:
            prefix (str, optional): Return only objects that start with the prefix
            props (str, optional): Comma-separated list of object properties to return. Default value is "name,size".
                Properties: "name", "size", "atime", "version", "checksum", "cached", "target_url", "status", "copies",
                "ec", "custom", "node".
            page_size (int, optional): Return at most "page_size" objects.
                The maximum number of objects in response depends on the bucket backend. E.g, AWS bucket cannot return
                    more than 5,000 objects in a single page.
                NOTE: If "page_size" is greater than a backend maximum, the backend maximum objects are returned.
                Defaults to "0" - return maximum number of objects.
            uuid (str, optional): Job ID, required to get the next page of objects
            continuation_token (str, optional): Marks the object to start reading the next page
            flags (List[ListObjectFlag], optional): Optional list of ListObjectFlag enums to include as flags in the
             request
            target(str, optional): Only list objects on this specific target node

        Returns:
            BucketList: the page of objects in the bucket and the continuation token to get the next page
            Empty continuation token marks the final page of the object list

        Raises:
            aistore.sdk.errors.AISError: All other types of errors with AIStore
            requests.ConnectionError: Connection error
            requests.ConnectionTimeout: Timed out connecting to AIStore
            requests.exceptions.HTTPError: Service unavailable
            requests.RequestException: "There was an ambiguous exception that occurred while handling..."
            requests.ReadTimeout: Timed out receiving response from AIStore
        """

        value = ListObjectsMsg(
            prefix=prefix,
            page_size=page_size,
            uuid=uuid,
            props=props,
            continuation_token=continuation_token,
            flags=[] if flags is None else flags,
            target=target,
        ).as_dict()
        action = ActionMsg(action=ACT_LIST, value=value).dict()

        bucket_list = self.client.request_deserialize(
            HTTP_METHOD_GET,
            path=f"{URL_PATH_BUCKETS}/{ self.name }",
            headers={HEADER_ACCEPT: MSGPACK_CONTENT_TYPE},
            res_model=BucketList,
            json=action,
            params=self.qparam,
        )

        for entry in bucket_list.entries:
            entry.object = self.object(entry.name)
        return bucket_list

    def list_objects_iter(
        self,
        prefix: str = "",
        props: str = "",
        page_size: int = 0,
        flags: List[ListObjectFlag] = None,
        target: str = "",
    ) -> ObjectIterator:
        """
        Returns an iterator for all objects in bucket

        Args:
            prefix (str, optional): Return only objects that start with the prefix
            props (str, optional): Comma-separated list of object properties to return. Default value is "name,size".
                Properties: "name", "size", "atime", "version", "checksum", "cached", "target_url", "status", "copies",
                "ec", "custom", "node".
            page_size (int, optional): return at most "page_size" objects
                The maximum number of objects in response depends on the bucket backend. E.g, AWS bucket cannot return
                    more than 5,000 objects in a single page.
                NOTE: If "page_size" is greater than a backend maximum, the backend maximum objects are returned.
                Defaults to "0" - return maximum number objects
            flags (List[ListObjectFlag], optional): Optional list of ListObjectFlag enums to include as flags in the
             request
            target(str, optional): Only list objects on this specific target node

        Returns:
            ObjectIterator: object iterator

        Raises:
            aistore.sdk.errors.AISError: All other types of errors with AIStore
            requests.ConnectionError: Connection error
            requests.ConnectionTimeout: Timed out connecting to AIStore
            requests.exceptions.HTTPError: Service unavailable
            requests.RequestException: "There was an ambiguous exception that occurred while handling..."
            requests.ReadTimeout: Timed out receiving response from AIStore
        """

        def fetch_objects(uuid, token):
            return self.list_objects(
                prefix,
                props,
                page_size,
                uuid=uuid,
                continuation_token=token,
                flags=flags,
                target=target,
            )

        return ObjectIterator(fetch_objects)

    def list_all_objects(
        self,
        prefix: str = "",
        props: str = "",
        page_size: int = 0,
        flags: List[ListObjectFlag] = None,
        target: str = "",
    ) -> List[BucketEntry]:
        """
        Returns a list of all objects in bucket

        Args:
            prefix (str, optional): return only objects that start with the prefix
            props (str, optional): comma-separated list of object properties to return. Default value is "name,size".
                Properties: "name", "size", "atime", "version", "checksum", "cached", "target_url", "status", "copies",
                "ec", "custom", "node".
            page_size (int, optional): return at most "page_size" objects
                The maximum number of objects in response depends on the bucket backend. E.g, AWS bucket cannot return
                    more than 5,000 objects in a single page.
                NOTE: If "page_size" is greater than a backend maximum, the backend maximum objects are returned.
                Defaults to "0" - return maximum number objects
            flags (List[ListObjectFlag], optional): Optional list of ListObjectFlag enums to include as flags in the
             request
            target(str, optional): Only list objects on this specific target node

        Returns:
            List[BucketEntry]: list of objects in bucket

        Raises:
            aistore.sdk.errors.AISError: All other types of errors with AIStore
            requests.ConnectionError: Connection error
            requests.ConnectionTimeout: Timed out connecting to AIStore
            requests.exceptions.HTTPError: Service unavailable
            requests.RequestException: "There was an ambiguous exception that occurred while handling..."
            requests.ReadTimeout: Timed out receiving response from AIStore
        """
        uuid = ""
        continuation_token = ""
        obj_list = None

        while True:
            resp = self.list_objects(
                prefix=prefix,
                props=props,
                page_size=page_size,
                uuid=uuid,
                continuation_token=continuation_token,
                flags=flags,
                target=target,
            )
            if obj_list:
                obj_list = obj_list + resp.entries
            obj_list = obj_list or resp.entries
            if resp.continuation_token == "":
                break
            continuation_token = resp.continuation_token
            uuid = resp.uuid
        return obj_list

    # pylint: disable=too-many-arguments
    def transform(
        self,
        etl_name: str,
        to_bck: Bucket,
        timeout: str = DEFAULT_ETL_TIMEOUT,
        prefix_filter: str = "",
        prepend: str = "",
        ext: Dict[str, str] = None,
        force: bool = False,
        dry_run: bool = False,
    ) -> str:
        """
        Visits all selected objects in the source bucket and for each object, puts the transformed
        result to the destination bucket

        Args:
            etl_name (str): name of etl to be used for transformations
            to_bck (str): destination bucket for transformations
            timeout (str, optional): Timeout of the ETL job (e.g. 5m for 5 minutes)
            prefix_filter (str, optional): Only transform objects with names starting with this prefix
            prepend (str, optional): Value to prepend to the name of resulting transformed objects
            ext (Dict[str, str], optional): dict of new extension followed by extension to be replaced
                (i.e. {"jpg": "txt"})
            dry_run (bool, optional): determines if the copy should actually happen or not
            force (bool, optional): override existing destination bucket

        Returns:
            Job ID (as str) that can be used to check the status of the operation
        """
        value = TCBckMsg(
            ext=ext,
            transform_msg=TransformBckMsg(etl_name=etl_name, timeout=timeout),
            copy_msg=CopyBckMsg(
                prefix=prefix_filter, prepend=prepend, force=force, dry_run=dry_run
            ),
        ).as_dict()

        params = self.qparam.copy()
        params[QPARAM_BCK_TO] = to_bck.get_path()
        return self.make_request(
            HTTP_METHOD_POST, ACT_ETL_BCK, value=value, params=params
        ).text

    def put_files(
        self,
        path: str,
        prefix_filter: str = "",
        pattern: str = "*",
        basename: bool = False,
        prepend: str = None,
        recursive: bool = False,
        dry_run: bool = False,
        verbose: bool = True,
    ) -> List[str]:
        """
        Puts files found in a given filepath as objects to a bucket in AIS storage.

        Args:
            path (str): Local filepath, can be relative or absolute
            prefix_filter (str, optional): Only put files with names starting with this prefix
            pattern (str, optional): Regex pattern to filter files
            basename (bool, optional): Whether to use the file names only as object names and omit the path information
            prepend (str, optional): Optional string to use as a prefix in the object name for all objects uploaded
                No delimiter ("/", "-", etc.) is automatically applied between the prepend value and the object name
            recursive (bool, optional): Whether to recurse through the provided path directories
            dry_run (bool, optional): Option to only show expected behavior without an actual put operation
            verbose (bool, optional): Whether to print upload info to standard output

        Returns:
            List of object names put to a bucket in AIS

        Raises:
            requests.RequestException: "There was an ambiguous exception that occurred while handling..."
            requests.ConnectionError: Connection error
            requests.ConnectionTimeout: Timed out connecting to AIStore
            requests.ReadTimeout: Timed out waiting response from AIStore
            ValueError: The path provided is not a valid directory
        """
        validate_directory(path)
        file_iterator = (
            Path(path).rglob(pattern) if recursive else Path(path).glob(pattern)
        )
        obj_names = []
        dry_run_prefix = "Dry-run enabled. Proposed action:" if dry_run else ""

        logger = logging.getLogger(f"{__name__}.put_files")
        logger.disabled = not verbose
        for file in file_iterator:
            if not file.is_file() or not str(file.name).startswith(prefix_filter):
                continue
            obj_name = self._get_uploaded_obj_name(file, path, basename, prepend)
            if not dry_run:
                self.object(obj_name).put_file(str(file))
            logger.info(
                "%s File '%s' uploaded as object '%s' with size %s",
                dry_run_prefix,
                file,
                obj_name,
                get_file_size(file),
            )
            obj_names.append(obj_name)
        logger.info(
            "%s Specified files from %s uploaded to bucket %s",
            dry_run_prefix,
            path,
            f"{self.provider}://{self.name}",
        )
        return obj_names

    @staticmethod
    def _get_uploaded_obj_name(file, root_path, basename, prepend):
        obj_name = str(file.relative_to(root_path)) if not basename else file.name
        if prepend:
            return prepend + obj_name
        return obj_name

    def object(self, obj_name: str) -> Object:
        """
        Factory constructor for an object in this bucket.
        Does not make any HTTP request, only instantiates an object in a bucket owned by the client.

        Args:
            obj_name (str): Name of object

        Returns:
            The object created.
        """
        return Object(
            bucket=self,
            name=obj_name,
        )

    def objects(
        self,
        obj_names: list = None,
        obj_range: ObjectRange = None,
        obj_template: str = None,
    ) -> ObjectGroup:
        """
        Factory constructor for multiple objects belonging to this bucket.

        Args:
            obj_names (list): Names of objects to include in the group
            obj_range (ObjectRange): Range of objects to include in the group
            obj_template (str): String template defining objects to include in the group

        Returns:
            The ObjectGroup created
        """
        return ObjectGroup(
            bck=self,
            obj_names=obj_names,
            obj_range=obj_range,
            obj_template=obj_template,
        )

    def make_request(
        self,
        method: str,
        action: str,
        value: dict = None,
        params: dict = None,
    ) -> requests.Response:
        """
        Use the bucket's client to make a request to the bucket endpoint on the AIS server

        Args:
            method (str): HTTP method to use, e.g. POST/GET/DELETE
            action (str): Action string used to create an ActionMsg to pass to the server
            value (dict): Additional value parameter to pass in the ActionMsg
            params (dict, optional): Optional parameters to pass in the request

        Returns:
            Response from the server

        """
        if self._client is None:
            raise ValueError(
                "Bucket requires a client to use functions. Try defining a client and accessing this bucket with "
                "client.bucket()"
            )
        json_val = ActionMsg(action=action, value=value).dict()
        return self._client.request(
            method,
            path=f"{URL_PATH_BUCKETS}/{self.name}",
            json=json_val,
            params=params if params else self.qparam,
        )

    def _verify_ais_bucket(self):
        """
        Verify the bucket provider is AIS
        """
        if self.provider is not PROVIDER_AIS:
            raise InvalidBckProvider(self.provider)

    def verify_cloud_bucket(self):
        """
        Verify the bucket provider is a cloud provider
        """
        if self.provider is PROVIDER_AIS:
            raise InvalidBckProvider(self.provider)

    def get_path(self) -> str:
        """
        Get the path representation of this bucket
        """
        namespace_path = self.namespace.get_path() if self.namespace else "@#"
        return f"{ self.provider }/{ namespace_path }/{ self.name }/"

    def as_model(self) -> BucketModel:
        """
        Return a data-model of the bucket

        Returns:
            BucketModel representation
        """
        return BucketModel(
            name=self.name, namespace=self.namespace, provider=self.provider
        )
