# Path: cloudvolume/exceptions.py
# class UnsupportedFormatError(Exception):
#   """Unable to interpret the format of this URI. e.g. precomputed://"""
#   pass
# 
# class DimensionError(BaseException):
#   """Wrong number of dimensions."""
#   pass
# 
# Path: cloudvolume/lib.py
# def generate_random_string(size=6):
#   return ''.join(random.SystemRandom().choice(
#     string.ascii_lowercase + string.digits) for _ in range(size)
#   )
# 
# Path: cloudvolume/paths.py
# def strict_extract(cloudpath, windows=None, disable_toabs=False):
#   """
#   Same as cloudvolume.paths.extract, but raise an additional 
#   cloudvolume.exceptions.UnsupportedProtocolError
#   if either dataset or layer is not set.
# 
#   Returns: ExtractedPath
#   """
#   path = extract(cloudpath, windows, disable_toabs)
# 
#   if path.dataset == '' or path.layer == '':
#     raise UnsupportedProtocolError(cloudpath_error(cloudpath))
# 
#   return path
# 
# def to_https_protocol(cloudpath):
#   if isinstance(cloudpath, ExtractedPath):
#     if cloudpath.protocol in ('gs', 's3', 'matrix', 'tigerdata'):
#       return extract(to_https_protocol(ascloudpath(cloudpath)))
#     return cloudpath
# 
#   return cloudfiles.paths.to_https_protocol(cloudpath)


import sys
import time
import multiprocessing as mp
import numpy as np
from typing import Optional, Union
from .exceptions import UnsupportedFormatError, DimensionError
from .lib import generate_random_string
from .paths import strict_extract, to_https_protocol
    from .frontends import CloudVolumePrecomputed
          Defaults to True in interactive python, False in script execution mode.
      provenance: (string, dict) In lieu of fetching a provenance 
          file, use this one. 
      secrets: (dict) provide per-instance authorization tokens. If not provided,
        defaults to looking in .cloudvolume/secrets for necessary tokens.
      skel_dir: (str) if not None, override the info['skeletons'] key before 
        pulling the skeleton info file.
      spatial_index_db: (str) A path to an sqlite3 or mysql database that follows 
        the following uri schema. sqlite is assumed if no scheme is present in 
        the uri.
          [sqlite://]filename.db
          mysql://<username>:<password>@<host>:<port>/<db_name>

        Igneous generated datasets include a JSON based spatial
        database that tiles the dataset. This can be fast enough up to about 100 TVx
        datasets. Above that, a proper database is required for efficient queries.
        We provide multiple SQL database types that the index can be hosted on.
      use_https: (bool) maps gs:// and s3:// to their respective https paths. The 
        https paths hit a cached, read-only version of the data and may be faster.
    """
    if use_https:
      cloudpath = to_https_protocol(cloudpath)

    kwargs = dict(locals())
    del kwargs['cls']

    path = strict_extract(cloudpath)
    if path.format in REGISTERED_PLUGINS:
      return REGISTERED_PLUGINS[path.format](**kwargs)
    else:

# Path: cloudvolume/exceptions.py
# class UnsupportedFormatError(Exception):
#   """Unable to interpret the format of this URI. e.g. precomputed://"""
#   pass
# 
# class DimensionError(BaseException):
#   """Wrong number of dimensions."""
#   pass
# 
# Path: cloudvolume/lib.py
# def generate_random_string(size=6):
#   return ''.join(random.SystemRandom().choice(
#     string.ascii_lowercase + string.digits) for _ in range(size)
#   )
# 
# Path: cloudvolume/paths.py
# def strict_extract(cloudpath, windows=None, disable_toabs=False):
#   """
#   Same as cloudvolume.paths.extract, but raise an additional 
#   cloudvolume.exceptions.UnsupportedProtocolError
#   if either dataset or layer is not set.
# 
#   Returns: ExtractedPath
#   """
#   path = extract(cloudpath, windows, disable_toabs)
# 
#   if path.dataset == '' or path.layer == '':
#     raise UnsupportedProtocolError(cloudpath_error(cloudpath))
# 
#   return path
# 
# def to_https_protocol(cloudpath):
#   if isinstance(cloudpath, ExtractedPath):
#     if cloudpath.protocol in ('gs', 's3', 'matrix', 'tigerdata'):
#       return extract(to_https_protocol(ascloudpath(cloudpath)))
#     return cloudpath
# 
#   return cloudfiles.paths.to_https_protocol(cloudpath)


# Path: cloudvolume/cloudvolume.py
import sys
import time
import multiprocessing as mp
import numpy as np
from typing import Optional, Union
from .exceptions import UnsupportedFormatError, DimensionError
from .lib import generate_random_string
from .paths import strict_extract, to_https_protocol
    from .frontends import CloudVolumePrecomputed

          Defaults to True in interactive python, False in script execution mode.
      provenance: (string, dict) In lieu of fetching a provenance 
          file, use this one. 
      secrets: (dict) provide per-instance authorization tokens. If not provided,
        defaults to looking in .cloudvolume/secrets for necessary tokens.
      skel_dir: (str) if not None, override the info['skeletons'] key before 
        pulling the skeleton info file.
      spatial_index_db: (str) A path to an sqlite3 or mysql database that follows 
        the following uri schema. sqlite is assumed if no scheme is present in 
        the uri.
          [sqlite://]filename.db
          mysql://<username>:<password>@<host>:<port>/<db_name>

        Igneous generated datasets include a JSON based spatial
        database that tiles the dataset. This can be fast enough up to about 100 TVx
        datasets. Above that, a proper database is required for efficient queries.
        We provide multiple SQL database types that the index can be hosted on.
      use_https: (bool) maps gs:// and s3:// to their respective https paths. The 
        https paths hit a cached, read-only version of the data and may be faster.
    """
    if use_https:
      cloudpath = to_https_protocol(cloudpath)

    kwargs = dict(locals())
    del kwargs['cls']

    path = strict_extract(cloudpath)
    if path.format in REGISTERED_PLUGINS:
      return REGISTERED_PLUGINS[path.format](**kwargs)
    else:

      raise UnsupportedFormatError(