# 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
        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(
        "Unknown format {}".format(path.format)
      )

  @classmethod
  def create_new_info(cls, *args, **kwargs):
    # For backwards compatibility, but this only 
    # makes sense for Precomputed anyway
    return CloudVolumePrecomputed.create_new_info(*args, **kwargs)

  @classmethod
  def from_numpy(cls, 
    arr, 

# 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

        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(
        "Unknown format {}".format(path.format)
      )

  @classmethod
  def create_new_info(cls, *args, **kwargs):
    # For backwards compatibility, but this only 
    # makes sense for Precomputed anyway
    return CloudVolumePrecomputed.create_new_info(*args, **kwargs)

  @classmethod
  def from_numpy(cls, 
    arr, 

    vol_path='file:///tmp/image/' + generate_random_string(),