





"""simple docstring"""


import importlib
import inspect
from functools import wraps
from typing import TYPE_CHECKING, Optional, Union

from .download.streaming_download_manager import (
    xbasename,
    xdirname,
    xet_parse,
    xexists,
    xgetsize,
    xglob,
    xgzip_open,
    xisdir,
    xisfile,
    xjoin,
    xlistdir,
    xnumpy_load,
    xopen,
    xpandas_read_csv,
    xpandas_read_excel,
    xPath,
    xrelpath,
    xsio_loadmat,
    xsplit,
    xsplitext,
    xwalk,
    xxml_dom_minidom_parse,
)
from .utils.logging import get_logger
from .utils.patching import patch_submodule
from .utils.py_utils import get_imports


a__   :       int   						=			get_logger(__name__)


if TYPE_CHECKING:
   from .builder import DatasetBuilder





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase = None		)      ->					Optional[Any]:

 '''simple docstring'''






 snake_case__      							=			importlib.import_module(_A		)

 # TODO(QL): always update the module to add subsequent new authentication
 if hasattr(_A		,       "_patched_for_streaming"		) and module._patched_for_streaming:
  return

 def wrap_auth(_UpperCAmelCase		):
  @wraps(_A		)
  def wrapper(*_UpperCAmelCase		,       **_UpperCAmelCase		):
   return function(*_A		,       use_auth_token=_A		,       **_A		)

  snake_case__      							=			"wrap_auth"
  return wrapper

 # open files in a streaming fashion
 patch_submodule(_A		,       "open"		,       wrap_auth(_A		)		).start()
 patch_submodule(_A		,       "os.listdir"		,       wrap_auth(_A		)		).start()
 patch_submodule(_A		,       "os.walk"		,       wrap_auth(_A		)		).start()
 patch_submodule(_A		,       "glob.glob"		,       wrap_auth(_A		)		).start()
 # allow to navigate in remote zip files
 patch_submodule(_A		,       "os.path.join"		,       _A		).start()
 patch_submodule(_A		,       "os.path.dirname"		,       _A		).start()
 patch_submodule(_A		,       "os.path.basename"		,       _A		).start()
 patch_submodule(_A		,       "os.path.relpath"		,       _A		).start()
 patch_submodule(_A		,       "os.path.split"		,       _A		).start()
 patch_submodule(_A		,       "os.path.splitext"		,       _A		).start()
 # allow checks on paths
 patch_submodule(_A		,       "os.path.exists"		,       wrap_auth(_A		)		).start()
 patch_submodule(_A		,       "os.path.isdir"		,       wrap_auth(_A		)		).start()
 patch_submodule(_A		,       "os.path.isfile"		,       wrap_auth(_A		)		).start()
 patch_submodule(_A		,       "os.path.getsize"		,       wrap_auth(_A		)		).start()
 patch_submodule(_A		,       "pathlib.Path"		,       _A		).start()
 # file readers
 patch_submodule(_A		,       "gzip.open"		,       wrap_auth(_A		)		).start()
 patch_submodule(_A		,       "numpy.load"		,       wrap_auth(_A		)		).start()
 patch_submodule(_A		,       "pandas.read_csv"		,       wrap_auth(_A		)		,       attrs=["__version__"]		).start()
 patch_submodule(_A		,       "pandas.read_excel"		,       wrap_auth(_A		)		,       attrs=["__version__"]		).start()
 patch_submodule(_A		,       "scipy.io.loadmat"		,       wrap_auth(_A		)		,       attrs=["__version__"]		).start()
 patch_submodule(_A		,       "xml.etree.ElementTree.parse"		,       wrap_auth(_A		)		).start()
 patch_submodule(_A		,       "xml.dom.minidom.parse"		,       wrap_auth(_A		)		).start()
 snake_case__      							=			True





def 							a__     (					_UpperCAmelCase		)      ->					Optional[int]:

 '''simple docstring'''






 extend_module_for_streaming(builder.__module__		,       use_auth_token=builder.use_auth_token		)
 # if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
 if not builder.__module__.startswith("datasets."		):  # check that it's not a packaged builder like csv
  for imports in get_imports(inspect.getfile(builder.__class__		)		):
   if imports[0] == "internal":
    snake_case__      							=			imports[1]
    snake_case__      							=			".".join(builder.__module__.split("."		)[:-1] + [internal_import_name]		)
    extend_module_for_streaming(_A		,       use_auth_token=builder.use_auth_token		)

    # builders can inherit from other builders that might use streaming functionality
    # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
    # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
 from .builder import DatasetBuilder

 snake_case__      							=			[
     cls.__module__
     for cls in type(_A		).__mro__[1:]  # make sure it's not the same module we've already patched
     if issubclass(cls		,       _A		) and cls.__module__ != DatasetBuilder.__module__
 ]  # check it's not a standard builder from datasets.builder
 for module in parent_builder_modules:
  extend_module_for_streaming(_A		,       use_auth_token=builder.use_auth_token		)








"""simple docstring"""


import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							Optional[int]										=       (LMSDiscreteScheduler,)
       snake_case__			:							int										=       10

       def 						_SCREAMING_SNAKE_CASE(     self				,   **__SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			{
            "num_train_timesteps": 1100,
            "beta_start": 0.0001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
        }

        config.update(**__SCREAMING_SNAKE_CASE       )
        return config

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        for timesteps in [10, 50, 100, 1000]:
         self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]				,   [0.0002, 0.002, 0.02]       ):
         self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE				,   beta_end=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        for schedule in ["linear", "scaled_linear"]:
         self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        for t in [0, 500, 800]:
         self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.scheduler_classes[0]
        snake_case__      							=			self.get_scheduler_config()
        snake_case__      							=			scheduler_class(**__SCREAMING_SNAKE_CASE       )

        scheduler.set_timesteps(self.num_inference_steps       )

        snake_case__      							=			self.dummy_model()
        snake_case__      							=			self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(scheduler.timesteps       ):
         snake_case__      							=			scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			scheduler.step(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )
         snake_case__      							=			output.prev_sample

        snake_case__      							=			torch.sum(torch.abs(__SCREAMING_SNAKE_CASE       )       )
        snake_case__      							=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

        assert abs(result_sum.item() - 1006.388       ) < 1e-2
        assert abs(result_mean.item() - 1.31       ) < 1e-3

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.scheduler_classes[0]
        snake_case__      							=			self.get_scheduler_config(prediction_type="v_prediction"       )
        snake_case__      							=			scheduler_class(**__SCREAMING_SNAKE_CASE       )

        scheduler.set_timesteps(self.num_inference_steps       )

        snake_case__      							=			self.dummy_model()
        snake_case__      							=			self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(scheduler.timesteps       ):
         snake_case__      							=			scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			scheduler.step(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )
         snake_case__      							=			output.prev_sample

        snake_case__      							=			torch.sum(torch.abs(__SCREAMING_SNAKE_CASE       )       )
        snake_case__      							=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

        assert abs(result_sum.item() - 0.0017       ) < 1e-2
        assert abs(result_mean.item() - 2.2676e-06       ) < 1e-3

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.scheduler_classes[0]
        snake_case__      							=			self.get_scheduler_config()
        snake_case__      							=			scheduler_class(**__SCREAMING_SNAKE_CASE       )

        scheduler.set_timesteps(self.num_inference_steps				,   device=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			self.dummy_model()
        snake_case__      							=			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        snake_case__      							=			sample.to(__SCREAMING_SNAKE_CASE       )

        for i, t in enumerate(scheduler.timesteps       ):
         snake_case__      							=			scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			scheduler.step(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )
         snake_case__      							=			output.prev_sample

        snake_case__      							=			torch.sum(torch.abs(__SCREAMING_SNAKE_CASE       )       )
        snake_case__      							=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

        assert abs(result_sum.item() - 1006.388       ) < 1e-2
        assert abs(result_mean.item() - 1.31       ) < 1e-3

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.scheduler_classes[0]
        snake_case__      							=			self.get_scheduler_config()
        snake_case__      							=			scheduler_class(**__SCREAMING_SNAKE_CASE				,   use_karras_sigmas=__SCREAMING_SNAKE_CASE       )

        scheduler.set_timesteps(self.num_inference_steps				,   device=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			self.dummy_model()
        snake_case__      							=			self.dummy_sample_deter.to(__SCREAMING_SNAKE_CASE       ) * scheduler.init_noise_sigma
        snake_case__      							=			sample.to(__SCREAMING_SNAKE_CASE       )

        for t in scheduler.timesteps:
         snake_case__      							=			scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			scheduler.step(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )
         snake_case__      							=			output.prev_sample

        snake_case__      							=			torch.sum(torch.abs(__SCREAMING_SNAKE_CASE       )       )
        snake_case__      							=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

        assert abs(result_sum.item() - 3812.9927       ) < 2e-2
        assert abs(result_mean.item() - 4.9648       ) < 1e-3

