
import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class 		lowercase_							(							unittest.TestCase					):
 # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
 A__    :					int    =     (ScoreSdeVeScheduler,)
 A__    :					Union[str, Any]    =     ()

 @property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							4
        UpperCamelCase_											=							3
        UpperCamelCase_											=							8
        UpperCamelCase_											=							8

        UpperCamelCase_											=							torch.rand((batch_size, num_channels, height, width)  )

        return sample

 @property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							4
        UpperCamelCase_											=							3
        UpperCamelCase_											=							8
        UpperCamelCase_											=							8

        UpperCamelCase_											=							batch_size * num_channels * height * width
        UpperCamelCase_											=							torch.arange(__UpperCamelCase  )
        UpperCamelCase_											=							sample.reshape(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  )
        UpperCamelCase_											=							sample / num_elems
        UpperCamelCase_											=							sample.permute(3  ,						0  ,						1  ,						2  )

        return sample

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        def model(__UpperCamelCase  ,						__UpperCamelCase  ,						*__UpperCamelCase  ):
               return sample * t / (t + 1)

        return model

 def lowerCamelCase_					(   self  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							{
            """num_train_timesteps""": 2_0_0_0,
            """snr""": 0.15,
            """sigma_min""": 0.01,
            """sigma_max""": 1_3_4_8,
            """sampling_eps""": 1e-5,
        }

        config.update(**__UpperCamelCase  )
        return config

 def lowerCamelCase_					(   self  ,						__UpperCamelCase=0  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )

        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.dummy_sample
               UpperCamelCase_											=							0.1 * sample

               UpperCamelCase_											=							self.get_scheduler_config(**__UpperCamelCase  )
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__UpperCamelCase  )
                      UpperCamelCase_											=							scheduler_class.from_pretrained(__UpperCamelCase  )

               UpperCamelCase_											=							scheduler.step_pred(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step_pred(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

               assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"

               UpperCamelCase_											=							scheduler.step_correct(__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step_correct(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

               assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler correction are not identical"

 def lowerCamelCase_					(   self  ,						__UpperCamelCase=0  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )
        kwargs.update(__UpperCamelCase  )

        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.dummy_sample
               UpperCamelCase_											=							0.1 * sample

               UpperCamelCase_											=							self.get_scheduler_config()
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__UpperCamelCase  )
                      UpperCamelCase_											=							scheduler_class.from_pretrained(__UpperCamelCase  )

               UpperCamelCase_											=							scheduler.step_pred(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step_pred(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

               assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"

               UpperCamelCase_											=							scheduler.step_correct(__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step_correct(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

               assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler correction are not identical"

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for timesteps in [1_0, 1_0_0, 1_0_0_0]:
               self.check_over_configs(num_train_timesteps=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]  ,						[1, 1_0_0, 1_0_0_0]  ):
               self.check_over_configs(sigma_min=__UpperCamelCase  ,						sigma_max=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for t in [0.1, 0.5, 0.75]:
               self.check_over_forward(time_step=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )

        UpperCamelCase_											=							self.scheduler_classes[0]
        UpperCamelCase_											=							self.get_scheduler_config()
        UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

        UpperCamelCase_											=							3

        UpperCamelCase_											=							self.dummy_model()
        UpperCamelCase_											=							self.dummy_sample_deter

        scheduler.set_sigmas(__UpperCamelCase  )
        scheduler.set_timesteps(__UpperCamelCase  )
        UpperCamelCase_											=							torch.manual_seed(0  )

        for i, t in enumerate(scheduler.timesteps  ):
               UpperCamelCase_											=							scheduler.sigmas[i]

               for _ in range(scheduler.config.correct_steps  ):
                      with torch.no_grad():
                             UpperCamelCase_											=							model(__UpperCamelCase  ,						__UpperCamelCase  )
                      UpperCamelCase_											=							scheduler.step_correct(__UpperCamelCase  ,						__UpperCamelCase  ,						generator=__UpperCamelCase  ,						**__UpperCamelCase  ).prev_sample

               with torch.no_grad():
                      UpperCamelCase_											=							model(__UpperCamelCase  ,						__UpperCamelCase  )

               UpperCamelCase_											=							scheduler.step_pred(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=__UpperCamelCase  ,						**__UpperCamelCase  )
               UpperCamelCase_							,					UpperCamelCase_											=							output.prev_sample, output.prev_sample_mean

        UpperCamelCase_											=							torch.sum(torch.abs(__UpperCamelCase  )  )
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert np.isclose(result_sum.item()  ,						14_372_758_528.0  )
        assert np.isclose(result_mean.item()  ,						18_714_530.0  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )

        UpperCamelCase_											=							kwargs.pop("""num_inference_steps"""  ,						__UpperCamelCase  )

        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.get_scheduler_config()
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

               UpperCamelCase_											=							self.dummy_sample
               UpperCamelCase_											=							0.1 * sample

               if num_inference_steps is not None and hasattr(__UpperCamelCase  ,						"""set_timesteps"""  ):
                      scheduler.set_timesteps(__UpperCamelCase  )
               elif num_inference_steps is not None and not hasattr(__UpperCamelCase  ,						"""set_timesteps"""  ):
                      UpperCamelCase_											=							num_inference_steps

               UpperCamelCase_											=							scheduler.step_pred(__UpperCamelCase  ,						0  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							scheduler.step_pred(__UpperCamelCase  ,						1  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

               self.assertEqual(output_a.shape  ,						sample.shape  )
               self.assertEqual(output_a.shape  ,						output_a.shape  )





import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class 		lowercase_							(							__SCREAMING_SNAKE_CASE					):

 def __init__(   self  ,						__UpperCamelCase  ,						__UpperCamelCase = None  ,						__UpperCamelCase = None  ,						__UpperCamelCase = None  ,						__UpperCamelCase = False  ,						__UpperCamelCase = False  ,						__UpperCamelCase = None  ,						**__UpperCamelCase  ,						):




        """simple docstring"""


        super().__init__(
            __UpperCamelCase  ,						split=__UpperCamelCase  ,						features=__UpperCamelCase  ,						cache_dir=__UpperCamelCase  ,						keep_in_memory=__UpperCamelCase  ,						streaming=__UpperCamelCase  ,						num_proc=__UpperCamelCase  ,						**__UpperCamelCase  ,						)
        UpperCamelCase_											=							path_or_paths if isinstance(__UpperCamelCase  ,						__UpperCamelCase  ) else {self.split: path_or_paths}
        UpperCamelCase_											=							Csv(
            cache_dir=__UpperCamelCase  ,						data_files=__UpperCamelCase  ,						features=__UpperCamelCase  ,						**__UpperCamelCase  ,						)

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        if self.streaming:
               UpperCamelCase_											=							self.builder.as_streaming_dataset(split=self.split  )
        # Build regular (map-style) dataset
        else:
               UpperCamelCase_											=							None
               UpperCamelCase_											=							None
               UpperCamelCase_											=							None
               UpperCamelCase_											=							None

               self.builder.download_and_prepare(
                   download_config=__UpperCamelCase  ,						download_mode=__UpperCamelCase  ,						verification_mode=__UpperCamelCase  ,						base_path=__UpperCamelCase  ,						num_proc=self.num_proc  ,						)
               UpperCamelCase_											=							self.builder.as_dataset(
                   split=self.split  ,						verification_mode=__UpperCamelCase  ,						in_memory=self.keep_in_memory  )
        return dataset



class 		lowercase_							:

 def __init__(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase = None  ,						__UpperCamelCase = None  ,						**__UpperCamelCase  ,						):




        """simple docstring"""


        if num_proc is not None and num_proc <= 0:
               raise ValueError(f'''num_proc {num_proc} must be an integer > 0.'''  )

        UpperCamelCase_											=							dataset
        UpperCamelCase_											=							path_or_buf
        UpperCamelCase_											=							batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        UpperCamelCase_											=							num_proc
        UpperCamelCase_											=							"""utf-8"""
        UpperCamelCase_											=							to_csv_kwargs

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.to_csv_kwargs.pop("""path_or_buf"""  ,						__UpperCamelCase  )
        UpperCamelCase_											=							self.to_csv_kwargs.pop("""header"""  ,						__UpperCamelCase  )
        UpperCamelCase_											=							self.to_csv_kwargs.pop("""index"""  ,						__UpperCamelCase  )

        if isinstance(self.path_or_buf  ,						(str, bytes, os.PathLike)  ):
               with open(self.path_or_buf  ,						"""wb+"""  ) as buffer:
                      UpperCamelCase_											=							self._write(file_obj=__UpperCamelCase  ,						header=__UpperCamelCase  ,						index=__UpperCamelCase  ,						**self.to_csv_kwargs  )
        else:
               UpperCamelCase_											=							self._write(file_obj=self.path_or_buf  ,						header=__UpperCamelCase  ,						index=__UpperCamelCase  ,						**self.to_csv_kwargs  )
        return written

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_											=							args

        UpperCamelCase_											=							query_table(
            table=self.dataset.data  ,						key=slice(__UpperCamelCase  ,						offset + self.batch_size  )  ,						indices=self.dataset._indices  ,						)
        UpperCamelCase_											=							batch.to_pandas().to_csv(
            path_or_buf=__UpperCamelCase  ,						header=header if (offset == 0) else False  ,						index=__UpperCamelCase  ,						**__UpperCamelCase  )
        return csv_str.encode(self.encoding  )

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							0

        if self.num_proc is None or self.num_proc == 1:
               for offset in logging.tqdm(
                   range(0  ,						len(self.dataset  )  ,						self.batch_size  )  ,						unit="""ba"""  ,						disable=not logging.is_progress_bar_enabled()  ,						desc="""Creating CSV from Arrow format"""  ,						):
                      UpperCamelCase_											=							self._batch_csv((offset, header, index, to_csv_kwargs)  )
                      written += file_obj.write(__UpperCamelCase  )

        else:
               UpperCamelCase_							,					UpperCamelCase_											=							len(self.dataset  ), self.batch_size
               with multiprocessing.Pool(self.num_proc  ) as pool:
                      for csv_str in logging.tqdm(
                          pool.imap(
                              self._batch_csv  ,						[(offset, header, index, to_csv_kwargs) for offset in range(0  ,						__UpperCamelCase  ,						__UpperCamelCase  )]  ,						)  ,						total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size  ,						unit="""ba"""  ,						disable=not logging.is_progress_bar_enabled()  ,						desc="""Creating CSV from Arrow format"""  ,						):
                             written += file_obj.write(__UpperCamelCase  )

        return written



