


import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Optional[Any]				      =     copy.deepcopy(__UpperCamelCase			)
     a_     :  Dict				      =     sorted(__UpperCamelCase			)
     a_     :  Dict				      =     [bisect.bisect_right(__UpperCamelCase	,	__UpperCamelCase			) for y in x]
     return quantized



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase=0			):
     a_     :  str				      =     np.arange(start=3	,	stop=__UpperCamelCase	,	step=4			).tolist() if k > 0 else [1_0]
     a_     :  Any				      =     _quantize(__UpperCamelCase	,	__UpperCamelCase			)
     # count number of elements per group
     a_     :  Optional[int]				      =     np.unique(__UpperCamelCase	,	return_counts=__UpperCamelCase			)[1]
     a_     :  List[str]				      =     [0] + bins + [np.inf]
     logger.info("""Using {} as bins for aspect lengths quantization""".format(__UpperCamelCase			)			)
     logger.info("""Count of instances per bin: {}""".format(__UpperCamelCase			)			)
     return groups


class 	a__						(    lowerCAmelCase_     ):

       def __init__( self   :  int   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  str     ):
            if not isinstance(lowerCamelCase_   ,				lowerCamelCase_     ):
                 raise ValueError(
                     """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(lowerCamelCase_     )     )
            a_     :  Optional[Any]				      =     sampler
            a_     :  List[Any]				      =     group_ids
            a_     :  Tuple				      =     batch_size

       def __iter__( self   :  Dict     ):
            a_     :  Tuple				      =     defaultdict(lowerCamelCase_     )
            a_     :  Any				      =     defaultdict(lowerCamelCase_     )

            a_     :  Any				      =     0
            for idx in self.sampler:
                 a_     :  str				      =     self.group_ids[idx]
                 buffer_per_group[group_id].append(lowerCamelCase_     )
                 samples_per_group[group_id].append(lowerCamelCase_     )
                 if len(buffer_per_group[group_id]     ) == self.batch_size:
                      yield buffer_per_group[group_id]  # TODO
                      num_batches += 1
                      del buffer_per_group[group_id]
                 assert len(buffer_per_group[group_id]     ) < self.batch_size

            # now we have run out of elements that satisfy
            # the group criteria, let's return the remaining
            # elements so that the size of the sampler is
            # deterministic
            a_     :  Optional[Any]				      =     len(self     )
            a_     :  Any				      =     expected_num_batches - num_batches
            if num_remaining > 0:
                 # for the remaining batches, group the batches by similar lengths
                 a_     :  str				      =     []
                 for group_id, idxs in sorted(buffer_per_group.items()   ,				key=lambda lowerCamelCase_     : x[0]     ):
                      batch_idx.extend(lowerCamelCase_     )
                      if len(lowerCamelCase_     ) >= self.batch_size:
                           yield batch_idx[: self.batch_size]
                           a_     :  Union[str, Any]				      =     batch_idx[self.batch_size :]
                           num_remaining -= 1
                 if len(lowerCamelCase_     ) > 0:
                      yield batch_idx
                      num_remaining -= 1
            assert num_remaining == 0

       def __len__( self   :  List[Any]     ):
            return (len(self.sampler     ) + self.batch_size - 1) // self.batch_size





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class 		lowercase_							(							__SCREAMING_SNAKE_CASE					):
 A__    :					List[Any]    =     (DPMSolverMultistepScheduler,)
 A__    :					List[str]    =     (("""num_inference_steps""", 25),)

 def lowerCamelCase_					(   self  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							{
            """num_train_timesteps""": 1_0_0_0,
            """beta_start""": 0.0_001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
            """solver_order""": 2,
            """prediction_type""": """epsilon""",
            """thresholding""": False,
            """sample_max_value""": 1.0,
            """algorithm_type""": """dpmsolver++""",
            """solver_type""": """midpoint""",
            """lower_order_final""": False,
            """lambda_min_clipped""": -float("""inf"""  ),
            """variance_type""": None,
        }

        config.update(**__UpperCamelCase  )
        return config

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




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )
        UpperCamelCase_											=							kwargs.pop("""num_inference_steps"""  ,						__UpperCamelCase  )
        UpperCamelCase_											=							self.dummy_sample
        UpperCamelCase_											=							0.1 * sample
        UpperCamelCase_											=							[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.get_scheduler_config(**__UpperCamelCase  )
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )
               scheduler.set_timesteps(__UpperCamelCase  )
               # copy over dummy past residuals
               UpperCamelCase_											=							dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__UpperCamelCase  )
                      UpperCamelCase_											=							scheduler_class.from_pretrained(__UpperCamelCase  )
                      new_scheduler.set_timesteps(__UpperCamelCase  )
                      # copy over dummy past residuals
                      UpperCamelCase_											=							dummy_past_residuals[: new_scheduler.config.solver_order]

               UpperCamelCase_							,					UpperCamelCase_											=							sample, sample
               for t in range(__UpperCamelCase  ,						time_step + scheduler.config.solver_order + 1  ):
                      UpperCamelCase_											=							scheduler.step(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						**__UpperCamelCase  ).prev_sample
                      UpperCamelCase_											=							new_scheduler.step(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						**__UpperCamelCase  ).prev_sample

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

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        pass

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




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )
        UpperCamelCase_											=							kwargs.pop("""num_inference_steps"""  ,						__UpperCamelCase  )
        UpperCamelCase_											=							self.dummy_sample
        UpperCamelCase_											=							0.1 * sample
        UpperCamelCase_											=							[residual + 0.2, residual + 0.15, residual + 0.10]

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

               # copy over dummy past residuals (must be after setting timesteps)
               UpperCamelCase_											=							dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__UpperCamelCase  )
                      UpperCamelCase_											=							scheduler_class.from_pretrained(__UpperCamelCase  )
                      # copy over dummy past residuals
                      new_scheduler.set_timesteps(__UpperCamelCase  )

                      # copy over dummy past residual (must be after setting timesteps)
                      UpperCamelCase_											=							dummy_past_residuals[: new_scheduler.config.solver_order]

               UpperCamelCase_											=							scheduler.step(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						**__UpperCamelCase  ).prev_sample

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

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




        """simple docstring"""


        if scheduler is None:
               UpperCamelCase_											=							self.scheduler_classes[0]
               UpperCamelCase_											=							self.get_scheduler_config(**__UpperCamelCase  )
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

        UpperCamelCase_											=							1_0
        UpperCamelCase_											=							self.dummy_model()
        UpperCamelCase_											=							self.dummy_sample_deter
        scheduler.set_timesteps(__UpperCamelCase  )

        for i, t in enumerate(scheduler.timesteps  ):
               UpperCamelCase_											=							model(__UpperCamelCase  ,						__UpperCamelCase  )
               UpperCamelCase_											=							scheduler.step(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ).prev_sample

        return sample

 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

               # copy over dummy past residuals (must be done after set_timesteps)
               UpperCamelCase_											=							[residual + 0.2, residual + 0.15, residual + 0.10]
               UpperCamelCase_											=							dummy_past_residuals[: scheduler.config.solver_order]

               UpperCamelCase_											=							scheduler.timesteps[5]
               UpperCamelCase_											=							scheduler.timesteps[6]

               UpperCamelCase_											=							scheduler.step(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							scheduler.step(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						**__UpperCamelCase  ).prev_sample

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

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
               self.check_over_configs(num_train_timesteps=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        self.check_over_configs(thresholding=__UpperCamelCase  )
        for order in [1, 2, 3]:
               for solver_type in ["midpoint", "heun"]:
                      for threshold in [0.5, 1.0, 2.0]:
                             for prediction_type in ["epsilon", "sample"]:
                                    self.check_over_configs(
                                        thresholding=__UpperCamelCase  ,						prediction_type=__UpperCamelCase  ,						sample_max_value=__UpperCamelCase  ,						algorithm_type="""dpmsolver++"""  ,						solver_order=__UpperCamelCase  ,						solver_type=__UpperCamelCase  ,						)

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


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

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
               for solver_type in ["midpoint", "heun"]:
                      for order in [1, 2, 3]:
                             for prediction_type in ["epsilon", "sample"]:
                                    if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                                           if order == 3:
                                                  continue
                                    else:
                                           self.check_over_configs(
                                               solver_order=__UpperCamelCase  ,						solver_type=__UpperCamelCase  ,						prediction_type=__UpperCamelCase  ,						algorithm_type=__UpperCamelCase  ,						)
                                    UpperCamelCase_											=							self.full_loop(
                                        solver_order=__UpperCamelCase  ,						solver_type=__UpperCamelCase  ,						prediction_type=__UpperCamelCase  ,						algorithm_type=__UpperCamelCase  ,						)
                                    assert not torch.isnan(__UpperCamelCase  ).any(), "Samples have nan numbers"

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        self.check_over_configs(lower_order_final=__UpperCamelCase  )
        self.check_over_configs(lower_order_final=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        self.check_over_configs(lambda_min_clipped=-float("""inf"""  )  )
        self.check_over_configs(lambda_min_clipped=-5.1  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        self.check_over_configs(variance_type=__UpperCamelCase  )
        self.check_over_configs(variance_type="""learned_range"""  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
               self.check_over_forward(num_inference_steps=__UpperCamelCase  ,						time_step=0  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.full_loop()
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert abs(result_mean.item() - 0.3_301  ) < 1e-3

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.full_loop(thresholding=__UpperCamelCase  ,						dynamic_thresholding_ratio=0.87  ,						sample_max_value=0.5  )
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert abs(result_mean.item() - 1.1_364  ) < 1e-3

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.full_loop(prediction_type="""v_prediction"""  )
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert abs(result_mean.item() - 0.2_251  ) < 1e-3

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.full_loop(prediction_type="""v_prediction"""  ,						use_karras_sigmas=__UpperCamelCase  )
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert abs(result_mean.item() - 0.2_096  ) < 1e-3

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							DPMSolverMultistepScheduler(**self.get_scheduler_config()  )
        UpperCamelCase_											=							self.full_loop(scheduler=__UpperCamelCase  )
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert abs(result_mean.item() - 0.3_301  ) < 1e-3

        UpperCamelCase_											=							DPMSolverSinglestepScheduler.from_config(scheduler.config  )
        UpperCamelCase_											=							UniPCMultistepScheduler.from_config(scheduler.config  )
        UpperCamelCase_											=							DEISMultistepScheduler.from_config(scheduler.config  )
        UpperCamelCase_											=							DPMSolverMultistepScheduler.from_config(scheduler.config  )

        UpperCamelCase_											=							self.full_loop(scheduler=__UpperCamelCase  )
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert abs(result_mean.item() - 0.3_301  ) < 1e-3

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


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

        UpperCamelCase_											=							1_0
        UpperCamelCase_											=							self.dummy_model()
        UpperCamelCase_											=							self.dummy_sample_deter.half()
        scheduler.set_timesteps(__UpperCamelCase  )

        for i, t in enumerate(scheduler.timesteps  ):
               UpperCamelCase_											=							model(__UpperCamelCase  ,						__UpperCamelCase  )
               UpperCamelCase_											=							scheduler.step(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ).prev_sample

        assert sample.dtype == torch.floataa

 def lowerCamelCase_					(   self  ,						**__UpperCamelCase  ):




        """simple docstring"""


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

               scheduler.set_timesteps(scheduler.config.num_train_timesteps  )
               assert len(scheduler.timesteps.unique()  ) == scheduler.num_inference_steps



