


import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class A  (       A__					):
      UpperCamelCase__							:  Dict				=(LMSDiscreteScheduler,)
      UpperCamelCase__							:  List[str]				=10






      def 	lowerCamelCase	( self						:					List[str]      ,       **lowercase_						:					Union[str, Any]			) ->						Dict:
             """simple docstring"""




             _lowerCamelCase	:		Optional[Any]          ={
                 'num_train_timesteps': 1100,
                 'beta_start': 0.0001,
                 'beta_end': 0.02,
                 'beta_schedule': 'linear',
             }

             config.update(**lowercase_			)
             return config






      def 	lowerCamelCase	( self						:					List[Any]			) ->						Optional[int]:
             """simple docstring"""




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






      def 	lowerCamelCase	( self						:					List[str]			) ->						Any:
             """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=lowercase_      ,       beta_end=lowercase_			)






      def 	lowerCamelCase	( self						:					List[Any]			) ->						int:
             """simple docstring"""




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






      def 	lowerCamelCase	( self						:					List[Any]			) ->						Optional[int]:
             """simple docstring"""




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






      def 	lowerCamelCase	( self						:					List[str]			) ->						str:
             """simple docstring"""




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






      def 	lowerCamelCase	( self						:					Union[str, Any]			) ->						Any:
             """simple docstring"""




             _lowerCamelCase	:		List[str]          =self.scheduler_classes[0]
             _lowerCamelCase	:		List[str]          =self.get_scheduler_config()
             _lowerCamelCase	:		List[str]          =scheduler_class(**lowercase_			)

             scheduler.set_timesteps(self.num_inference_steps			)

             _lowerCamelCase	:		Tuple          =self.dummy_model()
             _lowerCamelCase	:		Tuple          =self.dummy_sample_deter * scheduler.init_noise_sigma

             for i, t in enumerate(scheduler.timesteps			):
                    _lowerCamelCase	:		List[Any]          =scheduler.scale_model_input(lowercase_      ,       lowercase_			)

                    _lowerCamelCase	:		Tuple          =model(lowercase_      ,       lowercase_			)

                    _lowerCamelCase	:		Any          =scheduler.step(lowercase_      ,       lowercase_      ,       lowercase_			)
                    _lowerCamelCase	:		List[Any]          =output.prev_sample

             _lowerCamelCase	:		Dict          =torch.sum(torch.abs(lowercase_			)			)
             _lowerCamelCase	:		Any          =torch.mean(torch.abs(lowercase_			)			)

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






      def 	lowerCamelCase	( self						:					str			) ->						List[str]:
             """simple docstring"""




             _lowerCamelCase	:		Optional[int]          =self.scheduler_classes[0]
             _lowerCamelCase	:		Optional[Any]          =self.get_scheduler_config(prediction_type='v_prediction'			)
             _lowerCamelCase	:		Union[str, Any]          =scheduler_class(**lowercase_			)

             scheduler.set_timesteps(self.num_inference_steps			)

             _lowerCamelCase	:		str          =self.dummy_model()
             _lowerCamelCase	:		Optional[Any]          =self.dummy_sample_deter * scheduler.init_noise_sigma

             for i, t in enumerate(scheduler.timesteps			):
                    _lowerCamelCase	:		Optional[int]          =scheduler.scale_model_input(lowercase_      ,       lowercase_			)

                    _lowerCamelCase	:		Union[str, Any]          =model(lowercase_      ,       lowercase_			)

                    _lowerCamelCase	:		int          =scheduler.step(lowercase_      ,       lowercase_      ,       lowercase_			)
                    _lowerCamelCase	:		Tuple          =output.prev_sample

             _lowerCamelCase	:		List[str]          =torch.sum(torch.abs(lowercase_			)			)
             _lowerCamelCase	:		Union[str, Any]          =torch.mean(torch.abs(lowercase_			)			)

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






      def 	lowerCamelCase	( self						:					Dict			) ->						Any:
             """simple docstring"""




             _lowerCamelCase	:		Any          =self.scheduler_classes[0]
             _lowerCamelCase	:		Optional[int]          =self.get_scheduler_config()
             _lowerCamelCase	:		Any          =scheduler_class(**lowercase_			)

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

             _lowerCamelCase	:		List[str]          =self.dummy_model()
             _lowerCamelCase	:		Dict          =self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
             _lowerCamelCase	:		int          =sample.to(lowercase_			)

             for i, t in enumerate(scheduler.timesteps			):
                    _lowerCamelCase	:		Dict          =scheduler.scale_model_input(lowercase_      ,       lowercase_			)

                    _lowerCamelCase	:		List[Any]          =model(lowercase_      ,       lowercase_			)

                    _lowerCamelCase	:		Tuple          =scheduler.step(lowercase_      ,       lowercase_      ,       lowercase_			)
                    _lowerCamelCase	:		Any          =output.prev_sample

             _lowerCamelCase	:		List[Any]          =torch.sum(torch.abs(lowercase_			)			)
             _lowerCamelCase	:		int          =torch.mean(torch.abs(lowercase_			)			)

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






      def 	lowerCamelCase	( self						:					str			) ->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase	:		Optional[Any]          =self.scheduler_classes[0]
             _lowerCamelCase	:		Union[str, Any]          =self.get_scheduler_config()
             _lowerCamelCase	:		List[str]          =scheduler_class(**lowercase_      ,       use_karras_sigmas=lowercase_			)

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

             _lowerCamelCase	:		str          =self.dummy_model()
             _lowerCamelCase	:		Union[str, Any]          =self.dummy_sample_deter.to(lowercase_			) * scheduler.init_noise_sigma
             _lowerCamelCase	:		Union[str, Any]          =sample.to(lowercase_			)

             for t in scheduler.timesteps:
                    _lowerCamelCase	:		List[str]          =scheduler.scale_model_input(lowercase_      ,       lowercase_			)

                    _lowerCamelCase	:		Optional[Any]          =model(lowercase_      ,       lowercase_			)

                    _lowerCamelCase	:		List[Any]          =scheduler.step(lowercase_      ,       lowercase_      ,       lowercase_			)
                    _lowerCamelCase	:		Any          =output.prev_sample

             _lowerCamelCase	:		Optional[int]          =torch.sum(torch.abs(lowercase_			)			)
             _lowerCamelCase	:		Optional[Any]          =torch.mean(torch.abs(lowercase_			)			)

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








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_				(   SCREAMING_SNAKE_CASE__ :				Tuple		,      SCREAMING_SNAKE_CASE__ :				Optional[int]					):
       '''simple docstring'''





       _lowerCamelCase	:		Union[str, Any]          =copy.deepcopy(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		Union[str, Any]          =sorted(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		List[Any]          =[bisect.bisect_right(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					) for y in x]
       return quantized




def      a_				(   SCREAMING_SNAKE_CASE__ :				Any		,      SCREAMING_SNAKE_CASE__ :				Optional[int]=0					):
       '''simple docstring'''





       _lowerCamelCase	:		List[Any]          =np.arange(start=3		,      stop=SCREAMING_SNAKE_CASE__		,      step=4					).tolist() if k > 0 else [10]
       _lowerCamelCase	:		List[Any]          =_quantize(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
       # count number of elements per group
       _lowerCamelCase	:		Union[str, Any]          =np.unique(SCREAMING_SNAKE_CASE__		,      return_counts=SCREAMING_SNAKE_CASE__					)[1]
       _lowerCamelCase	:		int          =[0] + bins + [np.inf]
       logger.info('Using {} as bins for aspect lengths quantization'.format(SCREAMING_SNAKE_CASE__					)					)
       logger.info('Count of instances per bin: {}'.format(SCREAMING_SNAKE_CASE__					)					)
       return groups




class A  (       UpperCamelCase_					):






      def __init__( self						:					Optional[Any]      ,       lowercase_						:					int      ,       lowercase_						:					Optional[int]      ,       lowercase_						:					int			) ->						Any:
             """simple docstring"""




             if not isinstance(lowercase_      ,       lowercase_			):
                    raise ValueError(
                        'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(lowercase_			)			)
             _lowerCamelCase	:		Any          =sampler
             _lowerCamelCase	:		Dict          =group_ids
             _lowerCamelCase	:		Dict          =batch_size






      def __iter__( self						:					Optional[int]			) ->						str:
             """simple docstring"""




             _lowerCamelCase	:		Union[str, Any]          =defaultdict(lowercase_			)
             _lowerCamelCase	:		Union[str, Any]          =defaultdict(lowercase_			)

             _lowerCamelCase	:		Any          =0
             for idx in self.sampler:
                    _lowerCamelCase	:		Any          =self.group_ids[idx]
                    buffer_per_group[group_id].append(lowercase_			)
                    samples_per_group[group_id].append(lowercase_			)
                    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
             _lowerCamelCase	:		Union[str, Any]          =len(self			)
             _lowerCamelCase	:		Dict          =expected_num_batches - num_batches
             if num_remaining > 0:
                    # for the remaining batches, group the batches by similar lengths
                    _lowerCamelCase	:		Union[str, Any]          =[]
                    for group_id, idxs in sorted(buffer_per_group.items()      ,       key=lambda lowercase_			: x[0]			):
                           batch_idx.extend(lowercase_			)
                           if len(lowercase_			) >= self.batch_size:
                                  yield batch_idx[: self.batch_size]
                                  _lowerCamelCase	:		str          =batch_idx[self.batch_size :]
                                  num_remaining -= 1
                    if len(lowercase_			) > 0:
                           yield batch_idx
                           num_remaining -= 1
             assert num_remaining == 0






      def __len__( self						:					Optional[int]			) ->						Union[str, Any]:
             """simple docstring"""




             return (len(self.sampler			) + self.batch_size - 1) // self.batch_size


