



import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class 	lowerCamelCase      (						lowercase__  ):







     '''simple docstring'''







     lowerCAmelCase_				:    str									=  (DDPMParallelScheduler,)



     def    A__		(			self    ,	**lowerCAmelCase     ):
       UpperCAmelCase_						=				{
           'num_train_timesteps': 1000,
           'beta_start': 0.0001,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
           'variance_type': 'fixed_small',
           'clip_sample': True,
       }

       config.update(**lowerCAmelCase     )
       return config



     def    A__		(			self     ):







       for timesteps in [1, 5, 100, 1000]:
         self.check_over_configs(num_train_timesteps=lowerCAmelCase     )



     def    A__		(			self     ):







       for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]    ,	[0.002, 0.02, 0.2, 2]     ):
         self.check_over_configs(beta_start=lowerCAmelCase    ,	beta_end=lowerCAmelCase     )



     def    A__		(			self     ):







       for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=lowerCAmelCase     )



     def    A__		(			self     ):







       for variance in ["fixed_small", "fixed_large", "other"]:
         self.check_over_configs(variance_type=lowerCAmelCase     )



     def    A__		(			self     ):







       for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=lowerCAmelCase     )



     def    A__		(			self     ):
       self.check_over_configs(thresholding=lowerCAmelCase     )
       for threshold in [0.5, 1.0, 2.0]:
         for prediction_type in ["epsilon", "sample", "v_prediction"]:
           self.check_over_configs(
               thresholding=lowerCAmelCase    ,	prediction_type=lowerCAmelCase    ,	sample_max_value=lowerCAmelCase    ,	)



     def    A__		(			self     ):







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



     def    A__		(			self     ):







       for t in [0, 500, 999]:
         self.check_over_forward(time_step=lowerCAmelCase     )



     def    A__		(			self     ):
       UpperCAmelCase_						=				self.scheduler_classes[0]
       UpperCAmelCase_						=				self.get_scheduler_config()
       UpperCAmelCase_						=				scheduler_class(**lowerCAmelCase     )

       assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 0.0     )     ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(487     ) - 0.00979     )     ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(999     ) - 0.02     )     ) < 1e-5



     def    A__		(			self     ):
       UpperCAmelCase_						=				self.scheduler_classes[0]
       UpperCAmelCase_						=				self.get_scheduler_config()
       UpperCAmelCase_						=				scheduler_class(**lowerCAmelCase     )

       UpperCAmelCase_						=				len(lowerCAmelCase     )

       UpperCAmelCase_						=				self.dummy_model()
       UpperCAmelCase_						=				self.dummy_sample_deter
       UpperCAmelCase_						=				self.dummy_sample_deter + 0.1
       UpperCAmelCase_						=				self.dummy_sample_deter - 0.1

       UpperCAmelCase_						=				samplea.shape[0]
       UpperCAmelCase_						=				torch.stack([samplea, samplea, samplea]    ,	dim=0     )
       UpperCAmelCase_						=				torch.arange(lowerCAmelCase     )[0:3, None].repeat(1    ,	lowerCAmelCase     )

       UpperCAmelCase_						=				model(samples.flatten(0    ,	1     )    ,	timesteps.flatten(0    ,	1     )     )
       UpperCAmelCase_						=				scheduler.batch_step_no_noise(lowerCAmelCase    ,	timesteps.flatten(0    ,	1     )    ,	samples.flatten(0    ,	1     )     )

       UpperCAmelCase_						=				torch.sum(torch.abs(lowerCAmelCase     )     )
       UpperCAmelCase_						=				torch.mean(torch.abs(lowerCAmelCase     )     )

       assert abs(result_sum.item() - 1153.1833     ) < 1e-2
       assert abs(result_mean.item() - 0.5005     ) < 1e-3



     def    A__		(			self     ):
       UpperCAmelCase_						=				self.scheduler_classes[0]
       UpperCAmelCase_						=				self.get_scheduler_config()
       UpperCAmelCase_						=				scheduler_class(**lowerCAmelCase     )

       UpperCAmelCase_						=				len(lowerCAmelCase     )

       UpperCAmelCase_						=				self.dummy_model()
       UpperCAmelCase_						=				self.dummy_sample_deter
       UpperCAmelCase_						=				torch.manual_seed(0     )

       for t in reversed(range(lowerCAmelCase     )     ):
         # 1. predict noise residual
         UpperCAmelCase_						=				model(lowerCAmelCase    ,	lowerCAmelCase     )

         # 2. predict previous mean of sample x_t-1
         UpperCAmelCase_						=				scheduler.step(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	generator=lowerCAmelCase     ).prev_sample

         UpperCAmelCase_						=				pred_prev_sample

       UpperCAmelCase_						=				torch.sum(torch.abs(lowerCAmelCase     )     )
       UpperCAmelCase_						=				torch.mean(torch.abs(lowerCAmelCase     )     )

       assert abs(result_sum.item() - 258.9606     ) < 1e-2
       assert abs(result_mean.item() - 0.3372     ) < 1e-3



     def    A__		(			self     ):
       UpperCAmelCase_						=				self.scheduler_classes[0]
       UpperCAmelCase_						=				self.get_scheduler_config(prediction_type="v_prediction"     )
       UpperCAmelCase_						=				scheduler_class(**lowerCAmelCase     )

       UpperCAmelCase_						=				len(lowerCAmelCase     )

       UpperCAmelCase_						=				self.dummy_model()
       UpperCAmelCase_						=				self.dummy_sample_deter
       UpperCAmelCase_						=				torch.manual_seed(0     )

       for t in reversed(range(lowerCAmelCase     )     ):
         # 1. predict noise residual
         UpperCAmelCase_						=				model(lowerCAmelCase    ,	lowerCAmelCase     )

         # 2. predict previous mean of sample x_t-1
         UpperCAmelCase_						=				scheduler.step(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	generator=lowerCAmelCase     ).prev_sample

         UpperCAmelCase_						=				pred_prev_sample

       UpperCAmelCase_						=				torch.sum(torch.abs(lowerCAmelCase     )     )
       UpperCAmelCase_						=				torch.mean(torch.abs(lowerCAmelCase     )     )

       assert abs(result_sum.item() - 202.0296     ) < 1e-2
       assert abs(result_mean.item() - 0.2631     ) < 1e-3



     def    A__		(			self     ):
       UpperCAmelCase_						=				self.scheduler_classes[0]
       UpperCAmelCase_						=				self.get_scheduler_config()
       UpperCAmelCase_						=				scheduler_class(**lowerCAmelCase     )

       UpperCAmelCase_						=				[100, 87, 50, 1, 0]

       scheduler.set_timesteps(timesteps=lowerCAmelCase     )

       UpperCAmelCase_						=				scheduler.timesteps

       for i, timestep in enumerate(lowerCAmelCase     ):
         if i == len(lowerCAmelCase     ) - 1:
           UpperCAmelCase_						=				-1
         else:
           UpperCAmelCase_						=				timesteps[i + 1]

         UpperCAmelCase_						=				scheduler.previous_timestep(lowerCAmelCase     )
         UpperCAmelCase_						=				prev_t.item()

         self.assertEqual(lowerCAmelCase    ,	lowerCAmelCase     )



     def    A__		(			self     ):
       UpperCAmelCase_						=				self.scheduler_classes[0]
       UpperCAmelCase_						=				self.get_scheduler_config()
       UpperCAmelCase_						=				scheduler_class(**lowerCAmelCase     )

       UpperCAmelCase_						=				[100, 87, 50, 51, 0]

       with self.assertRaises(lowerCAmelCase    ,	msg="`custom_timesteps` must be in descending order."     ):
         scheduler.set_timesteps(timesteps=lowerCAmelCase     )



     def    A__		(			self     ):
       UpperCAmelCase_						=				self.scheduler_classes[0]
       UpperCAmelCase_						=				self.get_scheduler_config()
       UpperCAmelCase_						=				scheduler_class(**lowerCAmelCase     )

       UpperCAmelCase_						=				[100, 87, 50, 1, 0]
       UpperCAmelCase_						=				len(lowerCAmelCase     )

       with self.assertRaises(lowerCAmelCase    ,	msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."     ):
         scheduler.set_timesteps(num_inference_steps=lowerCAmelCase    ,	timesteps=lowerCAmelCase     )



     def    A__		(			self     ):
       UpperCAmelCase_						=				self.scheduler_classes[0]
       UpperCAmelCase_						=				self.get_scheduler_config()
       UpperCAmelCase_						=				scheduler_class(**lowerCAmelCase     )

       UpperCAmelCase_						=				[scheduler.config.num_train_timesteps]

       with self.assertRaises(
           lowerCAmelCase    ,	msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"    ,	):
         scheduler.set_timesteps(timesteps=lowerCAmelCase     )






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE     				=						{"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						["FNetTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						["FNetTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						[
			    "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "FNetForMaskedLM",
			    "FNetForMultipleChoice",
			    "FNetForNextSentencePrediction",
			    "FNetForPreTraining",
			    "FNetForQuestionAnswering",
			    "FNetForSequenceClassification",
			    "FNetForTokenClassification",
			    "FNetLayer",
			    "FNetModel",
			    "FNetPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet import FNetTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet_fast import FNetTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_fnet import (
						    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FNetForMaskedLM,
						    FNetForMultipleChoice,
						    FNetForNextSentencePrediction,
						    FNetForPreTraining,
						    FNetForQuestionAnswering,
						    FNetForSequenceClassification,
						    FNetForTokenClassification,
						    FNetLayer,
						    FNetModel,
						    FNetPreTrainedModel,
						)


else:
			import sys

			SCREAMING_SNAKE_CASE     				=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

