import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  (DDPMParallelScheduler,)



     def 		UpperCamelCase_						(					self				,				**A_						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				{
               '''num_train_timesteps''': 10_00,
               '''beta_start''': 0.0001,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
               '''variance_type''': '''fixed_small''',
               '''clip_sample''': True,
           }

           config.update(**A_						)
           return config



     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           for timesteps in [1, 5, 1_00, 10_00]:
                 self.check_over_configs(num_train_timesteps=A_						)



     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           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=A_				,				beta_end=A_						)



     def 		UpperCamelCase_						(					self						) ->				Any:






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



     def 		UpperCamelCase_						(					self						) ->				Tuple:






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



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=A_						)



     def 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           self.check_over_configs(thresholding=A_						)
           for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "sample", "v_prediction"]:
                       self.check_over_configs(
                           thresholding=A_				,				prediction_type=A_				,				sample_max_value=A_				,				)



     def 		UpperCamelCase_						(					self						) ->				str:






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



     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           for t in [0, 5_00, 9_99]:
                 self.check_over_forward(time_step=A_						)



     def 		UpperCamelCase_						(					self						) ->				Optional[int]:






           """simple docstring"""
           _lowerCamelCase			    =				self.scheduler_classes[0]
           _lowerCamelCase			    =				self.get_scheduler_config()
           _lowerCamelCase			    =				scheduler_class(**A_						)

           assert torch.sum(torch.abs(scheduler._get_variance(0						) - 0.0						)						) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(4_87						) - 0.00979						)						) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(9_99						) - 0.02						)						) < 1E-5



     def 		UpperCamelCase_						(					self						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				self.scheduler_classes[0]
           _lowerCamelCase			    =				self.get_scheduler_config()
           _lowerCamelCase			    =				scheduler_class(**A_						)

           _lowerCamelCase			    =				len(A_						)

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

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

           _lowerCamelCase			    =				model(samples.flatten(0				,				1						)				,				timesteps.flatten(0				,				1						)						)
           _lowerCamelCase			    =				scheduler.batch_step_no_noise(A_				,				timesteps.flatten(0				,				1						)				,				samples.flatten(0				,				1						)						)

           _lowerCamelCase			    =				torch.sum(torch.abs(A_						)						)
           _lowerCamelCase			    =				torch.mean(torch.abs(A_						)						)

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



     def 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase			    =				self.scheduler_classes[0]
           _lowerCamelCase			    =				self.get_scheduler_config()
           _lowerCamelCase			    =				scheduler_class(**A_						)

           _lowerCamelCase			    =				len(A_						)

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

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

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

                 _lowerCamelCase			    =				pred_prev_sample

           _lowerCamelCase			    =				torch.sum(torch.abs(A_						)						)
           _lowerCamelCase			    =				torch.mean(torch.abs(A_						)						)

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



     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				self.scheduler_classes[0]
           _lowerCamelCase			    =				self.get_scheduler_config(prediction_type='''v_prediction'''						)
           _lowerCamelCase			    =				scheduler_class(**A_						)

           _lowerCamelCase			    =				len(A_						)

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

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

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

                 _lowerCamelCase			    =				pred_prev_sample

           _lowerCamelCase			    =				torch.sum(torch.abs(A_						)						)
           _lowerCamelCase			    =				torch.mean(torch.abs(A_						)						)

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



     def 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase			    =				self.scheduler_classes[0]
           _lowerCamelCase			    =				self.get_scheduler_config()
           _lowerCamelCase			    =				scheduler_class(**A_						)

           _lowerCamelCase			    =				[1_00, 87, 50, 1, 0]

           scheduler.set_timesteps(timesteps=A_						)

           _lowerCamelCase			    =				scheduler.timesteps

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

                 _lowerCamelCase			    =				scheduler.previous_timestep(A_						)
                 _lowerCamelCase			    =				prev_t.item()

                 self.assertEqual(A_				,				A_						)



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				self.scheduler_classes[0]
           _lowerCamelCase			    =				self.get_scheduler_config()
           _lowerCamelCase			    =				scheduler_class(**A_						)

           _lowerCamelCase			    =				[1_00, 87, 50, 51, 0]

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



     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				self.scheduler_classes[0]
           _lowerCamelCase			    =				self.get_scheduler_config()
           _lowerCamelCase			    =				scheduler_class(**A_						)

           _lowerCamelCase			    =				[1_00, 87, 50, 1, 0]
           _lowerCamelCase			    =				len(A_						)

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





     def 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           _lowerCamelCase			    =				self.scheduler_classes[0]
           _lowerCamelCase			    =				self.get_scheduler_config()
           _lowerCamelCase			    =				scheduler_class(**A_						)

           _lowerCamelCase			    =				[scheduler.config.num_train_timesteps]

           with self.assertRaises(
               A_				,				msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''				,				):
                 scheduler.set_timesteps(timesteps=A_						)
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case__				= {
    'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WhisperConfig', 'WhisperOnnxConfig'],
    'feature_extraction_whisper': ['WhisperFeatureExtractor'],
    'processing_whisper': ['WhisperProcessor'],
    'tokenization_whisper': ['WhisperTokenizer'],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__				= ['WhisperTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__				= [
         'WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'WhisperForConditionalGeneration',
         'WhisperModel',
         'WhisperPreTrainedModel',
         'WhisperForAudioClassification',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__				= [
         'TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFWhisperForConditionalGeneration',
         'TFWhisperModel',
         'TFWhisperPreTrainedModel',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__				= [
         'FlaxWhisperForConditionalGeneration',
         'FlaxWhisperModel',
         'FlaxWhisperPreTrainedModel',
         'FlaxWhisperForAudioClassification',
     ]


if TYPE_CHECKING:
     from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
     from .feature_extraction_whisper import WhisperFeatureExtractor
     from .processing_whisper import WhisperProcessor
     from .tokenization_whisper import WhisperTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_whisper_fast import WhisperTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_whisper import (
              WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
              WhisperForAudioClassification,
              WhisperForConditionalGeneration,
              WhisperModel,
              WhisperPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_whisper import (
              TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFWhisperForConditionalGeneration,
              TFWhisperModel,
              TFWhisperPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_whisper import (
              FlaxWhisperForAudioClassification,
              FlaxWhisperForConditionalGeneration,
              FlaxWhisperModel,
              FlaxWhisperPreTrainedModel,
          )

else:
     import sys

     snake_case__				= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)