
'''simple docstring'''

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class      a_     (__A			):
       __lowerCAmelCase			: List[Any]			=					(UnCLIPScheduler,)




       def 							__UpperCamelCase				(   self      ,      **snake_case_     ):
         _lowerCAmelCase				:     int       =    {
             "num_train_timesteps": 1_0_0_0,
             "variance_type": "fixed_small_log",
             "clip_sample": True,
             "clip_sample_range": 1.0,
             "prediction_type": "epsilon",
         }

         config.update(**lowercase_     )
         return config




       def 							__UpperCamelCase				(   self     ):
         for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
           self.check_over_configs(num_train_timesteps=lowercase_     )




       def 							__UpperCamelCase				(   self     ):
         for variance in ["fixed_small_log", "learned_range"]:
           self.check_over_configs(variance_type=lowercase_     )




       def 							__UpperCamelCase				(   self     ):
         for clip_sample in [True, False]:
           self.check_over_configs(clip_sample=lowercase_     )




       def 							__UpperCamelCase				(   self     ):
         for clip_sample_range in [1, 5, 1_0, 2_0]:
           self.check_over_configs(clip_sample_range=lowercase_     )




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




       def 							__UpperCamelCase				(   self     ):
         for time_step in [0, 5_0_0, 9_9_9]:
           for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
             if prev_timestep is not None and prev_timestep >= time_step:
               continue

             self.check_over_forward(time_step=lowercase_      ,      prev_timestep=lowercase_     )




       def 							__UpperCamelCase				(   self     ):
         _lowerCAmelCase				:     Dict       =    self.scheduler_classes[0]
         _lowerCAmelCase				:     Optional[Any]       =    self.get_scheduler_config(variance_type="""fixed_small_log"""     )
         _lowerCAmelCase				:     Optional[int]       =    scheduler_class(**lowercase_     )

         assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 1.00_00E-10     )     ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_8_7     ) - 0.054_9625     )     ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_9_9     ) - 0.999_4987     )     ) < 1E-5




       def 							__UpperCamelCase				(   self     ):
         _lowerCAmelCase				:     Dict       =    self.scheduler_classes[0]
         _lowerCAmelCase				:     Optional[Any]       =    self.get_scheduler_config(variance_type="""learned_range"""     )
         _lowerCAmelCase				:     Any       =    scheduler_class(**lowercase_     )

         _lowerCAmelCase				:     Tuple       =    0.5

         assert scheduler._get_variance(1      ,      predicted_variance=lowercase_     ) - -10.171_2790 < 1E-5
         assert scheduler._get_variance(4_8_7      ,      predicted_variance=lowercase_     ) - -5.799_8052 < 1E-5
         assert scheduler._get_variance(9_9_9      ,      predicted_variance=lowercase_     ) - -0.001_0011 < 1E-5




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

         _lowerCAmelCase				:     Union[str, Any]       =    scheduler.timesteps

         _lowerCAmelCase				:     List[Any]       =    self.dummy_model()
         _lowerCAmelCase				:     str       =    self.dummy_sample_deter
         _lowerCAmelCase				:     int       =    torch.manual_seed(0     )

         for i, t in enumerate(lowercase_     ):
           # 1. predict noise residual
           _lowerCAmelCase				:     Optional[int]       =    model(lowercase_      ,      lowercase_     )

           # 2. predict previous mean of sample x_t-1
           _lowerCAmelCase				:     int       =    scheduler.step(lowercase_      ,      lowercase_      ,      lowercase_      ,      generator=lowercase_     ).prev_sample

           _lowerCAmelCase				:     List[str]       =    pred_prev_sample

         _lowerCAmelCase				:     List[str]       =    torch.sum(torch.abs(lowercase_     )     )
         _lowerCAmelCase				:     List[Any]       =    torch.mean(torch.abs(lowercase_     )     )

         assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5     ) < 1E-2
         assert abs(result_mean.item() - 0.328_4743     ) < 1E-3




       def 							__UpperCamelCase				(   self     ):
         _lowerCAmelCase				:     Optional[Any]       =    self.scheduler_classes[0]
         _lowerCAmelCase				:     Any       =    self.get_scheduler_config()
         _lowerCAmelCase				:     List[Any]       =    scheduler_class(**lowercase_     )

         scheduler.set_timesteps(2_5     )

         _lowerCAmelCase				:     Union[str, Any]       =    scheduler.timesteps

         _lowerCAmelCase				:     Tuple       =    self.dummy_model()
         _lowerCAmelCase				:     int       =    self.dummy_sample_deter
         _lowerCAmelCase				:     Optional[Any]       =    torch.manual_seed(0     )

         for i, t in enumerate(lowercase_     ):
           # 1. predict noise residual
           _lowerCAmelCase				:     Tuple       =    model(lowercase_      ,      lowercase_     )

           if i + 1 == timesteps.shape[0]:
             _lowerCAmelCase				:     int       =    None
           else:
             _lowerCAmelCase				:     Union[str, Any]       =    timesteps[i + 1]

           # 2. predict previous mean of sample x_t-1
           _lowerCAmelCase				:     str       =    scheduler.step(
               lowercase_      ,      lowercase_      ,      lowercase_      ,      prev_timestep=lowercase_      ,      generator=lowercase_     ).prev_sample

           _lowerCAmelCase				:     str       =    pred_prev_sample

         _lowerCAmelCase				:     Dict       =    torch.sum(torch.abs(lowercase_     )     )
         _lowerCAmelCase				:     List[Any]       =    torch.mean(torch.abs(lowercase_     )     )

         assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3     ) < 1E-2
         assert abs(result_mean.item() - 0.336_2038     ) < 1E-3




       def 							__UpperCamelCase				(   self     ):
         pass







       def 							__UpperCamelCase				(   self     ):
         pass



import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow



@is_pipeline_test
@require_torch
class   snake_case_       (		unittest.TestCase       ):






   @require_torch
   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						List[Any]:
       lowercase__ :   Union[str, Any]    					=				pipeline(
           task="zero-shot-audio-classification"	,						model="hf-internal-testing/tiny-clap-htsat-unfused"  )
       lowercase__ :   List[str]    					=				load_dataset("ashraq/esc50"  )
       lowercase__ :   List[Any]    					=				dataset["train"]["audio"][-1]["array"]
       lowercase__ :   Dict    					=				audio_classifier(lowercase_	,						candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"]  )
       self.assertEqual(
           nested_simplify(lowercase_  )	,						[{"score": 0.5_01, "label": "Sound of a dog"}, {"score": 0.4_99, "label": "Sound of vaccum cleaner"}]	,						)






   @unittest.skip("No models are available in TF"  )
   def       __UpperCamelCase     (      self				:     str  )					->						Optional[int]:
       pass






   @slow
   @require_torch
   def       __UpperCamelCase     (      self				:     List[str]  )					->						int:
       lowercase__ :   Tuple    					=				pipeline(
           task="zero-shot-audio-classification"	,						model="laion/clap-htsat-unfused"	,						)
       # This is an audio of a dog
       lowercase__ :   Union[str, Any]    					=				load_dataset("ashraq/esc50"  )
       lowercase__ :   Tuple    					=				dataset["train"]["audio"][-1]["array"]
       lowercase__ :   List[Any]    					=				audio_classifier(lowercase_	,						candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"]  )

       self.assertEqual(
           nested_simplify(lowercase_  )	,						[
               {"score": 0.9_99, "label": "Sound of a dog"},
               {"score": 0.0_01, "label": "Sound of vaccum cleaner"},
           ]	,						)

       lowercase__ :   int    					=				audio_classifier([audio] * 5	,						candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"]  )
       self.assertEqual(
           nested_simplify(lowercase_  )	,						[
               [
                   {"score": 0.9_99, "label": "Sound of a dog"},
                   {"score": 0.0_01, "label": "Sound of vaccum cleaner"},
               ],
           ]
           * 5	,						)
       lowercase__ :   Tuple    					=				audio_classifier(
           [audio] * 5	,						candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"]	,						batch_size=5  )
       self.assertEqual(
           nested_simplify(lowercase_  )	,						[
               [
                   {"score": 0.9_99, "label": "Sound of a dog"},
                   {"score": 0.0_01, "label": "Sound of vaccum cleaner"},
               ],
           ]
           * 5	,						)







   @unittest.skip("No models are available in TF"  )
   def       __UpperCamelCase     (      self				:     Union[str, Any]  )					->						Dict:
       pass



