import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							(UnCLIPScheduler,)




   def 			__a		(					self    :       Dict ,			**_lowercase    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   {
         """num_train_timesteps""": 10_00,
         """variance_type""": """fixed_small_log""",
         """clip_sample""": True,
         """clip_sample_range""": 1.0,
         """prediction_type""": """epsilon""",
     }

     config.update(**_lowercase   )
     return config




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     for timesteps in [1, 5, 1_00, 10_00]:
       self.check_over_configs(num_train_timesteps=_lowercase   )




   def 			__a		(					self    :       int   ):



     """simple docstring"""






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




   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






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




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     for clip_sample_range in [1, 5, 10, 20]:
       self.check_over_configs(clip_sample_range=_lowercase   )




   def 			__a		(					self    :       Union[str, Any]   ):



     """simple docstring"""






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




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     for time_step in [0, 5_00, 9_99]:
       for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
         if prev_timestep is not None and prev_timestep >= time_step:
           continue

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




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__    							=   self.get_scheduler_config(variance_type="""fixed_small_log"""   )
     SCREAMING_SNAKE_CASE__    							=   scheduler_class(**_lowercase   )

     assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 1.0000E-10   )   ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(4_87   ) - 0.0_54_96_25   )   ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(9_99   ) - 0.9_99_49_87   )   ) < 1E-5




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__    							=   self.get_scheduler_config(variance_type="""learned_range"""   )
     SCREAMING_SNAKE_CASE__    							=   scheduler_class(**_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   0.5

     assert scheduler._get_variance(1 ,			predicted_variance=_lowercase   ) - -10.1_71_27_90 < 1E-5
     assert scheduler._get_variance(4_87 ,			predicted_variance=_lowercase   ) - -5.7_99_80_52 < 1E-5
     assert scheduler._get_variance(9_99 ,			predicted_variance=_lowercase   ) - -0.0_01_00_11 < 1E-5




   def 			__a		(					self    :       Union[str, Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__    							=   self.get_scheduler_config()
     SCREAMING_SNAKE_CASE__    							=   scheduler_class(**_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   scheduler.timesteps

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

     for i, t in enumerate(_lowercase   ):
       # 1. predict noise residual
       SCREAMING_SNAKE_CASE__    							=   model(_lowercase ,			_lowercase   )

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

       SCREAMING_SNAKE_CASE__    							=   pred_prev_sample

     SCREAMING_SNAKE_CASE__    							=   torch.sum(torch.abs(_lowercase   )   )
     SCREAMING_SNAKE_CASE__    							=   torch.mean(torch.abs(_lowercase   )   )

     assert abs(result_sum.item() - 2_52.2_68_24_95   ) < 1E-2
     assert abs(result_mean.item() - 0.3_28_47_43   ) < 1E-3




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__    							=   self.get_scheduler_config()
     SCREAMING_SNAKE_CASE__    							=   scheduler_class(**_lowercase   )

     scheduler.set_timesteps(25   )

     SCREAMING_SNAKE_CASE__    							=   scheduler.timesteps

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

     for i, t in enumerate(_lowercase   ):
       # 1. predict noise residual
       SCREAMING_SNAKE_CASE__    							=   model(_lowercase ,			_lowercase   )

       if i + 1 == timesteps.shape[0]:
         SCREAMING_SNAKE_CASE__    							=   None
       else:
         SCREAMING_SNAKE_CASE__    							=   timesteps[i + 1]

       # 2. predict previous mean of sample x_t-1
       SCREAMING_SNAKE_CASE__    							=   scheduler.step(
           _lowercase ,			_lowercase ,			_lowercase ,			prev_timestep=_lowercase ,			generator=_lowercase   ).prev_sample

       SCREAMING_SNAKE_CASE__    							=   pred_prev_sample

     SCREAMING_SNAKE_CASE__    							=   torch.sum(torch.abs(_lowercase   )   )
     SCREAMING_SNAKE_CASE__    							=   torch.mean(torch.abs(_lowercase   )   )

     assert abs(result_sum.item() - 2_58.2_04_49_83   ) < 1E-2
     assert abs(result_mean.item() - 0.3_36_20_38   ) < 1E-3




   def 			__a		(					self    :       Union[str, Any]   ):



     """simple docstring"""






     pass




   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     pass

from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							"EncodecFeatureExtractor"
   lowerCAmelCase_														=							("T5Tokenizer", "T5TokenizerFast")




   def __init__(					self    :       List[Any] ,			_lowercase    :       int ,			_lowercase    :       Any   ):



     """simple docstring"""






     super().__init__(_lowercase ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   self.feature_extractor
     SCREAMING_SNAKE_CASE__    							=   False




   def 			__a		(					self    :       Tuple ,			_lowercase    :       str=None ,			_lowercase    :       int=None ,			_lowercase    :       Any=True   ):



     """simple docstring"""






     return self.tokenizer.get_decoder_prompt_ids(task=_lowercase ,			language=_lowercase ,			no_timestamps=_lowercase   )




   def __call__(					self    :       Tuple ,			*_lowercase    :       Tuple ,			**_lowercase    :       Union[str, Any]   ):



     """simple docstring"""






     if self._in_target_context_manager:
       return self.current_processor(*_lowercase ,			**_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""audio""" ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""sampling_rate""" ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""text""" ,			_lowercase   )
     if len(_lowercase   ) > 0:
       SCREAMING_SNAKE_CASE__    							=   args[0]
       SCREAMING_SNAKE_CASE__    							=   args[1:]

     if audio is None and text is None:
       raise ValueError("""You need to specify either an `audio` or `text` input to process."""   )

     if text is not None:
       SCREAMING_SNAKE_CASE__    							=   self.tokenizer(_lowercase ,			**_lowercase   )

     if audio is not None:
       SCREAMING_SNAKE_CASE__    							=   self.feature_extractor(_lowercase ,			*_lowercase ,			sampling_rate=_lowercase ,			**_lowercase   )

     if audio is None:
       return inputs

     elif text is None:
       return audio_inputs

     else:
       SCREAMING_SNAKE_CASE__    							=   audio_inputs["""input_values"""]
       if "padding_mask" in audio_inputs:
         SCREAMING_SNAKE_CASE__    							=   audio_inputs["""padding_mask"""]
       return inputs




   def 			__a		(					self    :       Dict ,			*_lowercase    :       Any ,			**_lowercase    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""audio""" ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""padding_mask""" ,			_lowercase   )

     if len(_lowercase   ) > 0:
       SCREAMING_SNAKE_CASE__    							=   args[0]
       SCREAMING_SNAKE_CASE__    							=   args[1:]

     if audio_values is not None:
       return self._decode_audio(_lowercase ,			padding_mask=_lowercase   )
     else:
       return self.tokenizer.batch_decode(*_lowercase ,			**_lowercase   )




   def 			__a		(					self    :       List[str] ,			*_lowercase    :       Tuple ,			**_lowercase    :       Optional[Any]   ):



     """simple docstring"""






     return self.tokenizer.decode(*_lowercase ,			**_lowercase   )




   def 			__a		(					self    :       List[str] ,			_lowercase    :       Tuple ,			_lowercase    :       Optional = None   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   to_numpy(_lowercase   )
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   audio_values.shape

     if padding_mask is None:
       return list(_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   to_numpy(_lowercase   )

     # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
     # token (so that the generated audio values are **not** treated as padded tokens)
     SCREAMING_SNAKE_CASE__    							=   seq_len - padding_mask.shape[-1]
     SCREAMING_SNAKE_CASE__    							=   1 - self.feature_extractor.padding_value
     SCREAMING_SNAKE_CASE__    							=   np.pad(_lowercase ,			((0, 0), (0, difference)) ,			"""constant""" ,			constant_values=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   audio_values.tolist()
     for i in range(_lowercase   ):
       SCREAMING_SNAKE_CASE__    							=   np.asarray(audio_values[i]   )[
           padding_mask[i][None, :] != self.feature_extractor.padding_value
       ]
       SCREAMING_SNAKE_CASE__    							=   sliced_audio.reshape(_lowercase ,			-1   )

     return audio_values
