

"""simple docstring"""




from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class      _UpperCAmelCase   (							_lowerCAmelCase       ):
	a__		: Dict					=					"EncodecFeatureExtractor"
	a__		: Tuple					=					("T5Tokenizer", "T5TokenizerFast")



	def __init__(  self     :       List[str]			,    _lowercase     :       Tuple			,    _lowercase     :       str						):
		super().__init__(_lowercase			,    _lowercase						)
		__UpperCAmelCase      		= self.feature_extractor
		__UpperCAmelCase      		= False



	def        a  (  self     :       List[str]			,    _lowercase     :       List[Any]=None			,    _lowercase     :       List[str]=None			,    _lowercase     :       Any=True						):
		return self.tokenizer.get_decoder_prompt_ids(task=_lowercase			,    language=_lowercase			,    no_timestamps=_lowercase						)



	def __call__(  self     :       Any			,    *_lowercase     :       Optional[Any]			,    **_lowercase     :       Union[str, Any]						):
		# For backward compatibility
		if self._in_target_context_manager:
			return self.current_processor(*_lowercase			,    **_lowercase						)

		__UpperCAmelCase      		= kwargs.pop('''audio'''			,    _lowercase						)
		__UpperCAmelCase      		= kwargs.pop('''sampling_rate'''			,    _lowercase						)
		__UpperCAmelCase      		= kwargs.pop('''text'''			,    _lowercase						)
		if len(_lowercase						) > 0:
			__UpperCAmelCase      		= args[0]
			__UpperCAmelCase      		= 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:
			__UpperCAmelCase      		= self.tokenizer(_lowercase			,    **_lowercase						)

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

		if audio is None:
			return inputs

		elif text is None:
			return audio_inputs

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



	def        a  (  self     :       str			,    *_lowercase     :       Dict			,    **_lowercase     :       List[str]						):
		__UpperCAmelCase      		= kwargs.pop('''audio'''			,    _lowercase						)
		__UpperCAmelCase      		= kwargs.pop('''padding_mask'''			,    _lowercase						)

		if len(_lowercase						) > 0:
			__UpperCAmelCase      		= args[0]
			__UpperCAmelCase      		= 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     :       Union[str, Any]			,    *_lowercase     :       int			,    **_lowercase     :       List[str]						):
		return self.tokenizer.decode(*_lowercase			,    **_lowercase						)



	def        a  (  self     :       List[str]			,    _lowercase     :       List[Any]			,    _lowercase     :       Optional = None						):
		__UpperCAmelCase      		= to_numpy(_lowercase						)
		__UpperCAmelCase		,							__UpperCAmelCase		,							__UpperCAmelCase      		= audio_values.shape

		if padding_mask is None:
			return list(_lowercase						)

		__UpperCAmelCase      		= 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)
		__UpperCAmelCase      		= seq_len - padding_mask.shape[-1]
		__UpperCAmelCase      		= 1 - self.feature_extractor.padding_value
		__UpperCAmelCase      		= np.pad(_lowercase			,    ((0, 0), (0, difference))			,    '''constant'''			,    constant_values=_lowercase						)

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

		return audio_values

'''simple docstring'''


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						(DDPMParallelScheduler,)




  def 		lowerCamelCase__  (		self      :       Optional[Any]   ,				**lowercase_      :       str      ):







    '''simple docstring'''


    lowercase_     =       {
        """num_train_timesteps""": 1_000,
        """beta_start""": 0.0_0_0_1,
        """beta_end""": 0.0_2,
        """beta_schedule""": """linear""",
        """variance_type""": """fixed_small""",
        """clip_sample""": True,
    }

    config.update(**lowercase_      )
    return config




  def 		lowerCamelCase__  (		self      :       Optional[Any]      ):







    '''simple docstring'''


    for timesteps in [1, 5, 100, 1_000]:
      self.check_over_configs(num_train_timesteps=lowercase_      )




  def 		lowerCamelCase__  (		self      :       List[Any]      ):







    '''simple docstring'''


    for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]   ,				[0.0_0_2, 0.0_2, 0.2, 2]      ):
      self.check_over_configs(beta_start=lowercase_   ,				beta_end=lowercase_      )




  def 		lowerCamelCase__  (		self      :       Any      ):







    '''simple docstring'''


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




  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


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




  def 		lowerCamelCase__  (		self      :       str      ):







    '''simple docstring'''


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




  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


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




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


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




  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


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




  def 		lowerCamelCase__  (		self      :       int      ):







    '''simple docstring'''


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

    assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 0.0      )      ) < 1e-5
    assert torch.sum(torch.abs(scheduler._get_variance(487      ) - 0.0_0_9_7_9      )      ) < 1e-5
    assert torch.sum(torch.abs(scheduler._get_variance(999      ) - 0.0_2      )      ) < 1e-5




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


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

    lowercase_     =       len(lowercase_      )

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

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

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

    lowercase_     =       torch.sum(torch.abs(lowercase_      )      )
    lowercase_     =       torch.mean(torch.abs(lowercase_      )      )

    assert abs(result_sum.item() - 1_1_5_3.1_8_3_3      ) < 1e-2
    assert abs(result_mean.item() - 0.5_0_0_5      ) < 1e-3




  def 		lowerCamelCase__  (		self      :       int      ):







    '''simple docstring'''


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

    lowercase_     =       len(lowercase_      )

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

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

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

      lowercase_     =       pred_prev_sample

    lowercase_     =       torch.sum(torch.abs(lowercase_      )      )
    lowercase_     =       torch.mean(torch.abs(lowercase_      )      )

    assert abs(result_sum.item() - 2_5_8.9_6_0_6      ) < 1e-2
    assert abs(result_mean.item() - 0.3_3_7_2      ) < 1e-3




  def 		lowerCamelCase__  (		self      :       str      ):







    '''simple docstring'''


    lowercase_     =       self.scheduler_classes[0]
    lowercase_     =       self.get_scheduler_config(prediction_type="""v_prediction"""      )
    lowercase_     =       scheduler_class(**lowercase_      )

    lowercase_     =       len(lowercase_      )

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

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

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

      lowercase_     =       pred_prev_sample

    lowercase_     =       torch.sum(torch.abs(lowercase_      )      )
    lowercase_     =       torch.mean(torch.abs(lowercase_      )      )

    assert abs(result_sum.item() - 2_0_2.0_2_9_6      ) < 1e-2
    assert abs(result_mean.item() - 0.2_6_3_1      ) < 1e-3




  def 		lowerCamelCase__  (		self      :       Any      ):







    '''simple docstring'''


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

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

    scheduler.set_timesteps(timesteps=lowercase_      )

    lowercase_     =       scheduler.timesteps

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

      lowercase_     =       scheduler.previous_timestep(lowercase_      )
      lowercase_     =       prev_t.item()

      self.assertEqual(lowercase_   ,				lowercase_      )




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


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

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

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




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


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

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

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





  def 		lowerCamelCase__  (		self      :       Optional[Any]      ):







    '''simple docstring'''


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

    lowercase_     =       [scheduler.config.num_train_timesteps]

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

