




# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput




@dataclass
class    __lowerCAmelCase       (							UpperCAmelCase__):
       _a 					=						42
       _a 					=						42







class    __lowerCAmelCase       (							UpperCAmelCase__      , UpperCAmelCase__):
       _a 					=						1






       @register_to_config
       def __init__(   self:					List[Any]       ,					_lowerCAmelCase:					int = 20_00       ,					_lowerCAmelCase:					float = 0.15       ,					_lowerCAmelCase:					float = 0.01       ,					_lowerCAmelCase:					float = 13_48.0       ,					_lowerCAmelCase:					float = 1e-5       ,					_lowerCAmelCase:					int = 1       ,					):
         lowercase    :List[str]      	=						sigma_max

         # setable values
         lowercase    :List[str]      	=						None

         self.set_sigmas(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					int       ,					_lowerCAmelCase:					torch.FloatTensor       ,					_lowerCAmelCase:					Optional[int] = None				):
         return sample






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple       ,					_lowerCAmelCase:					int       ,					_lowerCAmelCase:					float = None       ,					_lowerCAmelCase:					Union[str, torch.device] = None				):
         lowercase    :Dict      	=						sampling_eps if sampling_eps is not None else self.config.sampling_eps

         lowercase    :Any      	=						torch.linspace(1       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					device=__lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]       ,					_lowerCAmelCase:					int       ,					_lowerCAmelCase:					float = None       ,					_lowerCAmelCase:					float = None       ,					_lowerCAmelCase:					float = None				):
         lowercase    :Union[str, Any]      	=						sigma_min if sigma_min is not None else self.config.sigma_min
         lowercase    :int      	=						sigma_max if sigma_max is not None else self.config.sigma_max
         lowercase    :List[Any]      	=						sampling_eps if sampling_eps is not None else self.config.sampling_eps
         if self.timesteps is None:
           self.set_timesteps(__lowerCAmelCase       ,					__lowerCAmelCase				)

         lowercase    :List[str]      	=						sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
         lowercase    :Optional[Any]      	=						torch.exp(torch.linspace(math.log(__lowerCAmelCase				)       ,					math.log(__lowerCAmelCase				)       ,					__lowerCAmelCase				)				)
         lowercase    :Optional[Any]      	=						torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]				)






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]       ,					_lowerCAmelCase:					Optional[int]       ,					_lowerCAmelCase:					Optional[Any]				):
         return torch.where(
             timesteps == 0       ,					torch.zeros_like(t.to(timesteps.device				)				)       ,					self.discrete_sigmas[timesteps - 1].to(timesteps.device				)       ,					)






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]       ,					_lowerCAmelCase:					torch.FloatTensor       ,					_lowerCAmelCase:					int       ,					_lowerCAmelCase:					torch.FloatTensor       ,					_lowerCAmelCase:					Optional[torch.Generator] = None       ,					_lowerCAmelCase:					bool = True       ,					):






         if self.timesteps is None:
           raise ValueError(
               "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"				)

         lowercase    :Tuple      	=						timestep * torch.ones(
             sample.shape[0]       ,					device=sample.device				)  # torch.repeat_interleave(timestep, sample.shape[0])
         lowercase    :Tuple      	=						(timestep * (len(self.timesteps				) - 1)).long()

         # mps requires indices to be in the same device, so we use cpu as is the default with cuda
         lowercase    :Any      	=						timesteps.to(self.discrete_sigmas.device				)

         lowercase    :Optional[Any]      	=						self.discrete_sigmas[timesteps].to(sample.device				)
         lowercase    :List[Any]      	=						self.get_adjacent_sigma(__lowerCAmelCase       ,					__lowerCAmelCase				).to(sample.device				)
         lowercase    :Union[str, Any]      	=						torch.zeros_like(__lowerCAmelCase				)
         lowercase    :List[Any]      	=						(sigma**2 - adjacent_sigma**2) ** 0.5

         # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
         # also equation 47 shows the analog from SDE models to ancestral sampling methods
         lowercase    :int      	=						diffusion.flatten()
         while len(diffusion.shape				) < len(sample.shape				):
           lowercase    :List[Any]      	=						diffusion.unsqueeze(-1				)
         lowercase    :Dict      	=						drift - diffusion**2 * model_output

         #  equation 6: sample noise for the diffusion term of
         lowercase    :Optional[int]      	=						randn_tensor(
             sample.shape       ,					layout=sample.layout       ,					generator=__lowerCAmelCase       ,					device=sample.device       ,					dtype=sample.dtype				)
         lowercase    :int      	=						sample - drift  # subtract because `dt` is a small negative timestep
         # TODO is the variable diffusion the correct scaling term for the noise?
         lowercase    :Union[str, Any]      	=						prev_sample_mean + diffusion * noise  # add impact of diffusion field g

         if not return_dict:
           return (prev_sample, prev_sample_mean)

         return SdeVeOutput(prev_sample=__lowerCAmelCase       ,					prev_sample_mean=__lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]       ,					_lowerCAmelCase:					torch.FloatTensor       ,					_lowerCAmelCase:					torch.FloatTensor       ,					_lowerCAmelCase:					Optional[torch.Generator] = None       ,					_lowerCAmelCase:					bool = True       ,					):






         if self.timesteps is None:
           raise ValueError(
               "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"				)

         # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
         # sample noise for correction
         lowercase    :List[Any]      	=						randn_tensor(sample.shape       ,					layout=sample.layout       ,					generator=__lowerCAmelCase				).to(sample.device				)

         # compute step size from the model_output, the noise, and the snr
         lowercase    :Optional[Any]      	=						torch.norm(model_output.reshape(model_output.shape[0]       ,					-1				)       ,					dim=-1				).mean()
         lowercase    :str      	=						torch.norm(noise.reshape(noise.shape[0]       ,					-1				)       ,					dim=-1				).mean()
         lowercase    :Dict      	=						(self.config.snr * noise_norm / grad_norm) ** 2 * 2
         lowercase    :int      	=						step_size * torch.ones(sample.shape[0]				).to(sample.device				)
         # self.repeat_scalar(step_size, sample.shape[0])

         # compute corrected sample: model_output term and noise term
         lowercase    :int      	=						step_size.flatten()
         while len(step_size.shape				) < len(sample.shape				):
           lowercase    :Union[str, Any]      	=						step_size.unsqueeze(-1				)
         lowercase    :List[str]      	=						sample + step_size * model_output
         lowercase    :Any      	=						prev_sample_mean + ((step_size * 2) ** 0.5) * noise

         if not return_dict:
           return (prev_sample,)

         return SchedulerOutput(prev_sample=__lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]       ,					_lowerCAmelCase:					torch.FloatTensor       ,					_lowerCAmelCase:					torch.FloatTensor       ,					_lowerCAmelCase:					torch.FloatTensor       ,					):
         lowercase    :Any      	=						timesteps.to(original_samples.device				)
         lowercase    :Any      	=						self.discrete_sigmas.to(original_samples.device				)[timesteps]
         lowercase    :Optional[Any]      	=						(
             noise * sigmas[:, None, None, None]
             if noise is not None
             else torch.randn_like(__lowerCAmelCase				) * sigmas[:, None, None, None]
         )
         lowercase    :Optional[int]      	=						noise + original_samples
         return noisy_samples






       def __len__(   self:					List[Any]				):
         return self.config.num_train_timesteps









import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
     import torch

if is_datasets_available():
     from datasets import load_dataset

_UpperCAmelCase	:						List[str]			=     random.Random()



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase=1.0,			lowerCamelCase=None,			lowerCamelCase=None   ):
  if rng is None:
    lowercase    :Union[str, Any]      	=						global_rng

  lowercase    :Any      	=						[]
  for batch_idx in range(shape[0]   ):
    values.append([]   )
    for _ in range(shape[1]   ):
      values[-1].append(rng.random() * scale   )

  return values




class    __lowerCAmelCase       (							unittest.TestCase):






       def __init__(   self:					Optional[Any]       ,					_lowerCAmelCase:					Tuple       ,					_lowerCAmelCase:					Union[str, Any]=7       ,					_lowerCAmelCase:					str=4_00       ,					_lowerCAmelCase:					List[str]=20_00       ,					_lowerCAmelCase:					Dict=20_48       ,					_lowerCAmelCase:					Any=1_28       ,					_lowerCAmelCase:					Any=1       ,					_lowerCAmelCase:					List[Any]=5_12       ,					_lowerCAmelCase:					Optional[int]=30       ,					_lowerCAmelCase:					List[Any]=4_41_00       ,					):
         lowercase    :Any      	=						parent
         lowercase    :Any      	=						batch_size
         lowercase    :Optional[int]      	=						min_seq_length
         lowercase    :Optional[Any]      	=						max_seq_length
         lowercase    :Dict      	=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         lowercase    :List[str]      	=						spectrogram_length
         lowercase    :int      	=						feature_size
         lowercase    :Union[str, Any]      	=						num_audio_channels
         lowercase    :Optional[int]      	=						hop_length
         lowercase    :str      	=						chunk_length
         lowercase    :List[str]      	=						sampling_rate






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         return {
             "spectrogram_length": self.spectrogram_length,
             "feature_size": self.feature_size,
             "num_audio_channels": self.num_audio_channels,
             "hop_length": self.hop_length,
             "chunk_length": self.chunk_length,
             "sampling_rate": self.sampling_rate,
         }






       def 					SCREAMING_SNAKE_CASE    (   self:					int       ,					_lowerCAmelCase:					int=False       ,					_lowerCAmelCase:					int=False				):
         def _flatten(_lowerCAmelCase:					Optional[Any]				):
           return list(itertools.chain(*_lowerCAmelCase				)				)

         if equal_length:
           lowercase    :Any      	=						[floats_list((self.max_seq_length, self.feature_size)				) for _ in range(self.batch_size				)]
         else:
           # make sure that inputs increase in size
           lowercase    :Tuple      	=						[
               floats_list((x, self.feature_size)				)
               for x in range(self.min_seq_length       ,					self.max_seq_length       ,					self.seq_length_diff				)
           ]
         if numpify:
           lowercase    :Optional[int]      	=						[np.asarray(_lowerCAmelCase				) for x in speech_inputs]
         return speech_inputs







@require_torch
@require_torchaudio
class    __lowerCAmelCase       (							lowerCAmelCase      , unittest.TestCase):
       _a 					=						TvltFeatureExtractor






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         lowercase    :Dict      	=						TvltFeatureExtractionTester(self				)






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         lowercase    :List[Any]      	=						self.feature_extraction_class(**self.feat_extract_dict				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"spectrogram_length"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"feature_size"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"num_audio_channels"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"hop_length"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"chunk_length"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"sampling_rate"				)				)






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         lowercase    :Any      	=						self.feature_extraction_class(**self.feat_extract_dict				)

         with tempfile.TemporaryDirectory() as tmpdirname:
           lowercase    :Tuple      	=						feat_extract_first.save_pretrained(_lowerCAmelCase				)[0]
           check_json_file_has_correct_format(_lowerCAmelCase				)
           lowercase    :Tuple      	=						self.feature_extraction_class.from_pretrained(_lowerCAmelCase				)

         lowercase    :Optional[Any]      	=						feat_extract_first.to_dict()
         lowercase    :List[str]      	=						feat_extract_second.to_dict()
         lowercase    :Any      	=						dict_first.pop("mel_filters"				)
         lowercase    :Any      	=						dict_second.pop("mel_filters"				)
         self.assertTrue(np.allclose(_lowerCAmelCase       ,					_lowerCAmelCase				)				)
         self.assertEqual(_lowerCAmelCase       ,					_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         lowercase    :Union[str, Any]      	=						self.feature_extraction_class(**self.feat_extract_dict				)

         with tempfile.TemporaryDirectory() as tmpdirname:
           lowercase    :Any      	=						os.path.join(_lowerCAmelCase       ,					"feat_extract.json"				)
           feat_extract_first.to_json_file(_lowerCAmelCase				)
           lowercase    :List[Any]      	=						self.feature_extraction_class.from_json_file(_lowerCAmelCase				)

         lowercase    :Tuple      	=						feat_extract_first.to_dict()
         lowercase    :int      	=						feat_extract_second.to_dict()
         lowercase    :Tuple      	=						dict_first.pop("mel_filters"				)
         lowercase    :Tuple      	=						dict_second.pop("mel_filters"				)
         self.assertTrue(np.allclose(_lowerCAmelCase       ,					_lowerCAmelCase				)				)
         self.assertEqual(_lowerCAmelCase       ,					_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         # Initialize feature_extractor
         lowercase    :int      	=						self.feature_extraction_class(**self.feat_extract_dict				)

         # create three inputs of length 800, 1000, and 1200
         lowercase    :List[Any]      	=						[floats_list((1, x)				)[0] for x in range(8_00       ,					14_00       ,					2_00				)]
         lowercase    :Union[str, Any]      	=						[np.asarray(_lowerCAmelCase				) for speech_input in speech_inputs]

         # Test not batched input
         lowercase    :Any      	=						feature_extractor(np_speech_inputs[0]       ,					return_tensors="np"       ,					sampling_rate=4_41_00				).audio_values

         self.assertTrue(encoded_audios.ndim == 4				)
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)

         # Test batched
         lowercase    :Optional[int]      	=						feature_extractor(_lowerCAmelCase       ,					return_tensors="np"       ,					sampling_rate=4_41_00				).audio_values

         self.assertTrue(encoded_audios.ndim == 4				)
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)

         # Test audio masking
         lowercase    :Dict      	=						feature_extractor(
             _lowerCAmelCase       ,					return_tensors="np"       ,					sampling_rate=4_41_00       ,					mask_audio=_lowerCAmelCase				).audio_values

         self.assertTrue(encoded_audios.ndim == 4				)
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)

         # Test 2-D numpy arrays are batched.
         lowercase    :Optional[int]      	=						[floats_list((1, x)				)[0] for x in (8_00, 8_00, 8_00)]
         lowercase    :Union[str, Any]      	=						np.asarray(_lowerCAmelCase				)
         lowercase    :Dict      	=						feature_extractor(_lowerCAmelCase       ,					return_tensors="np"       ,					sampling_rate=4_41_00				).audio_values
         self.assertTrue(encoded_audios.ndim == 4				)
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]       ,					_lowerCAmelCase:					Optional[Any]				):
         lowercase    :str      	=						load_dataset("hf-internal-testing/librispeech_asr_dummy"       ,					"clean"       ,					split="validation"				)
         # automatic decoding with librispeech
         lowercase    :Any      	=						ds.sort("id"				).select(range(_lowerCAmelCase				)				)[:num_samples]["audio"]

         return [x["array"] for x in speech_samples]






       def 					SCREAMING_SNAKE_CASE    (   self:					Dict				):
         lowercase    :Optional[int]      	=						self._load_datasamples(1				)
         lowercase    :Union[str, Any]      	=						TvltFeatureExtractor()
         lowercase    :str      	=						feature_extractor(_lowerCAmelCase       ,					return_tensors="pt"				).audio_values

         self.assertEquals(audio_values.shape       ,					(1, 1, 1_92, 1_28)				)

         lowercase    :Optional[int]      	=						torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]]				)
         self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]       ,					_lowerCAmelCase       ,					atol=1e-4				)				)



