"""simple docstring"""






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

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 					UpperCAmelCase		(_UpperCAmelCase						):



    """simple docstring"""

    _UpperCAmelCase			:torch.FloatTensor
    _UpperCAmelCase			:Optional[torch.FloatTensor]							=				None



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase ,					__UpperCAmelCase=0.9_9_9 ,					__UpperCAmelCase="cosine" ,					)						->						int:
   if alpha_transform_type == "cosine":

      def alpha_bar_fn(__UpperCAmelCase				):
         return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2				) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(__UpperCAmelCase				):
         return math.exp(t * -1_2.0				)

   else:
      raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""				)

   lowercase__:     int      =   []
   for i in range(__UpperCAmelCase				):
      lowercase__:     Tuple      =   i / num_diffusion_timesteps
      lowercase__:     Union[str, Any]      =   (i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(__UpperCAmelCase				) / alpha_bar_fn(__UpperCAmelCase				) ,					__UpperCAmelCase				)				)
   return torch.tensor(__UpperCAmelCase ,					dtype=torch.floataa				)




class 					UpperCAmelCase		(_UpperCAmelCase					,_UpperCAmelCase						):



    """simple docstring"""

    _UpperCAmelCase			:int							=				[e.name for e in KarrasDiffusionSchedulers]
    _UpperCAmelCase			:Any							=				1






    @register_to_config
    def __init__(     self					,    _UpperCAmelCase = 1000					,    _UpperCAmelCase = 0.0_001					,    _UpperCAmelCase = 0.02					,    _UpperCAmelCase = "linear"					,    _UpperCAmelCase = None					,    _UpperCAmelCase = False					,    _UpperCAmelCase = "epsilon"					,    _UpperCAmelCase = "linspace"					,    _UpperCAmelCase = 0					,    ):
       if trained_betas is not None:
          lowercase__:     Union[str, Any]      =   torch.tensor(_UpperCAmelCase					,    dtype=torch.floataa							)
       elif beta_schedule == "linear":
          lowercase__:     str      =   torch.linspace(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    dtype=torch.floataa							)
       elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          lowercase__:     List[str]      =   (
              torch.linspace(beta_start**0.5					,    beta_end**0.5					,    _UpperCAmelCase					,    dtype=torch.floataa							) ** 2
          )
       elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          lowercase__:     Dict      =   betas_for_alpha_bar(_UpperCAmelCase							)
       else:
          raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""							)

       lowercase__:     int      =   1.0 - self.betas
       lowercase__:     Union[str, Any]      =   torch.cumprod(self.alphas					,    dim=0							)

       lowercase__:     Optional[int]      =   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
       lowercase__:     Any      =   np.concatenate([sigmas[::-1], [0.0]]							).astype(np.floataa							)
       lowercase__:     Optional[Any]      =   torch.from_numpy(_UpperCAmelCase							)

       # setable values
       lowercase__:     Optional[Any]      =   None
       lowercase__:     List[str]      =   use_karras_sigmas
       self.set_timesteps(_UpperCAmelCase					,    _UpperCAmelCase							)
       lowercase__:     Any      =   []
       lowercase__:     List[Any]      =   False






    @property
    def _snake_case    (     self							):
       # standard deviation of the initial noise distribution
       if self.config.timestep_spacing in ["linspace", "trailing"]:
          return self.sigmas.max()

       return (self.sigmas.max() ** 2 + 1) ** 0.5






    def _snake_case    (     self					,    _UpperCAmelCase					,    _UpperCAmelCase							):
       if isinstance(_UpperCAmelCase					,    torch.Tensor							):
          lowercase__:     List[Any]      =   timestep.to(self.timesteps.device							)
       lowercase__:     Union[str, Any]      =   (self.timesteps == timestep).nonzero().item()
       lowercase__:     Optional[int]      =   self.sigmas[step_index]
       lowercase__:     Any      =   sample / ((sigma**2 + 1) ** 0.5)
       lowercase__:     Union[str, Any]      =   True
       return sample






    def _snake_case    (     self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase							):

       def lms_derivative(_UpperCAmelCase							):
          lowercase__:     Optional[int]      =   1.0
          for k in range(_UpperCAmelCase							):
             if current_order == k:
                continue
             prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
          return prod

       lowercase__:     Optional[Any]      =   integrate.quad(_UpperCAmelCase					,    self.sigmas[t]					,    self.sigmas[t + 1]					,    epsrel=1e-4							)[0]

       return integrated_coeff






    def _snake_case    (     self					,    _UpperCAmelCase					,    _UpperCAmelCase = None							):
       lowercase__:     Optional[int]      =   num_inference_steps

       # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
       if self.config.timestep_spacing == "linspace":
          lowercase__:     Optional[int]      =   np.linspace(0					,    self.config.num_train_timesteps - 1					,    _UpperCAmelCase					,    dtype=_UpperCAmelCase							)[
              ::-1
          ].copy()
       elif self.config.timestep_spacing == "leading":
          lowercase__:     Tuple      =   self.config.num_train_timesteps // self.num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # casting to int to avoid issues when num_inference_step is power of 3
          lowercase__:     int      =   (np.arange(0					,    _UpperCAmelCase							) * step_ratio).round()[::-1].copy().astype(_UpperCAmelCase							)
          timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
          lowercase__:     int      =   self.config.num_train_timesteps / self.num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # casting to int to avoid issues when num_inference_step is power of 3
          lowercase__:     Union[str, Any]      =   (np.arange(self.config.num_train_timesteps					,    0					,    -step_ratio							)).round().copy().astype(_UpperCAmelCase							)
          timesteps -= 1
       else:
          raise ValueError(
              F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""							)

       lowercase__:     List[Any]      =   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
       lowercase__:     List[str]      =   np.log(_UpperCAmelCase							)
       lowercase__:     Any      =   np.interp(_UpperCAmelCase					,    np.arange(0					,    len(_UpperCAmelCase							)							)					,    _UpperCAmelCase							)

       if self.use_karras_sigmas:
          lowercase__:     int      =   self._convert_to_karras(in_sigmas=_UpperCAmelCase							)
          lowercase__:     str      =   np.array([self._sigma_to_t(_UpperCAmelCase					,    _UpperCAmelCase							) for sigma in sigmas]							)

       lowercase__:     Dict      =   np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)

       lowercase__:     Optional[Any]      =   torch.from_numpy(_UpperCAmelCase							).to(device=_UpperCAmelCase							)
       if str(_UpperCAmelCase							).startswith('''mps'''							):
          # mps does not support float64
          lowercase__:     Tuple      =   torch.from_numpy(_UpperCAmelCase							).to(_UpperCAmelCase					,    dtype=torch.floataa							)
       else:
          lowercase__:     Union[str, Any]      =   torch.from_numpy(_UpperCAmelCase							).to(device=_UpperCAmelCase							)

       lowercase__:     Any      =   []






    def _snake_case    (     self					,    _UpperCAmelCase					,    _UpperCAmelCase							):
       # get log sigma
       lowercase__:     Optional[Any]      =   np.log(_UpperCAmelCase							)

       # get distribution
       lowercase__:     str      =   log_sigma - log_sigmas[:, np.newaxis]

       # get sigmas range
       lowercase__:     str      =   np.cumsum((dists >= 0)					,    axis=0							).argmax(axis=0							).clip(max=log_sigmas.shape[0] - 2							)
       lowercase__:     Any      =   low_idx + 1

       lowercase__:     List[str]      =   log_sigmas[low_idx]
       lowercase__:     List[Any]      =   log_sigmas[high_idx]

       # interpolate sigmas
       lowercase__:     List[Any]      =   (low - log_sigma) / (low - high)
       lowercase__:     Dict      =   np.clip(_UpperCAmelCase					,    0					,    1							)

       # transform interpolation to time range
       lowercase__:     Optional[Any]      =   (1 - w) * low_idx + w * high_idx
       lowercase__:     Union[str, Any]      =   t.reshape(sigma.shape							)
       return t






    def _snake_case    (     self					,    _UpperCAmelCase							):
       lowercase__:     float      =   in_sigmas[-1].item()
       lowercase__:     float      =   in_sigmas[0].item()

       lowercase__:     Tuple      =   7.0  # 7.0 is the value used in the paper
       lowercase__:     Dict      =   np.linspace(0					,    1					,    self.num_inference_steps							)
       lowercase__:     List[str]      =   sigma_min ** (1 / rho)
       lowercase__:     List[str]      =   sigma_max ** (1 / rho)
       lowercase__:     int      =   (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
       return sigmas






    def _snake_case    (     self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase = 4					,    _UpperCAmelCase = True					,    ):
       if not self.is_scale_input_called:
          warnings.warn(
              '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
              '''See `StableDiffusionPipeline` for a usage example.'''							)

       if isinstance(_UpperCAmelCase					,    torch.Tensor							):
          lowercase__:     List[str]      =   timestep.to(self.timesteps.device							)
       lowercase__:     Optional[int]      =   (self.timesteps == timestep).nonzero().item()
       lowercase__:     Dict      =   self.sigmas[step_index]

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
          lowercase__:     Any      =   sample - sigma * model_output
       elif self.config.prediction_type == "v_prediction":
          # * c_out + input * c_skip
          lowercase__:     Union[str, Any]      =   model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
       elif self.config.prediction_type == "sample":
          lowercase__:     Optional[int]      =   model_output
       else:
          raise ValueError(
              F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""							)

       # 2. Convert to an ODE derivative
       lowercase__:     Optional[int]      =   (sample - pred_original_sample) / sigma
       self.derivatives.append(_UpperCAmelCase							)
       if len(self.derivatives							) > order:
          self.derivatives.pop(0							)

       # 3. Compute linear multistep coefficients
       lowercase__:     Optional[Any]      =   min(step_index + 1					,    _UpperCAmelCase							)
       lowercase__:     Any      =   [self.get_lms_coefficient(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase							) for curr_order in range(_UpperCAmelCase							)]

       # 4. Compute previous sample based on the derivatives path
       lowercase__:     int      =   sample + sum(
           coeff * derivative for coeff, derivative in zip(_UpperCAmelCase					,    reversed(self.derivatives							)							)							)

       if not return_dict:
          return (prev_sample,)

       return LMSDiscreteSchedulerOutput(prev_sample=_UpperCAmelCase					,    pred_original_sample=_UpperCAmelCase							)






    def _snake_case    (     self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    ):
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       lowercase__:     Optional[Any]      =   self.sigmas.to(device=original_samples.device					,    dtype=original_samples.dtype							)
       if original_samples.device.type == "mps" and torch.is_floating_point(_UpperCAmelCase							):
          # mps does not support float64
          lowercase__:     List[Any]      =   self.timesteps.to(original_samples.device					,    dtype=torch.floataa							)
          lowercase__:     str      =   timesteps.to(original_samples.device					,    dtype=torch.floataa							)
       else:
          lowercase__:     Optional[int]      =   self.timesteps.to(original_samples.device							)
          lowercase__:     Dict      =   timesteps.to(original_samples.device							)

       lowercase__:     int      =   [(schedule_timesteps == t).nonzero().item() for t in timesteps]

       lowercase__:     int      =   sigmas[step_indices].flatten()
       while len(sigma.shape							) < len(original_samples.shape							):
          lowercase__:     Optional[Any]      =   sigma.unsqueeze(-1							)

       lowercase__:     Any      =   original_samples + noise * sigma
       return noisy_samples






    def __len__(     self							):
       return self.config.num_train_timesteps

"""simple docstring"""






import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
       from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


__A         =				get_tests_dir("fixtures/test_sentencepiece.model")


@require_torch
@require_torchaudio
@require_sentencepiece
class 					UpperCAmelCase		(unittest.TestCase						):



    """simple docstring"""

    def _snake_case    (     self							):
       lowercase__:     Dict      =   tempfile.mkdtemp()

       lowercase__:     str      =   ['''<s>''', '''<pad>''', '''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
       lowercase__:     Optional[Any]      =   dict(zip(_UpperCAmelCase					,    range(len(_UpperCAmelCase							)							)							)							)
       lowercase__:     List[Any]      =   Path(self.tmpdirname							)
       save_json(_UpperCAmelCase					,    save_dir / VOCAB_FILES_NAMES['''vocab_file''']							)
       if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
          copyfile(_UpperCAmelCase					,    save_dir / VOCAB_FILES_NAMES['''spm_file''']							)

       lowercase__:     Tuple      =   SpeechaTextTokenizer.from_pretrained(self.tmpdirname							)
       tokenizer.save_pretrained(self.tmpdirname							)

       lowercase__:     Optional[int]      =   {
           '''feature_size''': 24,
           '''num_mel_bins''': 24,
           '''padding_value''': 0.0,
           '''sampling_rate''': 16000,
           '''return_attention_mask''': False,
           '''do_normalize''': True,
       }
       save_json(_UpperCAmelCase					,    save_dir / FEATURE_EXTRACTOR_NAME							)






    def _snake_case    (     self					,    **_UpperCAmelCase							):
       return SpeechaTextTokenizer.from_pretrained(self.tmpdirname					,    **_UpperCAmelCase							)






    def _snake_case    (     self					,    **_UpperCAmelCase							):
       return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname					,    **_UpperCAmelCase							)






    def _snake_case    (     self							):
       shutil.rmtree(self.tmpdirname							)






    def _snake_case    (     self							):
       lowercase__:     Tuple      =   self.get_tokenizer()
       lowercase__:     Optional[int]      =   self.get_feature_extractor()

       lowercase__:     int      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

       processor.save_pretrained(self.tmpdirname							)
       lowercase__:     Union[str, Any]      =   SpeechaTextProcessor.from_pretrained(self.tmpdirname							)

       self.assertEqual(processor.tokenizer.get_vocab()					,    tokenizer.get_vocab()							)
       self.assertIsInstance(processor.tokenizer					,    _UpperCAmelCase							)

       self.assertEqual(processor.feature_extractor.to_json_string()					,    feature_extractor.to_json_string()							)
       self.assertIsInstance(processor.feature_extractor					,    _UpperCAmelCase							)






    def _snake_case    (     self							):
       lowercase__:     Tuple      =   SpeechaTextProcessor(
           tokenizer=self.get_tokenizer()					,    feature_extractor=self.get_feature_extractor()							)
       processor.save_pretrained(self.tmpdirname							)

       lowercase__:     Any      =   self.get_tokenizer(bos_token='''(BOS)'''					,    eos_token='''(EOS)'''							)
       lowercase__:     Tuple      =   self.get_feature_extractor(do_normalize=_UpperCAmelCase					,    padding_value=1.0							)

       lowercase__:     List[str]      =   SpeechaTextProcessor.from_pretrained(
           self.tmpdirname					,    bos_token='''(BOS)'''					,    eos_token='''(EOS)'''					,    do_normalize=_UpperCAmelCase					,    padding_value=1.0							)

       self.assertEqual(processor.tokenizer.get_vocab()					,    tokenizer_add_kwargs.get_vocab()							)
       self.assertIsInstance(processor.tokenizer					,    _UpperCAmelCase							)

       self.assertEqual(processor.feature_extractor.to_json_string()					,    feature_extractor_add_kwargs.to_json_string()							)
       self.assertIsInstance(processor.feature_extractor					,    _UpperCAmelCase							)






    def _snake_case    (     self							):
       lowercase__:     Tuple      =   self.get_feature_extractor()
       lowercase__:     List[Any]      =   self.get_tokenizer()

       lowercase__:     Any      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

       lowercase__:     int      =   floats_list((3, 1000)							)

       lowercase__:     Optional[Any]      =   feature_extractor(_UpperCAmelCase					,    return_tensors='''np'''							)
       lowercase__:     Dict      =   processor(_UpperCAmelCase					,    return_tensors='''np'''							)

       for key in input_feat_extract.keys():
          self.assertAlmostEqual(input_feat_extract[key].sum()					,    input_processor[key].sum()					,    delta=1e-2							)






    def _snake_case    (     self							):
       lowercase__:     Tuple      =   self.get_feature_extractor()
       lowercase__:     Dict      =   self.get_tokenizer()

       lowercase__:     Union[str, Any]      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

       lowercase__:     Dict      =   '''This is a test string'''

       lowercase__:     int      =   processor(text=_UpperCAmelCase							)

       lowercase__:     Union[str, Any]      =   tokenizer(_UpperCAmelCase							)

       for key in encoded_tok.keys():
          self.assertListEqual(encoded_tok[key]					,    encoded_processor[key]							)






    def _snake_case    (     self							):
       lowercase__:     List[Any]      =   self.get_feature_extractor()
       lowercase__:     Union[str, Any]      =   self.get_tokenizer()

       lowercase__:     Tuple      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

       lowercase__:     List[str]      =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       lowercase__:     List[Any]      =   processor.batch_decode(_UpperCAmelCase							)
       lowercase__:     Dict      =   tokenizer.batch_decode(_UpperCAmelCase							)

       self.assertListEqual(_UpperCAmelCase					,    _UpperCAmelCase							)






    def _snake_case    (     self							):
       lowercase__:     Optional[Any]      =   self.get_feature_extractor()
       lowercase__:     List[str]      =   self.get_tokenizer()

       lowercase__:     Union[str, Any]      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

       self.assertListEqual(
           processor.model_input_names					,    feature_extractor.model_input_names					,    msg='''`processor` and `feature_extractor` model input names do not match'''					,    )
