



"""simple docstring"""





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def 						__lowerCAmelCase    (		__UpperCamelCase   :      Optional[int]       ,			__UpperCamelCase   :      str=0.999       ,			__UpperCamelCase   :      Dict="cosine"       ,			):


						'''simple docstring'''






						if alpha_transform_type == "cosine":

												def alpha_bar_fn(__UpperCamelCase   :      Optional[Any]     ):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(__UpperCamelCase   :      Optional[Any]     ):
																		return math.exp(t * -12.0     )

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

						snake_case_ :			Optional[int]            =      []
						for i in range(__UpperCamelCase     ):
												snake_case_ :			str            =      i / num_diffusion_timesteps
												snake_case_ :			Dict            =      (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  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__	,					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase      	=							[e.name for e in KarrasDiffusionSchedulers]
		_lowerCamelCase      	=							2





		@register_to_config
		def __init__(       self				,     _lowercase = 1_0_0_0				,     _lowercase = 0.0_0085				,     _lowercase = 0.012				,     _lowercase = "linear"				,     _lowercase = None				,     _lowercase = "epsilon"				,     _lowercase = "linspace"				,     _lowercase = 0				,     )			->    Union[str, Any]:







								'''simple docstring'''

								if trained_betas is not None:
														snake_case_ :			Tuple            =      torch.tensor(_lowercase				,     dtype=torch.floataa )
								elif beta_schedule == "linear":
														snake_case_ :			int            =      torch.linspace(_lowercase				,     _lowercase				,     _lowercase				,     dtype=torch.floataa )
								elif beta_schedule == "scaled_linear":
														# this schedule is very specific to the latent diffusion model.
														snake_case_ :			Optional[int]            =      (
														    torch.linspace(beta_start**0.5				,     beta_end**0.5				,     _lowercase				,     dtype=torch.floataa ) ** 2
														)
								elif beta_schedule == "squaredcos_cap_v2":
														# Glide cosine schedule
														snake_case_ :			List[str]            =      betas_for_alpha_bar(_lowercase )
								else:
														raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}' )

								snake_case_ :			Tuple            =      1.0 - self.betas
								snake_case_ :			str            =      torch.cumprod(self.alphas				,     dim=0 )

								#  set all values
								self.set_timesteps(_lowercase				,     _lowercase				,     _lowercase )





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase=None )			->    List[str]:







								'''simple docstring'''

								if schedule_timesteps is None:
														snake_case_ :			Optional[Any]            =      self.timesteps

								snake_case_ :			int            =      (schedule_timesteps == timestep).nonzero()

								# The sigma index that is taken for the **very** first `step`
								# is always the second index (or the last index if there is only 1)
								# This way we can ensure we don't accidentally skip a sigma in
								# case we start in the middle of the denoising schedule (e.g. for image-to-image)
								if len(self._index_counter ) == 0:
														snake_case_ :			Optional[Any]            =      1 if len(_lowercase ) > 1 else 0
								else:
														snake_case_ :			Optional[int]            =      timestep.cpu().item() if torch.is_tensor(_lowercase ) else timestep
														snake_case_ :			List[str]            =      self._index_counter[timestep_int]

								return indices[pos].item()





		@property
		def  UpperCAmelCase__				(       self )			->    Dict:







								'''simple docstring'''

								if self.config.timestep_spacing in ["linspace", "trailing"]:
														return self.sigmas.max()

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





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     )			->    torch.FloatTensor:







								'''simple docstring'''

								snake_case_ :			str            =      self.index_for_timestep(_lowercase )

								if self.state_in_first_order:
														snake_case_ :			Union[str, Any]            =      self.sigmas[step_index]
								else:
														snake_case_ :			List[Any]            =      self.sigmas_interpol[step_index]

								snake_case_ :			Dict            =      sample / ((sigma**2 + 1) ** 0.5)
								return sample





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase = None				,     _lowercase = None				,     )			->    Optional[Any]:







								'''simple docstring'''

								snake_case_ :			Any            =      num_inference_steps

								snake_case_ :			List[Any]            =      num_train_timesteps or self.config.num_train_timesteps

								# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
								if self.config.timestep_spacing == "linspace":
														snake_case_ :			Tuple            =      np.linspace(0				,     num_train_timesteps - 1				,     _lowercase				,     dtype=_lowercase )[::-1].copy()
								elif self.config.timestep_spacing == "leading":
														snake_case_ :			int            =      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
														snake_case_ :			Any            =      (np.arange(0				,     _lowercase ) * step_ratio).round()[::-1].copy().astype(_lowercase )
														timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
														snake_case_ :			Tuple            =      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
														snake_case_ :			Dict            =      (np.arange(_lowercase				,     0				,     -step_ratio )).round().copy().astype(_lowercase )
														timesteps -= 1
								else:
														raise ValueError(
														    f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.' )

								snake_case_ :			Dict            =      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
								snake_case_ :			List[str]            =      torch.from_numpy(np.log(_lowercase ) ).to(_lowercase )

								snake_case_ :			Optional[int]            =      np.interp(_lowercase				,     np.arange(0				,     len(_lowercase ) )				,     _lowercase )
								snake_case_ :			Union[str, Any]            =      np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
								snake_case_ :			str            =      torch.from_numpy(_lowercase ).to(device=_lowercase )

								# interpolate sigmas
								snake_case_ :			Union[str, Any]            =      sigmas.log().lerp(sigmas.roll(1 ).log()				,     0.5 ).exp()

								snake_case_ :			List[Any]            =      torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
								snake_case_ :			str            =      torch.cat(
								    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )

								if str(_lowercase ).startswith("""mps""" ):
														# mps does not support float64
														snake_case_ :			str            =      torch.from_numpy(_lowercase ).to(_lowercase				,     dtype=torch.floataa )
								else:
														snake_case_ :			Optional[int]            =      torch.from_numpy(_lowercase ).to(_lowercase )

								# interpolate timesteps
								snake_case_ :			Tuple            =      self.sigma_to_t(_lowercase ).to(_lowercase				,     dtype=timesteps.dtype )
								snake_case_ :			str            =      torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])				,     dim=-1 ).flatten()

								snake_case_ :			Any            =      torch.cat([timesteps[:1], interleaved_timesteps] )

								snake_case_ :			List[Any]            =      None

								# for exp beta schedules, such as the one for `pipeline_shap_e.py`
								# we need an index counter
								snake_case_ :			Union[str, Any]            =      defaultdict(_lowercase )





		def  UpperCAmelCase__				(       self				,     _lowercase )			->    Tuple:







								'''simple docstring'''

								snake_case_ :			List[Any]            =      sigma.log()

								# get distribution
								snake_case_ :			List[str]            =      log_sigma - self.log_sigmas[:, None]

								# get sigmas range
								snake_case_ :			Optional[Any]            =      dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
								snake_case_ :			int            =      low_idx + 1

								snake_case_ :			Tuple            =      self.log_sigmas[low_idx]
								snake_case_ :			List[str]            =      self.log_sigmas[high_idx]

								# interpolate sigmas
								snake_case_ :			Optional[int]            =      (low - log_sigma) / (low - high)
								snake_case_ :			Optional[Any]            =      w.clamp(0				,     1 )

								# transform interpolation to time range
								snake_case_ :			List[Any]            =      (1 - w) * low_idx + w * high_idx
								snake_case_ :			List[str]            =      t.view(sigma.shape )
								return t





		@property
		def  UpperCAmelCase__				(       self )			->    Dict:







								'''simple docstring'''

								return self.sample is None





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase = True				,     )			->    Union[SchedulerOutput, Tuple]:







								'''simple docstring'''

								snake_case_ :			List[str]            =      self.index_for_timestep(_lowercase )

								# advance index counter by 1
								snake_case_ :			List[Any]            =      timestep.cpu().item() if torch.is_tensor(_lowercase ) else timestep
								self._index_counter[timestep_int] += 1

								if self.state_in_first_order:
														snake_case_ :			int            =      self.sigmas[step_index]
														snake_case_ :			Union[str, Any]            =      self.sigmas_interpol[step_index + 1]
														snake_case_ :			List[str]            =      self.sigmas[step_index + 1]
								else:
														# 2nd order / KDPM2's method
														snake_case_ :			Optional[int]            =      self.sigmas[step_index - 1]
														snake_case_ :			Optional[int]            =      self.sigmas_interpol[step_index]
														snake_case_ :			int            =      self.sigmas[step_index]

								# currently only gamma=0 is supported. This usually works best anyways.
								# We can support gamma in the future but then need to scale the timestep before
								# passing it to the model which requires a change in API
								snake_case_ :			str            =      0
								snake_case_ :			Optional[int]            =      sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								if self.config.prediction_type == "epsilon":
														snake_case_ :			str            =      sigma_hat if self.state_in_first_order else sigma_interpol
														snake_case_ :			Optional[Any]            =      sample - sigma_input * model_output
								elif self.config.prediction_type == "v_prediction":
														snake_case_ :			Dict            =      sigma_hat if self.state_in_first_order else sigma_interpol
														snake_case_ :			int            =      model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
														    sample / (sigma_input**2 + 1)
														)
								elif self.config.prediction_type == "sample":
														raise NotImplementedError("""prediction_type not implemented yet: sample""" )
								else:
														raise ValueError(
														    f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`' )

								if self.state_in_first_order:
														# 2. Convert to an ODE derivative for 1st order
														snake_case_ :			List[str]            =      (sample - pred_original_sample) / sigma_hat
														# 3. delta timestep
														snake_case_ :			List[str]            =      sigma_interpol - sigma_hat

														# store for 2nd order step
														snake_case_ :			List[Any]            =      sample
								else:
														# DPM-Solver-2
														# 2. Convert to an ODE derivative for 2nd order
														snake_case_ :			List[Any]            =      (sample - pred_original_sample) / sigma_interpol

														# 3. delta timestep
														snake_case_ :			Optional[int]            =      sigma_next - sigma_hat

														snake_case_ :			Dict            =      self.sample
														snake_case_ :			int            =      None

								snake_case_ :			Any            =      sample + derivative * dt

								if not return_dict:
														return (prev_sample,)

								return SchedulerOutput(prev_sample=_lowercase )





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase				,     )			->    torch.FloatTensor:







								'''simple docstring'''

								snake_case_ :			Optional[Any]            =      self.sigmas.to(device=original_samples.device				,     dtype=original_samples.dtype )
								if original_samples.device.type == "mps" and torch.is_floating_point(_lowercase ):
														# mps does not support float64
														snake_case_ :			List[Any]            =      self.timesteps.to(original_samples.device				,     dtype=torch.floataa )
														snake_case_ :			Any            =      timesteps.to(original_samples.device				,     dtype=torch.floataa )
								else:
														snake_case_ :			Any            =      self.timesteps.to(original_samples.device )
														snake_case_ :			Union[str, Any]            =      timesteps.to(original_samples.device )

								snake_case_ :			Optional[Any]            =      [self.index_for_timestep(_lowercase				,     _lowercase ) for t in timesteps]

								snake_case_ :			Any            =      sigmas[step_indices].flatten()
								while len(sigma.shape ) < len(original_samples.shape ):
														snake_case_ :			Union[str, Any]            =      sigma.unsqueeze(-1 )

								snake_case_ :			Optional[Any]            =      original_samples + noise * sigma
								return noisy_samples





		def __len__(       self )			->    Union[str, Any]:







								'''simple docstring'''

								return self.config.num_train_timesteps



"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_            =    {
    '''configuration_speecht5''': [
        '''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
        '''SpeechT5Config''',
        '''SpeechT5HifiGanConfig''',
    ],
    '''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
    '''processing_speecht5''': ['''SpeechT5Processor'''],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    ['''SpeechT5Tokenizer''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''SpeechT5ForSpeechToText''',
       '''SpeechT5ForSpeechToSpeech''',
       '''SpeechT5ForTextToSpeech''',
       '''SpeechT5Model''',
       '''SpeechT5PreTrainedModel''',
       '''SpeechT5HifiGan''',
   ]

if TYPE_CHECKING:
   from .configuration_speechta import (
       SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
       SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
       SpeechTaConfig,
       SpeechTaHifiGanConfig,
   )
   from .feature_extraction_speechta import SpeechTaFeatureExtractor
   from .processing_speechta import SpeechTaProcessor

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_speechta import SpeechTaTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_speechta import (
          SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
          SpeechTaForSpeechToSpeech,
          SpeechTaForSpeechToText,
          SpeechTaForTextToSpeech,
          SpeechTaHifiGan,
          SpeechTaModel,
          SpeechTaPreTrainedModel,
      )

else:
   import sys

   SCREAMING_SNAKE_CASE_            =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



