
'''simple docstring'''





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

import numpy as np
import torch

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





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class 			_lowerCAmelCase      (			SCREAMING_SNAKE_CASE__ ):
				_lowercase    	=42
				_lowercase    	=None






def      lowerCamelCase__       (       __lowerCAmelCase						:						int					,							__lowerCAmelCase						:						Dict=0.999					,							__lowerCAmelCase						:						Optional[Any]="cosine"					,							):




			"""simple docstring"""


			if alpha_transform_type == "cosine":

						def alpha_bar_fn(__lowerCAmelCase						:						Any       ):
									return math.cos((t + 0.008) / 1.008 * math.pi / 2       ) ** 2

			elif alpha_transform_type == "exp":

						def alpha_bar_fn(__lowerCAmelCase						:						Union[str, Any]       ):
									return math.exp(t * -12.0       )

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

			lowerCAmelCase_      			=			[]
			for i in range(__UpperCamelCase       ):
						lowerCAmelCase_      			=			i / num_diffusion_timesteps
						lowerCAmelCase_      			=			(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__ ):




				@register_to_config
				def __init__(      self       ,       _UpperCamelCase = 1_000       ,       _UpperCamelCase = "fixed_small_log"       ,       _UpperCamelCase = True       ,       _UpperCamelCase = 1.0       ,       _UpperCamelCase = "epsilon"       ,       _UpperCamelCase = "squaredcos_cap_v2"       ,       )       ->   Tuple:

							if beta_schedule != "squaredcos_cap_v2":
										raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"  )

							lowerCAmelCase_      			=			betas_for_alpha_bar(_lowercase  )

							lowerCAmelCase_      			=			1.0 - self.betas
							lowerCAmelCase_      			=			torch.cumprod(self.alphas       ,       dim=0  )
							lowerCAmelCase_      			=			torch.tensor(1.0  )

							# standard deviation of the initial noise distribution
							lowerCAmelCase_      			=			1.0

							# setable values
							lowerCAmelCase_      			=			None
							lowerCAmelCase_      			=			torch.from_numpy(np.arange(0       ,       _lowercase  )[::-1].copy()  )

							lowerCAmelCase_      			=			variance_type




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = None  )       ->   torch.FloatTensor:
							return sample




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = None  )       ->   Union[str, Any]:
							lowerCAmelCase_      			=			num_inference_steps
							lowerCAmelCase_      			=			(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
							lowerCAmelCase_      			=			(np.arange(0       ,       _lowercase  ) * step_ratio).round()[::-1].copy().astype(np.intaa  )
							lowerCAmelCase_      			=			torch.from_numpy(_lowercase  ).to(_lowercase  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None  )       ->   Any:

							if prev_timestep is None:
										lowerCAmelCase_      			=			t - 1

							lowerCAmelCase_      			=			self.alphas_cumprod[t]
							lowerCAmelCase_      			=			self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
							lowerCAmelCase_      			=			1 - alpha_prod_t
							lowerCAmelCase_      			=			1 - alpha_prod_t_prev

							if prev_timestep == t - 1:
										lowerCAmelCase_      			=			self.betas[t]
							else:
										lowerCAmelCase_      			=			1 - alpha_prod_t / alpha_prod_t_prev

							# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
							# and sample from it to get previous sample
							# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
							lowerCAmelCase_      			=			beta_prod_t_prev / beta_prod_t * beta

							if variance_type is None:
										lowerCAmelCase_      			=			self.config.variance_type

							# hacks - were probably added for training stability
							if variance_type == "fixed_small_log":
										lowerCAmelCase_      			=			torch.log(torch.clamp(_lowercase       ,       min=1e-2_0  )  )
										lowerCAmelCase_      			=			torch.exp(0.5 * variance  )
							elif variance_type == "learned_range":
										# NOTE difference with DDPM scheduler
										lowerCAmelCase_      			=			variance.log()
										lowerCAmelCase_      			=			beta.log()

										lowerCAmelCase_      			=			(predicted_variance + 1) / 2
										lowerCAmelCase_      			=			frac * max_log + (1 - frac) * min_log

							return variance




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase = None       ,       _UpperCamelCase=None       ,       _UpperCamelCase = True       ,       )       ->   Union[UnCLIPSchedulerOutput, Tuple]:
							lowerCAmelCase_      			=			timestep

							if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
										lowerCAmelCase_      			=			torch.split(_lowercase       ,       sample.shape[1]       ,       dim=1  )
							else:
										lowerCAmelCase_      			=			None

							# 1. compute alphas, betas
							if prev_timestep is None:
										lowerCAmelCase_      			=			t - 1

							lowerCAmelCase_      			=			self.alphas_cumprod[t]
							lowerCAmelCase_      			=			self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
							lowerCAmelCase_      			=			1 - alpha_prod_t
							lowerCAmelCase_      			=			1 - alpha_prod_t_prev

							if prev_timestep == t - 1:
										lowerCAmelCase_      			=			self.betas[t]
										lowerCAmelCase_      			=			self.alphas[t]
							else:
										lowerCAmelCase_      			=			1 - alpha_prod_t / alpha_prod_t_prev
										lowerCAmelCase_      			=			1 - beta

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							if self.config.prediction_type == "epsilon":
										lowerCAmelCase_      			=			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif self.config.prediction_type == "sample":
										lowerCAmelCase_      			=			model_output
							else:
										raise ValueError(
										    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
										    " for the UnCLIPScheduler."  )

							# 3. Clip "predicted x_0"
							if self.config.clip_sample:
										lowerCAmelCase_      			=			torch.clamp(
										    _lowercase       ,       -self.config.clip_sample_range       ,       self.config.clip_sample_range  )

							# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							lowerCAmelCase_      			=			(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
							lowerCAmelCase_      			=			alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

							# 5. Compute predicted previous sample µ_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							lowerCAmelCase_      			=			pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							lowerCAmelCase_      			=			0
							if t > 0:
										lowerCAmelCase_      			=			randn_tensor(
										    model_output.shape       ,       dtype=model_output.dtype       ,       generator=_lowercase       ,       device=model_output.device  )

										lowerCAmelCase_      			=			self._get_variance(
										    _lowercase       ,       predicted_variance=_lowercase       ,       prev_timestep=_lowercase       ,       )

										if self.variance_type == "fixed_small_log":
													lowerCAmelCase_      			=			variance
										elif self.variance_type == "learned_range":
													lowerCAmelCase_      			=			(0.5 * variance).exp()
										else:
													raise ValueError(
													    f"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
													    " for the UnCLIPScheduler."  )

										lowerCAmelCase_      			=			variance * variance_noise

							lowerCAmelCase_      			=			pred_prev_sample + variance

							if not return_dict:
										return (pred_prev_sample,)

							return UnCLIPSchedulerOutput(prev_sample=_lowercase       ,       pred_original_sample=_lowercase  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       )       ->   torch.FloatTensor:
							lowerCAmelCase_      			=			self.alphas_cumprod.to(device=original_samples.device       ,       dtype=original_samples.dtype  )
							lowerCAmelCase_      			=			timesteps.to(original_samples.device  )

							lowerCAmelCase_      			=			alphas_cumprod[timesteps] ** 0.5
							lowerCAmelCase_      			=			sqrt_alpha_prod.flatten()
							while len(sqrt_alpha_prod.shape  ) < len(original_samples.shape  ):
										lowerCAmelCase_      			=			sqrt_alpha_prod.unsqueeze(-1  )

							lowerCAmelCase_      			=			(1 - alphas_cumprod[timesteps]) ** 0.5
							lowerCAmelCase_      			=			sqrt_one_minus_alpha_prod.flatten()
							while len(sqrt_one_minus_alpha_prod.shape  ) < len(original_samples.shape  ):
										lowerCAmelCase_      			=			sqrt_one_minus_alpha_prod.unsqueeze(-1  )

							lowerCAmelCase_      			=			sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
							return noisy_samples





from ...processing_utils import ProcessorMixin





class 			_lowerCAmelCase      (			__a ):
				_lowercase    	='''SpeechT5FeatureExtractor'''
				_lowercase    	='''SpeechT5Tokenizer'''




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   int:
							super().__init__(_UpperCamelCase       ,       _UpperCamelCase  )




				def __call__(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   Optional[int]:
							lowerCAmelCase_      			=			kwargs.pop("audio"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("text"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("text_target"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("audio_target"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("sampling_rate"       ,       _UpperCamelCase  )

							if audio is not None and text is not None:
										raise ValueError(
										    "Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"  )
							if audio_target is not None and text_target is not None:
										raise ValueError(
										    "Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"  )
							if audio is None and audio_target is None and text is None and text_target is None:
										raise ValueError(
										    "You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."  )

							if audio is not None:
										lowerCAmelCase_      			=			self.feature_extractor(_UpperCamelCase       ,       *_UpperCamelCase       ,       sampling_rate=_UpperCamelCase       ,       **_UpperCamelCase  )
							elif text is not None:
										lowerCAmelCase_      			=			self.tokenizer(_UpperCamelCase       ,       **_UpperCamelCase  )
							else:
										lowerCAmelCase_      			=			None

							if audio_target is not None:
										lowerCAmelCase_      			=			self.feature_extractor(audio_target=_UpperCamelCase       ,       *_UpperCamelCase       ,       sampling_rate=_UpperCamelCase       ,       **_UpperCamelCase  )
										lowerCAmelCase_      			=			targets["input_values"]
							elif text_target is not None:
										lowerCAmelCase_      			=			self.tokenizer(_UpperCamelCase       ,       **_UpperCamelCase  )
										lowerCAmelCase_      			=			targets["input_ids"]
							else:
										lowerCAmelCase_      			=			None

							if inputs is None:
										return targets

							if targets is not None:
										lowerCAmelCase_      			=			labels

										lowerCAmelCase_      			=			targets.get("attention_mask"  )
										if decoder_attention_mask is not None:
													lowerCAmelCase_      			=			decoder_attention_mask

							return inputs




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   str:
							lowerCAmelCase_      			=			kwargs.pop("input_values"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("input_ids"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("labels"       ,       _UpperCamelCase  )

							if input_values is not None and input_ids is not None:
										raise ValueError("Cannot process both `input_values` and `input_ids` inputs."  )
							if input_values is None and input_ids is None and labels is None:
										raise ValueError(
										    "You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."  )

							if input_values is not None:
										lowerCAmelCase_      			=			self.feature_extractor.pad(_UpperCamelCase       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )
							elif input_ids is not None:
										lowerCAmelCase_      			=			self.tokenizer.pad(_UpperCamelCase       ,       **_UpperCamelCase  )
							else:
										lowerCAmelCase_      			=			None

							if labels is not None:
										if "input_ids" in labels or (isinstance(_UpperCamelCase       ,       _UpperCamelCase  ) and "input_ids" in labels[0]):
													lowerCAmelCase_      			=			self.tokenizer.pad(_UpperCamelCase       ,       **_UpperCamelCase  )
													lowerCAmelCase_      			=			targets["input_ids"]
										else:
													lowerCAmelCase_      			=			self.feature_extractor.feature_size
													lowerCAmelCase_      			=			self.feature_extractor.num_mel_bins
													lowerCAmelCase_      			=			self.feature_extractor.pad(_UpperCamelCase       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )
													lowerCAmelCase_      			=			feature_size_hack
													lowerCAmelCase_      			=			targets["input_values"]
							else:
										lowerCAmelCase_      			=			None

							if inputs is None:
										return targets

							if targets is not None:
										lowerCAmelCase_      			=			labels

										lowerCAmelCase_      			=			targets.get("attention_mask"  )
										if decoder_attention_mask is not None:
													lowerCAmelCase_      			=			decoder_attention_mask

							return inputs




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   List[Any]:
							return self.tokenizer.batch_decode(*_UpperCamelCase       ,       **_UpperCamelCase  )




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   Optional[Any]:
							return self.tokenizer.decode(*_UpperCamelCase       ,       **_UpperCamelCase  )



