



'''simple docstring'''



import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow





@require_torch
class _UpperCamelCase		(     unittest.TestCase		):





						'''simple docstring'''





						def 				__lowerCamelCase     (							self  :  Union[str, Any]):







													'''simple docstring'''

													__lowercase       	='ylacombe/bark-small'
													__lowercase       	=tempfile.mkdtemp()
													__lowercase       	='en_speaker_1'
													__lowercase       	='This is a test string'
													__lowercase       	='speaker_embeddings_path.json'
													__lowercase       	='speaker_embeddings'

						def 				__lowerCamelCase     (							self  :  Any  ,       **_lowerCAmelCase  :  Any):







													'''simple docstring'''

													return AutoTokenizer.from_pretrained(self.checkpoint  ,       **_lowerCAmelCase)

						def 				__lowerCamelCase     (							self  :  List[Any]):







													'''simple docstring'''

													shutil.rmtree(self.tmpdirname)

						def 				__lowerCamelCase     (							self  :  List[Any]):







													'''simple docstring'''

													__lowercase       	=self.get_tokenizer()

													__lowercase       	=BarkProcessor(tokenizer=_lowerCAmelCase)

													processor.save_pretrained(self.tmpdirname)
													__lowercase       	=BarkProcessor.from_pretrained(self.tmpdirname)

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

						@slow
						def 				__lowerCamelCase     (							self  :  Union[str, Any]):







													'''simple docstring'''

													__lowercase       	=BarkProcessor.from_pretrained(
													    pretrained_processor_name_or_path=self.checkpoint  ,       speaker_embeddings_dict_path=self.speaker_embeddings_dict_path  ,       )
													processor.save_pretrained(
													    self.tmpdirname  ,       speaker_embeddings_dict_path=self.speaker_embeddings_dict_path  ,       speaker_embeddings_directory=self.speaker_embeddings_directory  ,       )

													__lowercase       	=self.get_tokenizer(bos_token='(BOS)'  ,       eos_token='(EOS)')

													__lowercase       	=BarkProcessor.from_pretrained(
													    self.tmpdirname  ,       self.speaker_embeddings_dict_path  ,       bos_token='(BOS)'  ,       eos_token='(EOS)'  ,       )

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

						def 				__lowerCamelCase     (							self  :  Union[str, Any]):







													'''simple docstring'''

													__lowercase       	=BarkProcessor.from_pretrained(
													    pretrained_processor_name_or_path=self.checkpoint  ,       speaker_embeddings_dict_path=self.speaker_embeddings_dict_path  ,       )

													__lowercase       	=3_5
													__lowercase       	=2
													__lowercase       	=8

													__lowercase       	={
													    'semantic_prompt': np.ones(_lowerCAmelCase),
													    'coarse_prompt': np.ones((nb_codebooks_coarse, seq_len)),
													    'fine_prompt': np.ones((nb_codebooks_total, seq_len)),
													}

													# test providing already loaded voice_preset
													__lowercase       	=processor(text=self.input_string  ,       voice_preset=_lowerCAmelCase)

													__lowercase       	=inputs['history_prompt']
													for key in voice_preset:
																				self.assertListEqual(voice_preset[key].tolist()  ,       processed_voice_preset.get(_lowerCAmelCase  ,       np.array([])).tolist())

													# test loading voice preset from npz file
													__lowercase       	=os.path.join(self.tmpdirname  ,       'file.npz')
													np.savez(_lowerCAmelCase  ,       **_lowerCAmelCase)
													__lowercase       	=processor(text=self.input_string  ,       voice_preset=_lowerCAmelCase)
													__lowercase       	=inputs['history_prompt']

													for key in voice_preset:
																				self.assertListEqual(voice_preset[key].tolist()  ,       processed_voice_preset.get(_lowerCAmelCase  ,       np.array([])).tolist())

													# test loading voice preset from the hub
													__lowercase       	=processor(text=self.input_string  ,       voice_preset=self.voice_preset)

						def 				__lowerCamelCase     (							self  :  Optional[Any]):







													'''simple docstring'''

													__lowercase       	=self.get_tokenizer()

													__lowercase       	=BarkProcessor(tokenizer=_lowerCAmelCase)

													__lowercase       	=processor(text=self.input_string)

													__lowercase       	=tokenizer(
													    self.input_string  ,       padding='max_length'  ,       max_length=2_5_6  ,       add_special_tokens=_lowerCAmelCase  ,       return_attention_mask=_lowerCAmelCase  ,       return_token_type_ids=_lowerCAmelCase  ,       )

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








'''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 _UpperCamelCase		(     A		):





				'''simple docstring'''





				lowerCAmelCase__					    =    42
				lowerCAmelCase__					    =    None



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase=0.9_99  ,   _lowerCAmelCase="cosine"  ,   ):



							"""simple docstring"""



							if alpha_transform_type == "cosine":

														def alpha_bar_fn(_lowerCAmelCase					):
																					return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2					) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(_lowerCAmelCase					):
																					return math.exp(t * -12.0					)

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

							__lowercase       	=[]
							for i in range(_lowerCAmelCase					):
														__lowercase       	=i / num_diffusion_timesteps
														__lowercase       	=(i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(_lowerCAmelCase					) / alpha_bar_fn(_lowerCAmelCase					)  ,   _lowerCAmelCase					)					)
							return torch.tensor(_lowerCAmelCase  ,   dtype=torch.floataa					)





class _UpperCamelCase		(     A      ,       A		):





				'''simple docstring'''





				@register_to_config
				def __init__(							self  :  Any  ,       _lowerCAmelCase  :  int = 1_0_0_0  ,       _lowerCAmelCase  :  str = "fixed_small_log"  ,       _lowerCAmelCase  :  bool = True  ,       _lowerCAmelCase  :  Optional[float] = 1.0  ,       _lowerCAmelCase  :  str = "epsilon"  ,       _lowerCAmelCase  :  str = "squaredcos_cap_v2"  ,       ):







											'''simple docstring'''

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

											__lowercase       	=betas_for_alpha_bar(_lowerCAmelCase)

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

											# standard deviation of the initial noise distribution
											__lowercase       	=1.0

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

											__lowercase       	=variance_type

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







											'''simple docstring'''

											return sample

				def 				__lowerCamelCase     (							self  :  Tuple  ,       _lowerCAmelCase  :  int  ,       _lowerCAmelCase  :  Union[str, torch.device] = None):







											'''simple docstring'''

											__lowercase       	=num_inference_steps
											__lowercase       	=(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
											__lowercase       	=(np.arange(0  ,       _lowerCAmelCase) * step_ratio).round()[::-1].copy().astype(np.intaa)
											__lowercase       	=torch.from_numpy(_lowerCAmelCase).to(_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  str  ,       _lowerCAmelCase  :  List[str]  ,       _lowerCAmelCase  :  Dict=None  ,       _lowerCAmelCase  :  Dict=None  ,       _lowerCAmelCase  :  int=None):







											'''simple docstring'''

											if prev_timestep is None:
																		__lowercase       	=t - 1

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

											if prev_timestep == t - 1:
																		__lowercase       	=self.betas[t]
											else:
																		__lowercase       	=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
											__lowercase       	=beta_prod_t_prev / beta_prod_t * beta

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

											# hacks - were probably added for training stability
											if variance_type == "fixed_small_log":
																		__lowercase       	=torch.log(torch.clamp(_lowerCAmelCase  ,       min=1e-20))
																		__lowercase       	=torch.exp(0.5 * variance)
											elif variance_type == "learned_range":
																		# NOTE difference with DDPM scheduler
																		__lowercase       	=variance.log()
																		__lowercase       	=beta.log()

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

											return variance

				def 				__lowerCamelCase     (							self  :  Union[str, Any]  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  int  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  Optional[int] = None  ,       _lowerCAmelCase  :  Optional[int]=None  ,       _lowerCAmelCase  :  bool = True  ,       ):







											'''simple docstring'''

											__lowercase       	=timestep

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

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

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

											if prev_timestep == t - 1:
																		__lowercase       	=self.betas[t]
																		__lowercase       	=self.alphas[t]
											else:
																		__lowercase       	=1 - alpha_prod_t / alpha_prod_t_prev
																		__lowercase       	=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":
																		__lowercase       	=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
											elif self.config.prediction_type == "sample":
																		__lowercase       	=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:
																		__lowercase       	=torch.clamp(
																		    _lowerCAmelCase  ,       -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
											__lowercase       	=(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
											__lowercase       	=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
											__lowercase       	=pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

																		__lowercase       	=self._get_variance(
																		    _lowerCAmelCase  ,       predicted_variance=_lowerCAmelCase  ,       prev_timestep=_lowerCAmelCase  ,       )

																		if self.variance_type == "fixed_small_log":
																									__lowercase       	=variance
																		elif self.variance_type == "learned_range":
																									__lowercase       	=(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.')

																		__lowercase       	=variance * variance_noise

											__lowercase       	=pred_prev_sample + variance

											if not return_dict:
																		return (pred_prev_sample,)

											return UnCLIPSchedulerOutput(prev_sample=_lowerCAmelCase  ,       pred_original_sample=_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  int  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  torch.IntTensor  ,       ):







											'''simple docstring'''

											__lowercase       	=self.alphas_cumprod.to(device=original_samples.device  ,       dtype=original_samples.dtype)
											__lowercase       	=timesteps.to(original_samples.device)

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

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

											__lowercase       	=sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
											return noisy_samples



