






"""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  lowerCAmelCase__ (						__magic_name__      ):
			SCREAMING_SNAKE_CASE_         =42
			SCREAMING_SNAKE_CASE_         =None




def   SCREAMING_SNAKE_CASE__					(     snake_case					:   Any ,				snake_case					:   List[Any]=0.999 ,				snake_case					:   Union[str, Any]="cosine" ,				)->		str:






					'''simple docstring'''



					if alpha_transform_type == "cosine":

										def alpha_bar_fn(snake_case					:   List[str]							):
															return math.cos((t + 0.008) / 1.008 * math.pi / 2							) ** 2

					elif alpha_transform_type == "exp":

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

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

					UpperCAmelCase__	:						Any       =       []
					for i in range(_UpperCAmelCase							):
										UpperCAmelCase__	:						Optional[Any]       =       i / num_diffusion_timesteps
										UpperCAmelCase__	:						str       =       (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__ (						__magic_name__       ,	__magic_name__      ):
			SCREAMING_SNAKE_CASE_         =[e.name for e in KarrasDiffusionSchedulers]
			SCREAMING_SNAKE_CASE_         =1
			@register_to_config
			def __init__(		self			:  List[str]       , snake_case__			:  int = 1_0_0_0       , snake_case__			:  float = 0.0001       , snake_case__			:  float = 0.02       , snake_case__			:  str = "linear"       , snake_case__			:  Optional[Union[np.ndarray, List[float]]] = None       , snake_case__			:  Optional[bool] = False       , snake_case__			:  str = "epsilon"       , snake_case__			:  str = "linspace"       , snake_case__			:  int = 0       , ):


								'''simple docstring'''
								if trained_betas is not None:
													UpperCAmelCase__	:						List[Any]       =       torch.tensor(UpperCAmelCase_       , dtype=torch.floataa       )
								elif beta_schedule == "linear":
													UpperCAmelCase__	:						List[Any]       =       torch.linspace(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , dtype=torch.floataa       )
								elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													UpperCAmelCase__	:						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
													UpperCAmelCase__	:						Dict       =       betas_for_alpha_bar(UpperCAmelCase_       )
								else:
													raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'       )

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

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

								# setable values
								UpperCAmelCase__	:						Tuple       =       None
								UpperCAmelCase__	:						Dict       =       use_karras_sigmas
								self.set_timesteps(UpperCAmelCase_       , UpperCAmelCase_       )
								UpperCAmelCase__	:						List[Any]       =       []
								UpperCAmelCase__	:						Optional[int]       =       False
			@property
			def 			__a       (		self			:  Dict       ):


								'''simple docstring'''
								# 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 			__a       (		self			:  Any       , snake_case__			:  torch.FloatTensor       , snake_case__			:  Union[float, torch.FloatTensor]       ):


								'''simple docstring'''
								if isinstance(UpperCAmelCase_       , torch.Tensor       ):
													UpperCAmelCase__	:						Dict       =       timestep.to(self.timesteps.device       )
								UpperCAmelCase__	:						Union[str, Any]       =       (self.timesteps == timestep).nonzero().item()
								UpperCAmelCase__	:						Dict       =       self.sigmas[step_index]
								UpperCAmelCase__	:						List[str]       =       sample / ((sigma**2 + 1) ** 0.5)
								UpperCAmelCase__	:						Optional[Any]       =       True
								return sample
			def 			__a       (		self			:  Any       , snake_case__			:  Any       , snake_case__			:  Tuple       , snake_case__			:  Optional[Any]       ):


								'''simple docstring'''

								def lms_derivative(snake_case__			:  int       ):
													UpperCAmelCase__	:						str       =       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

								UpperCAmelCase__	:						List[str]       =       integrate.quad(UpperCAmelCase_       , self.sigmas[t]       , self.sigmas[t + 1]       , epsrel=1e-4       )[0]

								return integrated_coeff
			def 			__a       (		self			:  int       , snake_case__			:  int       , snake_case__			:  Union[str, torch.device] = None       ):


								'''simple docstring'''
								UpperCAmelCase__	:						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":
													UpperCAmelCase__	:						Tuple       =       np.linspace(0       , self.config.num_train_timesteps - 1       , UpperCAmelCase_       , dtype=UpperCAmelCase_       )[
													    ::-1
													].copy()
								elif self.config.timestep_spacing == "leading":
													UpperCAmelCase__	:						Any       =       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
													UpperCAmelCase__	:						Tuple       =       (np.arange(0       , UpperCAmelCase_       ) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase_       )
													timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
													UpperCAmelCase__	:						List[str]       =       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
													UpperCAmelCase__	:						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\'.'       )

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

								if self.use_karras_sigmas:
													UpperCAmelCase__	:						List[Any]       =       self._convert_to_karras(in_sigmas=UpperCAmelCase_       )
													UpperCAmelCase__	:						List[Any]       =       np.array([self._sigma_to_t(UpperCAmelCase_       , UpperCAmelCase_       ) for sigma in sigmas]       )

								UpperCAmelCase__	:						Optional[int]       =       np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )

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

								UpperCAmelCase__	:						Any       =       []
			def 			__a       (		self			:  str       , snake_case__			:  Optional[Any]       , snake_case__			:  List[str]       ):


								'''simple docstring'''
								# get log sigma
								UpperCAmelCase__	:						Dict       =       np.log(UpperCAmelCase_       )

								# get distribution
								UpperCAmelCase__	:						Union[str, Any]       =       log_sigma - log_sigmas[:, np.newaxis]

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

								UpperCAmelCase__	:						Dict       =       log_sigmas[low_idx]
								UpperCAmelCase__	:						Union[str, Any]       =       log_sigmas[high_idx]

								# interpolate sigmas
								UpperCAmelCase__	:						Tuple       =       (low - log_sigma) / (low - high)
								UpperCAmelCase__	:						str       =       np.clip(UpperCAmelCase_       , 0       , 1       )

								# transform interpolation to time range
								UpperCAmelCase__	:						Optional[Any]       =       (1 - w) * low_idx + w * high_idx
								UpperCAmelCase__	:						List[str]       =       t.reshape(sigma.shape       )
								return t
			def 			__a       (		self			:  Any       , snake_case__			:  torch.FloatTensor       ):


								'''simple docstring'''
								UpperCAmelCase__	:						float       =       in_sigmas[-1].item()
								UpperCAmelCase__	:						float       =       in_sigmas[0].item()

								UpperCAmelCase__	:						int       =       7.0  # 7.0 is the value used in the paper
								UpperCAmelCase__	:						List[Any]       =       np.linspace(0       , 1       , self.num_inference_steps       )
								UpperCAmelCase__	:						int       =       sigma_min ** (1 / rho)
								UpperCAmelCase__	:						List[str]       =       sigma_max ** (1 / rho)
								UpperCAmelCase__	:						Optional[int]       =       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
								return sigmas
			def 			__a       (		self			:  Optional[int]       , snake_case__			:  torch.FloatTensor       , snake_case__			:  Union[float, torch.FloatTensor]       , snake_case__			:  torch.FloatTensor       , snake_case__			:  int = 4       , snake_case__			:  bool = True       , ):


								'''simple docstring'''
								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       ):
													UpperCAmelCase__	:						Tuple       =       timestep.to(self.timesteps.device       )
								UpperCAmelCase__	:						List[str]       =       (self.timesteps == timestep).nonzero().item()
								UpperCAmelCase__	:						str       =       self.sigmas[step_index]

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								if self.config.prediction_type == "epsilon":
													UpperCAmelCase__	:						str       =       sample - sigma * model_output
								elif self.config.prediction_type == "v_prediction":
													# * c_out + input * c_skip
													UpperCAmelCase__	:						Any       =       model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
								elif self.config.prediction_type == "sample":
													UpperCAmelCase__	:						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
								UpperCAmelCase__	:						Optional[Any]       =       (sample - pred_original_sample) / sigma
								self.derivatives.append(UpperCAmelCase_       )
								if len(self.derivatives       ) > order:
													self.derivatives.pop(0       )

								# 3. Compute linear multistep coefficients
								UpperCAmelCase__	:						str       =       min(step_index + 1       , UpperCAmelCase_       )
								UpperCAmelCase__	:						List[str]       =       [self.get_lms_coefficient(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       ) for curr_order in range(UpperCAmelCase_       )]

								# 4. Compute previous sample based on the derivatives path
								UpperCAmelCase__	:						List[str]       =       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 			__a       (		self			:  Optional[int]       , snake_case__			:  torch.FloatTensor       , snake_case__			:  torch.FloatTensor       , snake_case__			:  torch.FloatTensor       , ):


								'''simple docstring'''
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								UpperCAmelCase__	:						Optional[int]       =       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
													UpperCAmelCase__	:						List[Any]       =       self.timesteps.to(original_samples.device       , dtype=torch.floataa       )
													UpperCAmelCase__	:						Union[str, Any]       =       timesteps.to(original_samples.device       , dtype=torch.floataa       )
								else:
													UpperCAmelCase__	:						List[Any]       =       self.timesteps.to(original_samples.device       )
													UpperCAmelCase__	:						str       =       timesteps.to(original_samples.device       )

								UpperCAmelCase__	:						Dict       =       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

								UpperCAmelCase__	:						Any       =       original_samples + noise * sigma
								return noisy_samples




			def __len__(		self			:  Dict       ):


								'''simple docstring'''
								return self.config.num_train_timesteps










"""simple docstring"""



import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list




class  lowerCAmelCase__ (						unittest.TestCase      ):
   def 			__a       (		self			:  str       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Tuple       =       "<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z".split(" "       )
        UpperCAmelCase__	:						Dict       =       dict(zip(snake_case__       , range(len(snake_case__       )       )       )       )

        UpperCAmelCase__	:						Dict       =       {
            "pad_token": "<pad>",
            "unk_token": "<unk>",
            "bos_token": "<s>",
            "eos_token": "</s>",
        }
        UpperCAmelCase__	:						str       =       {
            "feature_size": 1,
            "padding_value": 0.0,
            "sampling_rate": 1_6_0_0_0,
            "return_attention_mask": False,
            "do_normalize": True,
        }

        UpperCAmelCase__	:						List[str]       =       tempfile.mkdtemp()
        UpperCAmelCase__	:						int       =       os.path.join(self.tmpdirname       , VOCAB_FILES_NAMES["vocab_file"]       )
        UpperCAmelCase__	:						List[str]       =       os.path.join(self.tmpdirname       , snake_case__       )
        with open(self.vocab_file       , "w"       , encoding="utf-8"       ) as fp:
             fp.write(json.dumps(snake_case__       ) + "\n"       )

        with open(self.feature_extraction_file       , "w"       , encoding="utf-8"       ) as fp:
             fp.write(json.dumps(snake_case__       ) + "\n"       )
   def 			__a       (		self			:  List[str]       , **snake_case__			:  Optional[int]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Dict       =       self.add_kwargs_tokens_map.copy()
        kwargs.update(snake_case__       )
        return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname       , **snake_case__       )
   def 			__a       (		self			:  List[Any]       , **snake_case__			:  str       ):


        '''simple docstring'''
        return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname       , **snake_case__       )
   def 			__a       (		self			:  List[str]       ):


        '''simple docstring'''
        shutil.rmtree(self.tmpdirname       )
   def 			__a       (		self			:  int       ):


        '''simple docstring'''
        UpperCAmelCase__	:						int       =       self.get_tokenizer()
        UpperCAmelCase__	:						List[Any]       =       self.get_feature_extractor()

        UpperCAmelCase__	:						Optional[Any]       =       WavaVecaProcessor(tokenizer=snake_case__       , feature_extractor=snake_case__       )

        processor.save_pretrained(self.tmpdirname       )
        UpperCAmelCase__	:						List[Any]       =       WavaVecaProcessor.from_pretrained(self.tmpdirname       )

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

        self.assertEqual(processor.feature_extractor.to_json_string()       , feature_extractor.to_json_string()       )
        self.assertIsInstance(processor.feature_extractor       , snake_case__       )
   def 			__a       (		self			:  Dict       ):


        '''simple docstring'''
        UpperCAmelCase__	:						List[Any]       =       WavaVecaProcessor(tokenizer=self.get_tokenizer()       , feature_extractor=self.get_feature_extractor()       )
        processor.save_pretrained(self.tmpdirname       )

        UpperCAmelCase__	:						str       =       self.get_tokenizer(bos_token="(BOS)"       , eos_token="(EOS)"       )
        UpperCAmelCase__	:						Optional[Any]       =       self.get_feature_extractor(do_normalize=snake_case__       , padding_value=1.0       )

        UpperCAmelCase__	:						Optional[Any]       =       WavaVecaProcessor.from_pretrained(
            self.tmpdirname       , bos_token="(BOS)"       , eos_token="(EOS)"       , do_normalize=snake_case__       , padding_value=1.0       )

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

        self.assertEqual(processor.feature_extractor.to_json_string()       , feature_extractor_add_kwargs.to_json_string()       )
        self.assertIsInstance(processor.feature_extractor       , snake_case__       )
   def 			__a       (		self			:  Tuple       ):


        '''simple docstring'''
        UpperCAmelCase__	:						List[str]       =       self.get_feature_extractor()
        UpperCAmelCase__	:						str       =       self.get_tokenizer()

        UpperCAmelCase__	:						str       =       WavaVecaProcessor(tokenizer=snake_case__       , feature_extractor=snake_case__       )

        UpperCAmelCase__	:						Optional[int]       =       floats_list((3, 1_0_0_0)       )

        UpperCAmelCase__	:						int       =       feature_extractor(snake_case__       , return_tensors="np"       )
        UpperCAmelCase__	:						Optional[Any]       =       processor(snake_case__       , 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 			__a       (		self			:  Optional[Any]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Dict       =       self.get_feature_extractor()
        UpperCAmelCase__	:						int       =       self.get_tokenizer()

        UpperCAmelCase__	:						Optional[int]       =       WavaVecaProcessor(tokenizer=snake_case__       , feature_extractor=snake_case__       )

        UpperCAmelCase__	:						Any       =       "This is a test string"

        UpperCAmelCase__	:						Optional[int]       =       processor(text=snake_case__       )

        UpperCAmelCase__	:						Optional[int]       =       tokenizer(snake_case__       )

        for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]       , encoded_processor[key]       )
   def 			__a       (		self			:  Optional[int]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						str       =       self.get_feature_extractor()
        UpperCAmelCase__	:						str       =       self.get_tokenizer()

        UpperCAmelCase__	:						Optional[int]       =       WavaVecaProcessor(tokenizer=snake_case__       , feature_extractor=snake_case__       )

        UpperCAmelCase__	:						Optional[int]       =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        UpperCAmelCase__	:						Dict       =       processor.batch_decode(snake_case__       )
        UpperCAmelCase__	:						Tuple       =       tokenizer.batch_decode(snake_case__       )

        self.assertListEqual(snake_case__       , snake_case__       )




   def 			__a       (		self			:  int       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Dict       =       self.get_feature_extractor()
        UpperCAmelCase__	:						Any       =       self.get_tokenizer()

        UpperCAmelCase__	:						Dict       =       WavaVecaProcessor(tokenizer=snake_case__       , feature_extractor=snake_case__       )

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


