

# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate


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







    """simple docstring"""





    lowerCamelCase		     =							42
    lowerCamelCase		     =							None




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





    '''simple docstring'''






    if alpha_transform_type == "cosine":

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

    elif alpha_transform_type == "exp":

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

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

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


class 			A						(							_UpperCAmelCase   ,							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							1


    @register_to_config
    def __init__(				self			:	Tuple,lowercase_			:	int = 1_0_0_0,lowercase_			:	float = 0.0_001,lowercase_			:	float = 0.02,lowercase_			:	str = "linear",lowercase_			:	Optional[Union[np.ndarray, List[float]]] = None,lowercase_			:	bool = True,lowercase_			:	bool = True,lowercase_			:	int = 0,lowercase_			:	str = "epsilon",lowercase_			:	float = 1.0,**lowercase_			:	Dict,)->   Dict:





        '''simple docstring'''







        if kwargs.get('set_alpha_to_one',lowercase_		) is not None:
            A__   				=       (
                'The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'
            )
            deprecate('set_alpha_to_one','1.0.0',lowercase_,standard_warn=lowercase_		)
            A__   				=       kwargs['set_alpha_to_one']
        if trained_betas is not None:
            A__   				=       torch.tensor(lowercase_,dtype=torch.floataa		)
        elif beta_schedule == "linear":
            A__   				=       torch.linspace(lowercase_,lowercase_,lowercase_,dtype=torch.floataa		)
        elif beta_schedule == "scaled_linear":
            # this schedule is very specific to the latent diffusion model.
            A__   				=       (
                torch.linspace(beta_start**0.5,beta_end**0.5,lowercase_,dtype=torch.floataa		) ** 2
            )
        elif beta_schedule == "squaredcos_cap_v2":
            # Glide cosine schedule
            A__   				=       betas_for_alpha_bar(lowercase_		)
        else:
            raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'		)

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

        # At every step in inverted ddim, we are looking into the next alphas_cumprod
        # For the final step, there is no next alphas_cumprod, and the index is out of bounds
        # `set_alpha_to_zero` decides whether we set this parameter simply to zero
        # in this case, self.step() just output the predicted noise
        # or whether we use the final alpha of the "non-previous" one.
        A__   				=       torch.tensor(0.0		) if set_alpha_to_zero else self.alphas_cumprod[-1]

        # standard deviation of the initial noise distribution
        A__   				=       1.0

        # setable values
        A__   				=       None
        A__   				=       torch.from_numpy(np.arange(0,lowercase_		).copy().astype(np.intaa		)		)


    def 						snake_case__					(				self			:	Optional[int],lowercase_			:	torch.FloatTensor,lowercase_			:	Optional[int] = None		)->   torch.FloatTensor:





        '''simple docstring'''







        return sample


    def 						snake_case__					(				self			:	Dict,lowercase_			:	int,lowercase_			:	Union[str, torch.device] = None		)->   str:





        '''simple docstring'''







        if num_inference_steps > self.config.num_train_timesteps:
            raise ValueError(
                F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
                F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
                F' maximal {self.config.num_train_timesteps} timesteps.'		)

        A__   				=       num_inference_steps
        A__   				=       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
        A__   				=       (np.arange(0,lowercase_		) * step_ratio).round().copy().astype(np.intaa		)
        A__   				=       torch.from_numpy(lowercase_		).to(lowercase_		)
        self.timesteps += self.config.steps_offset


    def 						snake_case__					(				self			:	Dict,lowercase_			:	torch.FloatTensor,lowercase_			:	int,lowercase_			:	torch.FloatTensor,lowercase_			:	float = 0.0,lowercase_			:	bool = False,lowercase_			:	Optional[torch.FloatTensor] = None,lowercase_			:	bool = True,)->   Union[DDIMSchedulerOutput, Tuple]:





        '''simple docstring'''







        A__   				=       timestep + self.config.num_train_timesteps // self.num_inference_steps

        # 2. compute alphas, betas
        # change original implementation to exactly match noise levels for analogous forward process
        A__   				=       self.alphas_cumprod[timestep]
        A__   				=       (
            self.alphas_cumprod[prev_timestep]
            if prev_timestep < self.config.num_train_timesteps
            else self.final_alpha_cumprod
        )

        A__   				=       1 - alpha_prod_t

        # 3. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        if self.config.prediction_type == "epsilon":
            A__   				=       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
            A__   				=       model_output
        elif self.config.prediction_type == "sample":
            A__   				=       model_output
            A__   				=       (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
        elif self.config.prediction_type == "v_prediction":
            A__   				=       (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
            A__   				=       (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
        else:
            raise ValueError(
                F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
                ' `v_prediction`'		)

        # 4. Clip or threshold "predicted x_0"
        if self.config.clip_sample:
            A__   				=       pred_original_sample.clamp(
                -self.config.clip_sample_range,self.config.clip_sample_range		)

        # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        A__   				=       (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

        # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        A__   				=       alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

        if not return_dict:
            return (prev_sample, pred_original_sample)
        return DDIMSchedulerOutput(prev_sample=lowercase_,pred_original_sample=lowercase_		)


    def __len__(				self			:	Optional[int]		)->   Tuple:





        '''simple docstring'''







        return self.config.num_train_timesteps




from jiwer import compute_measures

import datasets


lowercase_			=  "\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"

lowercase_			=  "\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n"

lowercase_			=  "\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> wer = datasets.load_metric(\"wer\")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n"


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,							_KWARGS_DESCRIPTION			)
class 			A						(							datasets.Metric			):







    """simple docstring"""





    def 						snake_case__					(				self			:	Any		)->   str:





        '''simple docstring'''







        return datasets.MetricInfo(
            description=_DESCRIPTION,citation=_CITATION,inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
                {
                    'predictions': datasets.Value('string',id='sequence'		),
                    'references': datasets.Value('string',id='sequence'		),
                }		),codebase_urls=['https://github.com/jitsi/jiwer/'],reference_urls=[
                'https://en.wikipedia.org/wiki/Word_error_rate',
            ],)


    def 						snake_case__					(				self			:	int,lowercase_			:	Any=None,lowercase_			:	List[str]=None,lowercase_			:	Dict=False		)->   Optional[int]:





        '''simple docstring'''







        if concatenate_texts:
            return compute_measures(lowercase_,lowercase_		)["wer"]
        else:
            A__   				=       0
            A__   				=       0
            for prediction, reference in zip(lowercase_,lowercase_		):
                A__   				=       compute_measures(lowercase_,lowercase_		)
                incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                total += measures["substitutions"] + measures["deletions"] + measures["hits"]
            return incorrect / total

