


"""simple docstring"""



import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline

def       lowerCamelCase					(a_						:Dataset				,				a_						:Dict[str, str])     ->							List[str]:
       lowercase		:List[str]       							=				args.log_outputs
       lowercase		:Tuple       							=				'''_'''.join(args.dataset.split('''/''') + [args.config, args.split])

       # load metric
       lowercase		:Any       							=				load_metric('''wer''')
       lowercase		:Tuple       							=				load_metric('''cer''')

       # compute metrics
       lowercase		:str       							=				wer.compute(references=result['''target''']				,				predictions=result['''prediction'''])
       lowercase		:int       							=				cer.compute(references=result['''target''']				,				predictions=result['''prediction'''])

       # print & log results
       lowercase		:List[Any]       							=				F"""WER: {wer_result}\nCER: {cer_result}"""
       print(_UpperCamelCase)

       with open(F"""{dataset_id}_eval_results.txt"""				,				'''w''') as f:
              f.write(_UpperCamelCase)

       # log all results in text file. Possibly interesting for analysis
       if log_outputs is not None:
              lowercase		:Dict       							=				F"""log_{dataset_id}_predictions.txt"""
              lowercase		:List[Any]       							=				F"""log_{dataset_id}_targets.txt"""

              with open(_UpperCamelCase				,				'''w''') as p, open(_UpperCamelCase				,				'''w''') as t:
                     # mapping function to write output
                     def write_to_file(a_						:Optional[Any]				,				a_						:List[Any]):
                            p.write(F"""{i}""" + '''\n''')
                            p.write(batch['''prediction'''] + '''\n''')
                            t.write(F"""{i}""" + '''\n''')
                            t.write(batch['''target'''] + '''\n''')

                     result.map(_UpperCamelCase				,				with_indices=_UpperCamelCase)

def       lowerCamelCase					(a_						:str)     ->							Union[str, Any]:
       lowercase		:List[Any]       							=				'''[,?.!\-\;\:"“%‘”�—’…–]'''  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

       lowercase		:int       							=				re.sub(_UpperCamelCase				,				''''''				,				text.lower())

       # In addition, we can normalize the target text, e.g. removing new lines characters etc...
       # note that order is important here!
       lowercase		:Optional[Any]       							=				['''\n\n''', '''\n''', '''   ''', '''  ''']

       for t in token_sequences_to_ignore:
              lowercase		:Union[str, Any]       							=				''' '''.join(text.split(_UpperCamelCase))

       return text

def       lowerCamelCase					(a_						:str)     ->							Optional[Any]:
       lowercase		:List[str]       							=				load_dataset(args.dataset				,				args.config				,				split=args.split				,				use_auth_token=_UpperCamelCase)

       # for testing: only process the first two examples as a test
       # dataset = dataset.select(range(10))

       # load processor
       lowercase		:int       							=				AutoFeatureExtractor.from_pretrained(args.model_id)
       lowercase		:List[Any]       							=				feature_extractor.sampling_rate

       # resample audio
       lowercase		:List[str]       							=				dataset.cast_column('''audio'''				,				Audio(sampling_rate=_UpperCamelCase))

       # load eval pipeline
       if args.device is None:
              lowercase		:str       							=				0 if torch.cuda.is_available() else -1
       lowercase		:Any       							=				pipeline('''automatic-speech-recognition'''				,				model=args.model_id				,				device=args.device)

       # map function to decode audio
       def map_to_pred(a_						:List[str]):
              lowercase		:Tuple       							=				asr(
                  batch['''audio''']['''array''']				,				chunk_length_s=args.chunk_length_s				,				stride_length_s=args.stride_length_s)

              lowercase		:int       							=				prediction['''text''']
              lowercase		:List[str]       							=				normalize_text(batch['''sentence'''])
              return batch

       # run inference on all examples
       lowercase		:Optional[Any]       							=				dataset.map(_UpperCamelCase				,				remove_columns=dataset.column_names)

       # compute and log_results
       # do not change function below
       log_results(_UpperCamelCase				,				_UpperCamelCase)


if __name__ == "__main__":
 UpperCAmelCase      		=			argparse.ArgumentParser()

 parser.add_argument(
     '''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
 )
 parser.add_argument(
     '''--dataset''',
     type=str,
     required=True,
     help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
 )
 parser.add_argument(
     '''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'`  for Common Voice'''
 )
 parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
 parser.add_argument(
     '''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
 )
 parser.add_argument(
     '''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
 )
 parser.add_argument(
     '''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
 )
 parser.add_argument(
     '''--device''',
     type=int,
     default=None,
     help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
 )
 UpperCAmelCase      		=			parser.parse_args()

 main(args)





"""simple docstring"""



# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      torch.FloatTensor
       __A			:      torch.FloatTensor







class 					__magic_name__		(			__UpperCAmelCase			,						__UpperCAmelCase     ):
       __A			:      Any        =   1






       @register_to_config
       def __init__(   self  :    int  ,     snake_case__  :    int = 2_0_0_0  ,     snake_case__  :    float = 0.15  ,     snake_case__  :    float = 0.01  ,     snake_case__  :    float = 13_48.0  ,     snake_case__  :    float = 1e-5  ,     snake_case__  :    int = 1  ,     ):

              '''simple docstring'''






              lowercase		:List[Any]       							=				sigma_max

              # setable values
              lowercase		:List[str]       							=				None

              self.set_sigmas(snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				)






       def  __snake_case						(   self  :    Optional[Any]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    Optional[int] = None				):

              '''simple docstring'''






              return sample






       def  __snake_case						(   self  :    Optional[int]  ,     snake_case__  :    int  ,     snake_case__  :    float = None  ,     snake_case__  :    Union[str, torch.device] = None				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				sampling_eps if sampling_eps is not None else self.config.sampling_eps

              lowercase		:Tuple       							=				torch.linspace(1  ,     snake_case__  ,     snake_case__  ,     device=snake_case__				)






       def  __snake_case						(   self  :    List[str]  ,     snake_case__  :    int  ,     snake_case__  :    float = None  ,     snake_case__  :    float = None  ,     snake_case__  :    float = None				):

              '''simple docstring'''






              lowercase		:int       							=				sigma_min if sigma_min is not None else self.config.sigma_min
              lowercase		:Any       							=				sigma_max if sigma_max is not None else self.config.sigma_max
              lowercase		:Tuple       							=				sampling_eps if sampling_eps is not None else self.config.sampling_eps
              if self.timesteps is None:
                     self.set_timesteps(snake_case__  ,     snake_case__				)

              lowercase		:Union[str, Any]       							=				sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
              lowercase		:str       							=				torch.exp(torch.linspace(math.log(snake_case__				)  ,     math.log(snake_case__				)  ,     snake_case__				)				)
              lowercase		:List[str]       							=				torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]				)






       def  __snake_case						(   self  :    Optional[Any]  ,     snake_case__  :    Tuple  ,     snake_case__  :    int				):

              '''simple docstring'''






              return torch.where(
                  timesteps == 0  ,     torch.zeros_like(t.to(timesteps.device				)				)  ,     self.discrete_sigmas[timesteps - 1].to(timesteps.device				)  ,     )






       def  __snake_case						(   self  :    List[Any]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    int  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    Optional[torch.Generator] = None  ,     snake_case__  :    bool = True  ,     ):

              '''simple docstring'''






              if self.timesteps is None:
                     raise ValueError(
                         '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''				)

              lowercase		:Optional[int]       							=				timestep * torch.ones(
                  sample.shape[0]  ,     device=sample.device				)  # torch.repeat_interleave(timestep, sample.shape[0])
              lowercase		:Optional[int]       							=				(timestep * (len(self.timesteps				) - 1)).long()

              # mps requires indices to be in the same device, so we use cpu as is the default with cuda
              lowercase		:Union[str, Any]       							=				timesteps.to(self.discrete_sigmas.device				)

              lowercase		:List[Any]       							=				self.discrete_sigmas[timesteps].to(sample.device				)
              lowercase		:str       							=				self.get_adjacent_sigma(snake_case__  ,     snake_case__				).to(sample.device				)
              lowercase		:Optional[Any]       							=				torch.zeros_like(snake_case__				)
              lowercase		:List[Any]       							=				(sigma**2 - adjacent_sigma**2) ** 0.5

              # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
              # also equation 47 shows the analog from SDE models to ancestral sampling methods
              lowercase		:Union[str, Any]       							=				diffusion.flatten()
              while len(diffusion.shape				) < len(sample.shape				):
                     lowercase		:Dict       							=				diffusion.unsqueeze(-1				)
              lowercase		:List[Any]       							=				drift - diffusion**2 * model_output

              #  equation 6: sample noise for the diffusion term of
              lowercase		:List[Any]       							=				randn_tensor(
                  sample.shape  ,     layout=sample.layout  ,     generator=snake_case__  ,     device=sample.device  ,     dtype=sample.dtype				)
              lowercase		:int       							=				sample - drift  # subtract because `dt` is a small negative timestep
              # TODO is the variable diffusion the correct scaling term for the noise?
              lowercase		:Any       							=				prev_sample_mean + diffusion * noise  # add impact of diffusion field g

              if not return_dict:
                     return (prev_sample, prev_sample_mean)

              return SdeVeOutput(prev_sample=snake_case__  ,     prev_sample_mean=snake_case__				)






       def  __snake_case						(   self  :    Union[str, Any]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    Optional[torch.Generator] = None  ,     snake_case__  :    bool = True  ,     ):

              '''simple docstring'''






              if self.timesteps is None:
                     raise ValueError(
                         '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''				)

              # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
              # sample noise for correction
              lowercase		:Tuple       							=				randn_tensor(sample.shape  ,     layout=sample.layout  ,     generator=snake_case__				).to(sample.device				)

              # compute step size from the model_output, the noise, and the snr
              lowercase		:List[Any]       							=				torch.norm(model_output.reshape(model_output.shape[0]  ,     -1				)  ,     dim=-1				).mean()
              lowercase		:List[str]       							=				torch.norm(noise.reshape(noise.shape[0]  ,     -1				)  ,     dim=-1				).mean()
              lowercase		:int       							=				(self.config.snr * noise_norm / grad_norm) ** 2 * 2
              lowercase		:Optional[Any]       							=				step_size * torch.ones(sample.shape[0]				).to(sample.device				)
              # self.repeat_scalar(step_size, sample.shape[0])

              # compute corrected sample: model_output term and noise term
              lowercase		:Union[str, Any]       							=				step_size.flatten()
              while len(step_size.shape				) < len(sample.shape				):
                     lowercase		:Union[str, Any]       							=				step_size.unsqueeze(-1				)
              lowercase		:Union[str, Any]       							=				sample + step_size * model_output
              lowercase		:Any       							=				prev_sample_mean + ((step_size * 2) ** 0.5) * noise

              if not return_dict:
                     return (prev_sample,)

              return SchedulerOutput(prev_sample=snake_case__				)






       def  __snake_case						(   self  :    List[str]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    torch.FloatTensor  ,     ):

              '''simple docstring'''






              lowercase		:List[Any]       							=				timesteps.to(original_samples.device				)
              lowercase		:str       							=				self.discrete_sigmas.to(original_samples.device				)[timesteps]
              lowercase		:Optional[int]       							=				(
                  noise * sigmas[:, None, None, None]
                  if noise is not None
                  else torch.randn_like(snake_case__				) * sigmas[:, None, None, None]
              )
              lowercase		:str       							=				noise + original_samples
              return noisy_samples







       def __len__(   self  :    Optional[Any]				):

              '''simple docstring'''






              return self.config.num_train_timesteps

