




import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_			,					lowerCAmelCase_      			=  image.size
						lowerCAmelCase_			,					lowerCAmelCase_      			=  (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
						lowerCAmelCase_      			=  image.resize((w, h) ,  resample=PIL_INTERPOLATION['''lanczos''']  )
						lowerCAmelCase_      			=  np.array(_A  ).astype(np.floataa  ) / 2_5_5.0
						lowerCAmelCase_      			=  image[None].transpose(0 ,  3 ,  1 ,  2  )
						lowerCAmelCase_      			=  torch.from_numpy(_A  )
						return 2.0 * image - 1.0






class  A		(  __UpperCAmelCase     ):



		def __init__(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	):







								"""simple docstring"""



								super().__init__()
								self.register_modules(vqvae=UpperCamelCase__,	unet=UpperCamelCase__,	scheduler=UpperCamelCase__   )







		@torch.no_grad()
		def __call__(     self,	UpperCamelCase__ = None,	UpperCamelCase__ = 1,	UpperCamelCase__ = 100,	UpperCamelCase__ = 0.0,	UpperCamelCase__ = None,	UpperCamelCase__ = "pil",	UpperCamelCase__ = True,	):







								"""simple docstring"""



								if isinstance(UpperCamelCase__,	PIL.Image.Image   ):
														lowerCAmelCase_      			=  1
								elif isinstance(UpperCamelCase__,	torch.Tensor   ):
														lowerCAmelCase_      			=  image.shape[0]
								else:
														raise ValueError(f"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(UpperCamelCase__   )}"   )

								if isinstance(UpperCamelCase__,	PIL.Image.Image   ):
														lowerCAmelCase_      			=  preprocess(UpperCamelCase__   )

								lowerCAmelCase_			,					lowerCAmelCase_      			=  image.shape[-2:]

								# in_channels should be 6: 3 for latents, 3 for low resolution image
								lowerCAmelCase_      			=  (batch_size, self.unet.config.in_channels // 2, height, width)
								lowerCAmelCase_      			=  next(self.unet.parameters()   ).dtype

								lowerCAmelCase_      			=  randn_tensor(UpperCamelCase__,	generator=UpperCamelCase__,	device=self.device,	dtype=UpperCamelCase__   )

								lowerCAmelCase_      			=  image.to(device=self.device,	dtype=UpperCamelCase__   )

								# set timesteps and move to the correct device
								self.scheduler.set_timesteps(UpperCamelCase__,	device=self.device   )
								lowerCAmelCase_      			=  self.scheduler.timesteps

								# scale the initial noise by the standard deviation required by the scheduler
								lowerCAmelCase_      			=  latents * self.scheduler.init_noise_sigma

								# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
								# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
								# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
								# and should be between [0, 1]
								lowerCAmelCase_      			=  '''eta''' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
								lowerCAmelCase_      			=  {}
								if accepts_eta:
														lowerCAmelCase_      			=  eta

								for t in self.progress_bar(UpperCamelCase__   ):
														# concat latents and low resolution image in the channel dimension.
														lowerCAmelCase_      			=  torch.cat([latents, image],	dim=1   )
														lowerCAmelCase_      			=  self.scheduler.scale_model_input(UpperCamelCase__,	UpperCamelCase__   )
														# predict the noise residual
														lowerCAmelCase_      			=  self.unet(UpperCamelCase__,	UpperCamelCase__   ).sample
														# compute the previous noisy sample x_t -> x_t-1
														lowerCAmelCase_      			=  self.scheduler.step(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	**UpperCamelCase__   ).prev_sample

								# decode the image latents with the VQVAE
								lowerCAmelCase_      			=  self.vqvae.decode(UpperCamelCase__   ).sample
								lowerCAmelCase_      			=  torch.clamp(UpperCamelCase__,	-1.0,	1.0   )
								lowerCAmelCase_      			=  image / 2 + 0.5
								lowerCAmelCase_      			=  image.cpu().permute(0,	2,	3,	1   ).numpy()

								if output_type == "pil":
														lowerCAmelCase_      			=  self.numpy_to_pil(UpperCamelCase__   )

								if not return_dict:
														return (image,)

								return ImagePipelineOutput(images=UpperCamelCase__   )










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 __UpperCamelCase			(       _A ,  _A  ):
						lowerCAmelCase_      			=  args.log_outputs
						lowerCAmelCase_      			=  '''_'''.join(args.dataset.split('''/'''  ) + [args.config, args.split]  )

						# load metric
						lowerCAmelCase_      			=  load_metric('''wer'''  )
						lowerCAmelCase_      			=  load_metric('''cer'''  )

						# compute metrics
						lowerCAmelCase_      			=  wer.compute(references=result['''target'''] ,  predictions=result['''prediction''']  )
						lowerCAmelCase_      			=  cer.compute(references=result['''target'''] ,  predictions=result['''prediction''']  )

						# print & log results
						lowerCAmelCase_      			=  f"WER: {wer_result}\nCER: {cer_result}"
						print(_A  )

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

						# log all results in text file. Possibly interesting for analysis
						if log_outputs is not None:
												lowerCAmelCase_      			=  f"log_{dataset_id}_predictions.txt"
												lowerCAmelCase_      			=  f"log_{dataset_id}_targets.txt"

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

																		result.map(_A ,  with_indices=_A  )





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  '''[,?.!\-\;\:"“%‘”�—’…–]'''  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

						lowerCAmelCase_      			=  re.sub(_A ,  '''''' ,  text.lower()  )

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

						for t in token_sequences_to_ignore:
												lowerCAmelCase_      			=  ''' '''.join(text.split(_A  )  )

						return text





def __UpperCamelCase			(       _A  ):
						# load dataset
						lowerCAmelCase_      			=  load_dataset(args.dataset ,  args.config ,  split=args.split ,  use_auth_token=_A  )

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

						# load processor
						lowerCAmelCase_      			=  AutoFeatureExtractor.from_pretrained(args.model_id  )
						lowerCAmelCase_      			=  feature_extractor.sampling_rate

						# resample audio
						lowerCAmelCase_      			=  dataset.cast_column('''audio''' ,  Audio(sampling_rate=_A  )  )

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

						# map function to decode audio
						def map_to_pred(_A  ):
												lowerCAmelCase_      			=  asr(
												    batch['''audio''']['''array'''] ,  chunk_length_s=args.chunk_length_s ,  stride_length_s=args.stride_length_s  )

												lowerCAmelCase_      			=  prediction['''text''']
												lowerCAmelCase_      			=  normalize_text(batch['''sentence''']  )
												return batch

						# run inference on all examples
						lowerCAmelCase_      			=  dataset.map(_A ,  remove_columns=dataset.column_names  )

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


if __name__ == "__main__":
						_A     =			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.''',
						)
						_A     =			parser.parse_args()

						main(args)




