




from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


_lowerCamelCase	:		Union[str, Any]							       =   transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Tuple:



       """simple docstring"""

       if isinstance(lowercase_ ,				torch.Tensor						):
              return image
       elif isinstance(lowercase_ ,				PIL.Image.Image						):
              A__       =    [image]

       A__       =    [trans(img.convert('''RGB'''						)						) for img in image]
       A__       =    torch.stack(lowercase_						)
       return image


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    def __init__(			self      :	List[str]					, UpperCAmelCase__      :	Union[str, Any]					, UpperCAmelCase__      :	Dict)						->Optional[Any]:





           '''simple docstring'''

           super().__init__()

           # make sure scheduler can always be converted to DDIM
           A__       =    DDIMScheduler.from_config(scheduler.config)

           self.register_modules(unet=UpperCAmelCase__					, scheduler=UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, UpperCAmelCase__      :	str)						->Dict:





           '''simple docstring'''

           if strength < 0 or strength > 1:
                  raise ValueError(f"""The value of strength should in [0.0, 1.0] but is {strength}""")


    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any]					, UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	List[Any]					, UpperCAmelCase__      :	List[Any])						->int:





           '''simple docstring'''

           A__       =    min(int(num_inference_steps * strength)					, UpperCAmelCase__)

           A__       =    max(num_inference_steps - init_timestep					, 0)
           A__       =    self.scheduler.timesteps[t_start:]

           return timesteps, num_inference_steps - t_start


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, UpperCAmelCase__      :	Any					, UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	str					, UpperCAmelCase__      :	Any					, UpperCAmelCase__      :	Tuple=None)						->Any:





           '''simple docstring'''

           if not isinstance(UpperCAmelCase__					, (torch.Tensor, PIL.Image.Image, list)):
                  raise ValueError(
                      f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(UpperCAmelCase__)}""")

           A__       =    image.to(device=UpperCAmelCase__					, dtype=UpperCAmelCase__)

           if isinstance(UpperCAmelCase__					, UpperCAmelCase__) and len(UpperCAmelCase__) != batch_size:
                  raise ValueError(
                      f"""You have passed a list of generators of length {len(UpperCAmelCase__)}, but requested an effective batch"""
                      f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""")

           A__       =    init_latents.shape
           A__       =    randn_tensor(UpperCAmelCase__					, generator=UpperCAmelCase__					, device=UpperCAmelCase__					, dtype=UpperCAmelCase__)

           # get latents
           print('''add noise to latents at timestep'''					, UpperCAmelCase__)
           A__       =    self.scheduler.add_noise(UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__)
           A__       =    init_latents

           return latents





    @torch.no_grad()
    def __call__(			self      :	Tuple					, UpperCAmelCase__      :	Union[torch.FloatTensor, PIL.Image.Image] = None					, UpperCAmelCase__      :	float = 0.8					, UpperCAmelCase__      :	int = 1					, UpperCAmelCase__      :	Optional[Union[torch.Generator, List[torch.Generator]]] = None					, UpperCAmelCase__      :	float = 0.0					, UpperCAmelCase__      :	int = 50					, UpperCAmelCase__      :	Optional[bool] = None					, UpperCAmelCase__      :	Optional[str] = "pil"					, UpperCAmelCase__      :	bool = True					, )						->Union[ImagePipelineOutput, Tuple]:





           '''simple docstring'''

           self.check_inputs(UpperCAmelCase__)

           # 2. Preprocess image
           A__       =    preprocess(UpperCAmelCase__)

           # 3. set timesteps
           self.scheduler.set_timesteps(UpperCAmelCase__					, device=self.device)
           A__ ,							A__       =    self.get_timesteps(UpperCAmelCase__					, UpperCAmelCase__					, self.device)
           A__       =    timesteps[:1].repeat(UpperCAmelCase__)

           # 4. Prepare latent variables
           A__       =    self.prepare_latents(UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__					, self.unet.dtype					, self.device					, UpperCAmelCase__)
           A__       =    latents

           # 5. Denoising loop
           for t in self.progress_bar(UpperCAmelCase__):
                  # 1. predict noise model_output
                  A__       =    self.unet(UpperCAmelCase__					, UpperCAmelCase__).sample

                  # 2. predict previous mean of image x_t-1 and add variance depending on eta
                  # eta corresponds to η in paper and should be between [0, 1]
                  # do x_t -> x_t-1
                  A__       =    self.scheduler.step(
                      UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__					, eta=UpperCAmelCase__					, use_clipped_model_output=UpperCAmelCase__					, generator=UpperCAmelCase__					, ).prev_sample

           A__       =    (image / 2 + 0.5).clamp(0					, 1)
           A__       =    image.cpu().permute(0					, 2					, 3					, 1).numpy()
           if output_type == "pil":
                  A__       =    self.numpy_to_pil(UpperCAmelCase__)

           if not return_dict:
                  return (image, latent_timestep.item())

           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 SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       Optional[Any]:



       """simple docstring"""

       A__       =    args.log_outputs
       A__       =    '''_'''.join(args.dataset.split('''/'''						) + [args.config, args.split]						)

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

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

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

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

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

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

                     result.map(lowercase_ ,				with_indices=lowercase_						)







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       str:



       """simple docstring"""

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

       A__       =    re.sub(lowercase_ ,				'''''' ,				text.lower()						)

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

       for t in token_sequences_to_ignore:
              A__       =    ''' '''.join(text.split(lowercase_						)						)

       return text







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       List[str]:



       """simple docstring"""

       A__       =    load_dataset(args.dataset ,				args.config ,				split=args.split ,				use_auth_token=lowercase_						)

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

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

       # resample audio
       A__       =    dataset.cast_column('''audio''' ,				Audio(sampling_rate=lowercase_						)						)

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

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

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

       # run inference on all examples
       A__       =    dataset.map(lowercase_ ,				remove_columns=dataset.column_names						)

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


if __name__ == "__main__":
  _lowerCamelCase	:		List[Any]							       =   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.""",
  )
  _lowerCamelCase	:		str							       =   parser.parse_args()

  main(args)


