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  ( __UpperCamelCase   :	Dataset			,					__UpperCamelCase   :	Dict[str, str]     )							-> Union[str, Any]:







  """simple docstring"""





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

  # load metric
  SCREAMING_SNAKE_CASE__      							=				load_metric("""wer"""     )
  SCREAMING_SNAKE_CASE__      							=				load_metric("""cer"""     )

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

  # print & log results
  SCREAMING_SNAKE_CASE__      							=				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:
    SCREAMING_SNAKE_CASE__      							=				f"""log_{dataset_id}_predictions.txt"""
    SCREAMING_SNAKE_CASE__      							=				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(__UpperCamelCase   :	Any			,					__UpperCamelCase   :	List[str]     ):
        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      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	str     )							-> str:







  """simple docstring"""





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

  SCREAMING_SNAKE_CASE__      							=				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!
  SCREAMING_SNAKE_CASE__      							=				["""\n\n""", """\n""", """   """, """  """]

  for t in token_sequences_to_ignore:
    SCREAMING_SNAKE_CASE__      							=				""" """.join(text.split(__UpperCamelCase     )     )

  return text



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[str]     )							-> List[str]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				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
  SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(args.model_id     )
  SCREAMING_SNAKE_CASE__      							=				feature_extractor.sampling_rate

  # resample audio
  SCREAMING_SNAKE_CASE__      							=				dataset.cast_column("""audio"""			,					Audio(sampling_rate=__UpperCamelCase     )     )

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

  # map function to decode audio
  def map_to_pred(__UpperCamelCase   :	int     ):
    SCREAMING_SNAKE_CASE__      							=				asr(
        batch["""audio"""]["""array"""]			,					chunk_length_s=args.chunk_length_s			,					stride_length_s=args.stride_length_s     )

    SCREAMING_SNAKE_CASE__      							=				prediction["""text"""]
    SCREAMING_SNAKE_CASE__      							=				normalize_text(batch["""sentence"""]     )
    return batch

  # run inference on all examples
  SCREAMING_SNAKE_CASE__      							=				dataset.map(__UpperCamelCase			,					remove_columns=dataset.column_names     )

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


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      :			Optional[Any]							       =		parser.parse_args()

   main(args)

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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
   import PIL

if is_torch_available():
   import torch


__lowerCamelCase      :			int							       =		logging.get_logger(__name__)







class __snake_case    (						lowerCamelCase_		):
   lowerCAmelCase_														=							["pixel_values"]




   def __init__(   self      :    int	,     _lowercase      :    bool = True	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    PILImageResampling = PILImageResampling.BILINEAR	,     _lowercase      :    bool = True	,     _lowercase      :    Union[int, float] = 1 / 2_55	,     _lowercase      :    bool = True	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    bool = True	,     **_lowercase      :    List[Any]	,     ):



     """simple docstring"""






     super().__init__(**_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				size if size is not None else {"""shortest_edge""": 2_24}
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     default_to_square=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				crop_size if crop_size is not None else {"""height""": 2_56, """width""": 2_56}
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     param_name="""crop_size"""				)

     SCREAMING_SNAKE_CASE__      							=				do_resize
     SCREAMING_SNAKE_CASE__      							=				size
     SCREAMING_SNAKE_CASE__      							=				resample
     SCREAMING_SNAKE_CASE__      							=				do_rescale
     SCREAMING_SNAKE_CASE__      							=				rescale_factor
     SCREAMING_SNAKE_CASE__      							=				do_center_crop
     SCREAMING_SNAKE_CASE__      							=				crop_size
     SCREAMING_SNAKE_CASE__      							=				do_flip_channel_order




   def      __a						(   self      :    List[Any]	,     _lowercase      :    np.ndarray	,     _lowercase      :    Dict[str, int]	,     _lowercase      :    PILImageResampling = PIL.Image.BILINEAR	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    str	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     default_to_square=_lowercase				)
     if "shortest_edge" not in size:
       raise ValueError(f"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""				)
     SCREAMING_SNAKE_CASE__      							=				get_resize_output_image_size(_lowercase	,     size=size["""shortest_edge"""]	,     default_to_square=_lowercase				)
     return resize(_lowercase	,     size=_lowercase	,     resample=_lowercase	,     data_format=_lowercase	,     **_lowercase				)




   def      __a						(   self      :    str	,     _lowercase      :    np.ndarray	,     _lowercase      :    Dict[str, int]	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    Any	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase				)
     if "height" not in size or "width" not in size:
       raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""				)
     return center_crop(_lowercase	,     size=(size["""height"""], size["""width"""])	,     data_format=_lowercase	,     **_lowercase				)




   def      __a						(   self      :    Optional[Any]	,     _lowercase      :    np.ndarray	,     _lowercase      :    Union[int, float]	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    Any	,     ):



     """simple docstring"""






     return rescale(_lowercase	,     scale=_lowercase	,     data_format=_lowercase	,     **_lowercase				)




   def      __a						(   self      :    Tuple	,     _lowercase      :    np.ndarray	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None				):



     """simple docstring"""






     return flip_channel_order(_lowercase	,     data_format=_lowercase				)




   def      __a						(   self      :    List[str]	,     _lowercase      :    ImageInput	,     _lowercase      :    bool = None	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    PILImageResampling = None	,     _lowercase      :    bool = None	,     _lowercase      :    float = None	,     _lowercase      :    bool = None	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    bool = None	,     _lowercase      :    Optional[Union[str, TensorType]] = None	,     _lowercase      :    ChannelDimension = ChannelDimension.FIRST	,     **_lowercase      :    int	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE__      							=				resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE__      							=				do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE__      							=				rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE__      							=				do_center_crop if do_center_crop is not None else self.do_center_crop
     SCREAMING_SNAKE_CASE__      							=				(
         do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
     )

     SCREAMING_SNAKE_CASE__      							=				size if size is not None else self.size
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     default_to_square=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				crop_size if crop_size is not None else self.crop_size
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     param_name="""crop_size"""				)

     SCREAMING_SNAKE_CASE__      							=				make_list_of_images(_lowercase				)

     if not valid_images(_lowercase				):
       raise ValueError(
           """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
           """torch.Tensor, tf.Tensor or jax.ndarray."""				)

     if do_resize and size is None:
       raise ValueError("""Size must be specified if do_resize is True."""				)

     if do_rescale and rescale_factor is None:
       raise ValueError("""Rescale factor must be specified if do_rescale is True."""				)

     if do_center_crop and crop_size is None:
       raise ValueError("""Crop size must be specified if do_center_crop is True."""				)

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE__      							=				[to_numpy_array(_lowercase				) for image in images]

     if do_resize:
       SCREAMING_SNAKE_CASE__      							=				[self.resize(image=_lowercase	,     size=_lowercase	,     resample=_lowercase				) for image in images]

     if do_center_crop:
       SCREAMING_SNAKE_CASE__      							=				[self.center_crop(image=_lowercase	,     size=_lowercase				) for image in images]

     if do_rescale:
       SCREAMING_SNAKE_CASE__      							=				[self.rescale(image=_lowercase	,     scale=_lowercase				) for image in images]

     # the pretrained checkpoints assume images are BGR, not RGB
     if do_flip_channel_order:
       SCREAMING_SNAKE_CASE__      							=				[self.flip_channel_order(image=_lowercase				) for image in images]

     SCREAMING_SNAKE_CASE__      							=				[to_channel_dimension_format(_lowercase	,     _lowercase				) for image in images]

     SCREAMING_SNAKE_CASE__      							=				{"""pixel_values""": images}
     return BatchFeature(data=_lowercase	,     tensor_type=_lowercase				)




   def      __a						(   self      :    List[Any]	,     _lowercase      :    Dict	,     _lowercase      :    List[Tuple] = None				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				outputs.logits

     # Resize logits and compute semantic segmentation maps
     if target_sizes is not None:
       if len(_lowercase				) != len(_lowercase				):
         raise ValueError(
             """Make sure that you pass in as many target sizes as the batch dimension of the logits"""				)

       if is_torch_tensor(_lowercase				):
         SCREAMING_SNAKE_CASE__      							=				target_sizes.numpy()

       SCREAMING_SNAKE_CASE__      							=				[]

       for idx in range(len(_lowercase				)				):
         SCREAMING_SNAKE_CASE__      							=				torch.nn.functional.interpolate(
             logits[idx].unsqueeze(dim=0				)	,     size=target_sizes[idx]	,     mode="""bilinear"""	,     align_corners=_lowercase				)
         SCREAMING_SNAKE_CASE__      							=				resized_logits[0].argmax(dim=0				)
         semantic_segmentation.append(_lowercase				)
     else:
       SCREAMING_SNAKE_CASE__      							=				logits.argmax(dim=1				)
       SCREAMING_SNAKE_CASE__      							=				[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]				)]

     return semantic_segmentation
