
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    A (      lowercase					, lowercase				)     ->						int:
  '''simple docstring'''

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

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

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

  # print & log results
  UpperCamelCase						=  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:
    UpperCamelCase						=  f'''log_{dataset_id}_predictions.txt'''
    UpperCamelCase						=  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    A (      lowercase				)     ->						str:
  '''simple docstring'''

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

  UpperCamelCase						=  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!
  UpperCamelCase						=  ['\n\n', '\n', '   ', '  ']

  for t in token_sequences_to_ignore:
    UpperCamelCase						=  ' '.join(text.split(lowercase				)				)

  return text


def    A (      lowercase				)     ->						Dict:
  '''simple docstring'''

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

  # resample audio
  UpperCamelCase						=  dataset.cast_column('audio'					, Audio(sampling_rate=lowercase				)				)

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

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

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

  # run inference on all examples
  UpperCamelCase						=  dataset.map(lowercase					, remove_columns=dataset.column_names				)

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


if __name__ == "__main__":
     _UpperCAmelCase      :				Optional[int]          = 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      :				Any          = parser.parse_args()

     main(args)


import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import DonutImageProcessor


class        lowercase      (	unittest.TestCase     ):



		def __init__(				self       ,   A_       ,   A_=7       ,   A_=3       ,   A_=18       ,   A_=30       ,   A_=400       ,   A_=True       ,   A_=None       ,   A_=True       ,   A_=False       ,   A_=True       ,   A_=True       ,   A_=[0.5, 0.5, 0.5]       ,   A_=[0.5, 0.5, 0.5]       ,   )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  parent
				UpperCamelCase						=  batch_size
				UpperCamelCase						=  num_channels
				UpperCamelCase						=  image_size
				UpperCamelCase						=  min_resolution
				UpperCamelCase						=  max_resolution
				UpperCamelCase						=  do_resize
				UpperCamelCase						=  size if size is not None else {'height': 18, 'width': 20}
				UpperCamelCase						=  do_thumbnail
				UpperCamelCase						=  do_align_axis
				UpperCamelCase						=  do_pad
				UpperCamelCase						=  do_normalize
				UpperCamelCase						=  image_mean
				UpperCamelCase						=  image_std



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				return {
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_thumbnail": self.do_thumbnail,
				    "do_align_long_axis": self.do_align_axis,
				    "do_pad": self.do_pad,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				}






@require_torch
@require_vision
class        lowercase      (	_SCREAMING_SNAKE_CASE						,     unittest.TestCase     ):
		__lowercase : Optional[int]			  =       DonutImageProcessor if is_vision_available() else None



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  DonutImageProcessingTester(self )



		@property
		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				return self.image_processor_tester.prepare_image_processor_dict()



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  self.image_processing_class(**self.image_processor_dict )
				self.assertTrue(hasattr(A_       ,   'do_resize' ) )
				self.assertTrue(hasattr(A_       ,   'size' ) )
				self.assertTrue(hasattr(A_       ,   'do_thumbnail' ) )
				self.assertTrue(hasattr(A_       ,   'do_align_long_axis' ) )
				self.assertTrue(hasattr(A_       ,   'do_pad' ) )
				self.assertTrue(hasattr(A_       ,   'do_normalize' ) )
				self.assertTrue(hasattr(A_       ,   'image_mean' ) )
				self.assertTrue(hasattr(A_       ,   'image_std' ) )



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				UpperCamelCase						=  self.image_processing_class.from_dict(self.image_processor_dict )
				self.assertEqual(image_processor.size       ,   {'height': 18, 'width': 20} )

				UpperCamelCase						=  self.image_processing_class.from_dict(self.image_processor_dict       ,   size=42 )
				self.assertEqual(image_processor.size       ,   {'height': 42, 'width': 42} )

				# Previous config had dimensions in (width, height) order
				UpperCamelCase						=  self.image_processing_class.from_dict(self.image_processor_dict       ,   size=(42, 84) )
				self.assertEqual(image_processor.size       ,   {'height': 84, 'width': 42} )



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				pass



		@is_flaky()
		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				# Initialize image_processing
				UpperCamelCase						=  self.image_processing_class(**self.image_processor_dict )
				# create random PIL images
				UpperCamelCase						=  prepare_image_inputs(self.image_processor_tester       ,   equal_resolution=A_ )
				for image in image_inputs:
						self.assertIsInstance(A_       ,   Image.Image )

				# Test not batched input
				UpperCamelCase						=  image_processing(image_inputs[0]       ,   return_tensors='pt' ).pixel_values
				self.assertEqual(
				    encoded_images.shape       ,   (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['height'],
				        self.image_processor_tester.size['width'],
				    )       ,   )

				# Test batched
				UpperCamelCase						=  image_processing(A_       ,   return_tensors='pt' ).pixel_values
				self.assertEqual(
				    encoded_images.shape       ,   (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['height'],
				        self.image_processor_tester.size['width'],
				    )       ,   )



		@is_flaky()
		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				# Initialize image_processing
				UpperCamelCase						=  self.image_processing_class(**self.image_processor_dict )
				# create random numpy tensors
				UpperCamelCase						=  prepare_image_inputs(self.image_processor_tester       ,   equal_resolution=A_       ,   numpify=A_ )
				for image in image_inputs:
						self.assertIsInstance(A_       ,   np.ndarray )

				# Test not batched input
				UpperCamelCase						=  image_processing(image_inputs[0]       ,   return_tensors='pt' ).pixel_values
				self.assertEqual(
				    encoded_images.shape       ,   (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['height'],
				        self.image_processor_tester.size['width'],
				    )       ,   )

				# Test batched
				UpperCamelCase						=  image_processing(A_       ,   return_tensors='pt' ).pixel_values
				self.assertEqual(
				    encoded_images.shape       ,   (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['height'],
				        self.image_processor_tester.size['width'],
				    )       ,   )



		@is_flaky()
		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				# Initialize image_processing
				UpperCamelCase						=  self.image_processing_class(**self.image_processor_dict )
				# create random PyTorch tensors
				UpperCamelCase						=  prepare_image_inputs(self.image_processor_tester       ,   equal_resolution=A_       ,   torchify=A_ )
				for image in image_inputs:
						self.assertIsInstance(A_       ,   torch.Tensor )

				# Test not batched input
				UpperCamelCase						=  image_processing(image_inputs[0]       ,   return_tensors='pt' ).pixel_values
				self.assertEqual(
				    encoded_images.shape       ,   (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['height'],
				        self.image_processor_tester.size['width'],
				    )       ,   )

				# Test batched
				UpperCamelCase						=  image_processing(A_       ,   return_tensors='pt' ).pixel_values
				self.assertEqual(
				    encoded_images.shape       ,   (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['height'],
				        self.image_processor_tester.size['width'],
				    )       ,   )
