
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)


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCAmelCase      :				Tuple          = logging.get_logger(__name__)

_UpperCAmelCase      :				Union[str, Any]          = {
    "facebook/data2vec-text-base": "https://huggingface.co/data2vec/resolve/main/config.json",
}


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Dict			  =       "data2vec-text"



		def __init__(				self       ,   A_=30_522       ,   A_=768       ,   A_=12       ,   A_=12       ,   A_=3_072       ,   A_="gelu"       ,   A_=0.1       ,   A_=0.1       ,   A_=512       ,   A_=2       ,   A_=0.02       ,   A_=1e-12       ,   A_=1       ,   A_=0       ,   A_=2       ,   A_="absolute"       ,   A_=True       ,   A_=None       ,   **A_       ,   )						->      Any:
				"""simple docstring"""
				super().__init__(pad_token_id=A_       ,   bos_token_id=A_       ,   eos_token_id=A_       ,   **A_ )

				UpperCamelCase						=  vocab_size
				UpperCamelCase						=  hidden_size
				UpperCamelCase						=  num_hidden_layers
				UpperCamelCase						=  num_attention_heads
				UpperCamelCase						=  hidden_act
				UpperCamelCase						=  intermediate_size
				UpperCamelCase						=  hidden_dropout_prob
				UpperCamelCase						=  attention_probs_dropout_prob
				UpperCamelCase						=  max_position_embeddings
				UpperCamelCase						=  type_vocab_size
				UpperCamelCase						=  initializer_range
				UpperCamelCase						=  layer_norm_eps
				UpperCamelCase						=  position_embedding_type
				UpperCamelCase						=  use_cache
				UpperCamelCase						=  classifier_dropout






class        lowercase      (	_SCREAMING_SNAKE_CASE     ):



		@property
		def __UpperCamelCase				(				self )						->      Mapping[str, Mapping[int, str]]:
				"""simple docstring"""
				if self.task == "multiple-choice":
						UpperCamelCase						=  {0: 'batch', 1: 'choice', 2: 'sequence'}
				else:
						UpperCamelCase						=  {0: 'batch', 1: 'sequence'}
				return OrderedDict(
				    [
				        ('input_ids', dynamic_axis),
				        ('attention_mask', dynamic_axis),
				    ] )
