'''simple docstring'''

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__      ):


      '''simple docstring'''






      _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(_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:
            _lowerCAmelCase										=F'''log_{dataset_id}_predictions.txt'''
            _lowerCAmelCase										=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(a__			,      a__      ):
                        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 							UpperCamelCase__			(			a__      ):


      '''simple docstring'''






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

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

      for t in token_sequences_to_ignore:
            _lowerCAmelCase										=' '.join(text.split(_UpperCamelCase      )      )

      return text



def 							UpperCamelCase__			(			a__      ):


      '''simple docstring'''






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

      # resample audio
      _lowerCAmelCase										=dataset.cast_column('audio'			,      Audio(sampling_rate=_UpperCamelCase      )      )

      # 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(_UpperCamelCase			,      remove_columns=dataset.column_names      )

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


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

       main(args)






'''simple docstring'''

from __future__ import annotations

from typing import Any
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""






		def __init__(   self		,						__A				) ->     None:
								_lowerCAmelCase										=num_of_nodes
								_lowerCAmelCase										=[]
								_lowerCAmelCase										={}






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     None:
								self.m_edges.append([u_node, v_node, weight]				)






		def 		UpperCamelCase__       (   self		,						__A				) ->     int:

								if self.m_component[u_node] == u_node:
														return u_node
								return self.find_component(self.m_component[u_node]				)






		def 		UpperCamelCase__       (   self		,						__A				) ->     None:

								if self.m_component[u_node] != u_node:
														for k in self.m_component:
																				_lowerCAmelCase										=self.find_component(__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     None:

								if component_size[u_node] <= component_size[v_node]:
														_lowerCAmelCase										=v_node
														component_size[v_node] += component_size[u_node]
														self.set_component(__A				)

								elif component_size[u_node] >= component_size[v_node]:
														_lowerCAmelCase										=self.find_component(__A				)
														component_size[u_node] += component_size[v_node]
														self.set_component(__A				)






		def 		UpperCamelCase__       (   self				) ->     None:
								_lowerCAmelCase										=[]
								_lowerCAmelCase										=0

								_lowerCAmelCase										=[-1] * self.m_num_of_nodes

								# A list of components (initialized to all of the nodes)
								for node in range(self.m_num_of_nodes				):
														self.m_component.update({node: node}				)
														component_size.append(1				)

								_lowerCAmelCase										=self.m_num_of_nodes

								while num_of_components > 1:
														for edge in self.m_edges:
																				_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=edge

																				_lowerCAmelCase										=self.m_component[u]
																				_lowerCAmelCase										=self.m_component[v]

																				if u_component != v_component:

																										for component in (u_component, v_component):
																																if (
																																    minimum_weight_edge[component] == -1
																																    or minimum_weight_edge[component][2] > w
																																):
																																						_lowerCAmelCase										=[u, v, w]

														for edge in minimum_weight_edge:
																				if isinstance(__A		,						__A				):
																										_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=edge

																										_lowerCAmelCase										=self.m_component[u]
																										_lowerCAmelCase										=self.m_component[v]

																										if u_component != v_component:
																																mst_weight += w
																																self.union(__A		,						__A		,						__A				)
																																print(F'''Added edge [{u} - {v}]\nAdded weight: {w}\n'''				)
																																num_of_components -= 1

														_lowerCAmelCase										=[-1] * self.m_num_of_nodes
								print(F'''The total weight of the minimal spanning tree is: {mst_weight}'''				)



def 							UpperCamelCase__			(			):


						'''simple docstring'''








if __name__ == "__main__":
							import doctest

							doctest.testmod()





