
import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin







class      A__							(     __magic_name__						,   unittest.TestCase	):
				lowercase			    =  TransfoXLTokenizer
				lowercase			    =  False
				lowercase			    =  False






				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							super().setUp()

							lowerCAmelCase__      :      Dict         =		[
							    '<unk>',
							    '[CLS]',
							    '[SEP]',
							    'want',
							    'unwanted',
							    'wa',
							    'un',
							    'running',
							    ',',
							    'low',
							    'l',
							]
							lowerCAmelCase__      :      Optional[Any]         =		os.path.join(self.tmpdirname    ,     VOCAB_FILES_NAMES['vocab_file']   )
							with open(self.vocab_file    ,     'w'    ,     encoding='utf-8'   ) as vocab_writer:
										vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]   )   )






				def 							_lowerCamelCase		(						self					:      Dict    ,     **a					:      str   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		True
							return TransfoXLTokenizer.from_pretrained(self.tmpdirname    ,     **a   )






				def 							_lowerCamelCase		(						self					:      Optional[int]    ,     a					:      Optional[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		'<unk> UNwanted , running'
							lowerCAmelCase__      :      Any         =		'<unk> unwanted, running'
							return input_text, output_text






				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Tuple         =		TransfoXLTokenizer(vocab_file=self.vocab_file    ,     lower_case=a   )

							lowerCAmelCase__      :      List[str]         =		tokenizer.tokenize('<unk> UNwanted , running'   )
							self.assertListEqual(a    ,     ['<unk>', 'unwanted', ',', 'running']   )

							self.assertListEqual(tokenizer.convert_tokens_to_ids(a   )    ,     [0, 4, 8, 7]   )






				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		TransfoXLTokenizer(lower_case=a   )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '   )    ,     ['hello', '!', 'how', 'are', 'you', '?']   )






				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		TransfoXLTokenizer(lower_case=a   )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '   )    ,     ['HeLLo', '!', 'how', 'Are', 'yoU', '?']   )






				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		TransfoXLTokenizer(lower_case=a   )
							lowerCAmelCase__      :      int         =		'Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
							lowerCAmelCase__      :      List[Any]         =		[
							    'Hello',
							    '(',
							    'bracket',
							    ')',
							    'and',
							    'side',
							    '@-@',
							    'scrolled',
							    '[',
							    'and',
							    ']',
							    'Henry',
							    '\'s',
							    '$',
							    '5',
							    '@,@',
							    '000',
							    'with',
							    '3',
							    '@.@',
							    '34',
							    'm',
							    '.',
							    'What',
							    '\'s',
							    'up',
							    '!',
							    '?',
							]

							self.assertListEqual(tokenizer.tokenize(a   )    ,     a   )

							self.assertEqual(tokenizer.convert_tokens_to_string(a   )    ,     a   )






				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		self.get_tokenizer()
							lowerCAmelCase__      :      Optional[Any]         =		len(a   )

							tokenizer.add_tokens(['new1', 'new2']   )
							tokenizer.move_added_token('new1'    ,     1   )

							# Check that moved token is not copied (duplicate)
							self.assertEqual(len(a   )    ,     original_len + 2   )
							# Check that token is moved to specified id
							self.assertEqual(tokenizer.encode('new1'   )    ,     [1]   )
							self.assertEqual(tokenizer.decode([1]   )    ,     'new1'   )

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  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		List[str]:
			lowerCAmelCase__      :      Optional[Any]         =		args.log_outputs
			lowerCAmelCase__      :      Union[str, Any]         =		'_'.join(args.dataset.split('/'     ) + [args.config, args.split]     )

			# load metric
			lowerCAmelCase__      :      Dict         =		load_metric('wer'     )
			lowerCAmelCase__      :      Tuple         =		load_metric('cer'     )

			# compute metrics
			lowerCAmelCase__      :      Dict         =		wer.compute(references=result['target']       ,							predictions=result['prediction']     )
			lowerCAmelCase__      :      int         =		cer.compute(references=result['target']       ,							predictions=result['prediction']     )

			# print & log results
			lowerCAmelCase__      :      Optional[int]         =		F'''WER: {wer_result}\nCER: {cer_result}'''
			print(SCREAMING_SNAKE_CASE_     )

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

			# log all results in text file. Possibly interesting for analysis
			if log_outputs is not None:
						lowerCAmelCase__      :      List[str]         =		F'''log_{dataset_id}_predictions.txt'''
						lowerCAmelCase__      :      Union[str, Any]         =		F'''log_{dataset_id}_targets.txt'''

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

									result.map(SCREAMING_SNAKE_CASE_       ,							with_indices=SCREAMING_SNAKE_CASE_     )
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		str:
			lowerCAmelCase__      :      List[str]         =		'[,?.!\-\;\:"“%‘”�—’…–]'  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

			lowerCAmelCase__      :      Union[str, Any]         =		re.sub(SCREAMING_SNAKE_CASE_       ,							''       ,							text.lower()     )

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

			for t in token_sequences_to_ignore:
						lowerCAmelCase__      :      Optional[int]         =		' '.join(text.split(SCREAMING_SNAKE_CASE_     )     )

			return text
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		List[Any]:
			# load dataset
			lowerCAmelCase__      :      Tuple         =		load_dataset(args.dataset       ,							args.config       ,							split=args.split       ,							use_auth_token=SCREAMING_SNAKE_CASE_     )

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

			# load processor
			lowerCAmelCase__      :      List[Any]         =		AutoFeatureExtractor.from_pretrained(args.model_id     )
			lowerCAmelCase__      :      Union[str, Any]         =		feature_extractor.sampling_rate

			# resample audio
			lowerCAmelCase__      :      Union[str, Any]         =		dataset.cast_column('audio'       ,							Audio(sampling_rate=SCREAMING_SNAKE_CASE_     )     )

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

			# map function to decode audio
			def map_to_pred(SCREAMING_SNAKE_CASE_     ):
						lowerCAmelCase__      :      Optional[int]         =		asr(
						    batch['audio']['array']       ,							chunk_length_s=args.chunk_length_s       ,							stride_length_s=args.stride_length_s     )

						lowerCAmelCase__      :      int         =		prediction['text']
						lowerCAmelCase__      :      Optional[int]         =		normalize_text(batch['sentence']     )
						return batch

			# run inference on all examples
			lowerCAmelCase__      :      Dict         =		dataset.map(SCREAMING_SNAKE_CASE_       ,							remove_columns=dataset.column_names     )

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


if __name__ == "__main__":
					lowerCamelCase__          =		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__          =		parser.parse_args()

					main(args)