



'''simple docstring'''






import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
						import tensorflow as tf

if is_tensorflow_text_available():
						from transformers.models.bert import TFBertTokenizer


UpperCamelCase__:     Optional[int]										=    ["bert-base-uncased", "bert-base-cased"]
UpperCamelCase__:     Any										=    "hf-internal-testing/tiny-bert-tf-only"

if is_tf_available():

						class        SCREAMING_SNAKE_CASE(							tf.keras.Model    ):







							"""simple docstring"""






							def __init__(		self    :  List[Any]	,    __snake_case    :  str	)    ->	Tuple:
														super().__init__()
														UpperCAmelCase				: Optional[Any]											=	tokenizer
														UpperCAmelCase				: List[Any]											=	AutoConfig.from_pretrained(__snake_case	)
														UpperCAmelCase				: Union[str, Any]											=	TFAutoModel.from_config(__snake_case	)







							def   A			(		self    :  List[str]	,    __snake_case    :  Dict	)    ->	Optional[int]:
														UpperCAmelCase				: List[Any]											=	self.tokenizer(__snake_case	)
														UpperCAmelCase				: Tuple											=	self.bert(**__snake_case	)
														return out["pooler_output"]

@require_tf
@require_tensorflow_text
class        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






	def   A			(		self    :  Any	)    ->	Tuple:
								super().setUp()

								UpperCAmelCase				: List[Any]											=	[
								    BertTokenizer.from_pretrained(__snake_case	) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
								]  # repeat for when fast_bert_tokenizer=false
								UpperCAmelCase				: List[Any]											=	[TFBertTokenizer.from_pretrained(__snake_case	) for checkpoint in TOKENIZER_CHECKPOINTS] + [
								    TFBertTokenizer.from_pretrained(__snake_case	,    use_fast_bert_tokenizer=__snake_case	)
								    for checkpoint in TOKENIZER_CHECKPOINTS
								]
								assert len(self.tokenizers	) == len(self.tf_tokenizers	)

								UpperCAmelCase				: Any											=	[
								    '''This is a straightforward English test sentence.''',
								    '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
								    '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
								    '''And some much more rare Chinese: 齉 堃 齉堃''',
								    '''Je vais aussi écrire en français pour tester les accents''',
								    '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
								]
								UpperCAmelCase				: Tuple											=	list(zip(self.test_sentences	,    self.test_sentences[::-1]	)	)







	def   A			(		self    :  Tuple	)    ->	Dict:
								for tokenizer, tf_tokenizer in zip(self.tokenizers	,    self.tf_tokenizers	):
															for test_inputs in (self.test_sentences, self.paired_sentences):
																						UpperCAmelCase				: Any											=	tokenizer(__snake_case	,    return_tensors='''tf'''	,    padding='''longest'''	)
																						UpperCAmelCase				: Optional[Any]											=	tf_tokenizer(__snake_case	)

																						for key in python_outputs.keys():
																													self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape	)	)
																													self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]	,    tf.intaa	) == tf_outputs[key]	)	)







	@slow
	def   A			(		self    :  List[Any]	)    ->	Tuple:
								for tf_tokenizer in self.tf_tokenizers:
															UpperCAmelCase				: Any											=	tf_tokenizer(self.paired_sentences	)
															UpperCAmelCase				: Union[str, Any]											=	tf_tokenizer(
															    text=[sentence[0] for sentence in self.paired_sentences]	,    text_pair=[sentence[1] for sentence in self.paired_sentences]	,    )
															for key in merged_outputs.keys():
																						self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]	,    tf.intaa	) == separated_outputs[key]	)	)







	@slow
	def   A			(		self    :  int	)    ->	int:
								for tf_tokenizer in self.tf_tokenizers:
															UpperCAmelCase				: Tuple											=	tf.function(__snake_case	)
															for test_inputs in (self.test_sentences, self.paired_sentences):
																						UpperCAmelCase				: Optional[Any]											=	tf.constant(__snake_case	)
																						UpperCAmelCase				: str											=	compiled_tokenizer(__snake_case	)
																						UpperCAmelCase				: List[Any]											=	tf_tokenizer(__snake_case	)

																						for key in eager_outputs.keys():
																													self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]	)	)







	@slow
	def   A			(		self    :  Union[str, Any]	)    ->	List[str]:
								for tf_tokenizer in self.tf_tokenizers:
															UpperCAmelCase				: Union[str, Any]											=	ModelToSave(tokenizer=__snake_case	)
															UpperCAmelCase				: Dict											=	tf.convert_to_tensor(self.test_sentences	)
															UpperCAmelCase				: Any											=	model(__snake_case	)  # Build model with some sample inputs
															with TemporaryDirectory() as tempdir:
																						UpperCAmelCase				: Union[str, Any]											=	Path(__snake_case	) / '''saved.model'''
																						model.save(__snake_case	)
																						UpperCAmelCase				: List[Any]											=	tf.keras.models.load_model(__snake_case	)
															UpperCAmelCase				: Tuple											=	loaded_model(__snake_case	)
															# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
															self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output	)	)	,    1E-5	)










'''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 				snake_case_  (      _lowerCAmelCase						:		Dataset     ,   _lowerCAmelCase						:		Dict[str, str]						)   ->     str:
							UpperCAmelCase				: Optional[int]											=	args.log_outputs
							UpperCAmelCase				: Any											=	'''_'''.join(args.dataset.split('''/'''						) + [args.config, args.split]						)

							# load metric
							UpperCAmelCase				: Union[str, Any]											=	load_metric('''wer'''						)
							UpperCAmelCase				: Dict											=	load_metric('''cer'''						)

							# compute metrics
							UpperCAmelCase				: Any											=	wer.compute(references=result['''target''']     ,   predictions=result['''prediction''']						)
							UpperCAmelCase				: str											=	cer.compute(references=result['''target''']     ,   predictions=result['''prediction''']						)

							# print & log results
							UpperCAmelCase				: Tuple											=	f"""WER: {wer_result}\nCER: {cer_result}"""
							print(_lowerCAmelCase						)

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

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

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

																					result.map(_lowerCAmelCase     ,   with_indices=_lowerCAmelCase						)


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

							UpperCAmelCase				: List[Any]											=	re.sub(_lowerCAmelCase     ,   ''''''     ,   text.lower()						)

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

							for t in token_sequences_to_ignore:
														UpperCAmelCase				: List[Any]											=	''' '''.join(text.split(_lowerCAmelCase						)						)

							return text


def 				snake_case_  (      _lowerCAmelCase						:		Optional[Any]						)   ->     Union[str, Any]:
							# load dataset
							UpperCAmelCase				: Optional[int]											=	load_dataset(args.dataset     ,   args.config     ,   split=args.split     ,   use_auth_token=_lowerCAmelCase						)

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

							# load processor
							UpperCAmelCase				: Any											=	AutoFeatureExtractor.from_pretrained(args.model_id						)
							UpperCAmelCase				: Optional[Any]											=	feature_extractor.sampling_rate

							# resample audio
							UpperCAmelCase				: Tuple											=	dataset.cast_column('''audio'''     ,   Audio(sampling_rate=_lowerCAmelCase						)						)

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

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

														UpperCAmelCase				: str											=	prediction['''text''']
														UpperCAmelCase				: int											=	normalize_text(batch['''sentence''']						)
														return batch

							# run inference on all examples
							UpperCAmelCase				: Tuple											=	dataset.map(_lowerCAmelCase     ,   remove_columns=dataset.column_names						)

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


if __name__ == "__main__":
						UpperCamelCase__:     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.",
						)
						UpperCamelCase__:     List[str]										=    parser.parse_args()

						main(args)





