


'''simple docstring'''







import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       __A = None    ,       ) ->			str:
	_snake_case     		=	{}

	if train_file is not None:
		_snake_case     		=	[train_file]
	if eval_file is not None:
		_snake_case     		=	[eval_file]
	if test_file is not None:
		_snake_case     		=	[test_file]

	_snake_case     		=	datasets.load_dataset('csv'    ,       data_files=_lowerCamelCase			)
	_snake_case     		=	list(ds[list(files.keys()			)[0]].features.keys()			)
	_snake_case     		=	features_name.pop(_lowerCamelCase			)
	_snake_case     		=	list(set(ds[list(files.keys()			)[0]][label_name]			)			)
	_snake_case     		=	{label: i for i, label in enumerate(_lowerCamelCase			)}
	_snake_case     		=	tokenizer.model_input_names
	_snake_case     		=	{}

	if len(_lowerCamelCase			) == 1:
		for k in files.keys():
			_snake_case     		=	ds[k].map(
			    lambda __A			: tokenizer.batch_encode_plus(
			        example[features_name[0]]    ,       truncation=_lowerCamelCase    ,       max_length=_lowerCamelCase    ,       padding='max_length'			)    ,       batched=_lowerCamelCase    ,       )
	elif len(_lowerCamelCase			) == 2:
		for k in files.keys():
			_snake_case     		=	ds[k].map(
			    lambda __A			: tokenizer.batch_encode_plus(
			        (example[features_name[0]], example[features_name[1]])    ,       truncation=_lowerCamelCase    ,       max_length=_lowerCamelCase    ,       padding='max_length'    ,       )    ,       batched=_lowerCamelCase    ,       )

	def gen_train():
		for ex in transformed_ds[datasets.Split.TRAIN]:
			_snake_case     		=	{k: v for k, v in ex.items() if k in input_names}
			_snake_case     		=	labelaid[ex[label_name]]
			yield (d, label)

	def gen_val():
		for ex in transformed_ds[datasets.Split.VALIDATION]:
			_snake_case     		=	{k: v for k, v in ex.items() if k in input_names}
			_snake_case     		=	labelaid[ex[label_name]]
			yield (d, label)

	def gen_test():
		for ex in transformed_ds[datasets.Split.TEST]:
			_snake_case     		=	{k: v for k, v in ex.items() if k in input_names}
			_snake_case     		=	labelaid[ex[label_name]]
			yield (d, label)

	_snake_case     		=	(
	    tf.data.Dataset.from_generator(
	        _lowerCamelCase    ,       ({k: tf.intaa for k in input_names}, tf.intaa)    ,       ({k: tf.TensorShape([None]			) for k in input_names}, tf.TensorShape([]			))    ,       )
	    if datasets.Split.TRAIN in transformed_ds
	    else None
	)

	if train_ds is not None:
		_snake_case     		=	train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]			)			)			)

	_snake_case     		=	(
	    tf.data.Dataset.from_generator(
	        _lowerCamelCase    ,       ({k: tf.intaa for k in input_names}, tf.intaa)    ,       ({k: tf.TensorShape([None]			) for k in input_names}, tf.TensorShape([]			))    ,       )
	    if datasets.Split.VALIDATION in transformed_ds
	    else None
	)

	if val_ds is not None:
		_snake_case     		=	val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]			)			)			)

	_snake_case     		=	(
	    tf.data.Dataset.from_generator(
	        _lowerCamelCase    ,       ({k: tf.intaa for k in input_names}, tf.intaa)    ,       ({k: tf.TensorShape([None]			) for k in input_names}, tf.TensorShape([]			))    ,       )
	    if datasets.Split.TEST in transformed_ds
	    else None
	)

	if test_ds is not None:
		_snake_case     		=	test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]			)			)			)

	return train_ds, val_ds, test_ds, labelaid


lowercase					:							List[Any]   =      logging.getLogger(__name__)


@dataclass
class 		__UpperCAmelCase   :
							__lowercase   	= field(metadata={"""help""": """Which column contains the label"""}		)
							__lowercase   	= field(default=lowercase__		,     metadata={"""help""": """The path of the training file"""}		)
							__lowercase   	= field(default=lowercase__		,     metadata={"""help""": """The path of the development file"""}		)
							__lowercase   	= field(default=lowercase__		,     metadata={"""help""": """The path of the test file"""}		)
							__lowercase   	= field(
							    default=1_28		,     metadata={
							        """help""": (
							            """The maximum total input sequence length after tokenization. Sequences longer """
							            """than this will be truncated, sequences shorter will be padded."""
							        )
							    }		,     )
							__lowercase   	= field(
							    default=lowercase__		,     metadata={"""help""": """Overwrite the cached training and evaluation sets"""}		)


@dataclass
class 		__UpperCAmelCase   :
							__lowercase   	= field(
							    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}		)
							__lowercase   	= field(
							    default=lowercase__		,     metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}		)
							__lowercase   	= field(
							    default=lowercase__		,     metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}		)
							__lowercase   	= field(default=lowercase__		,     metadata={"""help""": """Set this flag to use fast tokenization."""}		)
							# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
							# or just modify its tokenizer_config.json.
							__lowercase   	= field(
							    default=lowercase__		,     metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}		,     )







def        SCREAMING_SNAKE_CASE__			(     ) ->			List[Any]:
	_snake_case     		=	HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)			)
	_snake_case     		=	parser.parse_args_into_dataclasses()

	if (
	    os.path.exists(training_args.output_dir			)
	    and os.listdir(training_args.output_dir			)
	    and training_args.do_train
	    and not training_args.overwrite_output_dir
	):
		raise ValueError(
		    F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
		    ' --overwrite_output_dir to overcome.'			)

	# Setup logging
	logging.basicConfig(
	    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'    ,       datefmt='%m/%d/%Y %H:%M:%S'    ,       level=logging.INFO    ,       )
	logger.info(
	    F'n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1			)}, '
	    F'16-bits training: {training_args.fpaa}'			)
	logger.info(F'Training/evaluation parameters {training_args}'			)

	# Load pretrained model and tokenizer
	#
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.

	_snake_case     		=	AutoTokenizer.from_pretrained(
	    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path    ,       cache_dir=model_args.cache_dir    ,       )

	_snake_case     		=	get_tfds(
	    train_file=data_args.train_file    ,       eval_file=data_args.dev_file    ,       test_file=data_args.test_file    ,       tokenizer=_lowerCamelCase    ,       label_column_id=data_args.label_column_id    ,       max_seq_length=data_args.max_seq_length    ,       )

	_snake_case     		=	AutoConfig.from_pretrained(
	    model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,       num_labels=len(_lowerCamelCase			)    ,       labelaid=_lowerCamelCase    ,       idalabel={id: label for label, id in labelaid.items()}    ,       finetuning_task='text-classification'    ,       cache_dir=model_args.cache_dir    ,       )

	with training_args.strategy.scope():
		_snake_case     		=	TFAutoModelForSequenceClassification.from_pretrained(
		    model_args.model_name_or_path    ,       from_pt=bool('.bin' in model_args.model_name_or_path			)    ,       config=_lowerCamelCase    ,       cache_dir=model_args.cache_dir    ,       )

	def compute_metrics(__A			) -> Dict:
		_snake_case     		=	np.argmax(p.predictions    ,       axis=1			)

		return {"acc": (preds == p.label_ids).mean()}

	# Initialize our Trainer
	_snake_case     		=	TFTrainer(
	    model=_lowerCamelCase    ,       args=_lowerCamelCase    ,       train_dataset=_lowerCamelCase    ,       eval_dataset=_lowerCamelCase    ,       compute_metrics=_lowerCamelCase    ,       )

	# Training
	if training_args.do_train:
		trainer.train()
		trainer.save_model()
		tokenizer.save_pretrained(training_args.output_dir			)

	# Evaluation
	_snake_case     		=	{}
	if training_args.do_eval:
		logger.info('*** Evaluate ***'			)
		_snake_case     		=	trainer.evaluate()
		_snake_case     		=	os.path.join(training_args.output_dir    ,       'eval_results.txt'			)

		with open(_lowerCamelCase    ,       'w'			) as writer:
			logger.info('***** Eval results *****'			)

			for key, value in result.items():
				logger.info(F'  {key} = {value}'			)
				writer.write(F'{key} = {value}\n'			)

			results.update(_lowerCamelCase			)

	return results


if __name__ == "__main__":
					main()



'''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        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			int:
	_snake_case     		=	args.log_outputs
	_snake_case     		=	'_'.join(args.dataset.split('/'			) + [args.config, args.split]			)

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

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

	# print & log results
	_snake_case     		=	F'WER: {wer_result}\nCER: {cer_result}'
	print(__A			)

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

	# log all results in text file. Possibly interesting for analysis
	if log_outputs is not None:
		_snake_case     		=	F'log_{dataset_id}_predictions.txt'
		_snake_case     		=	F'log_{dataset_id}_targets.txt'

		with open(__A    ,       'w'			) as p, open(__A    ,       '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(__A    ,       with_indices=__A			)







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

	_snake_case     		=	re.sub(__A    ,       ''    ,       text.lower()			)

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

	for t in token_sequences_to_ignore:
		_snake_case     		=	' '.join(text.split(__A			)			)

	return text







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			Optional[int]:
	# load dataset
	_snake_case     		=	load_dataset(args.dataset    ,       args.config    ,       split=args.split    ,       use_auth_token=__A			)

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

	# load processor
	_snake_case     		=	AutoFeatureExtractor.from_pretrained(args.model_id			)
	_snake_case     		=	feature_extractor.sampling_rate

	# resample audio
	_snake_case     		=	dataset.cast_column('audio'    ,       Audio(sampling_rate=__A			)			)

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

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

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

	# run inference on all examples
	_snake_case     		=	dataset.map(__A    ,       remove_columns=dataset.column_names			)

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


if __name__ == "__main__":
					lowercase					:							str   =      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					:							Optional[int]   =      parser.parse_args()

					main(args)

