

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__			(       _a				,      _a):
	SCREAMING_SNAKE_CASE :   List[Any]					  =							args.log_outputs
	SCREAMING_SNAKE_CASE :   str					  =							"_".join(args.dataset.split("/") + [args.config, args.split])

	# load metric
	SCREAMING_SNAKE_CASE :   List[Any]					  =							load_metric("wer")
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							load_metric("cer")

	# compute metrics
	SCREAMING_SNAKE_CASE :   Dict					  =							wer.compute(references=result["target"]				,      predictions=result["prediction"])
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							cer.compute(references=result["target"]				,      predictions=result["prediction"])

	# print & log results
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							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:
		SCREAMING_SNAKE_CASE :   str					  =							f"log_{dataset_id}_predictions.txt"
		SCREAMING_SNAKE_CASE :   str					  =							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 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							"[,?.!\-\;\:\"“%‘”�—’…–]"  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

	SCREAMING_SNAKE_CASE :   Tuple					  =							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!
	SCREAMING_SNAKE_CASE :   Tuple					  =							["\n\n", "\n", "   ", "  "]

	for t in token_sequences_to_ignore:
		SCREAMING_SNAKE_CASE :   List[Any]					  =							" ".join(text.split(_a))

	return text

def 			lowerCamelCase__			(       _a):
	# load dataset
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							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
	SCREAMING_SNAKE_CASE :   Dict					  =							AutoFeatureExtractor.from_pretrained(args.model_id)
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							feature_extractor.sampling_rate

	# resample audio
	SCREAMING_SNAKE_CASE :   List[str]					  =							dataset.cast_column("audio"				,      Audio(sampling_rate=_a))

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

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

		SCREAMING_SNAKE_CASE :   Optional[Any]					  =							prediction["text"]
		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							normalize_text(batch["sentence"])
		return batch

	# run inference on all examples
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							dataset.map(_a				,      remove_columns=dataset.column_names)

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


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

			main(args)





import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
		from sagemaker import Session, TrainingJobAnalytics
		from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
    literal_eval(os.getenv('''TEST_SAGEMAKER'''			,  '''False'''     )     ) is not True			,  reason='''Skipping test because should only be run when releasing minor transformers version'''			,  )
@pytest.mark.usefixtures('''sm_env'''     )
@parameterized_class(
    [
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 650, '''eval_accuracy''': 0.7, '''eval_loss''': 0.6},
        },
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_ddp.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 600, '''eval_accuracy''': 0.7, '''eval_loss''': 0.6},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf_dist.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 600, '''eval_accuracy''': 0.6, '''eval_loss''': 0.7},
        },
    ]     )
class 						UpperCAmelCase  (							unittest.TestCase     ):







						'''simple docstring'''





						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							if self.framework == "pytorch":
								subprocess.run(
								    f"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split()			,      encoding="utf-8"			,      check=__lowercase			,      )
							assert hasattr(self			,      "env"		)






						def 			snake_case__		(		self				:	Dict			,      __lowercase				:	Optional[Any]		):






							"""simple docstring"""






							snake_case_						       =	f"{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"
							# distributed data settings
							snake_case_						       =	{"smdistributed": {"dataparallel": {"enabled": True}}} if self.script != "run_ddp.py" else None

							# creates estimator
							return HuggingFace(
							    entry_point=self.script			,      source_dir=self.env.test_path			,      role=self.env.role			,      image_uri=self.env.image_uri			,      base_job_name=__lowercase			,      instance_count=__lowercase			,      instance_type=self.instance_type			,      debugger_hook_config=__lowercase			,      hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}			,      metric_definitions=self.env.metric_definitions			,      distribution=__lowercase			,      py_version="py36"			,      )






						def 			snake_case__		(		self				:	Union[str, Any]			,      __lowercase				:	Tuple		):






							"""simple docstring"""






							TrainingJobAnalytics(__lowercase		).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv"		)






						@parameterized.expand([(2,)]		)
						def 			snake_case__		(		self				:	Union[str, Any]			,      __lowercase				:	Union[str, Any]		):






							"""simple docstring"""






							snake_case_						       =	self.create_estimator(__lowercase		)

							# run training
							estimator.fit()

							# result dataframe
							snake_case_						       =	TrainingJobAnalytics(estimator.latest_training_job.name		).dataframe()

							# extract kpis
							snake_case_						       =	list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]		)
							snake_case_						       =	list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]		)
							# get train time from SageMaker job, this includes starting, preprocessing, stopping
							snake_case_						       =	(
							    Session().describe_training_job(estimator.latest_training_job.name		).get("TrainingTimeInSeconds"			,      99_99_99		)
							)

							# assert kpis
							assert train_runtime <= self.results["train_runtime"]
							assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy		)
							assert all(t <= self.results["eval_loss"] for t in eval_loss		)

							# dump tests result into json file to share in PR
							with open(f"{estimator.latest_training_job.name}.json"			,      "w"		) as outfile:
								json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}			,      __lowercase		)
