

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      (lowercase_:				Dataset					,   lowercase_:				Dict[str, str]      )  ->   List[str]:
	A__							:					int       =	args.log_outputs
	A__							:					str       =	"""_""".join(args.dataset.split("""/"""      ) + [args.config, args.split]      )

	# load metric
	A__							:					str       =	load_metric("""wer"""      )
	A__							:					Optional[int]       =	load_metric("""cer"""      )

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

	# print & log results
	A__							:					Union[str, Any]       =	f"""WER: {wer_result}\nCER: {cer_result}"""
	print(lowercase_      )

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

	# log all results in text file. Possibly interesting for analysis
	if log_outputs is not None:
		A__							:					int       =	f"""log_{dataset_id}_predictions.txt"""
		A__							:					List[str]       =	f"""log_{dataset_id}_targets.txt"""

		with open(lowercase_					,   """w"""      ) as p, open(lowercase_					,   """w"""      ) as t:
			# mapping function to write output
			def write_to_file(lowercase_:				List[Any]					,   lowercase_:				List[Any]      ):
				p.write(f"""{i}""" + """\n"""      )
				p.write(batch["""prediction"""] + """\n"""      )
				t.write(f"""{i}""" + """\n"""      )
				t.write(batch["""target"""] + """\n"""      )

			result.map(lowercase_					,   with_indices=lowercase_      )

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

	A__							:					int       =	re.sub(lowercase_					,   """"""					,   text.lower()      )

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

	for t in token_sequences_to_ignore:
		A__							:					Optional[Any]       =	""" """.join(text.split(lowercase_      )      )

	return text

def 		UpperCamelCase      (lowercase_:				Dict      )  ->   Any:
	# load dataset
	A__							:					Any       =	load_dataset(args.dataset					,   args.config					,   split=args.split					,   use_auth_token=lowercase_      )

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

	# load processor
	A__							:					Tuple       =	AutoFeatureExtractor.from_pretrained(args.model_id      )
	A__							:					List[Any]       =	feature_extractor.sampling_rate

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

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

	# map function to decode audio
	def map_to_pred(lowercase_:				str      ):
		A__							:					Optional[Any]       =	asr(
		    batch["""audio"""]["""array"""]					,   chunk_length_s=args.chunk_length_s					,   stride_length_s=args.stride_length_s      )

		A__							:					str       =	prediction["""text"""]
		A__							:					Dict       =	normalize_text(batch["""sentence"""]      )
		return batch

	# run inference on all examples
	A__							:					int       =	dataset.map(lowercase_					,   remove_columns=dataset.column_names      )

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


if __name__ == "__main__":
	A_ :				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.',
	)
	A_ :				List[Any]						 =    parser.parse_args()

	main(args)






# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import doctest
import sys
import warnings
from os.path import abspath, dirname, join

import _pytest

from transformers.testing_utils import HfDoctestModule, HfDocTestParser


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
A_ :				Optional[int]						 =    abspath(join(dirname(__file__), 'src'))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)

def 		UpperCamelCase      (lowercase_:				List[str]      )  ->   Any:
	config.addinivalue_line(
	    """markers"""					,   """is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested"""      )
	config.addinivalue_line(
	    """markers"""					,   """is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested"""      )
	config.addinivalue_line("""markers"""					,   """is_pipeline_test: mark test to run only when pipelines are tested"""      )
	config.addinivalue_line("""markers"""					,   """is_staging_test: mark test to run only in the staging environment"""      )
	config.addinivalue_line("""markers"""					,   """accelerate_tests: mark test that require accelerate"""      )
	config.addinivalue_line("""markers"""					,   """tool_tests: mark the tool tests that are run on their specific schedule"""      )

def 		UpperCamelCase      (lowercase_:				Optional[int]      )  ->   Optional[Any]:
	from transformers.testing_utils import pytest_addoption_shared

	pytest_addoption_shared(lowercase_      )

def 		UpperCamelCase      (lowercase_:				List[str]      )  ->   Optional[Any]:
	from transformers.testing_utils import pytest_terminal_summary_main

	A__							:					List[Any]       =	terminalreporter.config.getoption("""--make-reports"""      )
	if make_reports:
		pytest_terminal_summary_main(lowercase_					,   id=lowercase_      )

def 		UpperCamelCase      (lowercase_:				Union[str, Any]					,   lowercase_:				int      )  ->   List[str]:
	# If no tests are collected, pytest exists with code 5, which makes the CI fail.
	if exitstatus == 5:
		A__							:					Tuple       =	0


# Doctest custom flag to ignore output.
A_ :				Tuple						 =    doctest.register_optionflag('IGNORE_RESULT')

A_ :				Dict						 =    doctest.OutputChecker



class 					_a		(__magic_name__       ):


					'''simple docstring'''






					def 						__A    (				self					, A__					, A__					, A__    ):
						if IGNORE_RESULT & optionflags:
							return True
						return OutputChecker.check_output(self					, A__					, A__					, A__    )



A_ :				str						 =    CustomOutputChecker
A_ :				Dict						 =    HfDoctestModule


A_ :				Optional[int]						 =    HfDocTestParser



