





"""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      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Tuple				   =			args.log_outputs
						UpperCamelCase				:		Optional[int]				   =			"""_""".join(args.dataset.split("""/"""     ) + [args.config, args.split]     )

						# load metric
						UpperCamelCase				:		Dict				   =			load_metric("""wer"""     )
						UpperCamelCase				:		str				   =			load_metric("""cer"""     )

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

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

						UpperCamelCase				:		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!
						UpperCamelCase				:		Optional[Any]				   =			["""\n\n""", """\n""", """   """, """  """]

						for t in token_sequences_to_ignore:
												UpperCamelCase				:		Union[str, Any]				   =			""" """.join(text.split(SCREAMING_SNAKE_CASE     )     )

						return text

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						# load dataset
						UpperCamelCase				:		Union[str, Any]				   =			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
						UpperCamelCase				:		str				   =			AutoFeatureExtractor.from_pretrained(args.model_id     )
						UpperCamelCase				:		Union[str, Any]				   =			feature_extractor.sampling_rate

						# resample audio
						UpperCamelCase				:		Optional[int]				   =			dataset.cast_column("""audio"""							,     Audio(sampling_rate=SCREAMING_SNAKE_CASE     )     )

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

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

												UpperCamelCase				:		str				   =			prediction["""text"""]
												UpperCamelCase				:		Any				   =			normalize_text(batch["""sentence"""]     )
												return batch

						# run inference on all examples
						UpperCamelCase				:		List[Any]				   =			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__":
		__magic_name__       :					Union[str, 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.""",
		)
		__magic_name__       :					int						=			parser.parse_args()

		main(args)










'''simple docstring'''




from collections import Counter
from timeit import timeit




def 						__lowerCamelCase			(	UpperCAmelCase_ = ""					,				)   ->bool:
	return sum(c % 2 for c in Counter(input_str.replace(' '					,				''							).lower()							).values()							) < 2




def 						__lowerCamelCase			(	UpperCAmelCase_ = ""							)   ->bool:
	if len(UpperCAmelCase_							) == 0:
		return True
	snake_case__	       =   input_str.replace(' '					,				''							).lower()
	# character_freq_dict: Stores the frequency of every character in the input string
	snake_case__	       =   {}

	for character in lower_case_input_str:
		snake_case__	       =   character_freq_dict.get(UpperCAmelCase_					,				0							) + 1
	snake_case__	       =   0

	for character_count in character_freq_dict.values():
		if character_count % 2:
			odd_char += 1
	if odd_char > 1:
		return False
	return True




def 						__lowerCamelCase			(	UpperCAmelCase_ = ""							)   ->None:
	print('\nFor string = '					,				UpperCAmelCase_					,				':'							)
	print(
	    '> can_string_be_rearranged_as_palindrome_counter()'					,				'\tans ='					,				can_string_be_rearranged_as_palindrome_counter(UpperCAmelCase_							)					,				'\ttime ='					,				timeit(
	        'z.can_string_be_rearranged_as_palindrome_counter(z.check_str)'					,				setup='import __main__ as z'					,				)					,				'seconds'					,				)
	print(
	    '> can_string_be_rearranged_as_palindrome()'					,				'\tans ='					,				can_string_be_rearranged_as_palindrome(UpperCAmelCase_							)					,				'\ttime ='					,				timeit(
	        'z.can_string_be_rearranged_as_palindrome(z.check_str)'					,				setup='import __main__ as z'					,				)					,				'seconds'					,				)


if __name__ == "__main__":
			a__					:			str          =     input(
			    '''Enter string to determine if it can be rearranged as a palindrome or not: '''
			).strip()
			benchmark(check_str)
			a__					:			List[Any]          =     can_string_be_rearranged_as_palindrome_counter(check_str)
			print(f"""{check_str} can {'' if status else 'not '}be rearranged as a palindrome""")



