



'''simple docstring'''






from collections import defaultdict

class        SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(		self    :  str	,    __snake_case    :  List[str]	,    __snake_case    :  Tuple	)    ->	Optional[Any]:
								UpperCAmelCase				: Optional[Any]											=	total  # total no of tasks (N)

								# DP table will have a dimension of (2^M)*N
								# initially all values are set to -1
								UpperCAmelCase				: Tuple											=	[
								    [-1 for i in range(total + 1	)] for j in range(2 ** len(__a	)	)
								]

								UpperCAmelCase				: List[Any]											=	defaultdict(__a	)  # stores the list of persons for each task

								# final_mask is used to check if all persons are included by setting all bits
								# to 1
								UpperCAmelCase				: List[Any]											=	(1 << len(__a	)) - 1







	def   A			(		self    :  Optional[int]	,    __snake_case    :  Optional[int]	,    __snake_case    :  Any	)    ->	Optional[int]:
								# if mask == self.finalmask all persons are distributed tasks, return 1
								if mask == self.final_mask:
															return 1

								# if not everyone gets the task and no more tasks are available, return 0
								if task_no > self.total_tasks:
															return 0

								# if case already considered
								if self.dp[mask][task_no] != -1:
															return self.dp[mask][task_no]

								# Number of ways when we don't this task in the arrangement
								UpperCAmelCase				: Optional[Any]											=	self.count_ways_until(__a	,    task_no + 1	)

								# now assign the tasks one by one to all possible persons and recursively
								# assign for the remaining tasks.
								if task_no in self.task:
															for p in self.task[task_no]:
																						# if p is already given a task
																						if mask & (1 << p):
																													continue

																						# assign this task to p and change the mask value. And recursively
																						# assign tasks with the new mask value.
																						total_ways_util += self.count_ways_until(mask | (1 << p)	,    task_no + 1	)

        # save the value.
								UpperCAmelCase				: Union[str, Any]											=	total_ways_util

								return self.dp[mask][task_no]







	def   A			(		self    :  str	,    __snake_case    :  Tuple	)    ->	List[str]:
								# Store the list of persons for each task
								for i in range(len(__a	)	):
															for j in task_performed[i]:
																						self.task[j].append(__a	)

        # call the function to fill the DP table, final answer is stored in dp[0][1]
								return self.count_ways_until(0	,    1	)


if __name__ == "__main__":
						UpperCamelCase__:     Any										=    5  # total no of tasks (the value of N)

						# the list of tasks that can be done by M persons.
						UpperCamelCase__:     Optional[int]										=    [[1, 3, 4], [1, 2, 5], [3, 4]]
						print(
						    AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
						        task_performed
						    )
						)










'''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)





