





'''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        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Dataset,			SCREAMING_SNAKE_CASE__      :			Dict[str, str]   ) ->	Optional[Any]:
						UpperCAmelCase_			:      Dict     						=  args.log_outputs
						UpperCAmelCase_			:      Optional[int]     						=  '''_'''.join(args.dataset.split('''/'''   ) + [args.config, args.split]   )

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

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

						# print & log results
						UpperCAmelCase_			:      str     						=  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_			:      Optional[int]     						=  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__      :			str,			SCREAMING_SNAKE_CASE__      :			Tuple   ):
																								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        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			str   ) ->	str:
						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_			:      Union[str, Any]     						=  ['''\n\n''', '''\n''', '''   ''', '''  ''']

						for t in token_sequences_to_ignore:
												UpperCAmelCase_			:      Optional[Any]     						=  ''' '''.join(text.split(SCREAMING_SNAKE_CASE__   )   )

						return text





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			List[str]   ) ->	Optional[int]:
						# load dataset
						UpperCAmelCase_			:      int     						=  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_			:      Tuple     						=  AutoFeatureExtractor.from_pretrained(args.model_id   )
						UpperCAmelCase_			:      Any     						=  feature_extractor.sampling_rate

						# resample audio
						UpperCAmelCase_			:      List[str]     						=  dataset.cast_column('''audio''',			Audio(sampling_rate=SCREAMING_SNAKE_CASE__   )   )

						# load eval pipeline
						if args.device is None:
												UpperCAmelCase_			:      int     						=  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(SCREAMING_SNAKE_CASE__      :			List[Any]   ):
												UpperCAmelCase_			:      List[str]     						=  asr(
												    batch['''audio''']['''array'''],			chunk_length_s=args.chunk_length_s,			stride_length_s=args.stride_length_s   )

												UpperCAmelCase_			:      Optional[Any]     						=  prediction['''text''']
												UpperCAmelCase_			:      Tuple     						=  normalize_text(batch['''sentence''']   )
												return batch

						# run inference on all examples
						UpperCAmelCase_			:      List[str]     						=  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__":
					snake_case_     :       Optional[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.",
					)
					snake_case_     :       Any						=  parser.parse_args()

					main(args)












'''simple docstring'''







import sys
import turtle





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			tuple[float, float],			SCREAMING_SNAKE_CASE__      :			tuple[float, float]   ) ->	tuple[float, float]:
						return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			tuple[float, float],			SCREAMING_SNAKE_CASE__      :			tuple[float, float],			SCREAMING_SNAKE_CASE__      :			tuple[float, float],			SCREAMING_SNAKE_CASE__      :			int,			) ->	None:
						my_pen.up()
						my_pen.goto(vertexa[0],			vertexa[1]   )
						my_pen.down()
						my_pen.goto(vertexa[0],			vertexa[1]   )
						my_pen.goto(vertexa[0],			vertexa[1]   )
						my_pen.goto(vertexa[0],			vertexa[1]   )

						if depth == 0:
												return

						triangle(SCREAMING_SNAKE_CASE__,			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			depth - 1   )
						triangle(SCREAMING_SNAKE_CASE__,			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			depth - 1   )
						triangle(SCREAMING_SNAKE_CASE__,			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			depth - 1   )


if __name__ == "__main__":
					if len(sys.argv) != 2:
										raise ValueError(
										    "Correct format for using this script: "
										    "python fractals.py <int:depth_for_fractal>"
										)
					snake_case_     :       Any						=  turtle.Turtle()
					my_pen.ht()
					my_pen.speed(5)
					my_pen.pencolor("red")

					snake_case_     :       Tuple						=  [(-1_75, -1_25), (0, 1_75), (1_75, -1_25)]  # vertices of triangle
					triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))





