






'''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 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	Tuple:
							lowerCamelCase__ :     str	  =	args.log_outputs
							lowerCamelCase__ :     Optional[int]	  =	"""_""".join(args.dataset.split("""/"""				) + [args.config, args.split]				)

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

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

							# print & log results
							lowerCamelCase__ :     List[str]	  =	f'''WER: {wer_result}\nCER: {cer_result}'''
							print(UpperCamelCase				)

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

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

														with open(UpperCamelCase    ,      """w"""				) as p, open(UpperCamelCase    ,      """w"""				) as t:
																					# mapping function to write output
																					def write_to_file(UpperCamelCase    ,      UpperCamelCase				):
																												p.write(f'''{i}''' + """\n"""				)
																												p.write(batch["""prediction"""] + """\n"""				)
																												t.write(f'''{i}''' + """\n"""				)
																												t.write(batch["""target"""] + """\n"""				)

																					result.map(UpperCamelCase    ,      with_indices=UpperCamelCase				)





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

							lowerCamelCase__ :     int	  =	re.sub(UpperCamelCase    ,      """"""    ,      text.lower()				)

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

							for t in token_sequences_to_ignore:
														lowerCamelCase__ :     str	  =	""" """.join(text.split(UpperCamelCase				)				)

							return text





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Tuple:
							# load dataset
							lowerCamelCase__ :     str	  =	load_dataset(args.dataset    ,      args.config    ,      split=args.split    ,      use_auth_token=UpperCamelCase				)

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

							# load processor
							lowerCamelCase__ :     List[str]	  =	AutoFeatureExtractor.from_pretrained(args.model_id				)
							lowerCamelCase__ :     Union[str, Any]	  =	feature_extractor.sampling_rate

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

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

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

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

							# run inference on all examples
							lowerCamelCase__ :     List[str]	  =	dataset.map(UpperCamelCase    ,      remove_columns=dataset.column_names				)

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


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     :      str   					=parser.parse_args()

			main(args)









'''simple docstring'''







import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential

if __name__ == "__main__":
			_A     :      Optional[int]   					=pd.read_csv('''sample_data.csv''', header=None)
			_A     :      Any   					=df.shape[:1][0]
			# If you're using some other dataset input the target column
			_A     :      List[str]   					=df.iloc[:, 1:2]
			_A     :      int   					=actual_data.values.reshape(len_data, 1)
			_A     :      Union[str, Any]   					=MinMaxScaler().fit_transform(actual_data)
			_A     :      Optional[int]   					=10
			_A     :      Union[str, Any]   					=5
			_A     :      Union[str, Any]   					=20
			_A     :      str   					=len_data - periods * look_back
			_A     :      List[Any]   					=actual_data[:division]
			_A     :      Optional[Any]   					=actual_data[division - look_back :]
			_A					,				_A     :      Tuple   					=[], []
			_A					,				_A     :      List[str]   					=[], []

			for i in range(0, len(train_data) - forward_days - look_back + 1):
						train_x.append(train_data[i : i + look_back])
						train_y.append(train_data[i + look_back : i + look_back + forward_days])
			for i in range(0, len(test_data) - forward_days - look_back + 1):
						test_x.append(test_data[i : i + look_back])
						test_y.append(test_data[i + look_back : i + look_back + forward_days])
			_A     :      List[Any]   					=np.array(train_x)
			_A     :      str   					=np.array(test_x)
			_A     :      List[Any]   					=np.array([list(i.ravel()) for i in train_y])
			_A     :      Any   					=np.array([list(i.ravel()) for i in test_y])

			_A     :      Optional[Any]   					=Sequential()
			model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
			model.add(LSTM(64, input_shape=(128, 1)))
			model.add(Dense(forward_days))
			model.compile(loss='''mean_squared_error''', optimizer='''adam''')
			_A     :      Dict   					=model.fit(
			    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
			)
			_A     :      List[str]   					=model.predict(x_test)

