

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     (   a__    :   Dataset			,a__    :   Dict[str, str]       )					->				Union[str, Any]:
	__A :			Union[str, Any]		    =			args.log_outputs
	__A :			List[Any]		    =			"""_""".join(args.dataset.split("""/"""       ) + [args.config, args.split]       )

	# load metric
	__A :			List[Any]		    =			load_metric("""wer"""       )
	__A :			List[str]		    =			load_metric("""cer"""       )

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

	# print & log results
	__A :			List[Any]		    =			f"""WER: {wer_result}\nCER: {cer_result}"""
	print(a__       )

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

	# 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 :			Union[str, Any]		    =			f"""log_{dataset_id}_targets.txt"""

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

			result.map(a__			,with_indices=a__       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str       )					->				str:
	__A :			Dict		    =			"""[,?.!\-\;\:\"“%‘”�—’…–]"""  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

	__A :			List[Any]		    =			re.sub(a__			,""""""			,text.lower()       )

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

	for t in token_sequences_to_ignore:
		__A :			str		    =			""" """.join(text.split(a__       )       )

	return text
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Dict       )					->				Optional[Any]:
	# load dataset
	__A :			Optional[Any]		    =			load_dataset(args.dataset			,args.config			,split=args.split			,use_auth_token=a__       )

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

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

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

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

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

		__A :			Optional[int]		    =			prediction["""text"""]
		__A :			int		    =			normalize_text(batch["""sentence"""]       )
		return batch

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

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


if __name__ == "__main__":
			UpperCAmelCase_							:					Optional[int]		 =   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_							:					int		 =   parser.parse_args()

			main(args)








from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   complex			,a__    :   str = "x"			,a__    :   float = 10**-10			,a__    :   int = 1			,)					->				complex:
	__A :			Tuple		    =			symbols(a__       )
	__A :			List[str]		    =			lambdify(a__			,a__       )
	__A :			Any		    =			lambdify(a__			,diff(a__			,a__       )       )

	__A :			Dict		    =			starting_point

	while True:
		if diff_function(a__       ) != 0:
			__A :			Optional[int]		    =			prev_guess - multiplicity * func(a__       ) / diff_function(
			    a__       )
		else:
			raise ZeroDivisionError("""Could not find root"""       ) from None

		# Precision is checked by comparing the difference of consecutive guesses
		if abs(next_guess - prev_guess       ) < precision:
			return next_guess

		__A :			List[Any]		    =			next_guess


# Let's Execute
if __name__ == "__main__":
			# Find root of trigonometric function
			# Find value of pi
			print(f"""The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}""")

			# Find root of polynomial
			# Find fourth Root of 5
			print(f"""The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5J)}""")

			# Find value of e
			print(
			    '''The root of log(y) - 1 = 0 is ''',
			    f"""{newton_raphson("log(y) - 1", 2, variable="y")}""",
			)

			# Exponential Roots
			print(
			    '''The root of exp(x) - 1 = 0 is''',
			    f"""{newton_raphson("exp(x) - 1", 10, precision=0.005)}""",
			)

			# Find root of cos(x)
			print(f"""The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}""")





