

from __future__ import annotations


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__ = None		,					UpperCAmelCase__ = None		,					UpperCAmelCase__ = False		,					):

					"""simple docstring"""






					__lowerCAmelCase					=							cipher_alphabet or [chr(UpperCAmelCase__      ) for i in range(97		,					123      )]

					# If the argument is None or the user provided an empty dictionary
					if not frequencies_dict:
										# Frequencies of letters in the english language (how much they show up)
										__lowerCAmelCase					=							{
										    'a': 0.08497,
										    'b': 0.01492,
										    'c': 0.02202,
										    'd': 0.04253,
										    'e': 0.11162,
										    'f': 0.02228,
										    'g': 0.02015,
										    'h': 0.06094,
										    'i': 0.07546,
										    'j': 0.00153,
										    'k': 0.01292,
										    'l': 0.04025,
										    'm': 0.02406,
										    'n': 0.06749,
										    'o': 0.07507,
										    'p': 0.01929,
										    'q': 0.00095,
										    'r': 0.07587,
										    's': 0.06327,
										    't': 0.09356,
										    'u': 0.02758,
										    'v': 0.00978,
										    'w': 0.02560,
										    'x': 0.00150,
										    'y': 0.01994,
										    'z': 0.00077,
										}
					else:
										# Custom frequencies dictionary
										__lowerCAmelCase					=							frequencies_dict

					if not case_sensitive:
										__lowerCAmelCase					=							ciphertext.lower()

					# Chi squared statistic values
					__lowerCAmelCase					=							{}

					# cycle through all of the shifts
					for shift in range(len(UpperCAmelCase__      )      ):
										__lowerCAmelCase					=							''

										# decrypt the message with the shift
										for letter in ciphertext:
															try:
																				# Try to index the letter in the alphabet
																				__lowerCAmelCase					=							(alphabet_letters.index(letter.lower()      ) - shift) % len(
																				    UpperCAmelCase__      )
																				decrypted_with_shift += (
																				    alphabet_letters[new_key].upper()
																				    if case_sensitive and letter.isupper()
																				    else alphabet_letters[new_key]
																				)
															except ValueError:
																				# Append the character if it isn't in the alphabet
																				decrypted_with_shift += letter

										__lowerCAmelCase					=							0.0

										# Loop through each letter in the decoded message with the shift
										for letter in decrypted_with_shift:
															if case_sensitive:
																				__lowerCAmelCase					=							letter.lower()
																				if letter in frequencies:
																									# Get the amount of times the letter occurs in the message
																									__lowerCAmelCase					=							decrypted_with_shift.lower().count(UpperCAmelCase__      )

																									# Get the excepcted amount of times the letter should appear based
																									# on letter frequencies
																									__lowerCAmelCase					=							frequencies[letter] * occurrences

																									# Complete the chi squared statistic formula
																									__lowerCAmelCase					=							((occurrences - expected) ** 2) / expected

																									# Add the margin of error to the total chi squared statistic
																									chi_squared_statistic += chi_letter_value
															else:
																				if letter.lower() in frequencies:
																									# Get the amount of times the letter occurs in the message
																									__lowerCAmelCase					=							decrypted_with_shift.count(UpperCAmelCase__      )

																									# Get the excepcted amount of times the letter should appear based
																									# on letter frequencies
																									__lowerCAmelCase					=							frequencies[letter] * occurrences

																									# Complete the chi squared statistic formula
																									__lowerCAmelCase					=							((occurrences - expected) ** 2) / expected

																									# Add the margin of error to the total chi squared statistic
																									chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
										__lowerCAmelCase					=							(
										    chi_squared_statistic,
										    decrypted_with_shift,
										)

					# Get the most likely cipher by finding the cipher with the smallest chi squared
					# statistic
					def chi_squared_statistic_values_sorting_key(UpperCAmelCase__      ) -> tuple[float, str]:
										return chi_squared_statistic_values[key]

					__lowerCAmelCase					=							min(
					    UpperCAmelCase__		,					key=UpperCAmelCase__		,					)

					# Get all the data from the most likely cipher (key, decoded message)
					(
					    (
					    __lowerCAmelCase
					),	(
					    __lowerCAmelCase
					),	
					)					=							chi_squared_statistic_values[most_likely_cipher]

					# Return the data on the most likely shift
					return (
					    most_likely_cipher,
					    most_likely_cipher_chi_squared_value,
					    decoded_most_likely_cipher,
					)









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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							args.log_outputs
					__lowerCAmelCase					=							'_'.join(args.dataset.split('/'      ) + [args.config, args.split]      )

					# load metric
					__lowerCAmelCase					=							load_metric('wer'      )
					__lowerCAmelCase					=							load_metric('cer'      )

					# compute metrics
					__lowerCAmelCase					=							wer.compute(references=result['target']		,					predictions=result['prediction']      )
					__lowerCAmelCase					=							cer.compute(references=result['target']		,					predictions=result['prediction']      )

					# print & log results
					__lowerCAmelCase					=							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					=							F"""log_{dataset_id}_predictions.txt"""
										__lowerCAmelCase					=							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        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							'[,?.!\-\;\:"“%‘”�—’…–]'  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

					__lowerCAmelCase					=							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					=							['\n\n', '\n', '   ', '  ']

					for t in token_sequences_to_ignore:
										__lowerCAmelCase					=							' '.join(text.split(UpperCAmelCase__      )      )

					return text


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							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					=							AutoFeatureExtractor.from_pretrained(args.model_id      )
					__lowerCAmelCase					=							feature_extractor.sampling_rate

					# resample audio
					__lowerCAmelCase					=							dataset.cast_column('audio'		,					Audio(sampling_rate=UpperCAmelCase__      )      )

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

					# map function to decode audio
					def map_to_pred(UpperCAmelCase__      ):
										__lowerCAmelCase					=							asr(
										    batch['audio']['array']		,					chunk_length_s=args.chunk_length_s		,					stride_length_s=args.stride_length_s      )

										__lowerCAmelCase					=							prediction['text']
										__lowerCAmelCase					=							normalize_text(batch['sentence']      )
										return batch

					# run inference on all examples
					__lowerCAmelCase					=							dataset.map(UpperCAmelCase__		,					remove_columns=dataset.column_names      )

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


if __name__ == "__main__":
		lowerCamelCase      				=     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.''',
		)
		lowerCamelCase      				=     parser.parse_args()

		main(args)






