



"""simple docstring"""





from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


lowercase  							=					logging.get_logger(__name__)




class 			lowercase__			(      A    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					['''audio_values''', '''audio_mask''']






							def __init__(  self	,       snake_case=2048	,       snake_case=1	,       snake_case=[16, 16]	,       snake_case=128	,       snake_case=44100	,       snake_case=86	,       snake_case=2048	,       snake_case=0.0	,       **snake_case	,       )			->							Any:
									super().__init__(
									    feature_size=snake_case	,       sampling_rate=snake_case	,       padding_value=snake_case	,       **snake_case	,       )

									_UpperCAmelCase							 =		spectrogram_length
									_UpperCAmelCase							 =		num_channels
									_UpperCAmelCase							 =		patch_size
									_UpperCAmelCase							 =		feature_size // self.patch_size[1]
									_UpperCAmelCase							 =		n_fft
									_UpperCAmelCase							 =		sampling_rate // hop_length_to_sampling_rate
									_UpperCAmelCase							 =		sampling_rate
									_UpperCAmelCase							 =		padding_value
									_UpperCAmelCase							 =		mel_filter_bank(
									    num_frequency_bins=1 + n_fft // 2	,       num_mel_filters=snake_case	,       min_frequency=0.0	,       max_frequency=22050.0	,       sampling_rate=snake_case	,       norm='slaney'	,       mel_scale='slaney'	,       ).T






							def        lowerCamelCase_   (  self	,       snake_case						)			->							np.ndarray:
									_UpperCAmelCase							 =		spectrogram(
									    snake_case	,       window_function(self.n_fft	,       'hann'						)	,       frame_length=self.n_fft	,       hop_length=self.hop_length	,       power=2.0	,       mel_filters=self.mel_filters.T	,       log_mel='dB'	,       db_range=80.0	,       )
									_UpperCAmelCase							 =		log_spec[:, :-1]
									_UpperCAmelCase							 =		log_spec - 20.0
									_UpperCAmelCase							 =		np.clip(log_spec / 40.0	,       -2.0	,       0.0						) + 1.0
									return log_spec






							def __call__(  self	,       snake_case	,       snake_case = None	,       snake_case = True	,       snake_case = None	,       snake_case = False	,       snake_case = False	,       **snake_case	,       )			->							BatchFeature:

									if sampling_rate is not None:
											if sampling_rate != self.sampling_rate:
													raise ValueError(
													    'This feature extractor is set to support sampling rate'
													    f' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'
													    f' with {self.sampling_rate} and not {sampling_rate}.'						)
									else:
											logger.warning(
											    'It is strongly recommended to pass the `sampling_rate` argument to this function. '
											    'Failing to do so can result in silent errors that might be hard to debug.'						)

									_UpperCAmelCase							 =		isinstance(snake_case	,       np.ndarray						) and len(raw_speech.shape						) > 1
									if is_batched_numpy and len(raw_speech.shape						) > 2:
											raise ValueError(f'Only mono-channel audio is supported for input to {self}'						)
									_UpperCAmelCase							 =		is_batched_numpy or (
									    isinstance(snake_case	,       (list, tuple)						) and (isinstance(raw_speech[0]	,       (np.ndarray, tuple, list)						))
									)
									if is_batched:
											_UpperCAmelCase							 =		[np.asarray([speech]	,       dtype=np.floataa						).T for speech in raw_speech]
									elif not is_batched and not isinstance(snake_case	,       np.ndarray						):
											_UpperCAmelCase							 =		np.asarray(snake_case	,       dtype=np.floataa						)
									elif isinstance(snake_case	,       np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
											_UpperCAmelCase							 =		raw_speech.astype(np.floataa						)
									# always return batch
									if not is_batched:
											_UpperCAmelCase							 =		[np.asarray([raw_speech]						).T]

									# Convert audio signals to log mel spectrograms, truncate by time axis
									_UpperCAmelCase							 =		[
									    self._np_extract_fbank_features(waveform.squeeze()						).T[: self.spectrogram_length] for waveform in raw_speech
									]
									if isinstance(audio_features[0]	,       snake_case						):
											_UpperCAmelCase							 =		[np.asarray(snake_case	,       dtype=np.floataa						) for feature in audio_features]

									# Create audio attention mask
									_UpperCAmelCase							 =		max(
									    [ceil(feature.shape[0] / self.patch_size[0]						) * self.freq_len for feature in audio_features]						)  # The maximum number of audio patches in a batch
									if return_attention_mask:
											_UpperCAmelCase							 =		[
											    (ceil(feature.shape[0] / self.patch_size[0]						) * self.freq_len) * [1]
											    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]						) * self.freq_len) * [0]
											    for feature in audio_features
											]
											_UpperCAmelCase							 =		np.array(snake_case						).astype(np.floataa						)

									# convert into correct format for padding
									_UpperCAmelCase							 =		max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
									_UpperCAmelCase							 =		np.ones([len(snake_case						), 1, max_time_len, self.feature_size]						).astype(np.floataa						)
									_UpperCAmelCase							 =		padded_audio_features * self.padding_value
									for i in range(len(snake_case						)						):
											_UpperCAmelCase							 =		audio_features[i]
											_UpperCAmelCase							 =		feature

									# return as BatchFeature
									if return_attention_mask:
											_UpperCAmelCase							 =		{'audio_values': padded_audio_features, 'audio_mask': audio_mask}
									else:
											_UpperCAmelCase							 =		{'audio_values': padded_audio_features}

									_UpperCAmelCase							 =		BatchFeature(data=snake_case	,       tensor_type=snake_case						)
									return encoded_inputs







"""simple docstring"""





import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
lowercase  							=					logging.getLogger()




def 	UpperCAmelCase     (		A	:						Path     ,				A	:						list       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		'\n'.join(A       )
		Path(A       ).open('w'       ).writelines(A       )


lowercase  							=					'''patrickvonplaten/t5-tiny-random'''
lowercase  							=					'''sshleifer/bart-tiny-random'''
lowercase  							=					'''sshleifer/tiny-mbart'''

lowercase  							=					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks






class 			lowercase__			(      A    ):
							'''simple docstring'''







							def        lowerCamelCase_   (  self	,       snake_case						)			->							str:
									_UpperCAmelCase							 =		Path(self.get_auto_remove_tmp_dir()						) / 'utest_input.source'
									_UpperCAmelCase							 =		input_file_name.parent / 'utest_output.txt'
									assert not output_file_name.exists()
									_UpperCAmelCase							 =		[' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
									_dump_articles(snake_case	,       snake_case						)

									_UpperCAmelCase							 =		str(Path(self.get_auto_remove_tmp_dir()						) / 'scores.json'						)
									_UpperCAmelCase							 =		'translation_en_to_de' if model == T5_TINY else 'summarization'
									_UpperCAmelCase							 =		f'\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            '.split()

									with patch.object(snake_case	,       'argv'	,       snake_case						):
											run_generate()
											assert Path(snake_case						).exists()
											# os.remove(Path(output_file_name))






							def        lowerCamelCase_   (  self						)			->							str:
									self.run_eval_tester(snake_case						)






							@parameterized.expand([BART_TINY, MBART_TINY]						)
							@slow
							def        lowerCamelCase_   (  self	,       snake_case						)			->							List[Any]:
									self.run_eval_tester(snake_case						)






							@parameterized.expand([T5_TINY, MBART_TINY]						)
							@slow
							def        lowerCamelCase_   (  self	,       snake_case						)			->							Dict:
									_UpperCAmelCase							 =		Path(self.get_auto_remove_tmp_dir()						) / 'utest_input.source'
									_UpperCAmelCase							 =		input_file_name.parent / 'utest_output.txt'
									assert not output_file_name.exists()

									_UpperCAmelCase							 =		{
									    'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
									    'de': [
									        'Maschinelles Lernen ist großartig, oder?',
									        'Ich esse gerne Bananen',
									        'Morgen ist wieder ein toller Tag!',
									    ],
									}

									_UpperCAmelCase							 =		Path(self.get_auto_remove_tmp_dir()						)
									_UpperCAmelCase							 =		str(tmp_dir / 'scores.json'						)
									_UpperCAmelCase							 =		str(tmp_dir / 'val.target'						)
									_dump_articles(snake_case	,       text['en']						)
									_dump_articles(snake_case	,       text['de']						)
									_UpperCAmelCase							 =		'translation_en_to_de' if model == T5_TINY else 'summarization'
									_UpperCAmelCase							 =		f'\n            run_eval_search.py\n            {model}\n            {str(snake_case						)}\n            {str(snake_case						)}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            '.split()
									testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']						)

									with patch.object(snake_case	,       'argv'	,       snake_case						):
											with CaptureStdout() as cs:
													run_search()
											_UpperCAmelCase							 =		[' num_beams | length_penalty', model, 'Best score args']
											_UpperCAmelCase							 =		['Info']
											if "translation" in task:
													expected_strings.append('bleu'						)
											else:
													expected_strings.extend(snake_case						)
											for w in expected_strings:
													assert w in cs.out
											for w in un_expected_strings:
													assert w not in cs.out
											assert Path(snake_case						).exists()
											os.remove(Path(snake_case						)						)


