


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


snake_case							:	List[str]					      =				logging.get_logger(__name__)







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   List[str]   				=      ['''audio_values''', '''audio_mask''']
					def __init__(				self       :Any      ,__snake_case       :str=20_48      ,__snake_case       :Dict=1      ,__snake_case       :Dict=[16, 16]      ,__snake_case       :Dict=1_28      ,__snake_case       :Optional[int]=4_41_00      ,__snake_case       :Union[str, Any]=86      ,__snake_case       :Union[str, Any]=20_48      ,__snake_case       :Union[str, Any]=0.0      ,**__snake_case       :Dict      ,) ->   int:
												super().__init__(
												    feature_size=__snake_case      ,sampling_rate=__snake_case      ,padding_value=__snake_case      ,**__snake_case      ,)

												a__					=					spectrogram_length
												a__					=					num_channels
												a__					=					patch_size
												a__					=					feature_size // self.patch_size[1]
												a__					=					n_fft
												a__					=					sampling_rate // hop_length_to_sampling_rate
												a__					=					sampling_rate
												a__					=					padding_value
												a__					=					mel_filter_bank(
												    num_frequency_bins=1 + n_fft // 2      ,num_mel_filters=__snake_case      ,min_frequency=0.0      ,max_frequency=2_20_50.0      ,sampling_rate=__snake_case      ,norm='slaney'      ,mel_scale='slaney'      ,).T
					def 			lowerCamelCase__(				self       :Any      ,__snake_case       :np.array				) ->   np.ndarray:
												a__					=					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      ,)
												a__					=					log_spec[:, :-1]
												a__					=					log_spec - 20.0
												a__					=					np.clip(log_spec / 40.0      ,-2.0      ,0.0				) + 1.0
												return log_spec



					def __call__(				self       :List[str]      ,__snake_case       :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,__snake_case       :Optional[Union[str, TensorType]] = None      ,__snake_case       :Optional[bool] = True      ,__snake_case       :Optional[int] = None      ,__snake_case       :bool = False      ,__snake_case       :bool = False      ,**__snake_case       :Dict      ,) ->   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.'				)

												a__					=					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}'				)
												a__					=					is_batched_numpy or (
												    isinstance(__snake_case      ,(list, tuple)				) and (isinstance(raw_speech[0]      ,(np.ndarray, tuple, list)				))
												)
												if is_batched:
																			a__					=					[np.asarray([speech]      ,dtype=np.floataa				).T for speech in raw_speech]
												elif not is_batched and not isinstance(__snake_case      ,np.ndarray				):
																			a__					=					np.asarray(__snake_case      ,dtype=np.floataa				)
												elif isinstance(__snake_case      ,np.ndarray				) and raw_speech.dtype is np.dtype(np.floataa				):
																			a__					=					raw_speech.astype(np.floataa				)
												# always return batch
												if not is_batched:
																			a__					=					[np.asarray([raw_speech]				).T]

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

												# Create audio attention mask
												a__					=					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:
																			a__					=					[
																			    (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
																			]
																			a__					=					np.array(__snake_case				).astype(np.floataa				)

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

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

												a__					=					BatchFeature(data=__snake_case      ,tensor_type=__snake_case				)
												return encoded_inputs








import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class 	snake_case_       (lowerCamelCase_     ,						unittest.TestCase				):
					UpperCAmelCase__ :   int   				=      MvpTokenizer
					UpperCAmelCase__ :   List[Any]   				=      MvpTokenizerFast
					UpperCAmelCase__ :   Optional[int]   				=      True
					UpperCAmelCase__ :   Tuple   				=      filter_roberta_detectors
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   Union[str, Any]:
												super().setUp()
												a__					=					[
												    'l',
												    'o',
												    'w',
												    'e',
												    'r',
												    's',
												    't',
												    'i',
												    'd',
												    'n',
												    '\u0120',
												    '\u0120l',
												    '\u0120n',
												    '\u0120lo',
												    '\u0120low',
												    'er',
												    '\u0120lowest',
												    '\u0120newer',
												    '\u0120wider',
												    '<unk>',
												]
												a__					=					dict(zip(__snake_case      ,range(len(__snake_case				)				)				)				)
												a__					=					['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
												a__					=					{'unk_token': '<unk>'}

												a__					=					os.path.join(self.tmpdirname      ,VOCAB_FILES_NAMES['vocab_file']				)
												a__					=					os.path.join(self.tmpdirname      ,VOCAB_FILES_NAMES['merges_file']				)
												with open(self.vocab_file      ,'w'      ,encoding='utf-8'				) as fp:
																			fp.write(json.dumps(__snake_case				) + '\n'				)
												with open(self.merges_file      ,'w'      ,encoding='utf-8'				) as fp:
																			fp.write('\n'.join(__snake_case				)				)
					def 			lowerCamelCase__(				self       :Optional[Any]      ,**__snake_case       :Tuple				) ->   int:
												kwargs.update(self.special_tokens_map				)
												return self.tokenizer_class.from_pretrained(self.tmpdirname      ,**__snake_case				)
					def 			lowerCamelCase__(				self       :Optional[Any]      ,**__snake_case       :Dict				) ->   Dict:
												kwargs.update(self.special_tokens_map				)
												return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,**__snake_case				)
					def 			lowerCamelCase__(				self       :Any      ,__snake_case       :Union[str, Any]				) ->   List[Any]:
												return "lower newer", "lower newer"
					@cached_property
					def 			lowerCamelCase__(				self       :Tuple				) ->   Optional[int]:
												return MvpTokenizer.from_pretrained('RUCAIBox/mvp'				)
					@cached_property
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   str:
												return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'				)
					@require_torch
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   str:
												a__					=					['A long paragraph for summarization.', 'Another paragraph for summarization.']
												a__					=					[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__					=					tokenizer(__snake_case      ,max_length=len(__snake_case				)      ,padding=__snake_case      ,return_tensors='pt'				)
																			self.assertIsInstance(__snake_case      ,__snake_case				)

																			self.assertEqual((2, 9)      ,batch.input_ids.shape				)
																			self.assertEqual((2, 9)      ,batch.attention_mask.shape				)
																			a__					=					batch.input_ids.tolist()[0]
																			self.assertListEqual(__snake_case      ,__snake_case				)
																			# Test that special tokens are reset
					@require_torch
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   List[Any]:
												a__					=					['A long paragraph for summarization.', 'Another paragraph for summarization.']
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__					=					tokenizer(__snake_case      ,padding=__snake_case      ,return_tensors='pt'				)
																			# check if input_ids are returned and no labels
																			self.assertIn('input_ids'      ,__snake_case				)
																			self.assertIn('attention_mask'      ,__snake_case				)
																			self.assertNotIn('labels'      ,__snake_case				)
																			self.assertNotIn('decoder_attention_mask'      ,__snake_case				)
					@require_torch
					def 			lowerCamelCase__(				self       :Any				) ->   int:
												a__					=					[
												    'Summary of the text.',
												    'Another summary.',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__					=					tokenizer(text_target=__snake_case      ,max_length=32      ,padding='max_length'      ,return_tensors='pt'				)
																			self.assertEqual(32      ,targets['input_ids'].shape[1]				)
					@require_torch
					def 			lowerCamelCase__(				self       :int				) ->   Union[str, Any]:
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__					=					tokenizer(
																			    ['I am a small frog' * 10_24, 'I am a small frog']      ,padding=__snake_case      ,truncation=__snake_case      ,return_tensors='pt'				)
																			self.assertIsInstance(__snake_case      ,__snake_case				)
																			self.assertEqual(batch.input_ids.shape      ,(2, 10_24)				)
					@require_torch
					def 			lowerCamelCase__(				self       :List[Any]				) ->   Any:
												a__					=					['A long paragraph for summarization.']
												a__					=					[
												    'Summary of the text.',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__					=					tokenizer(__snake_case      ,text_target=__snake_case      ,return_tensors='pt'				)
																			a__					=					inputs['input_ids']
																			a__					=					inputs['labels']
																			self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()				)
																			self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()				)
																			self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()				)
																			self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()				)
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   Tuple:
												pass



					def 			lowerCamelCase__(				self       :Tuple				) ->   Optional[Any]:
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
																										a__					=					self.rust_tokenizer_class.from_pretrained(__snake_case      ,**__snake_case				)
																										a__					=					self.tokenizer_class.from_pretrained(__snake_case      ,**__snake_case				)
																										a__					=					'A, <mask> AllenNLP sentence.'
																										a__					=					tokenizer_r.encode_plus(__snake_case      ,add_special_tokens=__snake_case      ,return_token_type_ids=__snake_case				)
																										a__					=					tokenizer_p.encode_plus(__snake_case      ,add_special_tokens=__snake_case      ,return_token_type_ids=__snake_case				)

																										# token_type_ids should put 0 everywhere
																										self.assertEqual(sum(tokens_r['token_type_ids']				)      ,sum(tokens_p['token_type_ids']				)				)

																										# attention_mask should put 1 everywhere, so sum over length should be 1
																										self.assertEqual(
																										    sum(tokens_r['attention_mask']				) / len(tokens_r['attention_mask']				)      ,sum(tokens_p['attention_mask']				) / len(tokens_p['attention_mask']				)      ,)

																										a__					=					tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']				)
																										a__					=					tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']				)

																										# Rust correctly handles the space before the mask while python doesnt
																										self.assertSequenceEqual(tokens_p['input_ids']      ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]				)
																										self.assertSequenceEqual(tokens_r['input_ids']      ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]				)

																										self.assertSequenceEqual(
																										    __snake_case      ,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']				)
																										self.assertSequenceEqual(
																										    __snake_case      ,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']				)




