

import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

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


_UpperCAmelCase	:     Union[str, Any]		       = logging.get_logger(__name__)







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				List[Any]     =    ['''input_features''']




				def __init__(      self   ,     snake_case=80   ,     snake_case=1_6000   ,     snake_case=160   ,     snake_case=30   ,     snake_case=400   ,     snake_case=0.0   ,     snake_case=False   ,     **snake_case   ,     ):
											super().__init__(
											    feature_size=snake_case   ,     sampling_rate=snake_case   ,     padding_value=snake_case   ,     return_attention_mask=snake_case   ,     **snake_case   ,     )
											snake_case_           =					n_fft
											snake_case_           =					hop_length
											snake_case_           =					chunk_length
											snake_case_           =					chunk_length * sampling_rate
											snake_case_           =					self.n_samples // hop_length
											snake_case_           =					sampling_rate
											snake_case_           =					mel_filter_bank(
											    num_frequency_bins=1 + n_fft // 2   ,     num_mel_filters=snake_case   ,     min_frequency=0.0   ,     max_frequency=80_00.0   ,     sampling_rate=snake_case   ,     norm='slaney'   ,     mel_scale='slaney'   ,     )




				def 					a							(      self   ,     snake_case ):
											snake_case_           =					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   ,     log_mel='log10'   ,     )
											snake_case_           =					log_spec[:, :-1]
											snake_case_           =					np.maximum(snake_case   ,     log_spec.max() - 8.0 )
											snake_case_           =					(log_spec + 4.0) / 4.0
											return log_spec




				@staticmethod
				# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
				def 					a							(      snake_case   ,     snake_case   ,     snake_case = 0.0 ):
											if attention_mask is not None:
																		snake_case_           =					np.array(snake_case   ,     np.intaa )
																		snake_case_           =					[]

																		for vector, length in zip(snake_case   ,     attention_mask.sum(-1 ) ):
																									snake_case_           =					(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7 )
																									if length < normed_slice.shape[0]:
																																snake_case_           =					padding_value

																									normed_input_values.append(snake_case )
											else:
																		snake_case_           =					[(x - x.mean()) / np.sqrt(x.var() + 1e-7 ) for x in input_values]

											return normed_input_values




				def __call__(      self   ,     snake_case   ,     snake_case = True   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = "max_length"   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     **snake_case   ,     ):

											if sampling_rate is not None:
																		if sampling_rate != self.sampling_rate:
																									raise ValueError(
																									    F'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
																									    F''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
																									    F''' was sampled 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.' )

											snake_case_           =					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}''' )
											snake_case_           =					is_batched_numpy or (
											    isinstance(snake_case   ,     (list, tuple) ) and (isinstance(raw_speech[0]   ,     (np.ndarray, tuple, list) ))
											)

											if is_batched:
																		snake_case_           =					[np.asarray([speech]   ,     dtype=np.floataa ).T for speech in raw_speech]
											elif not is_batched and not isinstance(snake_case   ,     np.ndarray ):
																		snake_case_           =					np.asarray(snake_case   ,     dtype=np.floataa )
											elif isinstance(snake_case   ,     np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
																		snake_case_           =					raw_speech.astype(np.floataa )

											# always return batch
											if not is_batched:
																		snake_case_           =					[np.asarray([raw_speech] ).T]

											snake_case_           =					BatchFeature({'input_features': raw_speech} )

											# convert into correct format for padding

											snake_case_           =					self.pad(
											    snake_case   ,     padding=snake_case   ,     max_length=max_length if max_length else self.n_samples   ,     truncation=snake_case   ,     pad_to_multiple_of=snake_case   ,     return_attention_mask=return_attention_mask or do_normalize   ,     )

											# zero-mean and unit-variance normalization
											if do_normalize:
																		snake_case_           =					self.zero_mean_unit_var_norm(
																		    padded_inputs['input_features']   ,     attention_mask=padded_inputs['attention_mask']   ,     padding_value=self.padding_value   ,     )
																		snake_case_           =					np.stack(padded_inputs['input_features']   ,     axis=0 )

											# make sure list is in array format
											snake_case_           =					padded_inputs.get('input_features' ).transpose(2   ,     0   ,     1 )

											snake_case_           =					[self._np_extract_fbank_features(snake_case ) for waveform in input_features[0]]

											if isinstance(input_features[0]   ,     snake_case ):
																		snake_case_           =					[np.asarray(snake_case   ,     dtype=np.floataa ) for feature in input_features]
											else:
																		snake_case_           =					input_features

											if return_attention_mask:
																		# rescale from sample (48000) to feature (3000)
																		snake_case_           =					padded_inputs['attention_mask'][:, :: self.hop_length]

											if return_tensors is not None:
																		snake_case_           =					padded_inputs.convert_to_tensors(snake_case )

											return padded_inputs




				def 					a							(      self ):
											snake_case_           =					copy.deepcopy(self.__dict__ )
											snake_case_           =					self.__class__.__name__
											if "mel_filters" in output:
																		del output["mel_filters"]
											return output







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 				lowercase       ( lowercase_			,						unittest.TestCase    ):
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    MvpTokenizer
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    MvpTokenizerFast
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    True
				__SCREAMING_SNAKE_CASE      :				Tuple     =    filter_roberta_detectors




				def 					a							(      self ):
											super().setUp()
											snake_case_           =					[
											    'l',
											    'o',
											    'w',
											    'e',
											    'r',
											    's',
											    't',
											    'i',
											    'd',
											    'n',
											    '\u0120',
											    '\u0120l',
											    '\u0120n',
											    '\u0120lo',
											    '\u0120low',
											    'er',
											    '\u0120lowest',
											    '\u0120newer',
											    '\u0120wider',
											    '<unk>',
											]
											snake_case_           =					dict(zip(snake_case   ,     range(len(snake_case ) ) ) )
											snake_case_           =					['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
											snake_case_           =					{'unk_token': '<unk>'}

											snake_case_           =					os.path.join(self.tmpdirname   ,     VOCAB_FILES_NAMES['vocab_file'] )
											snake_case_           =					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 					a							(      self   ,     **snake_case ):
											kwargs.update(self.special_tokens_map )
											return self.tokenizer_class.from_pretrained(self.tmpdirname   ,     **snake_case )




				def 					a							(      self   ,     **snake_case ):
											kwargs.update(self.special_tokens_map )
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,     **snake_case )




				def 					a							(      self   ,     snake_case ):
											return "lower newer", "lower newer"




				@cached_property
				def 					a							(      self ):
											return MvpTokenizer.from_pretrained('RUCAIBox/mvp' )




				@cached_property
				def 					a							(      self ):
											return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp' )




				@require_torch
				def 					a							(      self ):
											snake_case_           =					['A long paragraph for summarization.', 'Another paragraph for summarization.']
											snake_case_           =					[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		snake_case_           =					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 )
																		snake_case_           =					batch.input_ids.tolist()[0]
																		self.assertListEqual(snake_case   ,     snake_case )
																		# Test that special tokens are reset




				@require_torch
				def 					a							(      self ):
											snake_case_           =					['A long paragraph for summarization.', 'Another paragraph for summarization.']
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		snake_case_           =					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 					a							(      self ):
											snake_case_           =					[
											    'Summary of the text.',
											    'Another summary.',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		snake_case_           =					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 					a							(      self ):
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		snake_case_           =					tokenizer(
																		    ['I am a small frog' * 1024, '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, 1024) )




				@require_torch
				def 					a							(      self ):
											snake_case_           =					['A long paragraph for summarization.']
											snake_case_           =					[
											    'Summary of the text.',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		snake_case_           =					tokenizer(snake_case   ,     text_target=snake_case   ,     return_tensors='pt' )
																		snake_case_           =					inputs['input_ids']
																		snake_case_           =					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 					a							(      self ):
											pass




				def 					a							(      self ):
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
																									snake_case_           =					self.rust_tokenizer_class.from_pretrained(snake_case   ,     **snake_case )
																									snake_case_           =					self.tokenizer_class.from_pretrained(snake_case   ,     **snake_case )
																									snake_case_           =					'A, <mask> AllenNLP sentence.'
																									snake_case_           =					tokenizer_r.encode_plus(snake_case   ,     add_special_tokens=snake_case   ,     return_token_type_ids=snake_case )
																									snake_case_           =					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'] )   ,     )

																									snake_case_           =					tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
																									snake_case_           =					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, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] )
																									self.assertSequenceEqual(tokens_r['input_ids']   ,     [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 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>'] )




