


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

import numpy as np
import torch

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


a : Optional[Any]		=				logging.get_logger(__name__)
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				["input_features", "is_longer"]






					def __init__(				self ,       snake_case_=6_4 ,       snake_case_=4_8_0_0_0 ,       snake_case_=4_8_0 ,       snake_case_=1_0 ,       snake_case_=1_0_2_4 ,       snake_case_=0.0 ,       snake_case_=False ,       snake_case_ = 0 ,       snake_case_ = 1_4_0_0_0 ,       snake_case_ = None ,       snake_case_ = "fusion" ,       snake_case_ = "repeatpad" ,       **snake_case_ ,       )   ->			Tuple:







								'''simple docstring'''

								super().__init__(
								    feature_size=snake_case_ ,       sampling_rate=snake_case_ ,       padding_value=snake_case_ ,       return_attention_mask=snake_case_ ,       **snake_case_ ,       )
								__lowercase 					=   top_db
								__lowercase 					=   truncation
								__lowercase 					=   padding
								__lowercase 					=   fft_window_size
								__lowercase 					=   (fft_window_size >> 1) + 1
								__lowercase 					=   hop_length
								__lowercase 					=   max_length_s
								__lowercase 					=   max_length_s * sampling_rate
								__lowercase 					=   sampling_rate
								__lowercase 					=   frequency_min
								__lowercase 					=   frequency_max
								__lowercase 					=   mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins ,       num_mel_filters=snake_case_ ,       min_frequency=snake_case_ ,       max_frequency=snake_case_ ,       sampling_rate=snake_case_ ,       norm=snake_case_ ,       mel_scale='''htk''' ,       )
								__lowercase 					=   mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins ,       num_mel_filters=snake_case_ ,       min_frequency=snake_case_ ,       max_frequency=snake_case_ ,       sampling_rate=snake_case_ ,       norm='''slaney''' ,       mel_scale='''slaney''' ,       )






					def A      (				self						)   ->			Dict[str, Any]:







								'''simple docstring'''

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






					def A      (				self ,       snake_case_ ,       snake_case_ = None						)   ->			np.ndarray:







								'''simple docstring'''

								__lowercase 					=   spectrogram(
								    snake_case_ ,       window_function(self.fft_window_size ,       '''hann'''						) ,       frame_length=self.fft_window_size ,       hop_length=self.hop_length ,       power=2.0 ,       mel_filters=snake_case_ ,       log_mel='''dB''' ,       )
								return log_mel_spectrogram.T






					def A      (				self ,       snake_case_ ,       snake_case_ ,       snake_case_						)   ->			str:







								'''simple docstring'''

								__lowercase 					=   np.array_split(list(range(0 ,       total_frames - chunk_frames + 1						)						) ,       3						)
								if len(ranges[1]						) == 0:
											# if the audio is too short, we just use the first chunk
											__lowercase 					=   [0]
								if len(ranges[2]						) == 0:
											# if the audio is too short, we just use the first chunk
											__lowercase 					=   [0]
								# randomly choose index for each part
								__lowercase 					=   np.random.choice(ranges[0]						)
								__lowercase 					=   np.random.choice(ranges[1]						)
								__lowercase 					=   np.random.choice(ranges[2]						)

								__lowercase 					=   mel[idx_front : idx_front + chunk_frames, :]
								__lowercase 					=   mel[idx_middle : idx_middle + chunk_frames, :]
								__lowercase 					=   mel[idx_back : idx_back + chunk_frames, :]

								__lowercase 					=   torch.tensor(mel[None, None, :]						)
								__lowercase 					=   torch.nn.functional.interpolate(
								    snake_case_ ,       size=[chunk_frames, 6_4] ,       mode='''bilinear''' ,       align_corners=snake_case_						)
								__lowercase 					=   mel_shrink[0][0].numpy()
								__lowercase 					=   np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,       axis=0						)
								return mel_fusion






					def A      (				self ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_						)   ->			np.array:







								'''simple docstring'''

								if waveform.shape[0] > max_length:
											if truncation == "rand_trunc":
														__lowercase 					=   True
														# random crop to max_length (for compatibility) -> this should be handled by self.pad
														__lowercase 					=   len(snake_case_						) - max_length
														__lowercase 					=   np.random.randint(0 ,       overflow + 1						)
														__lowercase 					=   waveform[idx : idx + max_length]
														__lowercase 					=   self._np_extract_fbank_features(snake_case_ ,       self.mel_filters_slaney						)[None, :]
											elif truncation == "fusion":
														__lowercase 					=   self._np_extract_fbank_features(snake_case_ ,       self.mel_filters						)
														__lowercase 					=   max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
														__lowercase 					=   mel.shape[0]
														if chunk_frames == total_frames:
																	# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																	# In this case, we just use the whole audio.
																	__lowercase 					=   np.stack([mel, mel, mel, mel] ,       axis=0						)
																	__lowercase 					=   False
														else:
																	__lowercase 					=   self._random_mel_fusion(snake_case_ ,       snake_case_ ,       snake_case_						)
																	__lowercase 					=   True
											else:
														raise NotImplementedError(F'data_truncating {truncation} not implemented'						)

								else:
											__lowercase 					=   False
											# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
											if waveform.shape[0] < max_length:
														if padding == "repeat":
																	__lowercase 					=   int(max_length / len(snake_case_						)						)
																	__lowercase 					=   np.stack(np.tile(snake_case_ ,       n_repeat + 1						)						)[:max_length]
														if padding == "repeatpad":
																	__lowercase 					=   int(max_length / len(snake_case_						)						)
																	__lowercase 					=   np.stack(np.tile(snake_case_ ,       snake_case_						)						)
														__lowercase 					=   np.pad(snake_case_ ,       (0, max_length - waveform.shape[0]) ,       mode='''constant''' ,       constant_values=0						)

											if truncation == "fusion":
														__lowercase 					=   self._np_extract_fbank_features(snake_case_ ,       self.mel_filters						)
														__lowercase 					=   np.stack([input_mel, input_mel, input_mel, input_mel] ,       axis=0						)
											else:
														__lowercase 					=   self._np_extract_fbank_features(snake_case_ ,       self.mel_filters_slaney						)[None, :]

								return input_mel, longer






					def __call__(				self ,       snake_case_ ,       snake_case_ = None ,       snake_case_ = None ,       snake_case_ = None ,       snake_case_ = None ,       snake_case_ = None ,       **snake_case_ ,       )   ->			BatchFeature:







								'''simple docstring'''

								__lowercase 					=   truncation if truncation is not None else self.truncation
								__lowercase 					=   padding if padding else self.padding

								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.'''						)

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

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

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

								# convert to mel spectrogram, truncate and pad if needed.
								__lowercase 					=   [
								    self._get_input_mel(snake_case_ ,       max_length if max_length else self.nb_max_samples ,       snake_case_ ,       snake_case_						)
								    for waveform in raw_speech
								]

								__lowercase 					=   []
								__lowercase 					=   []
								for mel, longer in padded_inputs:
											input_mel.append(snake_case_						)
											is_longer.append(snake_case_						)

								if truncation == "fusion" and sum(snake_case_						) == 0:
											# if no audio is longer than 10s, then randomly select one audio to be longer
											__lowercase 					=   np.random.randint(0 ,       len(snake_case_						)						)
											__lowercase 					=   True

								if isinstance(input_mel[0] ,       snake_case_						):
											__lowercase 					=   [np.asarray(snake_case_ ,       dtype=np.floataa						) for feature in input_mel]

								# is_longer is a list of bool
								__lowercase 					=   [[longer] for longer in is_longer]

								__lowercase 					=   {'''input_features''': input_mel, '''is_longer''': is_longer}
								__lowercase 					=   BatchFeature(snake_case_						)

								if return_tensors is not None:
											__lowercase 					=   input_features.convert_to_tensors(snake_case_						)

								return input_features






import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType
class 	lowerCamelCase_       :



					'''simple docstring'''







					def __init__(				self ,       snake_case_=2 ,       snake_case_=3 ,       snake_case_=6_4 ,       snake_case_=None						)   ->			List[str]:







								'''simple docstring'''

								__lowercase 					=   np.random.default_rng(snake_case_						)
								__lowercase 					=   length
								__lowercase 					=   rng.normal(size=(length,)						).astype(np.floataa						)
								__lowercase 					=   a * self.x + b + rng.normal(scale=0.1 ,       size=(length,)						).astype(np.floataa						)






					def __len__(				self						)   ->			Dict:







								'''simple docstring'''

								return self.length






					def __getitem__(				self ,       snake_case_						)   ->			Union[str, Any]:







								'''simple docstring'''

								return {"x": self.x[i], "y": self.y[i]}




class 	lowerCamelCase_       (				torch.nn.Module				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_=0 ,       snake_case_=0 ,       snake_case_=False						)   ->			Optional[Any]:







								'''simple docstring'''

								super().__init__()
								__lowercase 					=   torch.nn.Parameter(torch.tensor([2, 3]						).float()						)
								__lowercase 					=   torch.nn.Parameter(torch.tensor([2, 3]						).float()						)
								__lowercase 					=   True






					def A      (				self ,       snake_case_=None						)   ->			List[Any]:







								'''simple docstring'''

								if self.first_batch:
											print(F'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'						)
											__lowercase 					=   False
								return x * self.a[0] + self.b[0]




class 	lowerCamelCase_       (				torch.nn.Module				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_=0 ,       snake_case_=0 ,       snake_case_=False						)   ->			List[str]:







								'''simple docstring'''

								super().__init__()
								__lowercase 					=   torch.nn.Parameter(torch.tensor(snake_case_						).float()						)
								__lowercase 					=   torch.nn.Parameter(torch.tensor(snake_case_						).float()						)
								__lowercase 					=   True






					def A      (				self ,       snake_case_=None						)   ->			str:







								'''simple docstring'''

								if self.first_batch:
											print(F'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'						)
											__lowercase 					=   False
								return x * self.a + self.b




def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase = 16  ):


			'''simple docstring'''







			from datasets import load_dataset
			from transformers import AutoTokenizer

			__lowercase 					=   AutoTokenizer.from_pretrained('''bert-base-cased'''  )
			__lowercase 					=   {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
			__lowercase 					=   load_dataset('''csv'''       ,			data_files=_UpperCamelCase  )
			__lowercase 					=   datasets['''train'''].unique('''label'''  )

			__lowercase 					=   {v: i for i, v in enumerate(_UpperCamelCase  )}

			def tokenize_function(_UpperCamelCase  ):
						# max_length=None => use the model max length (it's actually the default)
						__lowercase 					=   tokenizer(
						    examples['''sentence1''']       ,			examples['''sentence2''']       ,			truncation=_UpperCamelCase       ,			max_length=_UpperCamelCase       ,			padding='''max_length'''  )
						if "label" in examples:
									__lowercase 					=   [label_to_id[l] for l in examples['''label''']]
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			__lowercase 					=   datasets.map(
			    _UpperCamelCase       ,			batched=_UpperCamelCase       ,			remove_columns=['''sentence1''', '''sentence2''', '''label''']       ,			)

			def collate_fn(_UpperCamelCase  ):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(_UpperCamelCase       ,			padding='''max_length'''       ,			max_length=1_28       ,			return_tensors='''pt'''  )
						return tokenizer.pad(_UpperCamelCase       ,			padding='''longest'''       ,			return_tensors='''pt'''  )

			# Instantiate dataloaders.
			__lowercase 					=   DataLoader(tokenized_datasets['''train''']       ,			shuffle=_UpperCamelCase       ,			collate_fn=_UpperCamelCase       ,			batch_size=2  )
			__lowercase 					=   DataLoader(tokenized_datasets['''validation''']       ,			shuffle=_UpperCamelCase       ,			collate_fn=_UpperCamelCase       ,			batch_size=1  )

			return train_dataloader, eval_dataloader


