

'''simple docstring'''





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

from accelerate.utils.dataclasses import DistributedType





class 		__SCREAMING_SNAKE_CASE							:







				def __init__(       self     :		Dict		,					UpperCAmelCase__     :		Union[str, Any]=2		,					UpperCAmelCase__     :		str=3		,					UpperCAmelCase__     :		Optional[int]=64		,					UpperCAmelCase__     :		Union[str, Any]=None   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=np.random.default_rng(UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=length
										lowercase :							List[Any]    		=rng.normal(size=(length,)   ).astype(np.floataa   )
										lowercase :							Dict    		=a * self.x + b + rng.normal(scale=0.1		,					size=(length,)   ).astype(np.floataa   )







				def __len__(       self     :		Tuple   ):
										'''simple docstring'''
										return self.length







				def __getitem__(       self     :		Union[str, Any]		,					UpperCAmelCase__     :		Any   ):
										'''simple docstring'''
										return {"x": self.x[i], "y": self.y[i]}





class 		__SCREAMING_SNAKE_CASE							(			torch.nn.Module      ):







				def __init__(       self     :		Union[str, Any]		,					UpperCAmelCase__     :		List[Any]=0		,					UpperCAmelCase__     :		List[Any]=0		,					UpperCAmelCase__     :		str=False   ):
										'''simple docstring'''
										super().__init__()
										lowercase :							Any    		=torch.nn.Parameter(torch.tensor([2, 3]   ).float()   )
										lowercase :							List[str]    		=torch.nn.Parameter(torch.tensor([2, 3]   ).float()   )
										lowercase :							List[Any]    		=True







				def  lowerCamelCase_	(       self     :		str		,					UpperCAmelCase__     :		Any=None   ):
										'''simple docstring'''
										if self.first_batch:
																print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''   )
																lowercase :							Any    		=False
										return x * self.a[0] + self.b[0]





class 		__SCREAMING_SNAKE_CASE							(			torch.nn.Module      ):







				def __init__(       self     :		List[Any]		,					UpperCAmelCase__     :		Optional[Any]=0		,					UpperCAmelCase__     :		int=0		,					UpperCAmelCase__     :		Optional[Any]=False   ):
										'''simple docstring'''
										super().__init__()
										lowercase :							Optional[int]    		=torch.nn.Parameter(torch.tensor(UpperCAmelCase__   ).float()   )
										lowercase :							int    		=torch.nn.Parameter(torch.tensor(UpperCAmelCase__   ).float()   )
										lowercase :							Tuple    		=True







				def  lowerCamelCase_	(       self     :		List[Any]		,					UpperCAmelCase__     :		Optional[Any]=None   ):
										'''simple docstring'''
										if self.first_batch:
																print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''   )
																lowercase :							List[Any]    		=False
										return x * self.a + self.b






def 				_lowerCAmelCase			(   __magic_name__						:     Tuple   , __magic_name__						:     int = 16						)				->  Union[str, Any]:
						from datasets import load_dataset
						from transformers import AutoTokenizer

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

						lowercase :							List[str]    		={v: i for i, v in enumerate(__magic_name__						)}

						def tokenize_function(__magic_name__						:     Dict						):
												# max_length=None => use the model max length (it's actually the default)
												lowercase :							Dict    		=tokenizer(
												    examples['''sentence1''']   , examples['''sentence2''']   , truncation=__magic_name__   , max_length=__magic_name__   , padding='''max_length'''						)
												if "label" in examples:
																		lowercase :							List[Any]    		=[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 :							Optional[int]    		=datasets.map(
						    __magic_name__   , batched=__magic_name__   , remove_columns=['''sentence1''', '''sentence2''', '''label''']   , )

						def collate_fn(__magic_name__						:     Any						):
												# 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(__magic_name__   , padding='''max_length'''   , max_length=128   , return_tensors='''pt'''						)
												return tokenizer.pad(__magic_name__   , padding='''longest'''   , return_tensors='''pt'''						)

						# Instantiate dataloaders.
						lowercase :							Union[str, Any]    		=DataLoader(tokenized_datasets['''train''']   , shuffle=__magic_name__   , collate_fn=__magic_name__   , batch_size=2						)
						lowercase :							Tuple    		=DataLoader(tokenized_datasets['''validation''']   , shuffle=__magic_name__   , collate_fn=__magic_name__   , batch_size=1						)

						return train_dataloader, eval_dataloader












from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


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



class    A__  (			__snake_case     ):
      _UpperCAmelCase      :List[str]		       =  ['input_features', 'attention_mask']





      def __init__(					self			,				A_=80			,				A_=1_6000			,				A_=80			,				A_=0.0			,				A_=True			,				A_=True			,				A_=True			,				**A_			,				):
       '''simple docstring'''




       super().__init__(feature_size=A_			,				sampling_rate=A_			,				padding_value=A_			,				**A_  )
       UpperCamelCase      :   List[Any]			      =  num_mel_bins
       UpperCamelCase      :   Any			      =  do_ceptral_normalize
       UpperCamelCase      :   int			      =  normalize_means
       UpperCamelCase      :   Tuple			      =  normalize_vars
       UpperCamelCase      :   List[Any]			      =  True





      def 			__UpperCamelCase(					self			,				A_			,				):
       '''simple docstring'''




       UpperCamelCase      :   Optional[int]			      =  waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
       UpperCamelCase      :   Optional[Any]			      =  torch.from_numpy(A_  ).unsqueeze(0  )
       UpperCamelCase      :   Dict			      =  ta_kaldi.fbank(A_			,				num_mel_bins=self.num_mel_bins			,				sample_frequency=self.sampling_rate  )
       return features.numpy()





      @staticmethod
      def 			__UpperCamelCase(					A_			,				A_			,				A_ = True			,				A_ = True			,				A_ = 0.0			,				):
       '''simple docstring'''




       if normalize_means:
        UpperCamelCase      :   str			      =  x[:input_length].mean(axis=0  )
        UpperCamelCase      :   Any			      =  np.subtract(A_			,				A_  )
       if normalize_vars:
        UpperCamelCase      :   List[str]			      =  x[:input_length].std(axis=0  )
        UpperCamelCase      :   Optional[int]			      =  np.divide(A_			,				A_  )

       if input_length < x.shape[0]:
        UpperCamelCase      :   Dict			      =  padding_value

       # make sure array is in float32
       UpperCamelCase      :   Union[str, Any]			      =  x.astype(np.floataa  )

       return x





      def 			__UpperCamelCase(					self			,				A_			,				A_ = None  ):
       '''simple docstring'''




       UpperCamelCase      :   Optional[Any]			      =  attention_mask.sum(-1  ) if attention_mask is not None else [x.shape[0] for x in input_features]
       return [
           self.utterance_cmvn(A_			,				A_			,				self.normalize_means			,				self.normalize_vars			,				self.padding_value  )
           for x, n in zip(A_			,				A_  )
       ]





      def __call__(					self			,				A_			,				A_ = False			,				A_ = None			,				A_ = False			,				A_ = None			,				A_ = None			,				A_ = None			,				A_ = None			,				**A_			,				):
       '''simple docstring'''




       if sampling_rate is not None:
        if sampling_rate != self.sampling_rate:
         raise ValueError(
             F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
             F""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
             F""" {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      :   str			      =  isinstance(A_			,				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      :   List[Any]			      =  is_batched_numpy or (
           isinstance(A_			,				(list, tuple)  ) and (isinstance(raw_speech[0]			,				(np.ndarray, tuple, list)  ))
       )

       if is_batched:
        UpperCamelCase      :   List[str]			      =  [np.asarray(A_			,				dtype=np.floataa  ) for speech in raw_speech]
       elif not is_batched and not isinstance(A_			,				np.ndarray  ):
        UpperCamelCase      :   int			      =  np.asarray(A_			,				dtype=np.floataa  )
       elif isinstance(A_			,				np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
        UpperCamelCase      :   Union[str, Any]			      =  raw_speech.astype(np.floataa  )

       # always return batch
       if not is_batched:
        UpperCamelCase      :   Optional[int]			      =  [raw_speech]

       # extract fbank features
       UpperCamelCase      :   Optional[int]			      =  [self._extract_fbank_features(A_  ) for waveform in raw_speech]

       # convert into correct format for padding
       UpperCamelCase      :   List[Any]			      =  BatchFeature({"input_features": features}  )

       UpperCamelCase      :   Any			      =  self.pad(
           A_			,				padding=A_			,				max_length=A_			,				truncation=A_			,				pad_to_multiple_of=A_			,				return_attention_mask=A_			,				**A_			,				)

       # make sure list is in array format
       UpperCamelCase      :   Optional[int]			      =  padded_inputs.get("input_features"  )
       if isinstance(input_features[0]			,				A_  ):
        UpperCamelCase      :   str			      =  [np.asarray(A_			,				dtype=np.floataa  ) for feature in input_features]

       UpperCamelCase      :   Optional[int]			      =  padded_inputs.get("attention_mask"  )
       if attention_mask is not None:
        UpperCamelCase      :   Optional[int]			      =  [np.asarray(A_			,				dtype=np.intaa  ) for array in attention_mask]

       # Utterance-level cepstral mean and variance normalization
       if self.do_ceptral_normalize:
        UpperCamelCase      :   List[Any]			      =  (
            np.array(A_			,				dtype=np.intaa  )
            if self._get_padding_strategies(A_			,				max_length=A_  ) is not PaddingStrategy.DO_NOT_PAD
            else None
        )
        UpperCamelCase      :   int			      =  self.normalize(
            padded_inputs["input_features"]			,				attention_mask=A_  )

       if return_tensors is not None:
        UpperCamelCase      :   List[str]			      =  padded_inputs.convert_to_tensors(A_  )

       return padded_inputs
