






import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


__lowerCamelCase      :			Union[str, Any]							       =		'''true'''



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Optional[Any]			,					__UpperCamelCase   :	List[str]=82			,					__UpperCamelCase   :	str=16     )							-> Optional[int]:







  """simple docstring"""





  set_seed(42     )
  SCREAMING_SNAKE_CASE__      							=				RegressionModel()
  SCREAMING_SNAKE_CASE__      							=				deepcopy(SCREAMING_SNAKE_CASE_     )
  SCREAMING_SNAKE_CASE__      							=				RegressionDataset(length=SCREAMING_SNAKE_CASE_     )
  SCREAMING_SNAKE_CASE__      							=				DataLoader(SCREAMING_SNAKE_CASE_			,					batch_size=SCREAMING_SNAKE_CASE_     )
  model.to(accelerator.device     )
  SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				accelerator.prepare(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_     )
  return model, ddp_model, dataloader



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Accelerator			,					__UpperCamelCase   :	Optional[int]=False     )							-> List[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased"""     )
  SCREAMING_SNAKE_CASE__      							=				load_dataset("""glue"""			,					"""mrpc"""			,					split="""validation"""     )

  def tokenize_function(__UpperCamelCase   :	List[Any]     ):
    SCREAMING_SNAKE_CASE__      							=				tokenizer(examples["""sentence1"""]			,					examples["""sentence2"""]			,					truncation=SCREAMING_SNAKE_CASE_			,					max_length=SCREAMING_SNAKE_CASE_     )
    return outputs

  with accelerator.main_process_first():
    SCREAMING_SNAKE_CASE__      							=				dataset.map(
        SCREAMING_SNAKE_CASE_			,					batched=SCREAMING_SNAKE_CASE_			,					remove_columns=["""idx""", """sentence1""", """sentence2"""]			,					)

  SCREAMING_SNAKE_CASE__      							=				tokenized_datasets.rename_column("""label"""			,					"""labels"""     )

  def collate_fn(__UpperCamelCase   :	Union[str, Any]     ):
    if use_longest:
      return tokenizer.pad(SCREAMING_SNAKE_CASE_			,					padding="""longest"""			,					return_tensors="""pt"""     )
    return tokenizer.pad(SCREAMING_SNAKE_CASE_			,					padding="""max_length"""			,					max_length=1_28			,					return_tensors="""pt"""     )

  return DataLoader(SCREAMING_SNAKE_CASE_			,					shuffle=SCREAMING_SNAKE_CASE_			,					collate_fn=SCREAMING_SNAKE_CASE_			,					batch_size=16     )



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[str]			,					__UpperCamelCase   :	int     )							-> Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				Accelerator(dispatch_batches=SCREAMING_SNAKE_CASE_			,					split_batches=SCREAMING_SNAKE_CASE_     )
  SCREAMING_SNAKE_CASE__      							=				get_dataloader(SCREAMING_SNAKE_CASE_			,					not dispatch_batches     )
  SCREAMING_SNAKE_CASE__      							=				AutoModelForSequenceClassification.from_pretrained(
      """hf-internal-testing/mrpc-bert-base-cased"""			,					return_dict=SCREAMING_SNAKE_CASE_     )
  SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				accelerator.prepare(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_     )
  return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	str			,					__UpperCamelCase   :	List[str]			,					__UpperCamelCase   :	Optional[int]     )							-> Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[]
  for batch in dataloader:
    SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				batch.values()
    with torch.no_grad():
      SCREAMING_SNAKE_CASE__      							=				model(SCREAMING_SNAKE_CASE_     )
      SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				accelerator.gather_for_metrics((logit, target)     )
      logits_and_targets.append((logit, target)     )
  SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				[], []
  for logit, targ in logits_and_targets:
    logits.append(SCREAMING_SNAKE_CASE_     )
    targs.append(SCREAMING_SNAKE_CASE_     )
  SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				torch.cat(SCREAMING_SNAKE_CASE_     ), torch.cat(SCREAMING_SNAKE_CASE_     )
  return logits, targs



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Accelerator			,					__UpperCamelCase   :	Union[str, Any]=82			,					__UpperCamelCase   :	Union[str, Any]=False			,					__UpperCamelCase   :	Any=False			,					__UpperCamelCase   :	Optional[int]=16     )							-> Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				get_basic_setup(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_     )
  SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				generate_predictions(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_     )
  assert (
      len(SCREAMING_SNAKE_CASE_     ) == num_samples
  ), f"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(SCREAMING_SNAKE_CASE_     )}"""



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	bool = False			,					__UpperCamelCase   :	bool = False     )							-> Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				evaluate.load("""glue"""			,					"""mrpc"""     )
  SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				get_mrpc_setup(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_     )
  # First do baseline
  SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				setup["""no"""]
  model.to(SCREAMING_SNAKE_CASE_     )
  model.eval()
  for batch in dataloader:
    batch.to(SCREAMING_SNAKE_CASE_     )
    with torch.inference_mode():
      SCREAMING_SNAKE_CASE__      							=				model(**SCREAMING_SNAKE_CASE_     )
    SCREAMING_SNAKE_CASE__      							=				outputs.logits.argmax(dim=-1     )
    metric.add_batch(predictions=SCREAMING_SNAKE_CASE_			,					references=batch["""labels"""]     )
  SCREAMING_SNAKE_CASE__      							=				metric.compute()

  # Then do distributed
  SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				setup["""ddp"""]
  model.eval()
  for batch in dataloader:
    with torch.inference_mode():
      SCREAMING_SNAKE_CASE__      							=				model(**SCREAMING_SNAKE_CASE_     )
    SCREAMING_SNAKE_CASE__      							=				outputs.logits.argmax(dim=-1     )
    SCREAMING_SNAKE_CASE__      							=				batch["""labels"""]
    SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				accelerator.gather_for_metrics((preds, references)     )
    metric.add_batch(predictions=SCREAMING_SNAKE_CASE_			,					references=SCREAMING_SNAKE_CASE_     )
  SCREAMING_SNAKE_CASE__      							=				metric.compute()

  for key in "accuracy f1".split():
    assert math.isclose(
        baseline[key]			,					distributed[key]     ), f"""Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"""



def      __SCREAMING_SNAKE_CASE  ( )							-> List[str]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				Accelerator(split_batches=SCREAMING_SNAKE_CASE_			,					dispatch_batches=SCREAMING_SNAKE_CASE_     )
  if accelerator.is_local_main_process:
    datasets.utils.logging.set_verbosity_warning()
    transformers.utils.logging.set_verbosity_warning()
  else:
    datasets.utils.logging.set_verbosity_error()
    transformers.utils.logging.set_verbosity_error()
  # These are a bit slower so they should only be ran on the GPU or TPU
  if torch.cuda.is_available() or is_tpu_available():
    if accelerator.is_local_main_process:
      print("""**Testing gather_for_metrics**"""     )
    for split_batches in [True, False]:
      for dispatch_batches in [True, False]:
        if accelerator.is_local_main_process:
          print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"""     )
        test_mrpc(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_     )
        accelerator.state._reset_state()
  if accelerator.is_local_main_process:
    print("""**Test torch metrics**"""     )
  for split_batches in [True, False]:
    for dispatch_batches in [True, False]:
      SCREAMING_SNAKE_CASE__      							=				Accelerator(split_batches=SCREAMING_SNAKE_CASE_			,					dispatch_batches=SCREAMING_SNAKE_CASE_     )
      if accelerator.is_local_main_process:
        print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""     )
      test_torch_metrics(SCREAMING_SNAKE_CASE_			,					99     )
      accelerator.state._reset_state()
  if accelerator.is_local_main_process:
    print("""**Test last batch is not dropped when perfectly divisible**"""     )
  SCREAMING_SNAKE_CASE__      							=				Accelerator()
  test_torch_metrics(SCREAMING_SNAKE_CASE_			,					5_12     )
  accelerator.state._reset_state()



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[str]     )							-> str:







  """simple docstring"""





  main()


if __name__ == "__main__":
   main()

from typing import List, Optional, Union

import numpy as np

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


__lowerCamelCase      :			Any							       =		logging.get_logger(__name__)







class __snake_case    (						lowerCamelCase_		):
   lowerCAmelCase_														=							["input_features", "attention_mask"]




   def __init__(   self      :    int	,     _lowercase      :    Dict=80	,     _lowercase      :    Union[str, Any]=1_60_00	,     _lowercase      :    str=0.0	,     _lowercase      :    int=10	,     _lowercase      :    str=25	,     _lowercase      :    Union[str, Any]="hamming_window"	,     _lowercase      :    Dict=3_27_68.0	,     _lowercase      :    Optional[int]=0.97	,     _lowercase      :    Optional[int]=1.0	,     _lowercase      :    int=True	,     _lowercase      :    Union[str, Any]=True	,     _lowercase      :    Optional[int]=False	,     **_lowercase      :    Union[str, Any]	,     ):



     """simple docstring"""






     super().__init__(feature_size=_lowercase	,     sampling_rate=_lowercase	,     padding_value=_lowercase	,     **_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				feature_size
     SCREAMING_SNAKE_CASE__      							=				sampling_rate
     SCREAMING_SNAKE_CASE__      							=				padding_value
     SCREAMING_SNAKE_CASE__      							=				hop_length
     SCREAMING_SNAKE_CASE__      							=				win_length
     SCREAMING_SNAKE_CASE__      							=				frame_signal_scale
     SCREAMING_SNAKE_CASE__      							=				preemphasis_coeff
     SCREAMING_SNAKE_CASE__      							=				mel_floor
     SCREAMING_SNAKE_CASE__      							=				normalize_means
     SCREAMING_SNAKE_CASE__      							=				normalize_vars
     SCREAMING_SNAKE_CASE__      							=				win_function
     SCREAMING_SNAKE_CASE__      							=				return_attention_mask

     SCREAMING_SNAKE_CASE__      							=				win_length * sampling_rate // 10_00
     SCREAMING_SNAKE_CASE__      							=				hop_length * sampling_rate // 10_00

     SCREAMING_SNAKE_CASE__      							=				optimal_fft_length(self.sample_size				)
     SCREAMING_SNAKE_CASE__      							=				(self.n_fft // 2) + 1




   def      __a						(   self      :    str	,     _lowercase      :    np.array				):



     """simple docstring"""






     if self.win_function == "hamming_window":
       SCREAMING_SNAKE_CASE__      							=				window_function(window_length=self.sample_size	,     name=self.win_function	,     periodic=_lowercase				)
     else:
       SCREAMING_SNAKE_CASE__      							=				window_function(window_length=self.sample_size	,     name=self.win_function				)

     SCREAMING_SNAKE_CASE__      							=				mel_filter_bank(
         num_frequency_bins=self.n_freqs	,     num_mel_filters=self.feature_size	,     min_frequency=0.0	,     max_frequency=self.sampling_rate / 2.0	,     sampling_rate=self.sampling_rate	,     )

     SCREAMING_SNAKE_CASE__      							=				spectrogram(
         one_waveform * self.frame_signal_scale	,     window=_lowercase	,     frame_length=self.sample_size	,     hop_length=self.sample_stride	,     fft_length=self.n_fft	,     center=_lowercase	,     preemphasis=self.preemphasis_coeff	,     mel_filters=_lowercase	,     mel_floor=self.mel_floor	,     log_mel="""log"""	,     )
     return msfc_features.T




   def      __a						(   self      :    Tuple	,     _lowercase      :    Union[str, Any]	,     _lowercase      :    Optional[int]	,     _lowercase      :    int				):



     """simple docstring"""






     if self.normalize_means:
       SCREAMING_SNAKE_CASE__      							=				x[:input_length].mean(axis=0				)
       SCREAMING_SNAKE_CASE__      							=				np.subtract(_lowercase	,     _lowercase				)
     if self.normalize_vars:
       SCREAMING_SNAKE_CASE__      							=				x[:input_length].std(axis=0				)
       SCREAMING_SNAKE_CASE__      							=				np.divide(_lowercase	,     _lowercase				)

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

     # make sure array is in float32
     SCREAMING_SNAKE_CASE__      							=				x.astype(np.floataa				)

     return x




   def      __a						(   self      :    int	,     _lowercase      :    List[np.ndarray]	,     _lowercase      :    Optional[np.ndarray] = None				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				attention_mask.sum(-1				) if attention_mask is not None else [x.shape[0] for x in input_features]
     return [self._normalize_one(_lowercase	,     _lowercase	,     self.padding_value				) for x, n in zip(_lowercase	,     _lowercase				)]




   def __call__(   self      :    Dict	,     _lowercase      :    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,     _lowercase      :    Union[bool, str, PaddingStrategy] = False	,     _lowercase      :    Optional[int] = None	,     _lowercase      :    bool = False	,     _lowercase      :    Optional[int] = None	,     _lowercase      :    Optional[bool] = None	,     _lowercase      :    Optional[Union[str, TensorType]] = None	,     _lowercase      :    Optional[int] = None	,     **_lowercase      :    List[str]	,     ):



     """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."""				)

     SCREAMING_SNAKE_CASE__      							=				isinstance(_lowercase	,     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}"""				)
     SCREAMING_SNAKE_CASE__      							=				is_batched_numpy or (
         isinstance(_lowercase	,     (list, tuple)				) and (isinstance(raw_speech[0]	,     (np.ndarray, tuple, list)				))
     )

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

     # always return batch
     if not is_batched:
       SCREAMING_SNAKE_CASE__      							=				[raw_speech]

     # extract fbank features
     SCREAMING_SNAKE_CASE__      							=				[self._extract_mfsc_features(_lowercase				) for one_waveform in raw_speech]

     # convert into correct format for padding
     SCREAMING_SNAKE_CASE__      							=				BatchFeature({"""input_features""": features}				)

     SCREAMING_SNAKE_CASE__      							=				self.pad(
         _lowercase	,     padding=_lowercase	,     max_length=_lowercase	,     truncation=_lowercase	,     pad_to_multiple_of=_lowercase	,     return_attention_mask=_lowercase	,     **_lowercase	,     )
     # make sure list is in array format
     SCREAMING_SNAKE_CASE__      							=				padded_inputs.get("""input_features"""				)
     if isinstance(input_features[0]	,     _lowercase				):
       SCREAMING_SNAKE_CASE__      							=				[np.asarray(_lowercase	,     dtype=np.floataa				) for feature in input_features]

     SCREAMING_SNAKE_CASE__      							=				padded_inputs.get("""attention_mask"""				)
     if attention_mask is not None:
       SCREAMING_SNAKE_CASE__      							=				[np.asarray(_lowercase	,     dtype=np.intaa				) for array in attention_mask]

     if self.normalize_means or self.normalize_vars:
       SCREAMING_SNAKE_CASE__      							=				(
           np.array(_lowercase	,     dtype=np.intaa				)
           if self._get_padding_strategies(_lowercase	,     max_length=_lowercase				) is not PaddingStrategy.DO_NOT_PAD
           and padding
           else None
       )
       SCREAMING_SNAKE_CASE__      							=				self.normalize(
           padded_inputs["""input_features"""]	,     attention_mask=_lowercase				)

     if return_tensors is not None:
       SCREAMING_SNAKE_CASE__      							=				padded_inputs.convert_to_tensors(_lowercase				)

     return padded_inputs
