


"""simple docstring"""


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


A	:  Tuple            =     logging.get_logger(__name__)
class     _UpperCamelCase    (  lowerCamelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Any							 =["""input_features""", """attention_mask"""]

							def __init__(	self       ,  __a=80       ,  __a=1_60_00       ,  __a=80       ,  __a=0.0       ,  __a=True       ,  __a=True       ,  __a=True       ,  **__a       ,  ):
										super().__init__(feature_size=SCREAMING_SNAKE_CASE__       ,  sampling_rate=SCREAMING_SNAKE_CASE__       ,  padding_value=SCREAMING_SNAKE_CASE__       ,  **SCREAMING_SNAKE_CASE__      )
										__lowerCAmelCase							       =     num_mel_bins
										__lowerCAmelCase							       =     do_ceptral_normalize
										__lowerCAmelCase							       =     normalize_means
										__lowerCAmelCase							       =     normalize_vars
										__lowerCAmelCase							       =     True

							def      snake_case							(	self       ,  __a       ,  ):
										__lowerCAmelCase							       =     waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
										__lowerCAmelCase							       =     torch.from_numpy(SCREAMING_SNAKE_CASE__      ).unsqueeze(0      )
										__lowerCAmelCase							       =     ta_kaldi.fbank(SCREAMING_SNAKE_CASE__       ,  num_mel_bins=self.num_mel_bins       ,  sample_frequency=self.sampling_rate      )
										return features.numpy()

							@staticmethod
							def      snake_case							(	__a       ,  __a       ,  __a = True       ,  __a = True       ,  __a = 0.0       ,  ):



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

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

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

										return x

							def      snake_case							(	self       ,  __a       ,  __a = None      ):
										__lowerCAmelCase							       =     attention_mask.sum(-1      ) if attention_mask is not None else [x.shape[0] for x in input_features]
										return [
										    self.utterance_cmvn(SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__       ,  self.normalize_means       ,  self.normalize_vars       ,  self.padding_value      )
										    for x, n in zip(SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__      )
										]



							def __call__(	self       ,  __a       ,  __a = False       ,  __a = None       ,  __a = False       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = None       ,  **__a       ,  ):



										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."      )

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

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

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

										# extract fbank features
										__lowerCAmelCase							       =     [self._extract_fbank_features(SCREAMING_SNAKE_CASE__      ) for waveform in raw_speech]

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

										__lowerCAmelCase							       =     self.pad(
										    SCREAMING_SNAKE_CASE__       ,  padding=SCREAMING_SNAKE_CASE__       ,  max_length=SCREAMING_SNAKE_CASE__       ,  truncation=SCREAMING_SNAKE_CASE__       ,  pad_to_multiple_of=SCREAMING_SNAKE_CASE__       ,  return_attention_mask=SCREAMING_SNAKE_CASE__       ,  **SCREAMING_SNAKE_CASE__       ,  )

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

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

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

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

										return padded_inputs








"""simple docstring"""






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


UpperCamelCase__				:Dict			   =	"""true"""





def 				A_       ( snake_case__       ,					snake_case__=82       ,					snake_case__=16		)					->  List[Any]:
			set_seed(42		)
			_UpperCamelCase					:Dict					     =	RegressionModel()
			_UpperCamelCase					:Dict					     =	deepcopy(snake_case__		)
			_UpperCamelCase					:List[Any]					     =	RegressionDataset(length=snake_case__		)
			_UpperCamelCase					:Optional[int]					     =	DataLoader(snake_case__       ,					batch_size=snake_case__		)
			model.to(accelerator.device		)
			_UpperCamelCase     ,      _UpperCamelCase					:List[str]					     =	accelerator.prepare(snake_case__       ,					snake_case__		)
			return model, ddp_model, dataloader





def 				A_       ( snake_case__       ,					snake_case__=False		)					->  Optional[int]:
			_UpperCamelCase					:Optional[Any]					     =	AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''		)
			_UpperCamelCase					:int					     =	load_dataset('''glue'''       ,					'''mrpc'''       ,					split='''validation'''		)

			def tokenize_function(snake_case__		):
						_UpperCamelCase					:Optional[int]					     =	tokenizer(examples['''sentence1''']       ,					examples['''sentence2''']       ,					truncation=snake_case__       ,					max_length=snake_case__		)
						return outputs

			with accelerator.main_process_first():
						_UpperCamelCase					:Optional[int]					     =	dataset.map(
						    snake_case__       ,					batched=snake_case__       ,					remove_columns=['''idx''', '''sentence1''', '''sentence2''']       ,					)

			_UpperCamelCase					:Tuple					     =	tokenized_datasets.rename_column('''label'''       ,					'''labels'''		)

			def collate_fn(snake_case__		):
						if use_longest:
									return tokenizer.pad(snake_case__       ,					padding='''longest'''       ,					return_tensors='''pt'''		)
						return tokenizer.pad(snake_case__       ,					padding='''max_length'''       ,					max_length=1_28       ,					return_tensors='''pt'''		)

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





def 				A_       ( snake_case__       ,					snake_case__		)					->  Tuple:
			_UpperCamelCase					:Union[str, Any]					     =	Accelerator(dispatch_batches=snake_case__       ,					split_batches=snake_case__		)
			_UpperCamelCase					:Any					     =	get_dataloader(snake_case__       ,					not dispatch_batches		)
			_UpperCamelCase					:Optional[int]					     =	AutoModelForSequenceClassification.from_pretrained(
			    '''hf-internal-testing/mrpc-bert-base-cased'''       ,					return_dict=snake_case__		)
			_UpperCamelCase     ,      _UpperCamelCase					:int					     =	accelerator.prepare(snake_case__       ,					snake_case__		)
			return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  List[Any]:
			_UpperCamelCase					:Any					     =	[]
			for batch in dataloader:
						_UpperCamelCase     ,      _UpperCamelCase					:List[Any]					     =	batch.values()
						with torch.no_grad():
									_UpperCamelCase					:List[Any]					     =	model(snake_case__		)
									_UpperCamelCase     ,      _UpperCamelCase					:Optional[int]					     =	accelerator.gather_for_metrics((logit, target)		)
									logits_and_targets.append((logit, target)		)
			_UpperCamelCase     ,      _UpperCamelCase					:Optional[Any]					     =	[], []
			for logit, targ in logits_and_targets:
						logits.append(snake_case__		)
						targs.append(snake_case__		)
			_UpperCamelCase     ,      _UpperCamelCase					:List[str]					     =	torch.cat(snake_case__		), torch.cat(snake_case__		)
			return logits, targs





def 				A_       ( snake_case__       ,					snake_case__=82       ,					snake_case__=False       ,					snake_case__=False       ,					snake_case__=16		)					->  Dict:
			_UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase					:List[str]					     =	get_basic_setup(snake_case__       ,					snake_case__       ,					snake_case__		)
			_UpperCamelCase     ,      _UpperCamelCase					:Tuple					     =	generate_predictions(snake_case__       ,					snake_case__       ,					snake_case__		)
			assert (
			    len(snake_case__		) == num_samples
			), f"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(snake_case__		)}"





def 				A_       ( snake_case__ = False       ,					snake_case__ = False		)					->  Optional[Any]:
			_UpperCamelCase					:Any					     =	evaluate.load('''glue'''       ,					'''mrpc'''		)
			_UpperCamelCase     ,      _UpperCamelCase					:Optional[int]					     =	get_mrpc_setup(snake_case__       ,					snake_case__		)
			# First do baseline
			_UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase					:Tuple					     =	setup['''no''']
			model.to(snake_case__		)
			model.eval()
			for batch in dataloader:
						batch.to(snake_case__		)
						with torch.inference_mode():
									_UpperCamelCase					:str					     =	model(**snake_case__		)
						_UpperCamelCase					:Dict					     =	outputs.logits.argmax(dim=-1		)
						metric.add_batch(predictions=snake_case__       ,					references=batch['''labels''']		)
			_UpperCamelCase					:int					     =	metric.compute()

			# Then do distributed
			_UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase					:Optional[int]					     =	setup['''ddp''']
			model.eval()
			for batch in dataloader:
						with torch.inference_mode():
									_UpperCamelCase					:Dict					     =	model(**snake_case__		)
						_UpperCamelCase					:str					     =	outputs.logits.argmax(dim=-1		)
						_UpperCamelCase					:Union[str, Any]					     =	batch['''labels''']
						_UpperCamelCase     ,      _UpperCamelCase					:Tuple					     =	accelerator.gather_for_metrics((preds, references)		)
						metric.add_batch(predictions=snake_case__       ,					references=snake_case__		)
			_UpperCamelCase					:Optional[Any]					     =	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 				A_       ( )					->  Optional[Any]:
			_UpperCamelCase					:int					     =	Accelerator(split_batches=snake_case__       ,					dispatch_batches=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(snake_case__       ,					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]:
									_UpperCamelCase					:Tuple					     =	Accelerator(split_batches=snake_case__       ,					dispatch_batches=snake_case__		)
									if accelerator.is_local_main_process:
												print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"		)
									test_torch_metrics(snake_case__       ,					99		)
									accelerator.state._reset_state()
			if accelerator.is_local_main_process:
						print('''**Test last batch is not dropped when perfectly divisible**'''		)
			_UpperCamelCase					:Any					     =	Accelerator()
			test_torch_metrics(snake_case__       ,					5_12		)
			accelerator.state._reset_state()





def 				A_       ( snake_case__		)					->  Dict:
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
					main()

