





from typing import List, Optional, Union

import numpy as np

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


snake_case__					:							str													=     logging.get_logger(__name__)





class A_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = ["""input_values""", """padding_mask"""]




   def __init__(self	:str    ,   _UpperCamelCase	:int = 1    ,   _UpperCamelCase	:int = 2_4000    ,   _UpperCamelCase	:float = 0.0    ,   _UpperCamelCase	:float = None    ,   _UpperCamelCase	:float = None    ,   **_UpperCamelCase	:str    ,   )->					Tuple:
         super().__init__(feature_size=_UpperCamelCase    ,   sampling_rate=_UpperCamelCase    ,   padding_value=_UpperCamelCase    ,   **_UpperCamelCase )
         __A          =				chunk_length_s
         __A          =				overlap




   @property
   def _lowerCAmelCase      (self	:List[str] )->					Optional[int]:
         if self.chunk_length_s is None:
               return None
         else:
               return int(self.chunk_length_s * self.sampling_rate )




   @property
   def _lowerCAmelCase      (self	:List[Any] )->					Optional[int]:
         if self.chunk_length_s is None or self.overlap is None:
               return None
         else:
               return max(1    ,   int((1.0 - self.overlap) * self.chunk_length ) )






   def __call__(self	:List[str]    ,   _UpperCamelCase	:Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]    ,   _UpperCamelCase	:Optional[Union[bool, str, PaddingStrategy]] = None    ,   _UpperCamelCase	:Optional[bool] = False    ,   _UpperCamelCase	:Optional[int] = None    ,   _UpperCamelCase	:Optional[Union[str, TensorType]] = None    ,   _UpperCamelCase	:Optional[int] = None    ,   )->					BatchFeature:
         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 audio 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.''' )

         if padding and truncation:
               raise ValueError('''Both padding and truncation were set. Make sure you only set one.''' )
         elif padding is None:
               # by default let's pad the inputs
               __A          =				True

         __A          =				bool(
             isinstance(_UpperCamelCase    ,   (list, tuple) ) and (isinstance(raw_audio[0]    ,   (np.ndarray, tuple, list) )) )

         if is_batched:
               __A          =				[np.asarray(_UpperCamelCase    ,   dtype=np.floataa ).T for audio in raw_audio]
         elif not is_batched and not isinstance(_UpperCamelCase    ,   np.ndarray ):
               __A          =				np.asarray(_UpperCamelCase    ,   dtype=np.floataa )
         elif isinstance(_UpperCamelCase    ,   np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ):
               __A          =				raw_audio.astype(np.floataa )

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

         # verify inputs are valid
         for idx, example in enumerate(_UpperCamelCase ):
               if example.ndim > 2:
                     raise ValueError(f"""Expected input shape (channels, length) but got shape {example.shape}""" )
               if self.feature_size == 1 and example.ndim != 1:
                     raise ValueError(f"""Expected mono audio but example has {example.shape[-1]} channels""" )
               if self.feature_size == 2 and example.shape[-1] != 2:
                     raise ValueError(f"""Expected stereo audio but example has {example.shape[-1]} channels""" )

         __A          =				None
         __A          =				BatchFeature({'''input_values''': raw_audio} )
         if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
               if truncation:
                     __A          =				min(array.shape[0] for array in raw_audio )
                     __A          =				int(np.floor(max_length / self.chunk_stride ) )
                     __A          =				(nb_step - 1) * self.chunk_stride + self.chunk_length
               elif padding:
                     __A          =				max(array.shape[0] for array in raw_audio )
                     __A          =				int(np.ceil(max_length / self.chunk_stride ) )
                     __A          =				(nb_step - 1) * self.chunk_stride + self.chunk_length
                     __A          =				'''max_length'''
               else:
                     __A          =				input_values

        # normal padding on batch
         if padded_inputs is None:
               __A          =				self.pad(
                   _UpperCamelCase    ,   max_length=_UpperCamelCase    ,   truncation=_UpperCamelCase    ,   padding=_UpperCamelCase    ,   return_attention_mask=_UpperCamelCase    ,   )
               if padding:
                     __A          =				padded_inputs.pop('''attention_mask''' )

         __A          =				[]
         for example in padded_inputs.pop('''input_values''' ):
               if self.feature_size == 1:
                     __A          =				example[..., None]
               input_values.append(example.T )

         __A          =				input_values
         if return_tensors is not None:
               __A          =				padded_inputs.convert_to_tensors(_UpperCamelCase )

         return padded_inputs












import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


snake_case__					:							Union[str, Any]													=     500000

snake_case__						,    snake_case__					:							Optional[Any]													=     os.path.split(__file__)
snake_case__					:							List[Any]													=     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))



@get_duration
def _a				(	lowerCamelCase:   datasets.Dataset					,				**lowerCamelCase:   Optional[int] )	->						str:







      '''simple docstring'''






      __A          =				dataset.map(**lowerCamelCase )



@get_duration
def _a				(	lowerCamelCase:   datasets.Dataset					,				**lowerCamelCase:   Optional[int] )	->						str:







      '''simple docstring'''






      __A          =				dataset.filter(**lowerCamelCase )



def _a				(	)	->						List[Any]:







      '''simple docstring'''






      __A          =				{'''num examples''': SPEED_TEST_N_EXAMPLES}
      with tempfile.TemporaryDirectory() as tmp_dir:
            __A          =				datasets.Features({'''text''': datasets.Value('''string''' ), '''numbers''': datasets.Value('''float32''' )} )
            __A          =				generate_example_dataset(
                os.path.join(lowerCamelCase					,				'''dataset.arrow''' )					,				lowerCamelCase					,				num_examples=lowerCamelCase )

            __A          =				transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''					,				use_fast=lowerCamelCase )

            def tokenize(lowerCamelCase:   List[str] ):
                  return tokenizer(examples['''text'''] )

            __A          =				map(lowerCamelCase )

            __A          =				map(lowerCamelCase					,				batched=lowerCamelCase )

            __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''numpy''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''pandas''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''torch'''					,				columns='''numbers''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''tensorflow'''					,				columns='''numbers''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            __A          =				map(lowerCamelCase					,				function=lowerCamelCase					,				batched=lowerCamelCase )

            __A          =				filter(lowerCamelCase )

            # Activate later when tokenizer support batched inputs
            # with dataset.formatted_as(type='numpy'):
            #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

      with open(lowerCamelCase					,				'''wb''' ) as f:
            f.write(json.dumps(lowerCamelCase ).encode('''utf-8''' ) )


if __name__ == "__main__":  # useful to run the profiler
       benchmark_map_filter()





