





'''simple docstring'''

import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional

import yaml

from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool


# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
a   	=    sys.version_info >= (3, 10)

def     __magic_name__							(				__UpperCAmelCase=None				,						__UpperCAmelCase=None   )    ->       str:

   '''simple docstring'''







   return field(default_factory=lambda: default				,						metadata=__UpperCAmelCase   )







@dataclass
class __a			:
   __UpperCamelCase :			int
   __UpperCamelCase :			float
   __UpperCamelCase :			str
   __UpperCamelCase :			bool







@dataclass
class __a			:
   __UpperCamelCase :			int						   =						42
   __UpperCamelCase :			str						   =						field(default='toto', metadata={'help': 'help message'}      )







@dataclass
class __a			:
   __UpperCamelCase :			bool						   =						False
   __UpperCamelCase :			bool						   =						True
   __UpperCamelCase :			Optional[bool]						   =						None







class __a			( _snake_case      ):
   __UpperCamelCase :			Any						   =						'titi'
   __UpperCamelCase :			Union[str, Any]						   =						'toto'







class __a			( _snake_case      ):
   __UpperCamelCase :			Optional[int]						   =						'titi'
   __UpperCamelCase :			int						   =						'toto'
   __UpperCamelCase :			Optional[Any]						   =						42







@dataclass
class __a			:
   __UpperCamelCase :			BasicEnum						   =						"toto"





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							BasicEnum(self.foo     )







@dataclass
class __a			:
   __UpperCamelCase :			MixedTypeEnum						   =						"toto"





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							MixedTypeEnum(self.foo     )







@dataclass
class __a			:
   __UpperCamelCase :			Optional[int]						   =						None
   __UpperCamelCase :			Optional[float]						   =						field(default=_snake_case, metadata={'help': 'help message'}      )
   __UpperCamelCase :			Optional[str]						   =						None
   __UpperCamelCase :			Optional[List[str]]						   =						list_field(default=[]      )
   __UpperCamelCase :			Optional[List[int]]						   =						list_field(default=[]      )







@dataclass
class __a			:
   __UpperCamelCase :			List[int]						   =						list_field(default=[]      )
   __UpperCamelCase :			List[int]						   =						list_field(default=[1, 2, 3]      )
   __UpperCamelCase :			List[str]						   =						list_field(default=['Hallo', 'Bonjour', 'Hello']      )
   __UpperCamelCase :			List[float]						   =						list_field(default=[0.1, 0.2, 0.3]      )







@dataclass
class __a			:
   __UpperCamelCase :			List[int]						   =						field()
   __UpperCamelCase :			str						   =						field()
   __UpperCamelCase :			BasicEnum						   =						field()





   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							BasicEnum(self.required_enum     )







@dataclass
class __a			:
   __UpperCamelCase :			int
   __UpperCamelCase :			"BasicEnum"						   =						field()
   __UpperCamelCase :			"Optional[bool]"						   =						None
   __UpperCamelCase :			"str"						   =						field(default='toto', metadata={'help': 'help message'}      )
   __UpperCamelCase :			"List[str]"						   =						list_field(default=['Hallo', 'Bonjour', 'Hello']      )


if is_python_no_less_than_3_10:







    @dataclass
    class __a			:
       __UpperCamelCase :			bool						   =						False
       __UpperCamelCase :			bool						   =						True
       __UpperCamelCase :			bool | None						   =						None







    @dataclass
    class __a			:
       __UpperCamelCase :			int | None						   =						None
       __UpperCamelCase :			float | None						   =						field(default=_snake_case, metadata={'help': 'help message'}      )
       __UpperCamelCase :			str | None						   =						None
       __UpperCamelCase :			list[str] | None						   =						list_field(default=[]      )
       __UpperCamelCase :			list[int] | None						   =						list_field(default=[]      )







class __a			( unittest.TestCase      ):





   def   UpperCAmelCase__  (       self					:      List[str]					,lowerCamelCase					:      argparse.ArgumentParser					,lowerCamelCase					:      argparse.ArgumentParser     ):

      '''simple docstring'''

      self.assertEqual(len(a._actions     )					,len(b._actions     )     )
      for x, y in zip(a._actions					,b._actions     ):
         __SCREAMING_SNAKE_CASE   				=							{k: v for k, v in vars(lowerCamelCase     ).items() if k != """container"""}
         __SCREAMING_SNAKE_CASE   				=							{k: v for k, v in vars(lowerCamelCase     ).items() if k != """container"""}

         # Choices with mixed type have custom function as "type"
         # So we need to compare results directly for equality
         if xx.get("""choices"""					,lowerCamelCase     ) and yy.get("""choices"""					,lowerCamelCase     ):
            for expected_choice in yy["choices"] + xx["choices"]:
               self.assertEqual(xx["""type"""](lowerCamelCase     )					,yy["""type"""](lowerCamelCase     )     )
            del xx["type"], yy["type"]

         self.assertEqual(lowerCamelCase					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							HfArgumentParser(lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							argparse.ArgumentParser()
      expected.add_argument("""--foo"""					,type=lowerCamelCase					,required=lowerCamelCase     )
      expected.add_argument("""--bar"""					,type=lowerCamelCase					,required=lowerCamelCase     )
      expected.add_argument("""--baz"""					,type=lowerCamelCase					,required=lowerCamelCase     )
      expected.add_argument("""--flag"""					,type=lowerCamelCase					,default=lowerCamelCase					,const=lowerCamelCase					,nargs="""?"""     )
      self.argparsersEqual(lowerCamelCase					,lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							["""--foo""", """1""", """--baz""", """quux""", """--bar""", """0.5"""]
      ((__SCREAMING_SNAKE_CASE)				,  )   				=							parser.parse_args_into_dataclasses(lowerCamelCase					,look_for_args_file=lowerCamelCase     )
      self.assertFalse(example.flag     )





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							HfArgumentParser(lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							argparse.ArgumentParser()
      expected.add_argument("""--foo"""					,default=42					,type=lowerCamelCase     )
      expected.add_argument("""--baz"""					,default="""toto"""					,type=lowerCamelCase					,help="""help message"""     )
      self.argparsersEqual(lowerCamelCase					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							argparse.ArgumentParser()
      expected.add_argument("""--foo"""					,type=lowerCamelCase					,default=lowerCamelCase					,const=lowerCamelCase					,nargs="""?"""     )
      expected.add_argument("""--baz"""					,type=lowerCamelCase					,default=lowerCamelCase					,const=lowerCamelCase					,nargs="""?"""     )
      # A boolean no_* argument always has to come after its "default: True" regular counter-part
      # and its default must be set to False
      expected.add_argument("""--no_baz"""					,action="""store_false"""					,default=lowerCamelCase					,dest="""baz"""     )
      expected.add_argument("""--opt"""					,type=lowerCamelCase					,default=lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							[WithDefaultBoolExample]
      if is_python_no_less_than_3_10:
         dataclass_types.append(lowerCamelCase     )

      for dataclass_type in dataclass_types:
         __SCREAMING_SNAKE_CASE   				=							HfArgumentParser(lowerCamelCase     )
         self.argparsersEqual(lowerCamelCase					,lowerCamelCase     )

         __SCREAMING_SNAKE_CASE   				=							parser.parse_args([]     )
         self.assertEqual(lowerCamelCase					,Namespace(foo=lowerCamelCase					,baz=lowerCamelCase					,opt=lowerCamelCase     )     )

         __SCREAMING_SNAKE_CASE   				=							parser.parse_args(["""--foo""", """--no_baz"""]     )
         self.assertEqual(lowerCamelCase					,Namespace(foo=lowerCamelCase					,baz=lowerCamelCase					,opt=lowerCamelCase     )     )

         __SCREAMING_SNAKE_CASE   				=							parser.parse_args(["""--foo""", """--baz"""]     )
         self.assertEqual(lowerCamelCase					,Namespace(foo=lowerCamelCase					,baz=lowerCamelCase					,opt=lowerCamelCase     )     )

         __SCREAMING_SNAKE_CASE   				=							parser.parse_args(["""--foo""", """True""", """--baz""", """True""", """--opt""", """True"""]     )
         self.assertEqual(lowerCamelCase					,Namespace(foo=lowerCamelCase					,baz=lowerCamelCase					,opt=lowerCamelCase     )     )

         __SCREAMING_SNAKE_CASE   				=							parser.parse_args(["""--foo""", """False""", """--baz""", """False""", """--opt""", """False"""]     )
         self.assertEqual(lowerCamelCase					,Namespace(foo=lowerCamelCase					,baz=lowerCamelCase					,opt=lowerCamelCase     )     )





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							HfArgumentParser(lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							argparse.ArgumentParser()
      expected.add_argument(
          """--foo"""					,default="""toto"""					,choices=["""titi""", """toto""", 42]					,type=make_choice_type_function(["""titi""", """toto""", 42]     )					,)
      self.argparsersEqual(lowerCamelCase					,lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							parser.parse_args([]     )
      self.assertEqual(args.foo					,"""toto"""     )
      __SCREAMING_SNAKE_CASE   				=							parser.parse_args_into_dataclasses([]     )[0]
      self.assertEqual(enum_ex.foo					,MixedTypeEnum.toto     )

      __SCREAMING_SNAKE_CASE   				=							parser.parse_args(["""--foo""", """titi"""]     )
      self.assertEqual(args.foo					,"""titi"""     )
      __SCREAMING_SNAKE_CASE   				=							parser.parse_args_into_dataclasses(["""--foo""", """titi"""]     )[0]
      self.assertEqual(enum_ex.foo					,MixedTypeEnum.titi     )

      __SCREAMING_SNAKE_CASE   				=							parser.parse_args(["""--foo""", """42"""]     )
      self.assertEqual(args.foo					,42     )
      __SCREAMING_SNAKE_CASE   				=							parser.parse_args_into_dataclasses(["""--foo""", """42"""]     )[0]
      self.assertEqual(enum_ex.foo					,MixedTypeEnum.fourtytwo     )





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      @dataclass
      class __a			:
         __UpperCamelCase :			Literal["titi", "toto", 42]						   =						"toto"





      __SCREAMING_SNAKE_CASE   				=							HfArgumentParser(lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							argparse.ArgumentParser()
      expected.add_argument(
          """--foo"""					,default="""toto"""					,choices=("""titi""", """toto""", 42)					,type=make_choice_type_function(["""titi""", """toto""", 42]     )					,)
      self.argparsersEqual(lowerCamelCase					,lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							parser.parse_args([]     )
      self.assertEqual(args.foo					,"""toto"""     )

      __SCREAMING_SNAKE_CASE   				=							parser.parse_args(["""--foo""", """titi"""]     )
      self.assertEqual(args.foo					,"""titi"""     )

      __SCREAMING_SNAKE_CASE   				=							parser.parse_args(["""--foo""", """42"""]     )
      self.assertEqual(args.foo					,42     )





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							HfArgumentParser(lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							argparse.ArgumentParser()
      expected.add_argument("""--foo_int"""					,nargs="""+"""					,default=[]					,type=lowerCamelCase     )
      expected.add_argument("""--bar_int"""					,nargs="""+"""					,default=[1, 2, 3]					,type=lowerCamelCase     )
      expected.add_argument("""--foo_str"""					,nargs="""+"""					,default=["""Hallo""", """Bonjour""", """Hello"""]					,type=lowerCamelCase     )
      expected.add_argument("""--foo_float"""					,nargs="""+"""					,default=[0.1, 0.2, 0.3]					,type=lowerCamelCase     )

      self.argparsersEqual(lowerCamelCase					,lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							parser.parse_args([]     )
      self.assertEqual(
          lowerCamelCase					,Namespace(foo_int=[]					,bar_int=[1, 2, 3]					,foo_str=["""Hallo""", """Bonjour""", """Hello"""]					,foo_float=[0.1, 0.2, 0.3]     )					,)

      __SCREAMING_SNAKE_CASE   				=							parser.parse_args("""--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7""".split()     )
      self.assertEqual(lowerCamelCase					,Namespace(foo_int=[1]					,bar_int=[2, 3]					,foo_str=["""a""", """b""", """c"""]					,foo_float=[0.1, 0.7]     )     )





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							argparse.ArgumentParser()
      expected.add_argument("""--foo"""					,default=lowerCamelCase					,type=lowerCamelCase     )
      expected.add_argument("""--bar"""					,default=lowerCamelCase					,type=lowerCamelCase					,help="""help message"""     )
      expected.add_argument("""--baz"""					,default=lowerCamelCase					,type=lowerCamelCase     )
      expected.add_argument("""--ces"""					,nargs="""+"""					,default=[]					,type=lowerCamelCase     )
      expected.add_argument("""--des"""					,nargs="""+"""					,default=[]					,type=lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							[OptionalExample]
      if is_python_no_less_than_3_10:
         dataclass_types.append(lowerCamelCase     )

      for dataclass_type in dataclass_types:
         __SCREAMING_SNAKE_CASE   				=							HfArgumentParser(lowerCamelCase     )

         self.argparsersEqual(lowerCamelCase					,lowerCamelCase     )

         __SCREAMING_SNAKE_CASE   				=							parser.parse_args([]     )
         self.assertEqual(lowerCamelCase					,Namespace(foo=lowerCamelCase					,bar=lowerCamelCase					,baz=lowerCamelCase					,ces=[]					,des=[]     )     )

         __SCREAMING_SNAKE_CASE   				=							parser.parse_args("""--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3""".split()     )
         self.assertEqual(lowerCamelCase					,Namespace(foo=12					,bar=3.14					,baz="""42"""					,ces=["""a""", """b""", """c"""]					,des=[1, 2, 3]     )     )





   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							HfArgumentParser(lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							argparse.ArgumentParser()
      expected.add_argument("""--required_list"""					,nargs="""+"""					,type=lowerCamelCase					,required=lowerCamelCase     )
      expected.add_argument("""--required_str"""					,type=lowerCamelCase					,required=lowerCamelCase     )
      expected.add_argument(
          """--required_enum"""					,type=make_choice_type_function(["""titi""", """toto"""]     )					,choices=["""titi""", """toto"""]					,required=lowerCamelCase					,)
      self.argparsersEqual(lowerCamelCase					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							HfArgumentParser(lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							argparse.ArgumentParser()
      expected.add_argument("""--foo"""					,type=lowerCamelCase					,required=lowerCamelCase     )
      expected.add_argument(
          """--required_enum"""					,type=make_choice_type_function(["""titi""", """toto"""]     )					,choices=["""titi""", """toto"""]					,required=lowerCamelCase					,)
      expected.add_argument("""--opt"""					,type=lowerCamelCase					,default=lowerCamelCase     )
      expected.add_argument("""--baz"""					,default="""toto"""					,type=lowerCamelCase					,help="""help message"""     )
      expected.add_argument("""--foo_str"""					,nargs="""+"""					,default=["""Hallo""", """Bonjour""", """Hello"""]					,type=lowerCamelCase     )
      self.argparsersEqual(lowerCamelCase					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							HfArgumentParser(lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							{
          """foo""": 12,
          """bar""": 3.14,
          """baz""": """42""",
          """flag""": True,
      }

      __SCREAMING_SNAKE_CASE   				=							parser.parse_dict(lowerCamelCase     )[0]
      __SCREAMING_SNAKE_CASE   				=							BasicExample(**lowerCamelCase     )
      self.assertEqual(lowerCamelCase					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							HfArgumentParser(lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							{
          """foo""": 12,
          """bar""": 3.14,
          """baz""": """42""",
          """flag""": True,
          """extra""": 42,
      }

      self.assertRaises(lowerCamelCase					,parser.parse_dict					,lowerCamelCase					,allow_extra_keys=lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							HfArgumentParser(lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							{
          """foo""": 12,
          """bar""": 3.14,
          """baz""": """42""",
          """flag""": True,
      }
      with tempfile.TemporaryDirectory() as tmp_dir:
         __SCREAMING_SNAKE_CASE   				=							os.path.join(lowerCamelCase					,"""temp_json"""     )
         os.mkdir(lowerCamelCase     )
         with open(temp_local_path + """.json"""					,"""w+"""     ) as f:
            json.dump(lowerCamelCase					,lowerCamelCase     )
         __SCREAMING_SNAKE_CASE   				=							parser.parse_yaml_file(Path(temp_local_path + """.json"""     )     )[0]

      __SCREAMING_SNAKE_CASE   				=							BasicExample(**lowerCamelCase     )
      self.assertEqual(lowerCamelCase					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							HfArgumentParser(lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							{
          """foo""": 12,
          """bar""": 3.14,
          """baz""": """42""",
          """flag""": True,
      }
      with tempfile.TemporaryDirectory() as tmp_dir:
         __SCREAMING_SNAKE_CASE   				=							os.path.join(lowerCamelCase					,"""temp_yaml"""     )
         os.mkdir(lowerCamelCase     )
         with open(temp_local_path + """.yaml"""					,"""w+"""     ) as f:
            yaml.dump(lowerCamelCase					,lowerCamelCase     )
         __SCREAMING_SNAKE_CASE   				=							parser.parse_yaml_file(Path(temp_local_path + """.yaml"""     )     )[0]
      __SCREAMING_SNAKE_CASE   				=							BasicExample(**lowerCamelCase     )
      self.assertEqual(lowerCamelCase					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							HfArgumentParser(lowerCamelCase     )
      self.assertIsNotNone(lowerCamelCase     )












'''simple docstring'''

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   	=    logging.get_logger(__name__)







class __a			( _snake_case      ):
   __UpperCamelCase :			List[str]						   =						['input_features', 'is_longer']





   def __init__(       self					:      Union[str, Any]					,lowerCamelCase					:      Union[str, Any]=64					,lowerCamelCase					:      Any=4_8000					,lowerCamelCase					:      int=480					,lowerCamelCase					:      Any=10					,lowerCamelCase					:      Dict=1024					,lowerCamelCase					:      Union[str, Any]=0.0					,lowerCamelCase					:      int=False					,lowerCamelCase					:      float = 0					,lowerCamelCase					:      float = 1_4000					,lowerCamelCase					:      int = None					,lowerCamelCase					:      str = "fusion"					,lowerCamelCase					:      str = "repeatpad"					,**lowerCamelCase					:      Dict					,):

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							copy.deepcopy(self.__dict__     )
      __SCREAMING_SNAKE_CASE   				=							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   UpperCAmelCase__  (       self					:      str					,lowerCamelCase					:      np.array					,lowerCamelCase					:      Optional[np.array] = None     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							spectrogram(
          lowerCamelCase					,window_function(self.fft_window_size					,"""hann"""     )					,frame_length=self.fft_window_size					,hop_length=self.hop_length					,power=2.0					,mel_filters=lowerCamelCase					,log_mel="""dB"""					,)
      return log_mel_spectrogram.T





   def   UpperCAmelCase__  (       self					:      int					,lowerCamelCase					:      int					,lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							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
         __SCREAMING_SNAKE_CASE   				=							[0]
      if len(ranges[2]     ) == 0:
         # if the audio is too short, we just use the first chunk
         __SCREAMING_SNAKE_CASE   				=							[0]
      # randomly choose index for each part
      __SCREAMING_SNAKE_CASE   				=							np.random.choice(ranges[0]     )
      __SCREAMING_SNAKE_CASE   				=							np.random.choice(ranges[1]     )
      __SCREAMING_SNAKE_CASE   				=							np.random.choice(ranges[2]     )

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

      __SCREAMING_SNAKE_CASE   				=							torch.tensor(mel[None, None, :]     )
      __SCREAMING_SNAKE_CASE   				=							torch.nn.functional.interpolate(
          lowerCamelCase					,size=[chunk_frames, 64]					,mode="""bilinear"""					,align_corners=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							mel_shrink[0][0].numpy()
      __SCREAMING_SNAKE_CASE   				=							np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]					,axis=0     )
      return mel_fusion





   def   UpperCAmelCase__  (       self					:      Optional[Any]					,lowerCamelCase					:      np.array					,lowerCamelCase					:      str					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      Optional[Any]     ):

      '''simple docstring'''

      if waveform.shape[0] > max_length:
         if truncation == "rand_trunc":
            __SCREAMING_SNAKE_CASE   				=							True
            # random crop to max_length (for compatibility) -> this should be handled by self.pad
            __SCREAMING_SNAKE_CASE   				=							len(lowerCamelCase     ) - max_length
            __SCREAMING_SNAKE_CASE   				=							np.random.randint(0					,overflow + 1     )
            __SCREAMING_SNAKE_CASE   				=							waveform[idx : idx + max_length]
            __SCREAMING_SNAKE_CASE   				=							self._np_extract_fbank_features(lowerCamelCase					,self.mel_filters_slaney     )[None, :]
         elif truncation == "fusion":
            __SCREAMING_SNAKE_CASE   				=							self._np_extract_fbank_features(lowerCamelCase					,self.mel_filters     )
            __SCREAMING_SNAKE_CASE   				=							max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
            __SCREAMING_SNAKE_CASE   				=							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.
               __SCREAMING_SNAKE_CASE   				=							np.stack([mel, mel, mel, mel]					,axis=0     )
               __SCREAMING_SNAKE_CASE   				=							False
            else:
               __SCREAMING_SNAKE_CASE   				=							self._random_mel_fusion(lowerCamelCase					,lowerCamelCase					,lowerCamelCase     )
               __SCREAMING_SNAKE_CASE   				=							True
         else:
            raise NotImplementedError(f"""data_truncating {truncation} not implemented"""     )

      else:
         __SCREAMING_SNAKE_CASE   				=							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":
               __SCREAMING_SNAKE_CASE   				=							int(max_length / len(lowerCamelCase     )     )
               __SCREAMING_SNAKE_CASE   				=							np.stack(np.tile(lowerCamelCase					,n_repeat + 1     )     )[:max_length]
            if padding == "repeatpad":
               __SCREAMING_SNAKE_CASE   				=							int(max_length / len(lowerCamelCase     )     )
               __SCREAMING_SNAKE_CASE   				=							np.stack(np.tile(lowerCamelCase					,lowerCamelCase     )     )
            __SCREAMING_SNAKE_CASE   				=							np.pad(lowerCamelCase					,(0, max_length - waveform.shape[0])					,mode="""constant"""					,constant_values=0     )

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

      return input_mel, longer





   def __call__(       self					:      Optional[Any]					,lowerCamelCase					:      Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,lowerCamelCase					:      str = None					,lowerCamelCase					:      Optional[str] = None					,lowerCamelCase					:      Optional[int] = None					,lowerCamelCase					:      Optional[int] = None					,lowerCamelCase					:      Optional[Union[str, TensorType]] = None					,**lowerCamelCase					:      str					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							truncation if truncation is not None else self.truncation
      __SCREAMING_SNAKE_CASE   				=							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."""     )

      __SCREAMING_SNAKE_CASE   				=							isinstance(lowerCamelCase					,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(lowerCamelCase					,(list, tuple)     ) and (isinstance(raw_speech[0]					,(np.ndarray, tuple, list)     ))
      )

      if is_batched:
         __SCREAMING_SNAKE_CASE   				=							[np.asarray(lowerCamelCase					,dtype=np.floataa     ) for speech in raw_speech]
      elif not is_batched and not isinstance(lowerCamelCase					,np.ndarray     ):
         __SCREAMING_SNAKE_CASE   				=							np.asarray(lowerCamelCase					,dtype=np.floataa     )
      elif isinstance(lowerCamelCase					,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   				=							[np.asarray(lowerCamelCase     )]

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

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

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

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

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

      __SCREAMING_SNAKE_CASE   				=							{"""input_features""": input_mel, """is_longer""": is_longer}
      __SCREAMING_SNAKE_CASE   				=							BatchFeature(lowerCamelCase     )

      if return_tensors is not None:
         __SCREAMING_SNAKE_CASE   				=							input_features.convert_to_tensors(lowerCamelCase     )

      return input_features





