import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_xlnet import XLNetTokenizer
else:
      SCREAMING_SNAKE_CASE__		:					Tuple						=					None


SCREAMING_SNAKE_CASE__		:					int						=					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:					Tuple						=					{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE__		:					List[Any]						=					{
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
    },
}

SCREAMING_SNAKE_CASE__		:					int						=					{
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

SCREAMING_SNAKE_CASE__		:					Any						=					"▁"

# Segments (not really needed)
SCREAMING_SNAKE_CASE__		:					Any						=					0
SCREAMING_SNAKE_CASE__		:					Any						=					1
SCREAMING_SNAKE_CASE__		:					Union[str, Any]						=					2
SCREAMING_SNAKE_CASE__		:					List[str]						=					3
SCREAMING_SNAKE_CASE__		:					Tuple						=					4

class       snake_case					(      UpperCamelCase_						):
  lowercase_			=     VOCAB_FILES_NAMES
  lowercase_			=     PRETRAINED_VOCAB_FILES_MAP
  lowercase_			=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowercase_			=     'left'
  lowercase_			=     XLNetTokenizer




  def __init__(      self     :	Union[str, Any]     , a_     :	int=None     , a_     :	Dict=None     , a_     :	Optional[Any]=False     , a_     :	Tuple=True     , a_     :	Dict=False     , a_     :	str="<s>"     , a_     :	str="</s>"     , a_     :	int="<unk>"     , a_     :	Tuple="<sep>"     , a_     :	Union[str, Any]="<pad>"     , a_     :	List[str]="<cls>"     , a_     :	List[Any]="<mask>"     , a_     :	List[str]=["<eop>", "<eod>"]     , **a_     :	int     , )->       Optional[Any]:




       """simple docstring"""
       # Mask token behave like a normal word, i.e. include the space before it
       SCREAMING_SNAKE_CASE__				:				Any			=		AddedToken(a_     , lstrip=a_     , rstrip=a_				) if isinstance(a_     , a_				) else mask_token

       super().__init__(
           vocab_file=a_     , tokenizer_file=a_     , do_lower_case=a_     , remove_space=a_     , keep_accents=a_     , bos_token=a_     , eos_token=a_     , unk_token=a_     , sep_token=a_     , pad_token=a_     , cls_token=a_     , mask_token=a_     , additional_special_tokens=a_     , **a_     , )

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		3
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		do_lower_case
       SCREAMING_SNAKE_CASE__				:				str			=		remove_space
       SCREAMING_SNAKE_CASE__				:				Tuple			=		keep_accents
       SCREAMING_SNAKE_CASE__				:				str			=		vocab_file
       SCREAMING_SNAKE_CASE__				:				Any			=		False if not self.vocab_file else True




  def 	__lowercase(      self     :	Any     , a_     :	List[int]     , a_     :	Optional[List[int]] = None				)->       List[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		[self.sep_token_id]
       SCREAMING_SNAKE_CASE__				:				Dict			=		[self.cls_token_id]
       if token_ids_a is None:
            return token_ids_a + sep + cls
       return token_ids_a + sep + token_ids_a + sep + cls




  def 	__lowercase(      self     :	str     , a_     :	List[int]     , a_     :	Optional[List[int]] = None				)->       List[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[self.sep_token_id]
       SCREAMING_SNAKE_CASE__				:				Dict			=		[2]

       if token_ids_a is None:
            return len(token_ids_a + sep				) * [0] + cls_segment_id
       return len(token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1] + cls_segment_id




  def 	__lowercase(      self     :	List[Any]     , a_     :	str     , a_     :	Optional[str] = None				)->       Tuple[str]:




       """simple docstring"""
       if not self.can_save_slow_tokenizer:
            raise ValueError(
                'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                'tokenizer.'				)

       if not os.path.isdir(a_				):
            logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
            return
       SCREAMING_SNAKE_CASE__				:				Tuple			=		os.path.join(
           a_     , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)

       if os.path.abspath(self.vocab_file				) != os.path.abspath(a_				):
            copyfile(self.vocab_file     , a_				)

       return (out_vocab_file,)




import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
      import torch


SCREAMING_SNAKE_CASE__		:					Optional[Any]						=					random.Random()







def     _a				(      lowercase__  :			List[str]     ,  lowercase__  :			List[Any]=1.0     ,  lowercase__  :			Optional[int]=None     ,  lowercase__  :			List[str]=None ):







     '''simple docstring'''


     if rng is None:
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		global_rng

     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		[]
     for batch_idx in range(shape[0] ):
          values.append([] )
          for _ in range(shape[1] ):
               values[-1].append(rng.random() * scale )

     return values

@require_torch
class       snake_case					(      unittest.TestCase						):




  def __init__(      self     :	List[Any]     , a_     :	Optional[Any]     , a_     :	Union[str, Any]=7     , a_     :	Any=400     , a_     :	List[Any]=2000     , a_     :	Tuple=1     , a_     :	Optional[int]=0.0     , a_     :	Optional[Any]=1_6000     , a_     :	str=True     , a_     :	Union[str, Any]=80     , a_     :	Dict=16     , a_     :	Tuple=64     , a_     :	Any="hann_window"     , a_     :	Union[str, Any]=80     , a_     :	List[Any]=7600     , a_     :	Optional[Any]=1e-1_0     , a_     :	Dict=True     , )->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		parent
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		batch_size
       SCREAMING_SNAKE_CASE__				:				str			=		min_seq_length
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		max_seq_length
       SCREAMING_SNAKE_CASE__				:				str			=		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       SCREAMING_SNAKE_CASE__				:				int			=		feature_size
       SCREAMING_SNAKE_CASE__				:				str			=		padding_value
       SCREAMING_SNAKE_CASE__				:				Any			=		sampling_rate
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		do_normalize
       SCREAMING_SNAKE_CASE__				:				int			=		num_mel_bins
       SCREAMING_SNAKE_CASE__				:				int			=		hop_length
       SCREAMING_SNAKE_CASE__				:				str			=		win_length
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		win_function
       SCREAMING_SNAKE_CASE__				:				List[str]			=		fmin
       SCREAMING_SNAKE_CASE__				:				Dict			=		fmax
       SCREAMING_SNAKE_CASE__				:				int			=		mel_floor
       SCREAMING_SNAKE_CASE__				:				Tuple			=		return_attention_mask




  def 	__lowercase(      self     :	Dict				)->       Dict:




       """simple docstring"""
       return {
           "feature_size": self.feature_size,
           "padding_value": self.padding_value,
           "sampling_rate": self.sampling_rate,
           "do_normalize": self.do_normalize,
           "num_mel_bins": self.num_mel_bins,
           "hop_length": self.hop_length,
           "win_length": self.win_length,
           "win_function": self.win_function,
           "fmin": self.fmin,
           "fmax": self.fmax,
           "mel_floor": self.mel_floor,
           "return_attention_mask": self.return_attention_mask,
       }




  def 	__lowercase(      self     :	List[Any]     , a_     :	str=False     , a_     :	List[Any]=False				)->       Optional[Any]:




       """simple docstring"""
       def _flatten(a_     :	int				):
            return list(itertools.chain(*a_				)				)

       if equal_length:
            SCREAMING_SNAKE_CASE__				:				Tuple			=		floats_list((self.batch_size, self.max_seq_length)				)
       else:
            # make sure that inputs increase in size
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[
                _flatten(floats_list((x, self.feature_size)				)				)
                for x in range(self.min_seq_length     , self.max_seq_length     , self.seq_length_diff				)
            ]

       if numpify:
            SCREAMING_SNAKE_CASE__				:				int			=		[np.asarray(a_				) for x in speech_inputs]

       return speech_inputs




  def 	__lowercase(      self     :	Any     , a_     :	int=False     , a_     :	Any=False				)->       Union[str, Any]:




       """simple docstring"""
       if equal_length:
            SCREAMING_SNAKE_CASE__				:				str			=		[floats_list((self.max_seq_length, self.num_mel_bins)				) for _ in range(self.batch_size				)]
       else:
            # make sure that inputs increase in size
            SCREAMING_SNAKE_CASE__				:				Tuple			=		[
                floats_list((x, self.num_mel_bins)				)
                for x in range(self.min_seq_length     , self.max_seq_length     , self.seq_length_diff				)
            ]

       if numpify:
            SCREAMING_SNAKE_CASE__				:				List[str]			=		[np.asarray(a_				) for x in speech_inputs]

       return speech_inputs




@require_torch
class       snake_case					(      UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     SpeechTaFeatureExtractor




  def 	__lowercase(      self     :	List[Any]				)->       List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		SpeechTaFeatureExtractionTester(self				)




  def 	__lowercase(      self     :	Any     , a_     :	Optional[int]				)->       List[str]:




       """simple docstring"""
       self.assertTrue(np.all(np.mean(a_     , axis=0				) < 1e-3				)				)
       self.assertTrue(np.all(np.abs(np.var(a_     , axis=0				) - 1				) < 1e-3				)				)




  def 	__lowercase(      self     :	Tuple				)->       Dict:




       """simple docstring"""
       # Tests that all call wrap to encode_plus and batch_encode_plus
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
       # create three inputs of length 800, 1000, and 1200
       SCREAMING_SNAKE_CASE__				:				str			=		[floats_list((1, x)				)[0] for x in range(800     , 1400     , 200				)]
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[np.asarray(a_				) for speech_input in speech_inputs]

       # Test not batched input
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		feat_extract(speech_inputs[0]     , return_tensors='np'				).input_values
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		feat_extract(np_speech_inputs[0]     , return_tensors='np'				).input_values
       self.assertTrue(np.allclose(a_     , a_     , atol=1e-3				)				)

       # Test batched
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		feat_extract(a_     , return_tensors='np'				).input_values
       SCREAMING_SNAKE_CASE__				:				List[str]			=		feat_extract(a_     , return_tensors='np'				).input_values
       for enc_seq_a, enc_seq_a in zip(a_     , a_				):
            self.assertTrue(np.allclose(a_     , a_     , atol=1e-3				)				)




  def 	__lowercase(      self     :	List[Any]				)->       List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		[floats_list((1, x)				)[0] for x in range(800     , 1400     , 200				)]

       SCREAMING_SNAKE_CASE__				:				int			=		['longest', 'max_length', 'do_not_pad']
       SCREAMING_SNAKE_CASE__				:				Tuple			=		[None, 1600, None]
       for max_length, padding in zip(a_     , a_				):
            SCREAMING_SNAKE_CASE__				:				str			=		feat_extract(a_     , padding=a_     , max_length=a_     , return_tensors='np'				)
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		processed.input_values

            self._check_zero_mean_unit_variance(input_values[0][:800]				)
            self.assertTrue(input_values[0][800:].sum() < 1e-6				)
            self._check_zero_mean_unit_variance(input_values[1][:1000]				)
            self.assertTrue(input_values[0][1000:].sum() < 1e-6				)
            self._check_zero_mean_unit_variance(input_values[2][:1200]				)




  def 	__lowercase(      self     :	List[Any]				)->       List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		range(800     , 1400     , 200				)
       SCREAMING_SNAKE_CASE__				:				int			=		[floats_list((1, x)				)[0] for x in lengths]

       SCREAMING_SNAKE_CASE__				:				int			=		['longest', 'max_length', 'do_not_pad']
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		[None, 1600, None]

       for max_length, padding in zip(a_     , a_				):
            SCREAMING_SNAKE_CASE__				:				List[str]			=		feat_extract(a_     , max_length=a_     , padding=a_				)
            SCREAMING_SNAKE_CASE__				:				int			=		processed.input_values

            self._check_zero_mean_unit_variance(input_values[0][:800]				)
            self._check_zero_mean_unit_variance(input_values[1][:1000]				)
            self._check_zero_mean_unit_variance(input_values[2][:1200]				)




  def 	__lowercase(      self     :	int				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
       SCREAMING_SNAKE_CASE__				:				Tuple			=		[floats_list((1, x)				)[0] for x in range(800     , 1400     , 200				)]
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		feat_extract(
           a_     , truncation=a_     , max_length=1000     , padding='max_length'     , return_tensors='np'				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		processed.input_values

       self._check_zero_mean_unit_variance(input_values[0, :800]				)
       self._check_zero_mean_unit_variance(input_values[1]				)
       self._check_zero_mean_unit_variance(input_values[2]				)




  def 	__lowercase(      self     :	Optional[Any]				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[floats_list((1, x)				)[0] for x in range(800     , 1400     , 200				)]
       SCREAMING_SNAKE_CASE__				:				List[str]			=		feat_extract(
           a_     , truncation=a_     , max_length=1000     , padding='longest'     , return_tensors='np'				)
       SCREAMING_SNAKE_CASE__				:				int			=		processed.input_values

       self._check_zero_mean_unit_variance(input_values[0, :800]				)
       self._check_zero_mean_unit_variance(input_values[1, :1000]				)
       self._check_zero_mean_unit_variance(input_values[2]				)

       # make sure that if max_length < longest -> then pad to max_length
       self.assertTrue(input_values.shape == (3, 1000)				)

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[floats_list((1, x)				)[0] for x in range(800     , 1400     , 200				)]
       SCREAMING_SNAKE_CASE__				:				str			=		feat_extract(
           a_     , truncation=a_     , max_length=2000     , padding='longest'     , return_tensors='np'				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		processed.input_values

       self._check_zero_mean_unit_variance(input_values[0, :800]				)
       self._check_zero_mean_unit_variance(input_values[1, :1000]				)
       self._check_zero_mean_unit_variance(input_values[2]				)

       # make sure that if max_length > longest -> then pad to longest
       self.assertTrue(input_values.shape == (3, 1200)				)




  def 	__lowercase(      self     :	Any				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		np.random.rand(100				).astype(np.floataa				)
       SCREAMING_SNAKE_CASE__				:				int			=		np_speech_inputs.tolist()

       for inputs in [py_speech_inputs, np_speech_inputs]:
            SCREAMING_SNAKE_CASE__				:				Any			=		feature_extractor.pad([{'input_values': inputs}]     , return_tensors='np'				)
            self.assertTrue(np_processed.input_values.dtype == np.floataa				)
            SCREAMING_SNAKE_CASE__				:				Tuple			=		feature_extractor.pad([{'input_values': inputs}]     , return_tensors='pt'				)
            self.assertTrue(pt_processed.input_values.dtype == torch.floataa				)




  def 	__lowercase(      self     :	Any				)->       Optional[int]:




       """simple docstring"""
       # Tests that all call wrap to encode_plus and batch_encode_plus
       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
       # create three inputs of length 800, 1000, and 1200
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		[floats_list((1, x)				)[0] for x in range(800     , 1400     , 200				)]
       SCREAMING_SNAKE_CASE__				:				Dict			=		[np.asarray(a_				) for speech_input in speech_inputs]

       # Test feature size
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		feature_extractor(audio_target=a_     , padding=a_     , return_tensors='np'				).input_values
       self.assertTrue(input_values.ndim == 3				)
       self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins				)

       # Test not batched input
       SCREAMING_SNAKE_CASE__				:				Tuple			=		feature_extractor(speech_inputs[0]     , return_tensors='np'				).input_values
       SCREAMING_SNAKE_CASE__				:				int			=		feature_extractor(np_speech_inputs[0]     , return_tensors='np'				).input_values
       self.assertTrue(np.allclose(a_     , a_     , atol=1e-3				)				)

       # Test batched
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		feature_extractor(a_     , return_tensors='np'				).input_values
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		feature_extractor(a_     , return_tensors='np'				).input_values
       for enc_seq_a, enc_seq_a in zip(a_     , a_				):
            self.assertTrue(np.allclose(a_     , a_     , atol=1e-3				)				)

       # Test 2-D numpy arrays are batched.
       SCREAMING_SNAKE_CASE__				:				List[str]			=		[floats_list((1, x)				)[0] for x in (800, 800, 800)]
       SCREAMING_SNAKE_CASE__				:				List[str]			=		np.asarray(a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		feature_extractor(a_     , return_tensors='np'				).input_values
       SCREAMING_SNAKE_CASE__				:				str			=		feature_extractor(a_     , return_tensors='np'				).input_values
       for enc_seq_a, enc_seq_a in zip(a_     , a_				):
            self.assertTrue(np.allclose(a_     , a_     , atol=1e-3				)				)




  def 	__lowercase(      self     :	Dict				)->       int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		self.feat_extract_tester.prepare_inputs_for_target()
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.feature_extraction_class(**self.feat_extract_dict				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		feat_extract.model_input_names[0]

       SCREAMING_SNAKE_CASE__				:				Any			=		BatchFeature({input_name: speech_inputs}				)

       self.assertTrue(all(len(a_				) == len(a_				) for x, y in zip(a_     , processed_features[input_name]				)				)				)

       SCREAMING_SNAKE_CASE__				:				str			=		self.feat_extract_tester.prepare_inputs_for_target(equal_length=a_				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		BatchFeature({input_name: speech_inputs}     , tensor_type='np'				)

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		processed_features[input_name]

       if len(batch_features_input.shape				) < 3:
            SCREAMING_SNAKE_CASE__				:				int			=		batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0]				), self.feat_extract_tester.num_mel_bins)				)




  @require_torch
  def 	__lowercase(      self     :	List[Any]				)->       List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.feat_extract_tester.prepare_inputs_for_target(equal_length=a_				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		self.feature_extraction_class(**self.feat_extract_dict				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		feat_extract.model_input_names[0]

       SCREAMING_SNAKE_CASE__				:				Any			=		BatchFeature({input_name: speech_inputs}     , tensor_type='pt'				)

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		processed_features[input_name]

       if len(batch_features_input.shape				) < 3:
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0]				), self.feat_extract_tester.num_mel_bins)				)




  @require_torch
  def 	__lowercase(      self     :	Tuple				)->       List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		self.feature_extraction_class(**self.feat_extract_dict				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		self.feat_extract_tester.prepare_inputs_for_target()
       SCREAMING_SNAKE_CASE__				:				str			=		feat_extract.model_input_names[0]

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		BatchFeature({input_name: speech_inputs}				)

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		feat_extract.num_mel_bins  # hack!

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		feat_extract.pad(a_     , padding='longest'     , return_tensors='np'				)[input_name]
       SCREAMING_SNAKE_CASE__				:				Any			=		feat_extract.pad(a_     , padding='longest'     , return_tensors='pt'				)[input_name]

       self.assertTrue(abs(input_np.astype(np.floataa				).sum() - input_pt.numpy().astype(np.floataa				).sum()				) < 1e-2				)




  def 	__lowercase(      self     :	Any				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.feat_extract_dict
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		True
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.feature_extraction_class(**a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.feat_extract_tester.prepare_inputs_for_target()
       SCREAMING_SNAKE_CASE__				:				Any			=		[len(a_				) for x in speech_inputs]
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		feat_extract.model_input_names[0]

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		BatchFeature({input_name: speech_inputs}				)

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		feat_extract.num_mel_bins  # hack!

       SCREAMING_SNAKE_CASE__				:				Any			=		feat_extract.pad(a_     , padding='longest'     , return_tensors='np'				)
       self.assertIn('attention_mask'     , a_				)
       self.assertListEqual(list(processed.attention_mask.shape				)     , list(processed[input_name].shape[:2]				)				)
       self.assertListEqual(processed.attention_mask.sum(-1				).tolist()     , a_				)




  def 	__lowercase(      self     :	str				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		self.feat_extract_dict
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		True
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.feature_extraction_class(**a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.feat_extract_tester.prepare_inputs_for_target()
       SCREAMING_SNAKE_CASE__				:				Tuple			=		[len(a_				) for x in speech_inputs]
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		feat_extract.model_input_names[0]

       SCREAMING_SNAKE_CASE__				:				Dict			=		BatchFeature({input_name: speech_inputs}				)
       SCREAMING_SNAKE_CASE__				:				str			=		min(a_				)

       SCREAMING_SNAKE_CASE__				:				Any			=		feat_extract.num_mel_bins  # hack!

       SCREAMING_SNAKE_CASE__				:				int			=		feat_extract.pad(
           a_     , padding='max_length'     , max_length=a_     , truncation=a_     , return_tensors='np'				)
       self.assertIn('attention_mask'     , a_				)
       self.assertListEqual(
           list(processed_pad.attention_mask.shape				)     , [processed_pad[input_name].shape[0], max_length]				)
       self.assertListEqual(
           processed_pad.attention_mask[:, :max_length].sum(-1				).tolist()     , [max_length for x in speech_inputs]				)




  def 	__lowercase(      self     :	Optional[int]     , a_     :	List[str]				)->       Any:




       """simple docstring"""
       from datasets import load_dataset

       SCREAMING_SNAKE_CASE__				:				int			=		load_dataset('hf-internal-testing/librispeech_asr_dummy'     , 'clean'     , split='validation'				)
       # automatic decoding with librispeech
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		ds.sort('id'				).select(range(a_				)				)[:num_samples]['audio']

       return [x["array"] for x in speech_samples]




  def 	__lowercase(      self     :	List[str]				)->       List[Any]:




       """simple docstring"""
       # fmt: off
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.tensor(
           [2.3_8_0_4e-0_3, 2.0_7_5_2e-0_3, 1.9_8_3_6e-0_3, 2.1_0_5_7e-0_3, 1.6_1_7_4e-0_3,
            3.0_5_1_8e-0_4, 9.1_5_5_3e-0_5, 3.3_5_6_9e-0_4, 9.7_6_5_6e-0_4, 1.8_3_1_1e-0_3,
            2.0_1_4_2e-0_3, 2.1_0_5_7e-0_3, 1.7_3_9_5e-0_3, 4.5_7_7_6e-0_4, -3.9_6_7_3e-0_4,
            4.5_7_7_6e-0_4, 1.0_0_7_1e-0_3, 9.1_5_5_3e-0_5, 4.8_8_2_8e-0_4, 1.1_5_9_7e-0_3,
            7.3_2_4_2e-0_4, 9.4_6_0_4e-0_4, 1.8_0_0_5e-0_3, 1.8_3_1_1e-0_3, 8.8_5_0_1e-0_4,
            4.2_7_2_5e-0_4, 4.8_8_2_8e-0_4, 7.3_2_4_2e-0_4, 1.0_9_8_6e-0_3, 2.1_0_5_7e-0_3]				)
       # fmt: on

       SCREAMING_SNAKE_CASE__				:				List[str]			=		self._load_datasamples(1				)
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		SpeechTaFeatureExtractor()
       SCREAMING_SNAKE_CASE__				:				List[str]			=		feature_extractor(a_     , return_tensors='pt'				).input_values
       self.assertEquals(input_values.shape     , (1, 9_3680)				)
       self.assertTrue(torch.allclose(input_values[0, :30]     , a_     , atol=1e-6				)				)




  def 	__lowercase(      self     :	Tuple				)->       List[Any]:




       """simple docstring"""
       # fmt: off
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.tensor(
           [-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
            -3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
            -3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
            -3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998]				)
       # fmt: on

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self._load_datasamples(1				)
       SCREAMING_SNAKE_CASE__				:				int			=		SpeechTaFeatureExtractor()
       SCREAMING_SNAKE_CASE__				:				str			=		feature_extractor(audio_target=a_     , return_tensors='pt'				).input_values
       self.assertEquals(input_values.shape     , (1, 366, 80)				)
       self.assertTrue(torch.allclose(input_values[0, 0, :30]     , a_     , atol=1e-4				)				)



