




import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel

class  _lowerCamelCase						:

      """simple docstring"""

      def __init__(     self					: int ,       snake_case					: Tuple ,       snake_case					: Tuple=99 ,       snake_case					: Optional[Any]=13 ,       snake_case					: List[str]=7 ,       snake_case					: Optional[int]=9 ,       snake_case					: Any=True ,       snake_case					: str=True ,       snake_case					: Tuple=False ,       snake_case					: Any=32 ,       snake_case					: Optional[int]=5 ,       snake_case					: Dict=4 ,       snake_case					: List[str]=37 ,       snake_case					: List[str]=8 ,       snake_case					: Optional[Any]=0.1 ,       snake_case					: Optional[int]=0.0_02 ,       snake_case					: List[str]=1 ,       snake_case					: Optional[Any]=0 ,       snake_case					: Any=0 ,       snake_case					: List[Any]=None ,       snake_case					: Union[str, Any]=None ,       ):
          __UpperCamelCase  		=						parent
          __UpperCamelCase  		=						batch_size
          __UpperCamelCase  		=						encoder_seq_length
          __UpperCamelCase  		=						decoder_seq_length
          # For common tests
          __UpperCamelCase  		=						self.decoder_seq_length
          __UpperCamelCase  		=						is_training
          __UpperCamelCase  		=						use_attention_mask
          __UpperCamelCase  		=						use_labels
          __UpperCamelCase  		=						vocab_size
          __UpperCamelCase  		=						hidden_size
          __UpperCamelCase  		=						num_hidden_layers
          __UpperCamelCase  		=						num_attention_heads
          __UpperCamelCase  		=						d_ff
          __UpperCamelCase  		=						relative_attention_num_buckets
          __UpperCamelCase  		=						dropout_rate
          __UpperCamelCase  		=						initializer_factor
          __UpperCamelCase  		=						eos_token_id
          __UpperCamelCase  		=						pad_token_id
          __UpperCamelCase  		=						decoder_start_token_id
          __UpperCamelCase  		=						None
          __UpperCamelCase  		=						decoder_layers







      def       snake_case	(     self					: Dict				):
          return TaConfig.from_pretrained('''google/umt5-base'''				)







      def       snake_case	(     self					: List[str] ,       snake_case					: str ,       snake_case					: Optional[Any] ,       snake_case					: List[Any] ,       snake_case					: Any=None ,       snake_case					: Tuple=None ,       snake_case					: Dict=None ,       snake_case					: int=None ,       snake_case					: Union[str, Any]=None ,       ):
          if attention_mask is None:
              __UpperCamelCase  		=						input_ids.ne(config.pad_token_id				)
          if decoder_attention_mask is None:
              __UpperCamelCase  		=						decoder_input_ids.ne(config.pad_token_id				)
          if head_mask is None:
              __UpperCamelCase  		=						torch.ones(config.num_hidden_layers ,       config.num_attention_heads ,       device=snake_case				)
          if decoder_head_mask is None:
              __UpperCamelCase  		=						torch.ones(config.num_decoder_layers ,       config.num_attention_heads ,       device=snake_case				)
          if cross_attn_head_mask is None:
              __UpperCamelCase  		=						torch.ones(
                  config.num_decoder_layers ,       config.num_attention_heads ,       device=snake_case				)
          return {
              "input_ids": input_ids,
              "decoder_input_ids": decoder_input_ids,
              "attention_mask": attention_mask,
              "decoder_attention_mask": decoder_attention_mask,
              "head_mask": head_mask,
              "decoder_head_mask": decoder_head_mask,
              "cross_attn_head_mask": cross_attn_head_mask,
          }







      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						ids_tensor([self.batch_size, self.encoder_seq_length] ,       self.vocab_size				)
          __UpperCamelCase  		=						ids_tensor([self.batch_size, self.decoder_seq_length] ,       self.vocab_size				)

          # we need to clamp the input ids here to avoid having pad token in between
          # this is because for NllbMoe the position_ids are prepared such that
          # all pad tokens have pos id = 2 and rest are between 2..seq_length
          # and the seq_length here is seq_length - num_pad_tokens
          # but when using past, there is no way of knowing if the past input ids had
          # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
          # position_ids being off by num_pad_tokens in past input
          __UpperCamelCase  		=						input_ids.clamp(self.pad_token_id + 1				)
          __UpperCamelCase  		=						decoder_input_ids.clamp(self.pad_token_id + 1				)

          __UpperCamelCase  		=						self.get_config()
          __UpperCamelCase  		=						config.num_attention_heads
          __UpperCamelCase  		=						self.prepare_inputs_dict(snake_case ,       snake_case ,       snake_case				)
          return config, input_dict







      def       snake_case	(     self					: List[str]				):
          __UpperCamelCase		,      __UpperCamelCase  		=						self.prepare_config_and_inputs()
          return config, inputs_dict







      def       snake_case	(     self					: Union[str, Any]				):
          return TaConfig(
              vocab_size=166 ,       d_model=self.hidden_size ,       d_ff=self.d_ff ,       d_kv=self.hidden_size // self.num_attention_heads ,       num_layers=self.num_hidden_layers ,       num_decoder_layers=self.decoder_layers ,       num_heads=self.num_attention_heads ,       relative_attention_num_buckets=self.relative_attention_num_buckets ,       dropout_rate=self.dropout_rate ,       initializer_factor=self.initializer_factor ,       eos_token_id=self.eos_token_id ,       bos_token_id=self.pad_token_id ,       pad_token_id=self.pad_token_id ,       decoder_start_token_id=self.decoder_start_token_id ,       )







      def       snake_case	(     self					: List[Any]				):
          return TaConfig(
              vocab_size=self.vocab_size ,       d_model=self.hidden_size ,       d_ff=self.d_ff ,       d_kv=self.hidden_size // self.num_attention_heads ,       num_layers=self.num_hidden_layers ,       num_decoder_layers=self.decoder_layers ,       num_heads=self.num_attention_heads ,       relative_attention_num_buckets=self.relative_attention_num_buckets ,       dropout_rate=self.dropout_rate ,       initializer_factor=self.initializer_factor ,       eos_token_id=self.eos_token_id ,       bos_token_id=self.pad_token_id ,       pad_token_id=self.pad_token_id ,       decoder_start_token_id=self.decoder_start_token_id ,       )







      def       snake_case	(     self					: Union[str, Any] ,       snake_case					: Union[str, Any] ,       snake_case					: Any ,       snake_case					: Dict ,       snake_case					: Optional[int] ,       snake_case					: Any ,       snake_case					: Tuple ,       ):
          __UpperCamelCase  		=						UMTaModel(config=snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(
              input_ids=snake_case ,       decoder_input_ids=snake_case ,       attention_mask=snake_case ,       decoder_attention_mask=snake_case ,       )
          __UpperCamelCase  		=						model(input_ids=snake_case ,       decoder_input_ids=snake_case				)
          __UpperCamelCase  		=						result.last_hidden_state
          __UpperCamelCase  		=						result.past_key_values
          __UpperCamelCase  		=						result.encoder_last_hidden_state

          self.parent.assertEqual(encoder_output.size() ,       (self.batch_size, self.encoder_seq_length, self.hidden_size)				)
          self.parent.assertEqual(decoder_output.size() ,       (self.batch_size, self.decoder_seq_length, self.hidden_size)				)
          # There should be `num_layers` key value embeddings stored in decoder_past
          self.parent.assertEqual(len(snake_case				) ,       config.num_layers				)
          # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
          self.parent.assertEqual(len(decoder_past[0]				) ,       4				)







      def       snake_case	(     self					: List[str] ,       snake_case					: Union[str, Any] ,       snake_case					: Optional[Any] ,       snake_case					: List[str] ,       snake_case					: Optional[int] ,       snake_case					: Tuple ,       snake_case					: Tuple ,       ):
          __UpperCamelCase  		=						UMTaModel(config=snake_case				).get_decoder().to(snake_case				).eval()
          # first forward pass
          __UpperCamelCase  		=						model(snake_case ,       use_cache=snake_case				)
          __UpperCamelCase  		=						model(snake_case				)
          __UpperCamelCase  		=						model(snake_case ,       use_cache=snake_case				)

          self.parent.assertTrue(len(snake_case				) == len(snake_case				)				)
          self.parent.assertTrue(len(snake_case				) == len(snake_case				) + 1				)

          __UpperCamelCase		,      __UpperCamelCase  		=						outputs.to_tuple()

          # create hypothetical next token and extent to next_input_ids
          __UpperCamelCase  		=						ids_tensor((self.batch_size, 1) ,       config.vocab_size				)

          # append to next input_ids and
          __UpperCamelCase  		=						torch.cat([input_ids, next_tokens] ,       dim=-1				)

          __UpperCamelCase  		=						model(snake_case				)['''last_hidden_state''']
          __UpperCamelCase  		=						model(snake_case ,       past_key_values=snake_case				)['''last_hidden_state''']

          # select random slice
          __UpperCamelCase  		=						ids_tensor((1,) ,       output_from_past.shape[-1]				).item()
          __UpperCamelCase  		=						output_from_no_past[:, -1, random_slice_idx].detach()
          __UpperCamelCase  		=						output_from_past[:, 0, random_slice_idx].detach()

          # test that outputs are equal for slice
          self.parent.assertTrue(torch.allclose(snake_case ,       snake_case ,       atol=1E-3				)				)







      def       snake_case	(     self					: List[Any] ,       snake_case					: Tuple ,       snake_case					: Any ,       ):
          __UpperCamelCase  		=						UMTaModel(config=snake_case				).to(snake_case				).half().eval()
          __UpperCamelCase  		=						model(**snake_case				)['''last_hidden_state''']
          self.parent.assertFalse(torch.isnan(snake_case				).any().item()				)







@require_torch
class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE							,			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: str    =					(
          (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
      )
      lowerCAmelCase__			: Optional[int]    =					(UMTaForConditionalGeneration,) if is_torch_available() else ()
      lowerCAmelCase__			: List[str]    =					(
          {
              "conversational": UMTaForConditionalGeneration,
              "feature-extraction": UMTaModel,
              "summarization": UMTaForConditionalGeneration,
              "text2text-generation": UMTaForConditionalGeneration,
              "translation": UMTaForConditionalGeneration,
              "question-answering": UMTaForQuestionAnswering,
          }
          if is_torch_available()
          else {}
      )
      lowerCAmelCase__			: Union[str, Any]    =					True
      lowerCAmelCase__			: Any    =					False
      lowerCAmelCase__			: Tuple    =					False
      lowerCAmelCase__			: Tuple    =					True
      lowerCAmelCase__			: Tuple    =					True
      # The small UMT5 model needs higher percentages for CPU/MP tests
      lowerCAmelCase__			: Optional[Any]    =					[0.8, 0.9]







      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						UMTaModelTester(self				)







      @unittest.skip('''Test has a segmentation fault on torch 1.8.0'''				)
      def       snake_case	(     self					: str				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          __UpperCamelCase  		=						UMTaModel(config_and_inputs[0]				).to(snake_case				)
          with tempfile.TemporaryDirectory() as tmpdirname:
              torch.onnx.export(
                  snake_case ,       (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) ,       F"{tmpdirname}/t5_test.onnx" ,       export_params=snake_case ,       opset_version=9 ,       input_names=['''input_ids''', '''decoder_input_ids'''] ,       )







      @unittest.skipIf(torch_device == '''cpu''' ,       '''Cant do half precision'''				)
      def       snake_case	(     self					: List[Any]				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model_fpaa_forward(*snake_case				)







      def       snake_case	(     self					: List[str]				):
          __UpperCamelCase  		=						['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          __UpperCamelCase  		=						config_and_inputs[0]
          __UpperCamelCase  		=						UMTaForConditionalGeneration(snake_case				).eval()
          model.to(snake_case				)

          __UpperCamelCase  		=						{
              '''head_mask''': torch.zeros(config.num_layers ,       config.num_heads ,       device=snake_case				),
              '''decoder_head_mask''': torch.zeros(config.num_decoder_layers ,       config.num_heads ,       device=snake_case				),
              '''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers ,       config.num_heads ,       device=snake_case				),
          }

          for attn_name, (name, mask) in zip(snake_case ,       head_masking.items()				):
              __UpperCamelCase  		=						{name: mask}
              # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
              if name == "head_mask":
                  __UpperCamelCase  		=						torch.ones(
                      config.num_decoder_layers ,       config.num_heads ,       device=snake_case				)

              __UpperCamelCase  		=						model.generate(
                  config_and_inputs[1]['''input_ids'''] ,       num_beams=1 ,       max_length=3 ,       output_attentions=snake_case ,       return_dict_in_generate=snake_case ,       **snake_case ,       )
              # We check the state of decoder_attentions and cross_attentions just from the last step
              __UpperCamelCase  		=						out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
              self.assertEqual(sum([w.sum().item() for w in attn_weights]				) ,       0.0				)







      @unittest.skip('''Does not work on the tiny model as we keep hitting edge cases.'''				)
      def       snake_case	(     self					: Optional[Any]				):
          pass







@require_torch
@require_sentencepiece
@require_tokenizers
class  _lowerCamelCase						(			unittest.TestCase   ):

      """simple docstring"""

      @slow
      @unittest.skip(
          '''Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'''				)
      def       snake_case	(     self					: List[Any]				):
          __UpperCamelCase  		=						UMTaForConditionalGeneration.from_pretrained('''google/umt5-small''' ,       return_dict=snake_case				).to(snake_case				)
          __UpperCamelCase  		=						AutoTokenizer.from_pretrained('''google/umt5-small''' ,       use_fast=snake_case ,       legacy=snake_case				)
          __UpperCamelCase  		=						[
              '''Bonjour monsieur <extra_id_0> bien <extra_id_1>.''',
              '''No se como puedo <extra_id_0>.''',
              '''This is the reason why we <extra_id_0> them.''',
              '''The <extra_id_0> walks in <extra_id_1>, seats''',
              '''A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.''',
          ]
          __UpperCamelCase  		=						tokenizer(snake_case ,       return_tensors='''pt''' ,       padding=snake_case				).input_ids
          # fmt: off
          __UpperCamelCase  		=						torch.tensor(
              [
                  [ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                  [   826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                  [  1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
                  [   517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                  [   320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 1],
              ]				)
          # fmt: on
          torch.testing.assert_allclose(snake_case ,       snake_case				)

          __UpperCamelCase  		=						model.generate(input_ids.to(snake_case				)				)
          __UpperCamelCase  		=						[
              '''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
              '''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
              '''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
              '''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
              '''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
          ]
          __UpperCamelCase  		=						tokenizer.batch_decode(snake_case				)
          self.assertEqual(snake_case ,       snake_case				)












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  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Dict    =					["input_features", "is_longer"]







      def __init__(     self					: Dict ,       snake_case					: int=64 ,       snake_case					: Dict=48000 ,       snake_case					: Tuple=480 ,       snake_case					: Optional[Any]=10 ,       snake_case					: Union[str, Any]=1024 ,       snake_case					: Union[str, Any]=0.0 ,       snake_case					: List[str]=False ,       snake_case					: float = 0 ,       snake_case					: float = 14000 ,       snake_case					: int = None ,       snake_case					: str = "fusion" ,       snake_case					: str = "repeatpad" ,       **snake_case					: int ,       ):
          super().__init__(
              feature_size=snake_case ,       sampling_rate=snake_case ,       padding_value=snake_case ,       return_attention_mask=snake_case ,       **snake_case ,       )
          __UpperCamelCase  		=						top_db
          __UpperCamelCase  		=						truncation
          __UpperCamelCase  		=						padding
          __UpperCamelCase  		=						fft_window_size
          __UpperCamelCase  		=						(fft_window_size >> 1) + 1
          __UpperCamelCase  		=						hop_length
          __UpperCamelCase  		=						max_length_s
          __UpperCamelCase  		=						max_length_s * sampling_rate
          __UpperCamelCase  		=						sampling_rate
          __UpperCamelCase  		=						frequency_min
          __UpperCamelCase  		=						frequency_max
          __UpperCamelCase  		=						mel_filter_bank(
              num_frequency_bins=self.nb_frequency_bins ,       num_mel_filters=snake_case ,       min_frequency=snake_case ,       max_frequency=snake_case ,       sampling_rate=snake_case ,       norm=snake_case ,       mel_scale='''htk''' ,       )
          __UpperCamelCase  		=						mel_filter_bank(
              num_frequency_bins=self.nb_frequency_bins ,       num_mel_filters=snake_case ,       min_frequency=snake_case ,       max_frequency=snake_case ,       sampling_rate=snake_case ,       norm='''slaney''' ,       mel_scale='''slaney''' ,       )







      def       snake_case	(     self					: Union[str, Any]				):
          __UpperCamelCase  		=						copy.deepcopy(self.__dict__				)
          __UpperCamelCase  		=						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       snake_case	(     self					: Tuple ,       snake_case					: np.array ,       snake_case					: Optional[np.array] = None				):
          __UpperCamelCase  		=						spectrogram(
              snake_case ,       window_function(self.fft_window_size ,       '''hann'''				) ,       frame_length=self.fft_window_size ,       hop_length=self.hop_length ,       power=2.0 ,       mel_filters=snake_case ,       log_mel='''dB''' ,       )
          return log_mel_spectrogram.T







      def       snake_case	(     self					: Optional[int] ,       snake_case					: Any ,       snake_case					: List[Any] ,       snake_case					: int				):
          __UpperCamelCase  		=						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
              __UpperCamelCase  		=						[0]
          if len(ranges[2]				) == 0:
              # if the audio is too short, we just use the first chunk
              __UpperCamelCase  		=						[0]
          # randomly choose index for each part
          __UpperCamelCase  		=						np.random.choice(ranges[0]				)
          __UpperCamelCase  		=						np.random.choice(ranges[1]				)
          __UpperCamelCase  		=						np.random.choice(ranges[2]				)

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

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







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

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

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

          return input_mel, longer







      def __call__(     self					: List[str] ,       snake_case					: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,       snake_case					: str = None ,       snake_case					: Optional[str] = None ,       snake_case					: Optional[int] = None ,       snake_case					: Optional[int] = None ,       snake_case					: Optional[Union[str, TensorType]] = None ,       **snake_case					: Any ,       ):
          __UpperCamelCase  		=						truncation if truncation is not None else self.truncation
          __UpperCamelCase  		=						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.'''				)

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

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

          # always return batch
          if not is_batched:
              __UpperCamelCase  		=						[np.asarray(snake_case				)]

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

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

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

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

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

          __UpperCamelCase  		=						{'''input_features''': input_mel, '''is_longer''': is_longer}
          __UpperCamelCase  		=						BatchFeature(snake_case				)

          if return_tensors is not None:
              __UpperCamelCase  		=						input_features.convert_to_tensors(snake_case				)

          return input_features






