






import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

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


if is_torch_available():
     import torch

     from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
     from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder







def 		_UpperCamelCase				(      UpperCamelCase_    :   Optional[Any]      ,    UpperCamelCase_    :   Optional[Any]      ,    UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   Tuple=None      ,    UpperCamelCase_    :   str=None      ,    UpperCamelCase_    :   int=None      ,    UpperCamelCase_    :   Optional[int]=None      ,    UpperCamelCase_    :   Dict=None      ,    )					->				Tuple:

      """simple docstring"""



      if attention_mask is None:
            lowerCAmelCase__        =  input_ids.ne(config.pad_token_id						)
      if decoder_attention_mask is None:
            lowerCAmelCase__        =  decoder_input_ids.ne(config.pad_token_id						)
      if head_mask is None:
            lowerCAmelCase__        =  torch.ones(config.encoder_layers      ,    config.encoder_attention_heads      ,    device=UpperCamelCase_						)
      if decoder_head_mask is None:
            lowerCAmelCase__        =  torch.ones(config.decoder_layers      ,    config.decoder_attention_heads      ,    device=UpperCamelCase_						)
      if cross_attn_head_mask is None:
            lowerCAmelCase__        =  torch.ones(config.decoder_layers      ,    config.decoder_attention_heads      ,    device=UpperCamelCase_						)
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
          "cross_attn_head_mask": cross_attn_head_mask,
      }


class       __SCREAMING_SNAKE_CASE   :






      def __init__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase=13       ,  _UpperCamelCase=7       ,  _UpperCamelCase=True       ,  _UpperCamelCase=False       ,  _UpperCamelCase=99       ,  _UpperCamelCase=16       ,  _UpperCamelCase=2       ,  _UpperCamelCase=4       ,  _UpperCamelCase=4       ,  _UpperCamelCase="relu"       ,  _UpperCamelCase=0.1       ,  _UpperCamelCase=0.1       ,  _UpperCamelCase=0.0       ,  _UpperCamelCase=0.0       ,  _UpperCamelCase=20       ,  _UpperCamelCase=2       ,  _UpperCamelCase=1       ,  _UpperCamelCase=0       ,  ):






            """simple docstring"""
            lowerCAmelCase__        =  parent
            lowerCAmelCase__        =  batch_size
            lowerCAmelCase__        =  seq_length
            lowerCAmelCase__        =  is_training
            lowerCAmelCase__        =  use_labels
            lowerCAmelCase__        =  vocab_size
            lowerCAmelCase__        =  hidden_size
            lowerCAmelCase__        =  num_hidden_layers
            lowerCAmelCase__        =  num_attention_heads
            lowerCAmelCase__        =  intermediate_size
            lowerCAmelCase__        =  hidden_act
            lowerCAmelCase__        =  hidden_dropout_prob
            lowerCAmelCase__        =  attention_probs_dropout_prob
            lowerCAmelCase__        =  encoder_layerdrop
            lowerCAmelCase__        =  decoder_layerdrop
            lowerCAmelCase__        =  max_position_embeddings
            lowerCAmelCase__        =  eos_token_id
            lowerCAmelCase__        =  pad_token_id
            lowerCAmelCase__        =  bos_token_id






      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            lowerCAmelCase__        =  ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size				)
            lowerCAmelCase__        =  self.eos_token_id  # Eos Token
            lowerCAmelCase__        =  ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size				)

            # we need to clamp the input ids here to avoid having pad token in between
            # this is because for M2M100 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
            lowerCAmelCase__        =  input_ids.clamp(self.pad_token_id + 1				)
            lowerCAmelCase__        =  decoder_input_ids.clamp(self.pad_token_id + 1				)

            lowerCAmelCase__        =  self.get_config()
            lowerCAmelCase__        =  prepare_mam_aaa_inputs_dict(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				)
            return config, inputs_dict






      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            return MaMaaaConfig(
                vocab_size=self.vocab_size       ,  d_model=self.hidden_size       ,  encoder_layers=self.num_hidden_layers       ,  decoder_layers=self.num_hidden_layers       ,  encoder_attention_heads=self.num_attention_heads       ,  decoder_attention_heads=self.num_attention_heads       ,  encoder_ffn_dim=self.intermediate_size       ,  decoder_ffn_dim=self.intermediate_size       ,  dropout=self.hidden_dropout_prob       ,  attention_dropout=self.attention_probs_dropout_prob       ,  encoder_layerdrop=self.encoder_layerdrop       ,  decoder_layerdrop=self.decoder_layerdrop       ,  max_position_embeddings=self.max_position_embeddings       ,  eos_token_id=self.eos_token_id       ,  bos_token_id=self.bos_token_id       ,  pad_token_id=self.pad_token_id       ,  )






      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            lowerCAmelCase__  ,    lowerCAmelCase__        =  self.prepare_config_and_inputs()
            return config, inputs_dict






      def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase				):






            """simple docstring"""
            lowerCAmelCase__        =  MaMaaaModel(config=_UpperCamelCase				).get_decoder().to(_UpperCamelCase				).eval()
            lowerCAmelCase__        =  inputs_dict['input_ids']
            lowerCAmelCase__        =  inputs_dict['attention_mask']
            lowerCAmelCase__        =  inputs_dict['head_mask']

            # first forward pass
            lowerCAmelCase__        =  model(_UpperCamelCase       ,  attention_mask=_UpperCamelCase       ,  head_mask=_UpperCamelCase       ,  use_cache=_UpperCamelCase				)

            lowerCAmelCase__  ,    lowerCAmelCase__        =  outputs.to_tuple()

            # create hypothetical multiple next token and extent to next_input_ids
            lowerCAmelCase__        =  ids_tensor((self.batch_size, 3)       ,  config.vocab_size				)
            lowerCAmelCase__        =  ids_tensor((self.batch_size, 3)       ,  2				)

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

            lowerCAmelCase__        =  model(_UpperCamelCase       ,  attention_mask=_UpperCamelCase				)['last_hidden_state']
            lowerCAmelCase__        =  model(_UpperCamelCase       ,  attention_mask=_UpperCamelCase       ,  past_key_values=_UpperCamelCase				)[
                'last_hidden_state'
            ]

            # select random slice
            lowerCAmelCase__        =  ids_tensor((1,)       ,  output_from_past.shape[-1]				).item()
            lowerCAmelCase__        =  output_from_no_past[:, -3:, random_slice_idx].detach()
            lowerCAmelCase__        =  output_from_past[:, :, random_slice_idx].detach()

            self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]				)

            # test that outputs are equal for slice
            self.parent.assertTrue(torch.allclose(_UpperCamelCase       ,  _UpperCamelCase       ,  atol=1E-2				)				)







      def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase				):






            """simple docstring"""
            lowerCAmelCase__        =  MaMaaaModel(config=_UpperCamelCase				).to(_UpperCamelCase				).eval()
            lowerCAmelCase__        =  model(**_UpperCamelCase				)

            lowerCAmelCase__        =  outputs.encoder_last_hidden_state
            lowerCAmelCase__        =  outputs.last_hidden_state

            with tempfile.TemporaryDirectory() as tmpdirname:
                  lowerCAmelCase__        =  model.get_encoder()
                  encoder.save_pretrained(_UpperCamelCase				)
                  lowerCAmelCase__        =  MaMaaaEncoder.from_pretrained(_UpperCamelCase				).to(_UpperCamelCase				)

            lowerCAmelCase__        =  encoder(inputs_dict['input_ids']       ,  attention_mask=inputs_dict['attention_mask']				)[
                0
            ]

            self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3				)

            with tempfile.TemporaryDirectory() as tmpdirname:
                  lowerCAmelCase__        =  model.get_decoder()
                  decoder.save_pretrained(_UpperCamelCase				)
                  lowerCAmelCase__        =  MaMaaaDecoder.from_pretrained(_UpperCamelCase				).to(_UpperCamelCase				)

            lowerCAmelCase__        =  decoder(
                input_ids=inputs_dict['decoder_input_ids']       ,  attention_mask=inputs_dict['decoder_attention_mask']       ,  encoder_hidden_states=_UpperCamelCase       ,  encoder_attention_mask=inputs_dict['attention_mask']       ,  )[0]

            self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3				)




@require_torch
class       __SCREAMING_SNAKE_CASE   (				__lowercase						,   __lowercase						,   __lowercase						,   unittest.TestCase):
      _SCREAMING_SNAKE_CASE		:    Optional[Any]						=   (
          (
              MaMaaaModel,
              MaMaaaForConditionalGeneration,
          )
          if is_torch_available()
          else ()
      )
      _SCREAMING_SNAKE_CASE		:    Optional[int]						=   (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
      _SCREAMING_SNAKE_CASE		:    Optional[int]						=   (
          {
              '''conversational''': MaMaaaForConditionalGeneration,
              '''feature-extraction''': MaMaaaModel,
              '''summarization''': MaMaaaForConditionalGeneration,
              '''text2text-generation''': MaMaaaForConditionalGeneration,
              '''translation''': MaMaaaForConditionalGeneration,
          }
          if is_torch_available()
          else {}
      )
      _SCREAMING_SNAKE_CASE		:    Optional[int]						=   True
      _SCREAMING_SNAKE_CASE		:    Optional[Any]						=   True
      _SCREAMING_SNAKE_CASE		:    List[Any]						=   False
      _SCREAMING_SNAKE_CASE		:    Tuple						=   False






      def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






            """simple docstring"""
            if pipeline_test_casse_name == "TranslationPipelineTests":
                  # Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
                  # `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
                  return True

            return False






      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            lowerCAmelCase__        =  MaMaaaModelTester(self				)
            lowerCAmelCase__        =  ConfigTester(self       ,  config_class=_UpperCamelCase				)






      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            self.config_tester.run_common_tests()






      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            lowerCAmelCase__  ,    lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs()
            for model_class in self.all_model_classes:
                  lowerCAmelCase__        =  model_class(_UpperCamelCase				)

                  with tempfile.TemporaryDirectory() as tmpdirname:
                        model.save_pretrained(_UpperCamelCase				)
                        lowerCAmelCase__  ,    lowerCAmelCase__        =  model_class.from_pretrained(_UpperCamelCase       ,  output_loading_info=_UpperCamelCase				)
                  self.assertEqual(info['missing_keys']       ,  []				)






      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_decoder_model_past_large_inputs(*_UpperCamelCase				)






      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.check_encoder_decoder_model_standalone(*_UpperCamelCase				)






      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            lowerCAmelCase__  ,    lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
                  lowerCAmelCase__        =  model_class(_UpperCamelCase				)
                  model.to(_UpperCamelCase				)
                  model.eval()

                  lowerCAmelCase__        =  copy.deepcopy(self._prepare_for_class(_UpperCamelCase       ,  _UpperCamelCase				)				)

                  if not self.is_encoder_decoder:
                        lowerCAmelCase__        =  inputs['input_ids']
                        del inputs["input_ids"]
                  else:
                        lowerCAmelCase__        =  inputs['input_ids']
                        lowerCAmelCase__        =  inputs.get('decoder_input_ids'       ,  _UpperCamelCase				)
                        del inputs["input_ids"]
                        inputs.pop('decoder_input_ids'       ,  _UpperCamelCase				)

                  lowerCAmelCase__        =  model.get_input_embeddings()
                  if not self.is_encoder_decoder:
                        lowerCAmelCase__        =  wte(_UpperCamelCase				)
                  else:
                        lowerCAmelCase__        =  wte(_UpperCamelCase				)
                        lowerCAmelCase__        =  wte(_UpperCamelCase				)

                  with torch.no_grad():
                        model(**_UpperCamelCase				)[0]







      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            lowerCAmelCase__  ,    lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs()
            lowerCAmelCase__        =  input_dict['input_ids']
            lowerCAmelCase__        =  input_ids.ne(1				).to(_UpperCamelCase				)
            lowerCAmelCase__        =  MaMaaaForConditionalGeneration(_UpperCamelCase				).eval().to(_UpperCamelCase				)
            if torch_device == "cuda":
                  model.half()
            model.generate(_UpperCamelCase       ,  attention_mask=_UpperCamelCase				)
            model.generate(num_beams=4       ,  do_sample=_UpperCamelCase       ,  early_stopping=_UpperCamelCase       ,  num_return_sequences=3				)







def 		_UpperCamelCase				(      UpperCamelCase_    :   int						)					->				Dict:

      """simple docstring"""



      return torch.tensor(UpperCamelCase_      ,    dtype=torch.long      ,    device=UpperCamelCase_						)


__snake_case      :      List[str]             =       1e-4


@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class       __SCREAMING_SNAKE_CASE   (				unittest.TestCase):






      @cached_property
      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            return MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'				)






      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            lowerCAmelCase__        =  MaMaaaModel.from_pretrained('facebook/m2m100_418M'				).to(_UpperCamelCase				)
            lowerCAmelCase__        =  _long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]				)
            lowerCAmelCase__        =  _long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]				)
            lowerCAmelCase__        =  prepare_mam_aaa_inputs_dict(model.config       ,  _UpperCamelCase       ,  _UpperCamelCase				)
            with torch.no_grad():
                  lowerCAmelCase__        =  model(**_UpperCamelCase				)[0]
            lowerCAmelCase__        =  torch.Size((1, 11, 10_24)				)
            self.assertEqual(output.shape       ,  _UpperCamelCase				)
            # change to expected output here
            lowerCAmelCase__        =  torch.tensor(
                [[-0.77_80, -0.16_76, 0.10_38], [-6.75_56, -1.39_92, 0.05_67], [-7.53_83, -0.59_20, -0.27_79]]       ,  device=_UpperCamelCase				)
            self.assertTrue(torch.allclose(output[:, :3, :3]       ,  _UpperCamelCase       ,  atol=_UpperCamelCase				)				)






      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            lowerCAmelCase__        =  MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'				).to(_UpperCamelCase				)

            # change to intended input
            lowerCAmelCase__        =  _long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]				)
            lowerCAmelCase__        =  _long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]				)
            lowerCAmelCase__        =  prepare_mam_aaa_inputs_dict(model.config       ,  _UpperCamelCase       ,  _UpperCamelCase				)
            with torch.no_grad():
                  lowerCAmelCase__        =  model(**_UpperCamelCase				)[0]
            lowerCAmelCase__        =  torch.Size((1, 11, model.config.vocab_size)				)
            self.assertEqual(output.shape       ,  _UpperCamelCase				)
            # change to expected output here
            lowerCAmelCase__        =  torch.tensor(
                [[-1.04_48, -1.04_11, 3.79_92], [-3.21_91, -3.23_86, -1.34_51], [-3.62_10, -3.59_93, 0.49_25]]       ,  device=_UpperCamelCase				)
            self.assertTrue(torch.allclose(output[:, :3, :3]       ,  _UpperCamelCase       ,  atol=_UpperCamelCase				)				)







      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            lowerCAmelCase__        =  MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'				).to(_UpperCamelCase				)
            lowerCAmelCase__        =  MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'       ,  src_lang='fr'       ,  tgt_lang='en'				)

            lowerCAmelCase__        =  [
                'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
                'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
                'Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'
                ' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'
                ' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.',
            ]

            # The below article tests that we don't add any hypotheses outside of the top n_beams
            lowerCAmelCase__        =  tokenizer(_UpperCamelCase       ,  padding=_UpperCamelCase       ,  return_tensors='pt'				)

            lowerCAmelCase__        =  model.generate(
                input_ids=dct['input_ids'].to(_UpperCamelCase				)       ,  attention_mask=dct['attention_mask'].to(_UpperCamelCase				)       ,  num_beams=5       ,  forced_bos_token_id=tokenizer.get_lang_id('en'				)       ,  )

            lowerCAmelCase__        =  [
                'The NSA case highlights the total absence of intelligence debate',
                'I think there are two levels of response from the French government.',
                'When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'
                ' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'
                ' communications in France.',
            ]

            lowerCAmelCase__        =  tokenizer.batch_decode(
                hypotheses_batch.tolist()       ,  clean_up_tokenization_spaces=_UpperCamelCase       ,  skip_special_tokens=_UpperCamelCase				)
            assert generated == expected_en













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


__snake_case      :      Any             =       logging.get_logger(__name__)


class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    Tuple						=   ['''input_features''', '''is_longer''']






						def __init__(		self       ,  _UpperCamelCase=64       ,  _UpperCamelCase=4_80_00       ,  _UpperCamelCase=4_80       ,  _UpperCamelCase=10       ,  _UpperCamelCase=10_24       ,  _UpperCamelCase=0.0       ,  _UpperCamelCase=False       ,  _UpperCamelCase = 0       ,  _UpperCamelCase = 1_40_00       ,  _UpperCamelCase = None       ,  _UpperCamelCase = "fusion"       ,  _UpperCamelCase = "repeatpad"       ,  **_UpperCamelCase       ,  ):






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






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  copy.deepcopy(self.__dict__				)
												lowerCAmelCase__        =  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       ,  _UpperCamelCase       ,  _UpperCamelCase = None				):






												"""simple docstring"""
												lowerCAmelCase__        =  spectrogram(
												    _UpperCamelCase       ,  window_function(self.fft_window_size       ,  'hann'				)       ,  frame_length=self.fft_window_size       ,  hop_length=self.hop_length       ,  power=2.0       ,  mel_filters=_UpperCamelCase       ,  log_mel='dB'       ,  )
												return log_mel_spectrogram.T






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






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

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

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






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






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

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

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

												return input_mel, longer







						def __call__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  truncation if truncation is not None else self.truncation
												lowerCAmelCase__        =  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.'				)

												lowerCAmelCase__        =  isinstance(_UpperCamelCase       ,  np.ndarray				) and len(raw_speech.shape				) > 1
												if is_batched_numpy and len(raw_speech.shape				) > 2:
																		raise ValueError(F"Only mono-channel audio is supported for input to {self}"				)
												lowerCAmelCase__        =  is_batched_numpy or (
												    isinstance(_UpperCamelCase       ,  (list, tuple)				) and (isinstance(raw_speech[0]       ,  (np.ndarray, tuple, list)				))
												)

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

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

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

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

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

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

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

												lowerCAmelCase__        =  {'input_features': input_mel, 'is_longer': is_longer}
												lowerCAmelCase__        =  BatchFeature(_UpperCamelCase				)

												if return_tensors is not None:
																		lowerCAmelCase__        =  input_features.convert_to_tensors(_UpperCamelCase				)

												return input_features





