



'''simple docstring'''





import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import ASTForAudioClassification, ASTModel
  from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
      AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  )


if is_torchaudio_available():
  import torchaudio

  from transformers import ASTFeatureExtractor






class __A    :



 '''simple docstring'''







 def __init__(self				,     A				,     A=13				,     A=2				,     A=24				,     A=16				,     A=True				,     A=True				,     A=32				,     A=5				,     A=4				,     A=37				,     A="gelu"				,     A=0.1				,     A=0.1				,     A=10				,     A=0.02				,     A=None				,     A=2				,     A=2				,     )					->  List[Any]:


      """simple docstring"""



      _a											=						parent
      _a											=						batch_size
      _a											=						patch_size
      _a											=						max_length
      _a											=						num_mel_bins
      _a											=						is_training
      _a											=						use_labels
      _a											=						hidden_size
      _a											=						num_hidden_layers
      _a											=						num_attention_heads
      _a											=						intermediate_size
      _a											=						hidden_act
      _a											=						hidden_dropout_prob
      _a											=						attention_probs_dropout_prob
      _a											=						type_sequence_label_size
      _a											=						initializer_range
      _a											=						scope
      _a											=						frequency_stride
      _a											=						time_stride

      # in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
      _a											=						(self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
      _a											=						(self.max_length - self.patch_size) // self.time_stride + 1
      _a											=						frequency_out_dimension * time_out_dimension
      _a											=						num_patches + 2



 def       a__						(self )					->  Dict:


      """simple docstring"""



      _a											=						floats_tensor([self.batch_size, self.max_length, self.num_mel_bins] )

      _a											=						None
      if self.use_labels:
           _a											=						ids_tensor([self.batch_size]				,     self.type_sequence_label_size )

      _a											=						self.get_config()

      return config, input_values, labels



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


      """simple docstring"""



      return ASTConfig(
          patch_size=self.patch_size				,     max_length=self.max_length				,     num_mel_bins=self.num_mel_bins				,     hidden_size=self.hidden_size				,     num_hidden_layers=self.num_hidden_layers				,     num_attention_heads=self.num_attention_heads				,     intermediate_size=self.intermediate_size				,     hidden_act=self.hidden_act				,     hidden_dropout_prob=self.hidden_dropout_prob				,     attention_probs_dropout_prob=self.attention_probs_dropout_prob				,     is_decoder=A				,     initializer_range=self.initializer_range				,     frequency_stride=self.frequency_stride				,     time_stride=self.time_stride				,     )



 def       a__						(self				,     A				,     A				,     A )					->  Dict:


      """simple docstring"""



      _a											=						ASTModel(config=A )
      model.to(A )
      model.eval()
      _a											=						model(A )
      self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size) )



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


      """simple docstring"""



      _a											=						self.prepare_config_and_inputs()
      (
          (
          _a					
      )						,					(
          _a					
      )						,					(
          _a					
      )						,										
      )											=						config_and_inputs
      _a											=						{'''input_values''': input_values}
      return config, inputs_dict






@require_torch
class __A    (						A     ,     A     ,     unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase					: Optional[Any]    = (
     (
         ASTModel,
         ASTForAudioClassification,
     )
     if is_torch_available()
     else ()
 )
 __lowerCamelCase					: Optional[int]    = (
     {'audio-classification': ASTForAudioClassification, 'feature-extraction': ASTModel}
     if is_torch_available()
     else {}
 )
 __lowerCamelCase					: Dict    = False
 __lowerCamelCase					: Any    = False
 __lowerCamelCase					: List[str]    = False
 __lowerCamelCase					: str    = False



 def       a__						(self				,     A				,     A				,     A				,     A				,     A )					->  List[str]:


      """simple docstring"""



      if pipeline_test_casse_name == "AudioClassificationPipelineTests":
           return True

      return False



 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      _a											=						ASTModelTester(self )
      _a											=						ConfigTester(self				,     config_class=A				,     has_text_modality=A				,     hidden_size=37 )



 def       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



      self.config_tester.run_common_tests()



 @unittest.skip(reason='''AST does not use inputs_embeds''' )
 def       a__						(self )					->  Dict:


      """simple docstring"""



      pass



 def       a__						(self )					->  Dict:


      """simple docstring"""



      _a						,					_a											=						self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _a											=						model_class(A )
           self.assertIsInstance(model.get_input_embeddings()				,     (nn.Module) )
           _a											=						model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(A				,     nn.Linear ) )



 def       a__						(self )					->  Tuple:


      """simple docstring"""



      _a						,					_a											=						self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _a											=						model_class(A )
           _a											=						inspect.signature(model.forward )
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           _a											=						[*signature.parameters.keys()]

           _a											=						['''input_values''']
           self.assertListEqual(arg_names[:1]				,     A )



 def       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



      _a											=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A )



 @slow
 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a											=						ASTModel.from_pretrained(A )
           self.assertIsNotNone(A )





def lowerCAmelCase						():



     """simple docstring"""



     _a											=						hf_hub_download(
         repo_id='''nielsr/audio-spectogram-transformer-checkpoint'''						,			filename='''sample_audio.flac'''						,			repo_type='''dataset''')

     _a						,					_a											=						torchaudio.load(__A)

     return audio, sampling_rate






@require_torch
@require_torchaudio
class __A    (						unittest.TestCase		):



 '''simple docstring'''







 @cached_property
 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      return (
          ASTFeatureExtractor.from_pretrained('''MIT/ast-finetuned-audioset-10-10-0.4593''' )
          if is_torchaudio_available()
          else None
      )



 @slow
 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      _a											=						self.default_feature_extractor
      _a											=						ASTForAudioClassification.from_pretrained('''MIT/ast-finetuned-audioset-10-10-0.4593''' ).to(A )

      _a											=						self.default_feature_extractor
      _a						,					_a											=						prepare_audio()
      _a											=						audio.squeeze().numpy()
      _a											=						feature_extractor(A				,     sampling_rate=A				,     return_tensors='''pt''' ).to(A )

      # forward pass
      with torch.no_grad():
           _a											=						model(**A )

      # verify the logits
      _a											=						torch.Size((1, 527) )
      self.assertEqual(outputs.logits.shape				,     A )

      _a											=						torch.tensor([-0.8760, -7.0042, -8.6602] ).to(A )

      self.assertTrue(torch.allclose(outputs.logits[0, :3]				,     A				,     atol=1E-4 ) )








'''simple docstring'''





import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
  from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


lowercase_	 =						logging.get_logger(__name__)  # pylint: disable=invalid-name

lowercase_	 =						256






class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: Optional[int]    = ['melgan']



 def __init__(self				,     A				,     A				,     A				,     A				,     A				,     )					->  None:


      """simple docstring"""



      super().__init__()

      # From MELGAN
      _a											=						math.log(1E-5 )  # Matches MelGAN training.
      _a											=						4.0  # Largest value for most examples
      _a											=						128

      self.register_modules(
          notes_encoder=A				,     continuous_encoder=A				,     decoder=A				,     scheduler=A				,     melgan=A				,     )



 def       a__						(self				,     A				,     A=(-1.0, 1.0)				,     A=False )					->  List[str]:


      """simple docstring"""



      _a						,					_a											=						output_range
      if clip:
           _a											=						torch.clip(A				,     self.min_value				,     self.max_value )
      # Scale to [0, 1].
      _a											=						(features - self.min_value) / (self.max_value - self.min_value)
      # Scale to [min_out, max_out].
      return zero_one * (max_out - min_out) + min_out



 def       a__						(self				,     A				,     A=(-1.0, 1.0)				,     A=False )					->  Optional[Any]:


      """simple docstring"""



      _a						,					_a											=						input_range
      _a											=						torch.clip(A				,     A				,     A ) if clip else outputs
      # Scale to [0, 1].
      _a											=						(outputs - min_out) / (max_out - min_out)
      # Scale to [self.min_value, self.max_value].
      return zero_one * (self.max_value - self.min_value) + self.min_value



 def       a__						(self				,     A				,     A				,     A )					->  Optional[int]:


      """simple docstring"""



      _a											=						input_tokens > 0
      _a						,					_a											=						self.notes_encoder(
          encoder_input_tokens=A				,     encoder_inputs_mask=A )

      _a						,					_a											=						self.continuous_encoder(
          encoder_inputs=A				,     encoder_inputs_mask=A )

      return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]



 def       a__						(self				,     A				,     A				,     A )					->  Any:


      """simple docstring"""



      _a											=						noise_time
      if not torch.is_tensor(A ):
           _a											=						torch.tensor([timesteps]				,     dtype=torch.long				,     device=input_tokens.device )
      elif torch.is_tensor(A ) and len(timesteps.shape ) == 0:
           _a											=						timesteps[None].to(input_tokens.device )

      # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
      _a											=						timesteps * torch.ones(input_tokens.shape[0]				,     dtype=timesteps.dtype				,     device=timesteps.device )

      _a											=						self.decoder(
          encodings_and_masks=A				,     decoder_input_tokens=A				,     decoder_noise_time=A )
      return logits



 @torch.no_grad()
 def __call__(self				,     A				,     A = None				,     A = 100				,     A = True				,     A = "numpy"				,     A = None				,     A = 1				,     )					->  Union[AudioPipelineOutput, Tuple]:


      """simple docstring"""



      if (callback_steps is None) or (
          callback_steps is not None and (not isinstance(A				,     A ) or callback_steps <= 0)
      ):
           raise ValueError(
               f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
               f''' {type(A )}.''' )

      _a											=						np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]				,     dtype=np.floataa )
      _a											=						np.zeros([1, 0, self.n_dims]				,     np.floataa )
      _a											=						torch.ones((1, TARGET_FEATURE_LENGTH)				,     dtype=A				,     device=self.device )

      for i, encoder_input_tokens in enumerate(A ):
           if i == 0:
                _a											=						torch.from_numpy(pred_mel[:1].copy() ).to(
                    device=self.device				,     dtype=self.decoder.dtype )
                # The first chunk has no previous context.
                _a											=						torch.zeros((1, TARGET_FEATURE_LENGTH)				,     dtype=A				,     device=self.device )
           else:
                # The full song pipeline does not feed in a context feature, so the mask
                # will be all 0s after the feature converter. Because we know we're
                # feeding in a full context chunk from the previous prediction, set it
                # to all 1s.
                _a											=						ones

           _a											=						self.scale_features(
               A				,     output_range=[-1.0, 1.0]				,     clip=A )

           _a											=						self.encode(
               input_tokens=torch.IntTensor([encoder_input_tokens] ).to(device=self.device )				,     continuous_inputs=A				,     continuous_mask=A				,     )

           # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
           _a											=						randn_tensor(
               shape=encoder_continuous_inputs.shape				,     generator=A				,     device=self.device				,     dtype=self.decoder.dtype				,     )

           # set step values
           self.scheduler.set_timesteps(A )

           # Denoising diffusion loop
           for j, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
                _a											=						self.decode(
                    encodings_and_masks=A				,     input_tokens=A				,     noise_time=t / self.scheduler.config.num_train_timesteps				,     )

                # Compute previous output: x_t -> x_t-1
                _a											=						self.scheduler.step(A				,     A				,     A				,     generator=A ).prev_sample

           _a											=						self.scale_to_features(A				,     input_range=[-1.0, 1.0] )
           _a											=						mel[:1]
           _a											=						mel.cpu().float().numpy()

           _a											=						np.concatenate([full_pred_mel, pred_mel[:1]]				,     axis=1 )

           # call the callback, if provided
           if callback is not None and i % callback_steps == 0:
                callback(A				,     A )

           logger.info('''Generated segment'''				,     A )

      if output_type == "numpy" and not is_onnx_available():
           raise ValueError(
               '''Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.''' )
      elif output_type == "numpy" and self.melgan is None:
           raise ValueError(
               '''Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.''' )

      if output_type == "numpy":
           _a											=						self.melgan(input_features=full_pred_mel.astype(np.floataa ) )
      else:
           _a											=						full_pred_mel

      if not return_dict:
           return (output,)

      return AudioPipelineOutput(audios=A )



