



'''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 datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


lowercase_	 =						"\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",\n    author = \"Lin, Chin-Yew  and\n      Och, Franz Josef\",\n    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",\n    month = \"aug 23{--}aug 27\",\n    year = \"2004\",\n    address = \"Geneva, Switzerland\",\n    publisher = \"COLING\",\n    url = \"https://www.aclweb.org/anthology/C04-1072\",\n    pages = \"501--507\",\n}\n"

lowercase_	 =						"\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,\nthe better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n"

lowercase_	 =						"\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    'bleu': bleu score,\n    'precisions': geometric mean of n-gram precisions,\n    'brevity_penalty': brevity penalty,\n    'length_ratio': ratio of lengths,\n    'translation_length': translation_length,\n    'reference_length': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample\n    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)\n    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric(\"bleu\")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results[\"bleu\"])\n    1.0\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,     _KWARGS_DESCRIPTION		)
class __A    (						datasets.Metric		):



 '''simple docstring'''







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


      """simple docstring"""



      return datasets.MetricInfo(
          description=_DESCRIPTION				,     citation=_CITATION				,     inputs_description=_KWARGS_DESCRIPTION				,     features=datasets.Features(
              {
                  '''predictions''': datasets.Sequence(datasets.Value('''string'''				,     id='''token''' )				,     id='''sequence''' ),
                  '''references''': datasets.Sequence(
                      datasets.Sequence(datasets.Value('''string'''				,     id='''token''' )				,     id='''sequence''' )				,     id='''references''' ),
              } )				,     codebase_urls=['''https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py''']				,     reference_urls=[
              '''https://en.wikipedia.org/wiki/BLEU''',
              '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
          ]				,     )



 def       a__						(self				,     A				,     A				,     A=4				,     A=False )					->  Tuple:


      """simple docstring"""



      _a											=						compute_bleu(
          reference_corpus=A				,     translation_corpus=A				,     max_order=A				,     smooth=A )
      ((_a)						,					(_a)						,					(_a)						,					(_a)						,					(_a)						,					(_a))											=						score
      return {
          "bleu": bleu,
          "precisions": precisions,
          "brevity_penalty": bp,
          "length_ratio": ratio,
          "translation_length": translation_length,
          "reference_length": reference_length,
      }



