




"""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      UpperCAmelCase_  :




    def __init__(   self						:	int     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	Dict=13     ,			snake_case_						:	Optional[int]=2     ,			snake_case_						:	Union[str, Any]=24     ,			snake_case_						:	Any=16     ,			snake_case_						:	Optional[Any]=True     ,			snake_case_						:	Tuple=True     ,			snake_case_						:	Union[str, Any]=32     ,			snake_case_						:	str=5     ,			snake_case_						:	Union[str, Any]=4     ,			snake_case_						:	Optional[Any]=37     ,			snake_case_						:	Any="gelu"     ,			snake_case_						:	Tuple=0.1     ,			snake_case_						:	Tuple=0.1     ,			snake_case_						:	str=10     ,			snake_case_						:	Union[str, Any]=0.02     ,			snake_case_						:	List[str]=None     ,			snake_case_						:	Any=2     ,			snake_case_						:	Any=2     ,			)				->							int:




     '''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 							__magic_name__							(   self						:	Dict				)				->							List[Any]:




     '''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 							__magic_name__							(   self						:	Union[str, Any]				)				->							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=snake_case_     ,			initializer_range=self.initializer_range     ,			frequency_stride=self.frequency_stride     ,			time_stride=self.time_stride     ,			)




    def 							__magic_name__							(   self						:	Dict     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	Dict     ,			snake_case_						:	Optional[int]				)				->							int:




     '''simple docstring'''

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




    def 							__magic_name__							(   self						:	List[str]				)				->							int:




     '''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      UpperCAmelCase_  (						A_,       A_,       unittest.TestCase							):
    lowercase__						 =				(
        (
            ASTModel,
            ASTForAudioClassification,
        )
        if is_torch_available()
        else ()
    )
    lowercase__						 =				(
        {'''audio-classification''': ASTForAudioClassification, '''feature-extraction''': ASTModel}
        if is_torch_available()
        else {}
    )
    lowercase__						 =				False
    lowercase__						 =				False
    lowercase__						 =				False
    lowercase__						 =				False




    def 							__magic_name__							(   self						:	Dict     ,			snake_case_						:	str     ,			snake_case_						:	str     ,			snake_case_						:	List[str]     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	Optional[Any]				)				->							List[Any]:




     '''simple docstring'''

     if pipeline_test_casse_name == "AudioClassificationPipelineTests":
      return True

     return False




    def 							__magic_name__							(   self						:	Dict				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    ASTModelTester(self				)
     A__	=    ConfigTester(self     ,			config_class=snake_case_     ,			has_text_modality=snake_case_     ,			hidden_size=37				)




    def 							__magic_name__							(   self						:	Optional[int]				)				->							Dict:




     '''simple docstring'''

     self.config_tester.run_common_tests()




    @unittest.skip(reason="AST does not use inputs_embeds"				)
    def 							__magic_name__							(   self						:	List[str]				)				->							int:




     '''simple docstring'''

     pass




    def 							__magic_name__							(   self						:	List[str]				)				->							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(snake_case_				)
      self.assertIsInstance(model.get_input_embeddings()     ,			(nn.Module)				)
      A__	=    model.get_output_embeddings()
      self.assertTrue(x is None or isinstance(snake_case_     ,			nn.Linear				)				)




    def 							__magic_name__							(   self						:	int				)				->							str:




     '''simple docstring'''

     A__,				A__	=    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
      A__	=    model_class(snake_case_				)
      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]     ,			snake_case_				)




    def 							__magic_name__							(   self						:	Dict				)				->							Dict:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*snake_case_				)




    @slow
    def 							__magic_name__							(   self						:	List[str]				)				->							List[str]:




     '''simple docstring'''

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







def   _SCREAMING_SNAKE_CASE       (							)						->		str:
 A__	=    hf_hub_download(
     repo_id="nielsr/audio-spectogram-transformer-checkpoint"      ,			filename="sample_audio.flac"      ,			repo_type="dataset"						)

 A__,				A__	=    torchaudio.load(lowercase_						)

 return audio, sampling_rate






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




    @cached_property
    def 							__magic_name__							(   self						:	Tuple				)				->							int:




     '''simple docstring'''

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




    @slow
    def 							__magic_name__							(   self						:	Dict				)				->							List[Any]:




     '''simple docstring'''

     A__	=    self.default_feature_extractor
     A__	=    ASTForAudioClassification.from_pretrained("MIT/ast-finetuned-audioset-10-10-0.4593"				).to(snake_case_				)

     A__	=    self.default_feature_extractor
     A__,				A__	=    prepare_audio()
     A__	=    audio.squeeze().numpy()
     A__	=    feature_extractor(snake_case_     ,			sampling_rate=snake_case_     ,			return_tensors="pt"				).to(snake_case_				)

     # forward pass
     with torch.no_grad():
      A__	=    model(**snake_case_				)

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

     A__	=    torch.tensor([-0.8760, -7.0042, -8.6602]				).to(snake_case_				)

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











"""simple docstring"""



SCREAMING_SNAKE_CASE    =	"Alexander Joslin"

import operator as op

from .stack import Stack







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		int:
 A__	=    {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}

 A__	=    Stack()
 A__	=    Stack()

 for i in equation:
  if i.isdigit():
   # RULE 1
   operand_stack.push(int(lowercase_						)						)
  elif i in operators:
   # RULE 2
   operator_stack.push(lowercase_						)
  elif i == ")":
   # RULE 4
   A__	=    operator_stack.peek()
   operator_stack.pop()
   A__	=    operand_stack.peek()
   operand_stack.pop()
   A__	=    operand_stack.peek()
   operand_stack.pop()

   A__	=    operators[opr](lowercase_      ,			lowercase_						)
   operand_stack.push(lowercase_						)

    # RULE 5
 return operand_stack.peek()


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE    =	"(5 + ((4 * 2) * (2 + 3)))"
      # answer = 45
      print(f'{equation} = {dijkstras_two_stack_algorithm(equation)}')





