





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 						lowerCamelCase       :







       """simple docstring"""




       def __init__( self     :					Any,			_UpperCAmelCase     :					Tuple,			_UpperCAmelCase     :					List[Any]=1_3,			_UpperCAmelCase     :					Dict=2,			_UpperCAmelCase     :					int=2_4,			_UpperCAmelCase     :					List[str]=1_6,			_UpperCAmelCase     :					Union[str, Any]=True,			_UpperCAmelCase     :					Any=True,			_UpperCAmelCase     :					Optional[int]=3_2,			_UpperCAmelCase     :					int=5,			_UpperCAmelCase     :					Dict=4,			_UpperCAmelCase     :					str=3_7,			_UpperCAmelCase     :					Optional[int]="gelu",			_UpperCAmelCase     :					List[str]=0.1,			_UpperCAmelCase     :					Optional[Any]=0.1,			_UpperCAmelCase     :					List[str]=1_0,			_UpperCAmelCase     :					List[Any]=0.02,			_UpperCAmelCase     :					Union[str, Any]=None,			_UpperCAmelCase     :					Optional[int]=2,			_UpperCAmelCase     :					List[Any]=2,			)				->			Optional[int]:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Tuple     =						parent
              SCREAMING_SNAKE_CASE__       :      str     =						batch_size
              SCREAMING_SNAKE_CASE__       :      List[str]     =						patch_size
              SCREAMING_SNAKE_CASE__       :      Dict     =						max_length
              SCREAMING_SNAKE_CASE__       :      List[str]     =						num_mel_bins
              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						is_training
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						use_labels
              SCREAMING_SNAKE_CASE__       :      int     =						hidden_size
              SCREAMING_SNAKE_CASE__       :      int     =						num_hidden_layers
              SCREAMING_SNAKE_CASE__       :      str     =						num_attention_heads
              SCREAMING_SNAKE_CASE__       :      Dict     =						intermediate_size
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						hidden_act
              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						hidden_dropout_prob
              SCREAMING_SNAKE_CASE__       :      Any     =						attention_probs_dropout_prob
              SCREAMING_SNAKE_CASE__       :      Tuple     =						type_sequence_label_size
              SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						initializer_range
              SCREAMING_SNAKE_CASE__       :      str     =						scope
              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						frequency_stride
              SCREAMING_SNAKE_CASE__       :      str     =						time_stride

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






       def  A_     ( self     :					Dict						)				->			int:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      List[str]     =						floats_tensor([self.batch_size, self.max_length, self.num_mel_bins]						)

              SCREAMING_SNAKE_CASE__       :      Tuple     =						None
              if self.use_labels:
                     SCREAMING_SNAKE_CASE__       :      int     =						ids_tensor([self.batch_size],			self.type_sequence_label_size						)

              SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.get_config()

              return config, input_values, labels






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





              """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=_UpperCAmelCase,			initializer_range=self.initializer_range,			frequency_stride=self.frequency_stride,			time_stride=self.time_stride,			)






       def  A_     ( self     :					Optional[int],			_UpperCAmelCase     :					Tuple,			_UpperCAmelCase     :					Any,			_UpperCAmelCase     :					int						)				->			Union[str, Any]:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Dict     =						ASTModel(config=_UpperCAmelCase						)
              model.to(_UpperCAmelCase						)
              model.eval()
              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						model(_UpperCAmelCase						)
              self.parent.assertEqual(result.last_hidden_state.shape,			(self.batch_size, self.seq_length, self.hidden_size)						)






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





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      str     =						self.prepare_config_and_inputs()
              (
                  SCREAMING_SNAKE_CASE__
              )       :      Union[str, Any]     =						config_and_inputs
              SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						{"input_values": input_values}
              return config, inputs_dict


@require_torch
class 						lowerCamelCase       (__lowerCamelCase							,     __lowerCamelCase							,     unittest.TestCase      ):







       """simple docstring"""




       UpperCAmelCase_      						=   (
           (
               ASTModel,
               ASTForAudioClassification,
           )
           if is_torch_available()
           else ()
       )
       UpperCAmelCase_      						=   (
           {"audio-classification": ASTForAudioClassification, "feature-extraction": ASTModel}
           if is_torch_available()
           else {}
       )
       UpperCAmelCase_      						=   False
       UpperCAmelCase_      						=   False
       UpperCAmelCase_      						=   False
       UpperCAmelCase_      						=   False






       def  A_     ( self     :					Tuple,			_UpperCAmelCase     :					str,			_UpperCAmelCase     :					Optional[Any],			_UpperCAmelCase     :					Optional[int],			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					Optional[Any]						)				->			Any:





              """simple docstring"""
              if pipeline_test_casse_name == "AudioClassificationPipelineTests":
                     return True

              return False






       def  A_     ( self     :					List[Any]						)				->			Dict:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						ASTModelTester(self						)
              SCREAMING_SNAKE_CASE__       :      Tuple     =						ConfigTester(self,			config_class=_UpperCAmelCase,			has_text_modality=_UpperCAmelCase,			hidden_size=3_7						)






       def  A_     ( self     :					Optional[Any]						)				->			int:





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






       @unittest.skip(reason="AST does not use inputs_embeds"						)
       def  A_     ( self     :					Optional[int]						)				->			Tuple:





              """simple docstring"""
              pass






       def  A_     ( self     :					Tuple						)				->			Optional[Any]:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.model_tester.prepare_config_and_inputs_for_common()

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






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





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     SCREAMING_SNAKE_CASE__       :      Tuple     =						model_class(_UpperCAmelCase						)
                     SCREAMING_SNAKE_CASE__       :      str     =						inspect.signature(model.forward						)
                     # signature.parameters is an OrderedDict => so arg_names order is deterministic
                     SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						[*signature.parameters.keys()]

                     SCREAMING_SNAKE_CASE__       :      Tuple     =						["input_values"]
                     self.assertListEqual(arg_names[:1],			_UpperCAmelCase						)






       def  A_     ( self     :					List[str]						)				->			str:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Dict     =						self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*_UpperCAmelCase						)






       @slow
       def  A_     ( self     :					str						)				->			str:





              """simple docstring"""
              for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						ASTModel.from_pretrained(_UpperCAmelCase						)
                     self.assertIsNotNone(_UpperCAmelCase						)




def _a (  )	->    str:

       '''simple docstring'''






       SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						hf_hub_download(
           repo_id="nielsr/audio-spectogram-transformer-checkpoint"			,				filename="sample_audio.flac"			,				repo_type="dataset"       )

       SCREAMING_SNAKE_CASE__       :      Dict     =						torchaudio.load(SCREAMING_SNAKE_CASE__       )

       return audio, sampling_rate







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







       """simple docstring"""




       @cached_property
       def  A_     ( self     :					List[Any]						)				->			List[str]:





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






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





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      int     =						self.default_feature_extractor
              SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						ASTForAudioClassification.from_pretrained("MIT/ast-finetuned-audioset-10-10-0.4593"						).to(_UpperCAmelCase						)

              SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.default_feature_extractor
              SCREAMING_SNAKE_CASE__       :      str     =						prepare_audio()
              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						audio.squeeze().numpy()
              SCREAMING_SNAKE_CASE__       :      Any     =						feature_extractor(_UpperCAmelCase,			sampling_rate=_UpperCAmelCase,			return_tensors="pt"						).to(_UpperCAmelCase						)

              # forward pass
              with torch.no_grad():
                     SCREAMING_SNAKE_CASE__       :      Dict     =						model(**_UpperCAmelCase						)

              # verify the logits
              SCREAMING_SNAKE_CASE__       :      List[str]     =						torch.Size((1, 5_2_7)						)
              self.assertEqual(outputs.logits.shape,			_UpperCAmelCase						)

              SCREAMING_SNAKE_CASE__       :      Dict     =						torch.tensor([-0.8760, -7.0042, -8.6602]						).to(_UpperCAmelCase						)

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













from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403




def _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  complex			,				SCREAMING_SNAKE_CASE__      :  str = "x"			,				SCREAMING_SNAKE_CASE__      :  float = 10**-10			,				SCREAMING_SNAKE_CASE__      :  int = 1			,				)	->    complex:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						symbols(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      str     =						lambdify(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      int     =						lambdify(SCREAMING_SNAKE_CASE__			,				diff(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )       )

							SCREAMING_SNAKE_CASE__       :      Any     =						starting_point

							while True:
														if diff_function(SCREAMING_SNAKE_CASE__       ) != 0:
																					SCREAMING_SNAKE_CASE__       :      Any     =						prev_guess - multiplicity * func(SCREAMING_SNAKE_CASE__       ) / diff_function(
																					    SCREAMING_SNAKE_CASE__       )
														else:
																					raise ZeroDivisionError("Could not find root"       ) from None

														# Precision is checked by comparing the difference of consecutive guesses
														if abs(next_guess - prev_guess       ) < precision:
																					return next_guess

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						next_guess


# Let's Execute
if __name__ == "__main__":
		# Find root of trigonometric function
		# Find value of pi
		print(f"The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}")

		# Find root of polynomial
		# Find fourth Root of 5
		print(f"The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5J)}")

		# Find value of e
		print(
		    '''The root of log(y) - 1 = 0 is ''',
		    f"{newton_raphson('log(y) - 1', 2, variable='y')}",
		)

		# Exponential Roots
		print(
		    '''The root of exp(x) - 1 = 0 is''',
		    f"{newton_raphson('exp(x) - 1', 1_0, precision=0.005)}",
		)

		# Find root of cos(x)
		print(f"The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}")






