


'''simple docstring'''

import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowerCamelCase							:   List[str]  						=	random.Random()

def _SCREAMING_SNAKE_CASE      (A  , A=1.0  , A=None  , A=None       )       ->       List[Any]:






       """simple docstring"""







       if rng is None:
              lowercase__      			= global_rng

       lowercase__      			= []
       for batch_idx in range(shape[0]       ):
              values.append([]       )
              for _ in range(shape[1]       ):
                     values[-1].append(rng.random() * scale       )

       return values
class   __lowerCAmelCase						(unittest.TestCase     ):





      '''simple docstring'''






      def __init__(self    :    Optional[Any]   ,						UpperCamelCase    :    List[str]   ,						UpperCamelCase    :    Optional[Any]=7   ,						UpperCamelCase    :    Union[str, Any]=400   ,						UpperCamelCase    :    List[Any]=2000   ,						UpperCamelCase    :    Optional[Any]=1   ,						UpperCamelCase    :    Dict=0.0   ,						UpperCamelCase    :    int=16000   ,						UpperCamelCase    :    Dict=True   ,						UpperCamelCase    :    List[str]=True   ,						):

             '''simple docstring'''







             lowercase__      			= parent
             lowercase__      			= batch_size
             lowercase__      			= min_seq_length
             lowercase__      			= max_seq_length
             lowercase__      			= (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
             lowercase__      			= feature_size
             lowercase__      			= padding_value
             lowercase__      			= sampling_rate
             lowercase__      			= return_attention_mask
             lowercase__      			= do_normalize



      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             return {
                 "feature_size": self.feature_size,
                 "padding_value": self.padding_value,
                 "sampling_rate": self.sampling_rate,
                 "return_attention_mask": self.return_attention_mask,
                 "do_normalize": self.do_normalize,
             }






      def       UpperCamelCase__		(self    :    Optional[Any]   ,						UpperCamelCase    :    Dict=False   ,						UpperCamelCase    :    Tuple=False ):

             '''simple docstring'''







             def _flatten(UpperCamelCase    :    Dict ):
                    return list(itertools.chain(*UpperCamelCase ) )

             if equal_length:
                    lowercase__      			= floats_list((self.batch_size, self.max_seq_length) )
             else:
                    # make sure that inputs increase in size
                    lowercase__      			= [
                        _flatten(floats_list((x, self.feature_size) ) )
                        for x in range(self.min_seq_length   ,						self.max_seq_length   ,						self.seq_length_diff )
                    ]

             if numpify:
                    lowercase__      			= [np.asarray(UpperCamelCase ) for x in speech_inputs]

             return speech_inputs






class   __lowerCAmelCase						(lowercase_  ,     unittest.TestCase     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Union[str, Any]  					=							WavaVecaFeatureExtractor



      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             lowercase__      			= WavaVecaFeatureExtractionTester(self )



      def       UpperCamelCase__		(self    :    List[Any]   ,						UpperCamelCase    :    Tuple ):

             '''simple docstring'''







             self.assertTrue(np.all(np.mean(UpperCamelCase   ,						axis=0 ) < 1E-3 ) )
             self.assertTrue(np.all(np.abs(np.var(UpperCamelCase   ,						axis=0 ) - 1 ) < 1E-3 ) )



      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







             lowercase__      			= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
             # create three inputs of length 800, 1000, and 1200
             lowercase__      			= [floats_list((1, x) )[0] for x in range(800   ,						1400   ,						200 )]
             lowercase__      			= [np.asarray(UpperCamelCase ) for speech_input in speech_inputs]

             # Test not batched input
             lowercase__      			= feat_extract(speech_inputs[0]   ,						return_tensors='''np''' ).input_values
             lowercase__      			= feat_extract(np_speech_inputs[0]   ,						return_tensors='''np''' ).input_values
             self.assertTrue(np.allclose(UpperCamelCase   ,						UpperCamelCase   ,						atol=1E-3 ) )

             # Test batched
             lowercase__      			= feat_extract(UpperCamelCase   ,						return_tensors='''np''' ).input_values
             lowercase__      			= feat_extract(UpperCamelCase   ,						return_tensors='''np''' ).input_values
             for enc_seq_a, enc_seq_a in zip(UpperCamelCase   ,						UpperCamelCase ):
                    self.assertTrue(np.allclose(UpperCamelCase   ,						UpperCamelCase   ,						atol=1E-3 ) )

             # Test 2-D numpy arrays are batched.
             lowercase__      			= [floats_list((1, x) )[0] for x in (800, 800, 800)]
             lowercase__      			= np.asarray(UpperCamelCase )
             lowercase__      			= feat_extract(UpperCamelCase   ,						return_tensors='''np''' ).input_values
             lowercase__      			= feat_extract(UpperCamelCase   ,						return_tensors='''np''' ).input_values
             for enc_seq_a, enc_seq_a in zip(UpperCamelCase   ,						UpperCamelCase ):
                    self.assertTrue(np.allclose(UpperCamelCase   ,						UpperCamelCase   ,						atol=1E-3 ) )



      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







             lowercase__      			= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
             lowercase__      			= [floats_list((1, x) )[0] for x in range(800   ,						1400   ,						200 )]

             lowercase__      			= ['''longest''', '''max_length''', '''do_not_pad''']
             lowercase__      			= [None, 1600, None]
             for max_length, padding in zip(UpperCamelCase   ,						UpperCamelCase ):
                    lowercase__      			= feat_extract(UpperCamelCase   ,						padding=UpperCamelCase   ,						max_length=UpperCamelCase   ,						return_tensors='''np''' )
                    lowercase__      			= processed.input_values

                    self._check_zero_mean_unit_variance(input_values[0][:800] )
                    self.assertTrue(input_values[0][800:].sum() < 1E-6 )
                    self._check_zero_mean_unit_variance(input_values[1][:1000] )
                    self.assertTrue(input_values[0][1000:].sum() < 1E-6 )
                    self._check_zero_mean_unit_variance(input_values[2][:1200] )



      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             lowercase__      			= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
             lowercase__      			= range(800   ,						1400   ,						200 )
             lowercase__      			= [floats_list((1, x) )[0] for x in lengths]

             lowercase__      			= ['''longest''', '''max_length''', '''do_not_pad''']
             lowercase__      			= [None, 1600, None]

             for max_length, padding in zip(UpperCamelCase   ,						UpperCamelCase ):
                    lowercase__      			= feat_extract(UpperCamelCase   ,						max_length=UpperCamelCase   ,						padding=UpperCamelCase )
                    lowercase__      			= processed.input_values

                    self._check_zero_mean_unit_variance(input_values[0][:800] )
                    self._check_zero_mean_unit_variance(input_values[1][:1000] )
                    self._check_zero_mean_unit_variance(input_values[2][:1200] )



      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             lowercase__      			= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
             lowercase__      			= [floats_list((1, x) )[0] for x in range(800   ,						1400   ,						200 )]
             lowercase__      			= feat_extract(
                 UpperCamelCase   ,						truncation=UpperCamelCase   ,						max_length=1000   ,						padding='''max_length'''   ,						return_tensors='''np''' )
             lowercase__      			= processed.input_values

             self._check_zero_mean_unit_variance(input_values[0, :800] )
             self._check_zero_mean_unit_variance(input_values[1] )
             self._check_zero_mean_unit_variance(input_values[2] )



      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             lowercase__      			= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
             lowercase__      			= [floats_list((1, x) )[0] for x in range(800   ,						1400   ,						200 )]
             lowercase__      			= feat_extract(
                 UpperCamelCase   ,						truncation=UpperCamelCase   ,						max_length=1000   ,						padding='''longest'''   ,						return_tensors='''np''' )
             lowercase__      			= processed.input_values

             self._check_zero_mean_unit_variance(input_values[0, :800] )
             self._check_zero_mean_unit_variance(input_values[1, :1000] )
             self._check_zero_mean_unit_variance(input_values[2] )

             # make sure that if max_length < longest -> then pad to max_length
             self.assertTrue(input_values.shape == (3, 1000) )

             lowercase__      			= [floats_list((1, x) )[0] for x in range(800   ,						1400   ,						200 )]
             lowercase__      			= feat_extract(
                 UpperCamelCase   ,						truncation=UpperCamelCase   ,						max_length=2000   ,						padding='''longest'''   ,						return_tensors='''np''' )
             lowercase__      			= processed.input_values

             self._check_zero_mean_unit_variance(input_values[0, :800] )
             self._check_zero_mean_unit_variance(input_values[1, :1000] )
             self._check_zero_mean_unit_variance(input_values[2] )

             # make sure that if max_length > longest -> then pad to longest
             self.assertTrue(input_values.shape == (3, 1200) )



      @require_torch
      def       UpperCamelCase__		(self    :    Optional[int] ):

             '''simple docstring'''







             import torch

             lowercase__      			= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
             lowercase__      			= np.random.rand(100 ).astype(np.floataa )
             lowercase__      			= np_speech_inputs.tolist()

             for inputs in [py_speech_inputs, np_speech_inputs]:
                    lowercase__      			= feature_extractor.pad([{'''input_values''': inputs}]   ,						return_tensors='''np''' )
                    self.assertTrue(np_processed.input_values.dtype == np.floataa )
                    lowercase__      			= feature_extractor.pad([{'''input_values''': inputs}]   ,						return_tensors='''pt''' )
                    self.assertTrue(pt_processed.input_values.dtype == torch.floataa )






      @slow
      @require_torch
      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
                    lowercase__      			= WavaVecaConfig.from_pretrained(UpperCamelCase )
                    lowercase__      			= WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase )

                    # only "layer" feature extraction norm should make use of
                    # attention_mask
                    self.assertEqual(feat_extract.return_attention_mask   ,						config.feat_extract_norm == '''layer''' )










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


UpperCamelCase__    			=			{
    """configuration_audio_spectrogram_transformer""": [
        """AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ASTConfig""",
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__    			=			[
					    """AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """ASTForAudioClassification""",
					    """ASTModel""",
					    """ASTPreTrainedModel""",
					]

try:
					if not is_speech_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__    			=			["""ASTFeatureExtractor"""]

if TYPE_CHECKING:
					from .configuration_audio_spectrogram_transformer import (
					    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    ASTConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_audio_spectrogram_transformer import (
										    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ASTForAudioClassification,
										    ASTModel,
										    ASTPreTrainedModel,
										)

					try:
										if not is_speech_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
					import sys

					UpperCamelCase__    			=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




