






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


lowercase  :							Dict			  =    random.Random()

def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			Optional[int]=1.0  ,      _a       :			List[str]=None  ,      _a       :			str=None    ):
		if rng is None:
				snake_case_				:							List[Any]						 =       global_rng

		snake_case_				:							Union[str, Any]						 =       []
		for batch_idx in range(shape[0]    ):
				values.append([]    )
				for _ in range(shape[1]    ):
						values[-1].append(rng.random() * scale    )

		return values




class 		UpperCAmelCase_						(	unittest.TestCase					):


						'''simple docstring'''




						def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE=7		,				_SCREAMING_SNAKE_CASE=400		,				_SCREAMING_SNAKE_CASE=2000		,				_SCREAMING_SNAKE_CASE=1		,				_SCREAMING_SNAKE_CASE=0.0		,				_SCREAMING_SNAKE_CASE=1_6000		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=True		,				) ->				List[Any]:
								snake_case_				:							Dict						 =       parent
								snake_case_				:							Optional[int]						 =       batch_size
								snake_case_				:							Dict						 =       min_seq_length
								snake_case_				:							Tuple						 =       max_seq_length
								snake_case_				:							Optional[int]						 =       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								snake_case_				:							Dict						 =       feature_size
								snake_case_				:							List[Any]						 =       padding_value
								snake_case_				:							str						 =       sampling_rate
								snake_case_				:							Optional[int]						 =       return_attention_mask
								snake_case_				:							Optional[int]						 =       do_normalize






						def       _lowerCAmelCase       (	self				) ->				List[str]:
								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       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE=False				) ->				str:
								def _flatten(_SCREAMING_SNAKE_CASE				):
										return list(itertools.chain(*_SCREAMING_SNAKE_CASE				)				)

								if equal_length:
										snake_case_				:							Tuple						 =       floats_list((self.batch_size, self.max_seq_length)				)
								else:
										# make sure that inputs increase in size
										snake_case_				:							str						 =       [
										    _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:
										snake_case_				:							int						 =       [np.asarray(_SCREAMING_SNAKE_CASE				) for x in speech_inputs]

								return speech_inputs




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					,  unittest.TestCase					):


						'''simple docstring'''




						A     :   List[str]						=					WavaVecaFeatureExtractor






						def       _lowerCAmelCase       (	self				) ->				List[str]:
								snake_case_				:							Tuple						 =       WavaVecaFeatureExtractionTester(self				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Union[str, Any]:
								self.assertTrue(np.all(np.mean(_SCREAMING_SNAKE_CASE		,				axis=0				) < 1e-3				)				)
								self.assertTrue(np.all(np.abs(np.var(_SCREAMING_SNAKE_CASE		,				axis=0				) - 1				) < 1e-3				)				)






						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								# Tests that all call wrap to encode_plus and batch_encode_plus
								snake_case_				:							List[Any]						 =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
								# create three inputs of length 800, 1000, and 1200
								snake_case_				:							List[str]						 =       [floats_list((1, x)				)[0] for x in range(800		,				1400		,				200				)]
								snake_case_				:							Dict						 =       [np.asarray(_SCREAMING_SNAKE_CASE				) for speech_input in speech_inputs]

								# Test not batched input
								snake_case_				:							str						 =       feat_extract(speech_inputs[0]		,				return_tensors="np"				).input_values
								snake_case_				:							Dict						 =       feat_extract(np_speech_inputs[0]		,				return_tensors="np"				).input_values
								self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				atol=1e-3				)				)

								# Test batched
								snake_case_				:							str						 =       feat_extract(_SCREAMING_SNAKE_CASE		,				return_tensors="np"				).input_values
								snake_case_				:							str						 =       feat_extract(_SCREAMING_SNAKE_CASE		,				return_tensors="np"				).input_values
								for enc_seq_a, enc_seq_a in zip(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				):
										self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				atol=1e-3				)				)

								# Test 2-D numpy arrays are batched.
								snake_case_				:							Optional[int]						 =       [floats_list((1, x)				)[0] for x in (800, 800, 800)]
								snake_case_				:							str						 =       np.asarray(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Tuple						 =       feat_extract(_SCREAMING_SNAKE_CASE		,				return_tensors="np"				).input_values
								snake_case_				:							Any						 =       feat_extract(_SCREAMING_SNAKE_CASE		,				return_tensors="np"				).input_values
								for enc_seq_a, enc_seq_a in zip(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				):
										self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				atol=1e-3				)				)






						def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								snake_case_				:							List[Any]						 =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
								snake_case_				:							Tuple						 =       [floats_list((1, x)				)[0] for x in range(800		,				1400		,				200				)]

								snake_case_				:							List[Any]						 =       ["longest", "max_length", "do_not_pad"]
								snake_case_				:							int						 =       [None, 1600, None]
								for max_length, padding in zip(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				):
										snake_case_				:							Any						 =       feat_extract(_SCREAMING_SNAKE_CASE		,				padding=_SCREAMING_SNAKE_CASE		,				max_length=_SCREAMING_SNAKE_CASE		,				return_tensors="np"				)
										snake_case_				:							Optional[Any]						 =       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       _lowerCAmelCase       (	self				) ->				Optional[int]:
								snake_case_				:							Dict						 =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
								snake_case_				:							Any						 =       range(800		,				1400		,				200				)
								snake_case_				:							Tuple						 =       [floats_list((1, x)				)[0] for x in lengths]

								snake_case_				:							Optional[Any]						 =       ["longest", "max_length", "do_not_pad"]
								snake_case_				:							Optional[Any]						 =       [None, 1600, None]

								for max_length, padding in zip(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				):
										snake_case_				:							Optional[int]						 =       feat_extract(_SCREAMING_SNAKE_CASE		,				max_length=_SCREAMING_SNAKE_CASE		,				padding=_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Any						 =       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       _lowerCAmelCase       (	self				) ->				Tuple:
								snake_case_				:							str						 =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
								snake_case_				:							Union[str, Any]						 =       [floats_list((1, x)				)[0] for x in range(800		,				1400		,				200				)]
								snake_case_				:							str						 =       feat_extract(
								    _SCREAMING_SNAKE_CASE		,				truncation=_SCREAMING_SNAKE_CASE		,				max_length=1000		,				padding="max_length"		,				return_tensors="np"				)
								snake_case_				:							int						 =       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       _lowerCAmelCase       (	self				) ->				str:
								snake_case_				:							int						 =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
								snake_case_				:							Any						 =       [floats_list((1, x)				)[0] for x in range(800		,				1400		,				200				)]
								snake_case_				:							Any						 =       feat_extract(
								    _SCREAMING_SNAKE_CASE		,				truncation=_SCREAMING_SNAKE_CASE		,				max_length=1000		,				padding="longest"		,				return_tensors="np"				)
								snake_case_				:							int						 =       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)				)

								snake_case_				:							Dict						 =       [floats_list((1, x)				)[0] for x in range(800		,				1400		,				200				)]
								snake_case_				:							Optional[int]						 =       feat_extract(
								    _SCREAMING_SNAKE_CASE		,				truncation=_SCREAMING_SNAKE_CASE		,				max_length=2000		,				padding="longest"		,				return_tensors="np"				)
								snake_case_				:							Tuple						 =       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       _lowerCAmelCase       (	self				) ->				Any:
								import torch

								snake_case_				:							int						 =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
								snake_case_				:							List[Any]						 =       np.random.rand(100				).astype(np.floataa				)
								snake_case_				:							Any						 =       np_speech_inputs.tolist()

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






						@slow
						@require_torch
						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								# this test makes sure that models that are using
								# group norm don't have their feature extractor return the
								# attention_mask
								for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
										snake_case_				:							Dict						 =       WavaVecaConfig.from_pretrained(_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Union[str, Any]						 =       WavaVecaFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE				)

										# 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


lowercase  :							int			  =    {
    '''configuration_audio_spectrogram_transformer''': [
        '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ASTConfig''',
    ]
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Optional[Any]			  =    [
							    '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''ASTForAudioClassification''',
							    '''ASTModel''',
							    '''ASTPreTrainedModel''',
							]

try:
							if not is_speech_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Optional[int]			  =    ['''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

							lowercase  :							Optional[int]			  =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
