
'''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


_lowercase    =		random.Random()


def A				(__lowerCamelCase				:Dict			,     __lowerCamelCase				:List[Any]=1.0			,     __lowerCamelCase				:int=None			,     __lowerCamelCase				:str=None		):
						if rng is None:
												_lowerCAmelCase  						=			global_rng

						_lowerCAmelCase  						=			[]
						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   ,       _lowercase   ,       _lowercase=7   ,       _lowercase=400   ,       _lowercase=2_000   ,       _lowercase=1   ,       _lowercase=0.0   ,       _lowercase=16_000   ,       _lowercase=True   ,       _lowercase=True   ,       ):

												"""simple docstring"""



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




						def  _lowercase     ( self				):

												"""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  _lowercase     ( self   ,       _lowercase=False   ,       _lowercase=False				):

												"""simple docstring"""



												def _flatten(_lowercase				):
																		return list(itertools.chain(*_lowercase				)				)

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

												return speech_inputs






class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE	,							unittest.TestCase			):






						'''simple docstring'''






						_lowercase		:				List[str]									=       WavaVecaFeatureExtractor




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			WavaVecaFeatureExtractionTester(self				)




						def  _lowercase     ( self   ,       _lowercase				):

												"""simple docstring"""



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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
												# create three inputs of length 800, 1000, and 1200
												_lowerCAmelCase  						=			[floats_list((1, x)				)[0] for x in range(800   ,       1_400   ,       200				)]
												_lowerCAmelCase  						=			[np.asarray(_lowercase				) for speech_input in speech_inputs]

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

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

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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
												_lowerCAmelCase  						=			[floats_list((1, x)				)[0] for x in range(800   ,       1_400   ,       200				)]

												_lowerCAmelCase  						=			["""longest""", """max_length""", """do_not_pad"""]
												_lowerCAmelCase  						=			[None, 1_600, None]
												for max_length, padding in zip(_lowercase   ,       _lowercase				):
																		_lowerCAmelCase  						=			feat_extract(_lowercase   ,       padding=_lowercase   ,       max_length=_lowercase   ,       return_tensors="""np"""				)
																		_lowerCAmelCase  						=			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][:1_000]				)
																		self.assertTrue(input_values[0][1_000:].sum() < 1e-6				)
																		self._check_zero_mean_unit_variance(input_values[2][:1_200]				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
												_lowerCAmelCase  						=			range(800   ,       1_400   ,       200				)
												_lowerCAmelCase  						=			[floats_list((1, x)				)[0] for x in lengths]

												_lowerCAmelCase  						=			["""longest""", """max_length""", """do_not_pad"""]
												_lowerCAmelCase  						=			[None, 1_600, None]

												for max_length, padding in zip(_lowercase   ,       _lowercase				):
																		_lowerCAmelCase  						=			feat_extract(_lowercase   ,       max_length=_lowercase   ,       padding=_lowercase				)
																		_lowerCAmelCase  						=			processed.input_values

																		self._check_zero_mean_unit_variance(input_values[0][:800]				)
																		self._check_zero_mean_unit_variance(input_values[1][:1_000]				)
																		self._check_zero_mean_unit_variance(input_values[2][:1_200]				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
												_lowerCAmelCase  						=			[floats_list((1, x)				)[0] for x in range(800   ,       1_400   ,       200				)]
												_lowerCAmelCase  						=			feat_extract(
												    _lowercase   ,       truncation=_lowercase   ,       max_length=1_000   ,       padding="""max_length"""   ,       return_tensors="""np"""				)
												_lowerCAmelCase  						=			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  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
												_lowerCAmelCase  						=			[floats_list((1, x)				)[0] for x in range(800   ,       1_400   ,       200				)]
												_lowerCAmelCase  						=			feat_extract(
												    _lowercase   ,       truncation=_lowercase   ,       max_length=1_000   ,       padding="""longest"""   ,       return_tensors="""np"""				)
												_lowerCAmelCase  						=			processed.input_values

												self._check_zero_mean_unit_variance(input_values[0, :800]				)
												self._check_zero_mean_unit_variance(input_values[1, :1_000]				)
												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, 1_000)				)

												_lowerCAmelCase  						=			[floats_list((1, x)				)[0] for x in range(800   ,       1_400   ,       200				)]
												_lowerCAmelCase  						=			feat_extract(
												    _lowercase   ,       truncation=_lowercase   ,       max_length=2_000   ,       padding="""longest"""   ,       return_tensors="""np"""				)
												_lowerCAmelCase  						=			processed.input_values

												self._check_zero_mean_unit_variance(input_values[0, :800]				)
												self._check_zero_mean_unit_variance(input_values[1, :1_000]				)
												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, 1_200)				)




						@require_torch
						def  _lowercase     ( self				):

												"""simple docstring"""



												import torch

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

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




						@slow
						@require_torch
						def  _lowercase     ( self				):

												"""simple docstring"""



												for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
																		_lowerCAmelCase  						=			WavaVecaConfig.from_pretrained(_lowercase				)
																		_lowerCAmelCase  						=			WavaVecaFeatureExtractor.from_pretrained(_lowercase				)

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


'''simple docstring'''



from typing import TYPE_CHECKING

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


_lowercase    =		{"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase    =		[
						    """VAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """VanForImageClassification""",
						    """VanModel""",
						    """VanPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_van import (
												    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    VanForImageClassification,
												    VanModel,
												    VanPreTrainedModel,
												)

else:
						import sys

						_lowercase    =		_LazyModule(__name__, globals()["""__file__"""], _import_structure)
