





"""simple docstring"""


import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
      from transformers import SpeechaTextFeatureExtractor

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





def SCREAMING_SNAKE_CASE_  (			snake_case  :   List[str]		,    snake_case  :   Tuple=1.0		,    snake_case  :   str=None		,    snake_case  :   int=None						)->						int:




       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







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



     def __init__(			self   ,	a__   ,	a__=7   ,	a__=4_00   ,	a__=20_00   ,	a__=24   ,	a__=24   ,	a__=0.0   ,	a__=1_60_00   ,	a__=True   ,	a__=True   ,	):
            _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             =   num_mel_bins
            _lowerCamelCase             =   padding_value
            _lowerCamelCase             =   sampling_rate
            _lowerCamelCase             =   return_attention_mask
            _lowerCamelCase             =   do_normalize



     def 		snake_case_     (			self  ):
            return {
                "feature_size": self.feature_size,
                "num_mel_bins": self.num_mel_bins,
                "padding_value": self.padding_value,
                "sampling_rate": self.sampling_rate,
                "return_attention_mask": self.return_attention_mask,
                "do_normalize": self.do_normalize,
            }



     def 		snake_case_     (			self   ,	a__=False   ,	a__=False  ):


            def _flatten(a__  ):
                   return list(itertools.chain(*_UpperCamelCase  )  )

            if equal_length:
                   _lowerCamelCase             =   [floats_list((self.max_seq_length, self.feature_size)  ) for _ in range(self.batch_size  )]
            else:
                   # make sure that inputs increase in size
                   _lowerCamelCase             =   [
                       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(_UpperCamelCase  ) for x in speech_inputs]
            return speech_inputs







@require_torch
@require_torchaudio
class       __a  (    snake_case__      ,		unittest.TestCase						):
     SCREAMING_SNAKE_CASE__					: str									=     SpeechaTextFeatureExtractor if is_speech_available() else None



     def 		snake_case_     (			self  ):
            _lowerCamelCase             =   SpeechaTextFeatureExtractionTester(self  )



     def 		snake_case_     (			self   ,	a__  ):
            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 		snake_case_     (			self  ):
            _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(8_00   ,	14_00   ,	2_00  )]
            _lowerCamelCase             =   [np.asarray(_UpperCamelCase  ) for speech_input in speech_inputs]

            # Test feature size
            _lowerCamelCase             =   feature_extractor(_UpperCamelCase   ,	padding=_UpperCamelCase   ,	return_tensors='np'  ).input_features
            self.assertTrue(input_features.ndim == 3  )
            self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size  )

            # Test not batched input
            _lowerCamelCase             =   feature_extractor(speech_inputs[0]   ,	return_tensors='np'  ).input_features
            _lowerCamelCase             =   feature_extractor(np_speech_inputs[0]   ,	return_tensors='np'  ).input_features
            self.assertTrue(np.allclose(_UpperCamelCase   ,	_UpperCamelCase   ,	atol=1e-3  )  )

            # Test batched
            _lowerCamelCase             =   feature_extractor(_UpperCamelCase   ,	return_tensors='np'  ).input_features
            _lowerCamelCase             =   feature_extractor(_UpperCamelCase   ,	return_tensors='np'  ).input_features
            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.
            _lowerCamelCase             =   [floats_list((1, x)  )[0] for x in (8_00, 8_00, 8_00)]
            _lowerCamelCase             =   np.asarray(_UpperCamelCase  )
            _lowerCamelCase             =   feature_extractor(_UpperCamelCase   ,	return_tensors='np'  ).input_features
            _lowerCamelCase             =   feature_extractor(_UpperCamelCase   ,	return_tensors='np'  ).input_features
            for enc_seq_a, enc_seq_a in zip(_UpperCamelCase   ,	_UpperCamelCase  ):
                   self.assertTrue(np.allclose(_UpperCamelCase   ,	_UpperCamelCase   ,	atol=1e-3  )  )



     def 		snake_case_     (			self  ):
            _lowerCamelCase             =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
            _lowerCamelCase             =   [floats_list((1, x)  )[0] for x in range(8_00   ,	14_00   ,	2_00  )]

            _lowerCamelCase             =   ['longest', 'max_length', 'do_not_pad']
            _lowerCamelCase             =   [None, 16, None]
            for max_length, padding in zip(_UpperCamelCase   ,	_UpperCamelCase  ):
                   _lowerCamelCase             =   feature_extractor(
                       _UpperCamelCase   ,	padding=_UpperCamelCase   ,	max_length=_UpperCamelCase   ,	return_attention_mask=_UpperCamelCase  )
                   _lowerCamelCase             =   inputs.input_features
                   _lowerCamelCase             =   inputs.attention_mask
                   _lowerCamelCase             =   [np.sum(_UpperCamelCase  ) for x in attention_mask]

                   self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]  )
                   self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]  )
                   self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]  )



     def 		snake_case_     (			self  ):
            _lowerCamelCase             =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
            _lowerCamelCase             =   [floats_list((1, x)  )[0] for x in range(8_00   ,	14_00   ,	2_00  )]

            _lowerCamelCase             =   ['longest', 'max_length', 'do_not_pad']
            _lowerCamelCase             =   [None, 16, None]
            for max_length, padding in zip(_UpperCamelCase   ,	_UpperCamelCase  ):
                   _lowerCamelCase             =   feature_extractor(
                       _UpperCamelCase   ,	max_length=_UpperCamelCase   ,	padding=_UpperCamelCase   ,	return_tensors='np'   ,	return_attention_mask=_UpperCamelCase  )
                   _lowerCamelCase             =   inputs.input_features
                   _lowerCamelCase             =   inputs.attention_mask
                   _lowerCamelCase             =   [np.sum(_UpperCamelCase  ) for x in attention_mask]

                   self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]  )
                   self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6  )
                   self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]  )
                   self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6  )
                   self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]  )



     def 		snake_case_     (			self  ):
            _lowerCamelCase             =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
            _lowerCamelCase             =   [floats_list((1, x)  )[0] for x in range(8_00   ,	14_00   ,	2_00  )]
            _lowerCamelCase             =   feature_extractor(
                _UpperCamelCase   ,	padding='max_length'   ,	max_length=4   ,	truncation=_UpperCamelCase   ,	return_tensors='np'   ,	return_attention_mask=_UpperCamelCase   ,	)
            _lowerCamelCase             =   inputs.input_features
            _lowerCamelCase             =   inputs.attention_mask
            _lowerCamelCase             =   np.sum(attention_mask == 1   ,	axis=1  )

            self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]  )
            self._check_zero_mean_unit_variance(input_features[1]  )
            self._check_zero_mean_unit_variance(input_features[2]  )



     def 		snake_case_     (			self  ):
            _lowerCamelCase             =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
            _lowerCamelCase             =   [floats_list((1, x)  )[0] for x in range(8_00   ,	14_00   ,	2_00  )]
            _lowerCamelCase             =   feature_extractor(
                _UpperCamelCase   ,	padding='longest'   ,	max_length=4   ,	truncation=_UpperCamelCase   ,	return_tensors='np'   ,	return_attention_mask=_UpperCamelCase   ,	)
            _lowerCamelCase             =   inputs.input_features
            _lowerCamelCase             =   inputs.attention_mask
            _lowerCamelCase             =   np.sum(attention_mask == 1   ,	axis=1  )

            self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]  )
            self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]  )
            self._check_zero_mean_unit_variance(input_features[2]  )

            # make sure that if max_length < longest -> then pad to max_length
            self.assertEqual(input_features.shape   ,	(3, 4, 24)  )

            _lowerCamelCase             =   [floats_list((1, x)  )[0] for x in range(8_00   ,	14_00   ,	2_00  )]
            _lowerCamelCase             =   feature_extractor(
                _UpperCamelCase   ,	padding='longest'   ,	max_length=16   ,	truncation=_UpperCamelCase   ,	return_tensors='np'   ,	return_attention_mask=_UpperCamelCase   ,	)
            _lowerCamelCase             =   inputs.input_features
            _lowerCamelCase             =   inputs.attention_mask
            _lowerCamelCase             =   np.sum(attention_mask == 1   ,	axis=1  )

            self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]  )
            self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]  )
            self._check_zero_mean_unit_variance(input_features[2]  )

            # make sure that if max_length < longest -> then pad to max_length
            self.assertEqual(input_features.shape   ,	(3, 6, 24)  )



     def 		snake_case_     (			self  ):
            import torch

            _lowerCamelCase             =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
            _lowerCamelCase             =   np.random.rand(1_00   ,	32  ).astype(np.floataa  )
            _lowerCamelCase             =   np_speech_inputs.tolist()

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



     def 		snake_case_     (			self   ,	a__  ):
            from datasets import load_dataset

            _lowerCamelCase             =   load_dataset('hf-internal-testing/librispeech_asr_dummy'   ,	'clean'   ,	split='validation'  )
            # automatic decoding with librispeech
            _lowerCamelCase             =   ds.sort('id'  ).select(range(_UpperCamelCase  )  )[:num_samples]['audio']

            return [x["array"] for x in speech_samples]



     def 		snake_case_     (			self  ):
            _lowerCamelCase             =   np.array([
                -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
                -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
                -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
            ]  )
            # fmt: on

            _lowerCamelCase             =   self._load_datasamples(1  )
            _lowerCamelCase             =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
            _lowerCamelCase             =   feature_extractor(_UpperCamelCase   ,	return_tensors='pt'  ).input_features
            self.assertEquals(input_features.shape   ,	(1, 5_84, 24)  )
            self.assertTrue(np.allclose(input_features[0, 0, :30]   ,	_UpperCamelCase   ,	atol=1e-4  )  )









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase__		 =     {
    '''configuration_convnext''': ['''CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvNextConfig''', '''ConvNextOnnxConfig''']
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__		 =     ['''ConvNextFeatureExtractor''']
    UpperCamelCase__		 =     ['''ConvNextImageProcessor''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__		 =     [
        '''CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''ConvNextForImageClassification''',
        '''ConvNextModel''',
        '''ConvNextPreTrainedModel''',
        '''ConvNextBackbone''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__		 =     [
        '''TFConvNextForImageClassification''',
        '''TFConvNextModel''',
        '''TFConvNextPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_convnext import ConvNextFeatureExtractor
        from .image_processing_convnext import ConvNextImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_convnext import (
            CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
            ConvNextBackbone,
            ConvNextForImageClassification,
            ConvNextModel,
            ConvNextPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
    import sys

    UpperCamelCase__		 =     _LazyModule(__name__, globals()['''__file__'''], _import_structure)

