"""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


__SCREAMING_SNAKE_CASE    	=random.Random()



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,			__SCREAMING_SNAKE_CASE							:     Optional[Any]=1.0     ,			__SCREAMING_SNAKE_CASE							:     Optional[int]=None     ,			__SCREAMING_SNAKE_CASE							:     Optional[Any]=None      ):
     if rng is None:
          lowercase_	:				Optional[int]													=	global_rng

     lowercase_	:				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  ):
   def __init__(			self     ,__UpperCamelCase     ,__UpperCamelCase=7     ,__UpperCamelCase=400     ,__UpperCamelCase=2000     ,__UpperCamelCase=1     ,__UpperCamelCase=0.0     ,__UpperCamelCase=1_6000     ,__UpperCamelCase=True     ,__UpperCamelCase=True     ,)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				str													=	parent
        lowercase_	:				Any													=	batch_size
        lowercase_	:				List[Any]													=	min_seq_length
        lowercase_	:				List[str]													=	max_seq_length
        lowercase_	:				List[str]													=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        lowercase_	:				Union[str, Any]													=	feature_size
        lowercase_	:				Optional[Any]													=	padding_value
        lowercase_	:				Union[str, Any]													=	sampling_rate
        lowercase_	:				Dict													=	return_attention_mask
        lowercase_	:				int													=	do_normalize
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''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     ,__UpperCamelCase=False     ,__UpperCamelCase=False			)	->      List[Any]:


        '''simple docstring'''


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

        if equal_length:
             lowercase_	:				Union[str, Any]													=	floats_list((self.batch_size, self.max_seq_length)			)
        else:
             # make sure that inputs increase in size
             lowercase_	:				Any													=	[
                 _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_	:				List[str]													=	[np.asarray(__UpperCamelCase			) for x in speech_inputs]

        return speech_inputs







class        UpperCamelCase		(			lowercase_						,      unittest.TestCase  ):
   lowercase							=  WavaVecaFeatureExtractor
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_	:				Tuple													=	WavaVecaFeatureExtractionTester(self			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      Union[str, Any]:


        '''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			)	->      List[str]:


        '''simple docstring'''


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

        # Test not batched input
        lowercase_	:				Any													=	feat_extract(speech_inputs[0]     ,return_tensors='np'			).input_values
        lowercase_	:				List[Any]													=	feat_extract(np_speech_inputs[0]     ,return_tensors='np'			).input_values
        self.assertTrue(np.allclose(__UpperCamelCase     ,__UpperCamelCase     ,atol=1e-3			)			)

        # Test batched
        lowercase_	:				Dict													=	feat_extract(__UpperCamelCase     ,return_tensors='np'			).input_values
        lowercase_	:				List[Any]													=	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_	:				int													=	[floats_list((1, x)			)[0] for x in (800, 800, 800)]
        lowercase_	:				str													=	np.asarray(__UpperCamelCase			)
        lowercase_	:				Optional[Any]													=	feat_extract(__UpperCamelCase     ,return_tensors='np'			).input_values
        lowercase_	:				Dict													=	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			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				Optional[Any]													=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
        lowercase_	:				Union[str, Any]													=	[floats_list((1, x)			)[0] for x in range(800     ,1400     ,200			)]

        lowercase_	:				Tuple													=	['longest', 'max_length', 'do_not_pad']
        lowercase_	:				Dict													=	[None, 1600, None]
        for max_length, padding in zip(__UpperCamelCase     ,__UpperCamelCase			):
             lowercase_	:				Optional[int]													=	feat_extract(__UpperCamelCase     ,padding=__UpperCamelCase     ,max_length=__UpperCamelCase     ,return_tensors='np'			)
             lowercase_	:				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 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
        lowercase_	:				List[str]													=	range(800     ,1400     ,200			)
        lowercase_	:				Optional[int]													=	[floats_list((1, x)			)[0] for x in lengths]

        lowercase_	:				Tuple													=	['longest', 'max_length', 'do_not_pad']
        lowercase_	:				Any													=	[None, 1600, None]

        for max_length, padding in zip(__UpperCamelCase     ,__UpperCamelCase			):
             lowercase_	:				List[Any]													=	feat_extract(__UpperCamelCase     ,max_length=__UpperCamelCase     ,padding=__UpperCamelCase			)
             lowercase_	:				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][:1200]			)
   def 			_UpperCAmelCase   (			self			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
        lowercase_	:				Any													=	[floats_list((1, x)			)[0] for x in range(800     ,1400     ,200			)]
        lowercase_	:				Any													=	feat_extract(
            __UpperCamelCase     ,truncation=__UpperCamelCase     ,max_length=1000     ,padding='max_length'     ,return_tensors='np'			)
        lowercase_	:				Any													=	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			)	->      int:


        '''simple docstring'''


        lowercase_	:				int													=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
        lowercase_	:				int													=	[floats_list((1, x)			)[0] for x in range(800     ,1400     ,200			)]
        lowercase_	:				int													=	feat_extract(
            __UpperCamelCase     ,truncation=__UpperCamelCase     ,max_length=1000     ,padding='longest'     ,return_tensors='np'			)
        lowercase_	:				List[str]													=	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_	:				Tuple													=	[floats_list((1, x)			)[0] for x in range(800     ,1400     ,200			)]
        lowercase_	:				Optional[int]													=	feat_extract(
            __UpperCamelCase     ,truncation=__UpperCamelCase     ,max_length=2000     ,padding='longest'     ,return_tensors='np'			)
        lowercase_	:				Optional[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 longest
        self.assertTrue(input_values.shape == (3, 1200)			)
   @require_torch
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        import torch

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

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







   @slow
   @require_torch
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
             lowercase_	:				int													=	WavaVecaConfig.from_pretrained(__UpperCamelCase			)
             lowercase_	:				Any													=	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'			)


"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)







class        UpperCamelCase		(			lowercase_  ):
   def __init__(			self     ,*__UpperCamelCase     ,**__UpperCamelCase			)	->      None:


        '''simple docstring'''


        warnings.warn(
            'The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
            ' use BeitImageProcessor instead.'     ,__UpperCamelCase     ,)
        super().__init__(*__UpperCamelCase     ,**__UpperCamelCase			)

