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


UpperCAmelCase_			    =							random.Random()




def       lowerCAmelCase_     (		lowercase:							List[str]  ,					lowercase:							Optional[int]=1.0  ,					lowercase:							Any=None  ,					lowercase:							Tuple=None		)    ->				Optional[Any]:







 '''simple docstring'''
 if rng is None:
  _UpperCamelCase:     Dict							= global_rng

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

 return values




class  __magic_name__  (		unittest.TestCase    ):





   """simple docstring"""




   def __init__(	self					:  Optional[Any]		,  _lowercase					:  Any		,  _lowercase					:  List[str]=7		,  _lowercase					:  Dict=400		,  _lowercase					:  Any=2_000		,  _lowercase					:  List[Any]=1		,  _lowercase					:  Union[str, Any]=0.0		,  _lowercase					:  Optional[Any]=16_000		,  _lowercase					:  Dict=True		,  _lowercase					:  List[str]=True		,  ):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= parent
    _UpperCamelCase:     Dict							= batch_size
    _UpperCamelCase:     Any							= min_seq_length
    _UpperCamelCase:     int							= max_seq_length
    _UpperCamelCase:     Tuple							= (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
    _UpperCamelCase:     Any							= feature_size
    _UpperCamelCase:     int							= padding_value
    _UpperCamelCase:     List[str]							= sampling_rate
    _UpperCamelCase:     Tuple							= return_attention_mask
    _UpperCamelCase:     Optional[int]							= do_normalize



   def   lowerCAmelCase						(	self					:  List[str]							):



    """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   lowerCAmelCase						(	self					:  Optional[Any]		,  _lowercase					:  List[Any]=False		,  _lowercase					:  str=False							):



    """simple docstring"""



    def _flatten(_lowercase					:  List[Any]							):
     return list(itertools.chain(*_lowercase							)							)

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

    return speech_inputs




class  __magic_name__  (		__a      ,	unittest.TestCase    ):





   """simple docstring"""




   lowerCAmelCase      :    Optional[int]  			=   WavaVecaFeatureExtractor



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



    """simple docstring"""



    _UpperCamelCase:     List[Any]							= WavaVecaFeatureExtractionTester(self							)



   def   lowerCAmelCase						(	self					:  Dict		,  _lowercase					:  int							):



    """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   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



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

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

    # Test batched
    _UpperCamelCase:     int							= feat_extract(_lowercase		,  return_tensors='''np'''							).input_values
    _UpperCamelCase:     Union[str, Any]							= 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.
    _UpperCamelCase:     Any							= [floats_list((1, x)							)[0] for x in (800, 800, 800)]
    _UpperCamelCase:     Tuple							= np.asarray(_lowercase							)
    _UpperCamelCase:     Dict							= feat_extract(_lowercase		,  return_tensors='''np'''							).input_values
    _UpperCamelCase:     str							= 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   lowerCAmelCase						(	self					:  Optional[int]							):



    """simple docstring"""



    _UpperCamelCase:     Optional[int]							= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
    _UpperCamelCase:     Union[str, Any]							= [floats_list((1, x)							)[0] for x in range(800		,  1_400		,  200							)]

    _UpperCamelCase:     Dict							= ['''longest''', '''max_length''', '''do_not_pad''']
    _UpperCamelCase:     int							= [None, 1_600, None]
    for max_length, padding in zip(_lowercase		,  _lowercase							):
     _UpperCamelCase:     Any							= feat_extract(_lowercase		,  padding=_lowercase		,  max_length=_lowercase		,  return_tensors='''np'''							)
     _UpperCamelCase:     Dict							= 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   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     Optional[int]							= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
    _UpperCamelCase:     Optional[int]							= range(800		,  1_400		,  200							)
    _UpperCamelCase:     List[str]							= [floats_list((1, x)							)[0] for x in lengths]

    _UpperCamelCase:     Union[str, Any]							= ['''longest''', '''max_length''', '''do_not_pad''']
    _UpperCamelCase:     Optional[int]							= [None, 1_600, None]

    for max_length, padding in zip(_lowercase		,  _lowercase							):
     _UpperCamelCase:     int							= feat_extract(_lowercase		,  max_length=_lowercase		,  padding=_lowercase							)
     _UpperCamelCase:     str							= 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   lowerCAmelCase						(	self					:  str							):



    """simple docstring"""



    _UpperCamelCase:     str							= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
    _UpperCamelCase:     Any							= [floats_list((1, x)							)[0] for x in range(800		,  1_400		,  200							)]
    _UpperCamelCase:     Dict							= feat_extract(
        _lowercase		,  truncation=_lowercase		,  max_length=1_000		,  padding='''max_length'''		,  return_tensors='''np'''							)
    _UpperCamelCase:     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   lowerCAmelCase						(	self					:  List[str]							):



    """simple docstring"""



    _UpperCamelCase:     List[Any]							= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
    _UpperCamelCase:     int							= [floats_list((1, x)							)[0] for x in range(800		,  1_400		,  200							)]
    _UpperCamelCase:     Tuple							= feat_extract(
        _lowercase		,  truncation=_lowercase		,  max_length=1_000		,  padding='''longest'''		,  return_tensors='''np'''							)
    _UpperCamelCase:     List[Any]							= 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)							)

    _UpperCamelCase:     List[Any]							= [floats_list((1, x)							)[0] for x in range(800		,  1_400		,  200							)]
    _UpperCamelCase:     int							= feat_extract(
        _lowercase		,  truncation=_lowercase		,  max_length=2_000		,  padding='''longest'''		,  return_tensors='''np'''							)
    _UpperCamelCase:     int							= 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   lowerCAmelCase						(	self					:  Tuple							):



    """simple docstring"""



    import torch

    _UpperCamelCase:     Tuple							= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
    _UpperCamelCase:     Tuple							= np.random.rand(100							).astype(np.floataa							)
    _UpperCamelCase:     str							= np_speech_inputs.tolist()

    for inputs in [py_speech_inputs, np_speech_inputs]:
     _UpperCamelCase:     Optional[Any]							= feature_extractor.pad([{'''input_values''': inputs}]		,  return_tensors='''np'''							)
     self.assertTrue(np_processed.input_values.dtype == np.floataa							)
     _UpperCamelCase:     List[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					:  Union[str, Any]							):



    """simple docstring"""



    for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
     _UpperCamelCase:     Tuple							= WavaVecaConfig.from_pretrained(_lowercase							)
     _UpperCamelCase:     Dict							= 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'''							)
import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


UpperCAmelCase_			    =							logging.get_logger(__name__)




class  __magic_name__  (		__a    ):





   """simple docstring"""




   def __init__(	self					:  str		,  *_lowercase					:  str		,  **_lowercase					:  int							):



    """simple docstring"""



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