




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


_lowerCamelCase      			=       random.Random()




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						List[str]	,					__UpperCamelCase      :						Optional[int]=1.0	,					__UpperCamelCase      :						int=None	,					__UpperCamelCase      :						Tuple=None				)  ->							Tuple:





    if rng is None:
        UpperCAmelCase_										=					global_rng

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

    return values



class       a					(       unittest.TestCase      ):

     '''simple docstring'''





     def __init__(     self       :      List[str] ,   __snake_case       :      str ,   __snake_case       :      Optional[Any]=7 ,   __snake_case       :      Optional[Any]=4_00 ,   __snake_case       :      Union[str, Any]=20_00 ,   __snake_case       :      str=1 ,   __snake_case       :      Dict=0.0 ,   __snake_case       :      List[str]=1_60_00 ,   __snake_case       :      List[str]=True ,   __snake_case       :      List[str]=True ,   ):
         UpperCAmelCase_										=					parent
         UpperCAmelCase_										=					batch_size
         UpperCAmelCase_										=					min_seq_length
         UpperCAmelCase_										=					max_seq_length
         UpperCAmelCase_										=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         UpperCAmelCase_										=					feature_size
         UpperCAmelCase_										=					padding_value
         UpperCAmelCase_										=					sampling_rate
         UpperCAmelCase_										=					return_attention_mask
         UpperCAmelCase_										=					do_normalize
     def   lowerCamelCase_       (     self       :      Optional[Any]    ):
         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       :      str ,   __snake_case       :      List[Any]=False ,   __snake_case       :      List[Any]=False    ):
         def _flatten(__snake_case       :      int    ):
             return list(itertools.chain(*__snake_case    )    )

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

         return speech_inputs



class       a					(       __UpperCamelCase     ,       unittest.TestCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	Union[str, Any]       		=  WavaVecaFeatureExtractor
     def   lowerCamelCase_       (     self       :      Dict    ):
         UpperCAmelCase_										=					WavaVecaFeatureExtractionTester(self    )
     def   lowerCamelCase_       (     self       :      str ,   __snake_case       :      Dict    ):
         self.assertTrue(np.all(np.mean(__snake_case ,   axis=0    ) < 1E-3    )    )
         self.assertTrue(np.all(np.abs(np.var(__snake_case ,   axis=0    ) - 1    ) < 1E-3    )    )
     def   lowerCamelCase_       (     self       :      Dict    ):
         # Tests that all call wrap to encode_plus and batch_encode_plus
         UpperCAmelCase_										=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
         # create three inputs of length 800, 1000, and 1200
         UpperCAmelCase_										=					[floats_list((1, x)    )[0] for x in range(8_00 ,   14_00 ,   2_00    )]
         UpperCAmelCase_										=					[np.asarray(__snake_case    ) for speech_input in speech_inputs]

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

         # Test batched
         UpperCAmelCase_										=					feat_extract(__snake_case ,   return_tensors='''np'''    ).input_values
         UpperCAmelCase_										=					feat_extract(__snake_case ,   return_tensors='''np'''    ).input_values
         for enc_seq_a, enc_seq_a in zip(__snake_case ,   __snake_case    ):
             self.assertTrue(np.allclose(__snake_case ,   __snake_case ,   atol=1E-3    )    )

         # Test 2-D numpy arrays are batched.
         UpperCAmelCase_										=					[floats_list((1, x)    )[0] for x in (8_00, 8_00, 8_00)]
         UpperCAmelCase_										=					np.asarray(__snake_case    )
         UpperCAmelCase_										=					feat_extract(__snake_case ,   return_tensors='''np'''    ).input_values
         UpperCAmelCase_										=					feat_extract(__snake_case ,   return_tensors='''np'''    ).input_values
         for enc_seq_a, enc_seq_a in zip(__snake_case ,   __snake_case    ):
             self.assertTrue(np.allclose(__snake_case ,   __snake_case ,   atol=1E-3    )    )
     def   lowerCamelCase_       (     self       :      Any    ):
         UpperCAmelCase_										=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
         UpperCAmelCase_										=					[floats_list((1, x)    )[0] for x in range(8_00 ,   14_00 ,   2_00    )]

         UpperCAmelCase_										=					["""longest""", """max_length""", """do_not_pad"""]
         UpperCAmelCase_										=					[None, 16_00, None]
         for max_length, padding in zip(__snake_case ,   __snake_case    ):
             UpperCAmelCase_										=					feat_extract(__snake_case ,   padding=__snake_case ,   max_length=__snake_case ,   return_tensors='''np'''    )
             UpperCAmelCase_										=					processed.input_values

             self._check_zero_mean_unit_variance(input_values[0][:8_00]    )
             self.assertTrue(input_values[0][8_00:].sum() < 1E-6    )
             self._check_zero_mean_unit_variance(input_values[1][:10_00]    )
             self.assertTrue(input_values[0][10_00:].sum() < 1E-6    )
             self._check_zero_mean_unit_variance(input_values[2][:12_00]    )
     def   lowerCamelCase_       (     self       :      int    ):
         UpperCAmelCase_										=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
         UpperCAmelCase_										=					range(8_00 ,   14_00 ,   2_00    )
         UpperCAmelCase_										=					[floats_list((1, x)    )[0] for x in lengths]

         UpperCAmelCase_										=					["""longest""", """max_length""", """do_not_pad"""]
         UpperCAmelCase_										=					[None, 16_00, None]

         for max_length, padding in zip(__snake_case ,   __snake_case    ):
             UpperCAmelCase_										=					feat_extract(__snake_case ,   max_length=__snake_case ,   padding=__snake_case    )
             UpperCAmelCase_										=					processed.input_values

             self._check_zero_mean_unit_variance(input_values[0][:8_00]    )
             self._check_zero_mean_unit_variance(input_values[1][:10_00]    )
             self._check_zero_mean_unit_variance(input_values[2][:12_00]    )
     def   lowerCamelCase_       (     self       :      Any    ):
         UpperCAmelCase_										=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
         UpperCAmelCase_										=					[floats_list((1, x)    )[0] for x in range(8_00 ,   14_00 ,   2_00    )]
         UpperCAmelCase_										=					feat_extract(
             __snake_case ,   truncation=__snake_case ,   max_length=10_00 ,   padding='''max_length''' ,   return_tensors='''np'''    )
         UpperCAmelCase_										=					processed.input_values

         self._check_zero_mean_unit_variance(input_values[0, :8_00]    )
         self._check_zero_mean_unit_variance(input_values[1]    )
         self._check_zero_mean_unit_variance(input_values[2]    )
     def   lowerCamelCase_       (     self       :      Dict    ):
         UpperCAmelCase_										=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
         UpperCAmelCase_										=					[floats_list((1, x)    )[0] for x in range(8_00 ,   14_00 ,   2_00    )]
         UpperCAmelCase_										=					feat_extract(
             __snake_case ,   truncation=__snake_case ,   max_length=10_00 ,   padding='''longest''' ,   return_tensors='''np'''    )
         UpperCAmelCase_										=					processed.input_values

         self._check_zero_mean_unit_variance(input_values[0, :8_00]    )
         self._check_zero_mean_unit_variance(input_values[1, :10_00]    )
         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, 10_00)    )

         UpperCAmelCase_										=					[floats_list((1, x)    )[0] for x in range(8_00 ,   14_00 ,   2_00    )]
         UpperCAmelCase_										=					feat_extract(
             __snake_case ,   truncation=__snake_case ,   max_length=20_00 ,   padding='''longest''' ,   return_tensors='''np'''    )
         UpperCAmelCase_										=					processed.input_values

         self._check_zero_mean_unit_variance(input_values[0, :8_00]    )
         self._check_zero_mean_unit_variance(input_values[1, :10_00]    )
         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, 12_00)    )
     @require_torch
     def   lowerCamelCase_       (     self       :      str    ):
         import torch

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

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




     @slow
     @require_torch
     def   lowerCamelCase_       (     self       :      Tuple    ):
         # 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:
             UpperCAmelCase_										=					WavaVecaConfig.from_pretrained(__snake_case    )
             UpperCAmelCase_										=					WavaVecaFeatureExtractor.from_pretrained(__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'''    )











import numpy as np




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						np.array				)  ->							np.array:
    return (2 / (1 + np.exp(-2 * vector				))) - 1


if __name__ == "__main__":
 import doctest

 doctest.testmod()





