





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


__A									=				random.Random()





def      lowerCAmelCase_ (       __a	,    __a=1.0	,    __a=None	,    __a=None		)     ->		Optional[Any]:







  """simple docstring"""





  if rng is None:
    lowerCamelCase__:      Union[str, Any]			  =global_rng

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

  return values



class    _SCREAMING_SNAKE_CASE						(	unittest.TestCase    ):







 '''simple docstring'''

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



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->Dict:



   '''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 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any]       , UpperCAmelCase_				:   Optional[int]=False       , UpperCAmelCase_				:   str=False)       ->List[Any]:



   '''simple docstring'''



   def _flatten(UpperCAmelCase_				:   Optional[int]):
     return list(itertools.chain(*UpperCAmelCase_))

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

   return speech_inputs



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ,							unittest.TestCase    ):







 '''simple docstring'''
 lowercase_		=			WavaVecaFeatureExtractor

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Any			  =WavaVecaFeatureExtractionTester(self)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any]       , UpperCAmelCase_				:   List[Any])       ->str:



   '''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 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->int:



   '''simple docstring'''



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

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

   # Test batched
   lowerCamelCase__:      List[str]			  =feat_extract(UpperCAmelCase_       , return_tensors="np").input_values
   lowerCamelCase__:      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))

   # Test 2-D numpy arrays are batched.
   lowerCamelCase__:      Union[str, Any]			  =[floats_list((1, x))[0] for x in (800, 800, 800)]
   lowerCamelCase__:      Union[str, Any]			  =np.asarray(UpperCAmelCase_)
   lowerCamelCase__:      Optional[int]			  =feat_extract(UpperCAmelCase_       , return_tensors="np").input_values
   lowerCamelCase__:      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 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->List[str]:



   '''simple docstring'''



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

   lowerCamelCase__:      List[Any]			  =["longest", "max_length", "do_not_pad"]
   lowerCamelCase__:      Optional[Any]			  =[None, 1_600, None]
   for max_length, padding in zip(UpperCAmelCase_       , UpperCAmelCase_):
     lowerCamelCase__:      str			  =feat_extract(UpperCAmelCase_       , padding=UpperCAmelCase_       , max_length=UpperCAmelCase_       , return_tensors="np")
     lowerCamelCase__:      Tuple			  =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 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->Dict:



   '''simple docstring'''



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

   lowerCamelCase__:      Any			  =["longest", "max_length", "do_not_pad"]
   lowerCamelCase__:      int			  =[None, 1_600, None]

   for max_length, padding in zip(UpperCAmelCase_       , UpperCAmelCase_):
     lowerCamelCase__:      int			  =feat_extract(UpperCAmelCase_       , max_length=UpperCAmelCase_       , padding=UpperCAmelCase_)
     lowerCamelCase__:      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][:1_200])

 def 							SCREAMING_SNAKE_CASE_					(self				:   str)       ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
   lowerCamelCase__:      Union[str, Any]			  =[floats_list((1, x))[0] for x in range(800       , 1_400       , 200)]
   lowerCamelCase__:      Tuple			  =feat_extract(
       UpperCAmelCase_       , truncation=UpperCAmelCase_       , max_length=1_000       , padding="max_length"       , return_tensors="np")
   lowerCamelCase__:      int			  =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 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->List[str]:



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
   lowerCamelCase__:      Any			  =[floats_list((1, x))[0] for x in range(800       , 1_400       , 200)]
   lowerCamelCase__:      str			  =feat_extract(
       UpperCAmelCase_       , truncation=UpperCAmelCase_       , max_length=1_000       , padding="longest"       , return_tensors="np")
   lowerCamelCase__:      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 max_length
   self.assertTrue(input_values.shape == (3, 1_000))

   lowerCamelCase__:      Optional[Any]			  =[floats_list((1, x))[0] for x in range(800       , 1_400       , 200)]
   lowerCamelCase__:      str			  =feat_extract(
       UpperCAmelCase_       , truncation=UpperCAmelCase_       , max_length=2_000       , padding="longest"       , return_tensors="np")
   lowerCamelCase__:      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 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->Any:



   '''simple docstring'''



   import torch

   lowerCamelCase__:      Tuple			  =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
   lowerCamelCase__:      int			  =np.random.rand(100).astype(np.floataa)
   lowerCamelCase__:      Any			  =np_speech_inputs.tolist()

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







 @slow
 @require_torch
 def 							SCREAMING_SNAKE_CASE_					(self				:   str)       ->Optional[int]:



   '''simple docstring'''



   for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
     lowerCamelCase__:      Optional[int]			  =WavaVecaConfig.from_pretrained(UpperCAmelCase_)
     lowerCamelCase__:      List[str]			  =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")




def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase = False    ):

						if not isinstance(_UpperCAmelCase				,	_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: str  		=       f"Expected string as input, found {type(_UpperCAmelCase    )}"
												raise ValueError(_UpperCAmelCase    )
						if not isinstance(_UpperCAmelCase				,	_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       f"Expected boolean as use_pascal parameter, found {type(_UpperCAmelCase    )}"
												raise ValueError(_UpperCAmelCase    )

						SCREAMING_SNAKE_CASE_: Tuple  		=       input_str.split("_"    )

						SCREAMING_SNAKE_CASE_: str  		=       0 if use_pascal else 1

						SCREAMING_SNAKE_CASE_: int  		=       words[start_index:]

						SCREAMING_SNAKE_CASE_: List[str]  		=       [word[0].upper() + word[1:] for word in words_to_capitalize]

						SCREAMING_SNAKE_CASE_: List[Any]  		=       "" if use_pascal else words[0]

						return "".join([initial_word, *capitalized_words]    )


if __name__ == "__main__":
						from doctest import testmod

						testmod()






