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


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







def       A__   (						snake_case_			:		Union[str, Any]							,					snake_case_			:		Optional[Any]=1.0							,					snake_case_			:		Dict=None							,					snake_case_			:		List[str]=None   ):
 if rng is None:
  SCREAMING_SNAKE_CASE__: List[Any]=   global_rng

 SCREAMING_SNAKE_CASE__: 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 		_lowerCamelCase		( unittest.TestCase     ):
    def __init__(	self			,			lowerCAmelCase			,			lowerCAmelCase=7			,			lowerCAmelCase=400			,			lowerCAmelCase=2000			,			lowerCAmelCase=1			,			lowerCAmelCase=0.0			,			lowerCAmelCase=16000			,			lowerCAmelCase=True			,			lowerCAmelCase=True			,			)							->       Dict:
     SCREAMING_SNAKE_CASE__: str=   parent
     SCREAMING_SNAKE_CASE__: str=   batch_size
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   min_seq_length
     SCREAMING_SNAKE_CASE__: Tuple=   max_seq_length
     SCREAMING_SNAKE_CASE__: List[str]=   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   feature_size
     SCREAMING_SNAKE_CASE__: Optional[int]=   padding_value
     SCREAMING_SNAKE_CASE__: str=   sampling_rate
     SCREAMING_SNAKE_CASE__: Optional[int]=   return_attention_mask
     SCREAMING_SNAKE_CASE__: str=   do_normalize
    def     UpperCamelCase_					(	self )							->       Dict:
     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			,			lowerCAmelCase=False			,			lowerCAmelCase=False )							->       int:
     def _flatten(lowerCAmelCase ):
      return list(itertools.chain(*__A ) )

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

     return speech_inputs






class 		_lowerCamelCase		( UpperCamelCase_     ,  unittest.TestCase     ):
    __a						=   WavaVecaFeatureExtractor
    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   WavaVecaFeatureExtractionTester(self )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase )							->       List[str]:
     self.assertTrue(np.all(np.mean(__A			,			axis=0 ) < 1e-3 ) )
     self.assertTrue(np.all(np.abs(np.var(__A			,			axis=0 ) - 1 ) < 1e-3 ) )
    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     # Tests that all call wrap to encode_plus and batch_encode_plus
     SCREAMING_SNAKE_CASE__: Dict=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
     # create three inputs of length 800, 1000, and 1200
     SCREAMING_SNAKE_CASE__: List[str]=   [floats_list((1, x) )[0] for x in range(800			,			1400			,			200 )]
     SCREAMING_SNAKE_CASE__: List[Any]=   [np.asarray(__A ) for speech_input in speech_inputs]

     # Test not batched input
     SCREAMING_SNAKE_CASE__: int=   feat_extract(speech_inputs[0]			,			return_tensors='''np''' ).input_values
     SCREAMING_SNAKE_CASE__: Dict=   feat_extract(np_speech_inputs[0]			,			return_tensors='''np''' ).input_values
     self.assertTrue(np.allclose(__A			,			__A			,			atol=1e-3 ) )

     # Test batched
     SCREAMING_SNAKE_CASE__: int=   feat_extract(__A			,			return_tensors='''np''' ).input_values
     SCREAMING_SNAKE_CASE__: List[Any]=   feat_extract(__A			,			return_tensors='''np''' ).input_values
     for enc_seq_a, enc_seq_a in zip(__A			,			__A ):
      self.assertTrue(np.allclose(__A			,			__A			,			atol=1e-3 ) )

     # Test 2-D numpy arrays are batched.
     SCREAMING_SNAKE_CASE__: Any=   [floats_list((1, x) )[0] for x in (800, 800, 800)]
     SCREAMING_SNAKE_CASE__: List[Any]=   np.asarray(__A )
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   feat_extract(__A			,			return_tensors='''np''' ).input_values
     SCREAMING_SNAKE_CASE__: List[str]=   feat_extract(__A			,			return_tensors='''np''' ).input_values
     for enc_seq_a, enc_seq_a in zip(__A			,			__A ):
      self.assertTrue(np.allclose(__A			,			__A			,			atol=1e-3 ) )
    def     UpperCamelCase_					(	self )							->       Tuple:
     SCREAMING_SNAKE_CASE__: Optional[Any]=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
     SCREAMING_SNAKE_CASE__: Optional[int]=   [floats_list((1, x) )[0] for x in range(800			,			1400			,			200 )]

     SCREAMING_SNAKE_CASE__: List[Any]=   ['''longest''', '''max_length''', '''do_not_pad''']
     SCREAMING_SNAKE_CASE__: Any=   [None, 1600, None]
     for max_length, padding in zip(__A			,			__A ):
      SCREAMING_SNAKE_CASE__: int=   feat_extract(__A			,			padding=__A			,			max_length=__A			,			return_tensors='''np''' )
      SCREAMING_SNAKE_CASE__: List[str]=   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 )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: Any=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
     SCREAMING_SNAKE_CASE__: Any=   range(800			,			1400			,			200 )
     SCREAMING_SNAKE_CASE__: List[str]=   [floats_list((1, x) )[0] for x in lengths]

     SCREAMING_SNAKE_CASE__: Tuple=   ['''longest''', '''max_length''', '''do_not_pad''']
     SCREAMING_SNAKE_CASE__: Dict=   [None, 1600, None]

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

     SCREAMING_SNAKE_CASE__: Union[str, Any]=   [floats_list((1, x) )[0] for x in range(800			,			1400			,			200 )]
     SCREAMING_SNAKE_CASE__: Any=   feat_extract(
         __A			,			truncation=__A			,			max_length=2000			,			padding='''longest'''			,			return_tensors='''np''' )
     SCREAMING_SNAKE_CASE__: 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 )							->       Any:
     import torch

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

     for inputs in [py_speech_inputs, np_speech_inputs]:
      SCREAMING_SNAKE_CASE__: List[str]=   feature_extractor.pad([{'''input_values''': inputs}]			,			return_tensors='''np''' )
      self.assertTrue(np_processed.input_values.dtype == np.floataa )
      SCREAMING_SNAKE_CASE__: List[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 )							->       Optional[int]:
     # 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:
      SCREAMING_SNAKE_CASE__: List[str]=   WavaVecaConfig.from_pretrained(__A )
      SCREAMING_SNAKE_CASE__: List[Any]=   WavaVecaFeatureExtractor.from_pretrained(__A )

      # only "layer" feature extraction norm should make use of
      # attention_mask
      self.assertEqual(feat_extract.return_attention_mask			,			config.feat_extract_norm == '''layer''' )



def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int | float | str				)			->						tuple[int, int]:
       try:
              SCREAMING_SNAKE_CASE_							:				int		=float(UpperCAmelCase_				)
       except ValueError:
              raise ValueError('''Please enter a valid number'''				)
       SCREAMING_SNAKE_CASE_							:				Any		=decimal - int(UpperCAmelCase_				)
       if fractional_part == 0:
              return int(UpperCAmelCase_				), 1
       else:
              SCREAMING_SNAKE_CASE_							:				Any		=len(str(UpperCAmelCase_				).split('''.'''				)[1]				)
              SCREAMING_SNAKE_CASE_							:				str		=int(decimal * (1_0**number_of_frac_digits)				)
              SCREAMING_SNAKE_CASE_							:				Any		=1_0**number_of_frac_digits
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Tuple		=denominator, numerator
              while True:
                     SCREAMING_SNAKE_CASE_							:				Any		=dividend % divisor
                     if remainder == 0:
                            break
                     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[int]		=divisor, remainder
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[int]		=numerator / divisor, denominator / divisor
              return int(UpperCAmelCase_				), int(UpperCAmelCase_				)


if __name__ == "__main__":
 print(F"{decimal_to_fraction(2) = }")
 print(F"{decimal_to_fraction(89.0) = }")
 print(F"{decimal_to_fraction('67') = }")
 print(F"{decimal_to_fraction('45.0') = }")
 print(F"{decimal_to_fraction(1.5) = }")
 print(F"{decimal_to_fraction('6.25') = }")
 print(F"{decimal_to_fraction('78td') = }")
