





"""simple docstring"""


# Imports
import numpy as np


class _lowerCamelCase    :






   def __init__(		self       :		List[Any]   ,     UpperCamelCase       :		str=None   ,     UpperCamelCase       :		Optional[int]=None   ,     UpperCamelCase       :		int=None   ,     UpperCamelCase       :		Any=None   ,     UpperCamelCase       :		List[str]=None   )			->						Tuple:

         """simple docstring"""
         self.set_matricies(red=_a   ,     green=_a   ,     blue=_a   ,     red_edge=_a   ,     nir=_a   )






   def 	_lowerCAmelCase   (		self       :		int   ,     UpperCamelCase       :		List[Any]=None   ,     UpperCamelCase       :		Any=None   ,     UpperCamelCase       :		str=None   ,     UpperCamelCase       :		Any=None   ,     UpperCamelCase       :		int=None   )			->						Union[str, Any]:

         """simple docstring"""
         if red is not None:
               lowerCAmelCase__      :			Any   =	red
         if green is not None:
               lowerCAmelCase__      :			int   =	green
         if blue is not None:
               lowerCAmelCase__      :			List[Any]   =	blue
         if red_edge is not None:
               lowerCAmelCase__      :			List[Any]   =	red_edge
         if nir is not None:
               lowerCAmelCase__      :			int   =	nir
         return True






   def 	_lowerCAmelCase   (		self       :		Optional[int]   ,     UpperCamelCase       :		Tuple=""   ,     UpperCamelCase       :		Optional[Any]=None   ,     UpperCamelCase       :		Optional[Any]=None   ,     UpperCamelCase       :		Any=None   ,     UpperCamelCase       :		Union[str, Any]=None   ,     UpperCamelCase       :		Dict=None   )			->						Tuple:

         """simple docstring"""
         self.set_matricies(red=_a   ,     green=_a   ,     blue=_a   ,     red_edge=_a   ,     nir=_a   )
         lowerCAmelCase__      :			Optional[Any]   =	{
             """ARVI2""": self.arvaa,
             """CCCI""": self.ccci,
             """CVI""": self.cvi,
             """GLI""": self.gli,
             """NDVI""": self.ndvi,
             """BNDVI""": self.bndvi,
             """redEdgeNDVI""": self.red_edge_ndvi,
             """GNDVI""": self.gndvi,
             """GBNDVI""": self.gbndvi,
             """GRNDVI""": self.grndvi,
             """RBNDVI""": self.rbndvi,
             """PNDVI""": self.pndvi,
             """ATSAVI""": self.atsavi,
             """BWDRVI""": self.bwdrvi,
             """CIgreen""": self.ci_green,
             """CIrededge""": self.ci_rededge,
             """CI""": self.ci,
             """CTVI""": self.ctvi,
             """GDVI""": self.gdvi,
             """EVI""": self.evi,
             """GEMI""": self.gemi,
             """GOSAVI""": self.gosavi,
             """GSAVI""": self.gsavi,
             """Hue""": self.hue,
             """IVI""": self.ivi,
             """IPVI""": self.ipvi,
             """I""": self.i,
             """RVI""": self.rvi,
             """MRVI""": self.mrvi,
             """MSAVI""": self.m_savi,
             """NormG""": self.norm_g,
             """NormNIR""": self.norm_nir,
             """NormR""": self.norm_r,
             """NGRDI""": self.ngrdi,
             """RI""": self.ri,
             """S""": self.s,
             """IF""": self._if,
             """DVI""": self.dvi,
             """TVI""": self.tvi,
             """NDRE""": self.ndre,
         }

         try:
               return funcs[index]()
         except KeyError:
               print("""Index not in the list!"""   )
               return False






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						List[str]:

         """simple docstring"""
         return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))






   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   )			->						int:

         """simple docstring"""
         return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
             (self.nir - self.red) / (self.nir + self.red)
         )






   def 	_lowerCAmelCase   (		self       :		int   )			->						Tuple:

         """simple docstring"""
         return self.nir * (self.red / (self.green**2))






   def 	_lowerCAmelCase   (		self       :		Any   )			->						Any:

         """simple docstring"""
         return (2 * self.green - self.red - self.blue) / (
             2 * self.green + self.red + self.blue
         )






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						Any:

         """simple docstring"""
         return (self.nir - self.red) / (self.nir + self.red)






   def 	_lowerCAmelCase   (		self       :		Any   )			->						Optional[Any]:

         """simple docstring"""
         return (self.nir - self.blue) / (self.nir + self.blue)






   def 	_lowerCAmelCase   (		self       :		Dict   )			->						List[Any]:

         """simple docstring"""
         return (self.redEdge - self.red) / (self.redEdge + self.red)






   def 	_lowerCAmelCase   (		self       :		str   )			->						int:

         """simple docstring"""
         return (self.nir - self.green) / (self.nir + self.green)






   def 	_lowerCAmelCase   (		self       :		List[Any]   )			->						Union[str, Any]:

         """simple docstring"""
         return (self.nir - (self.green + self.blue)) / (
             self.nir + (self.green + self.blue)
         )






   def 	_lowerCAmelCase   (		self       :		List[Any]   )			->						Dict:

         """simple docstring"""
         return (self.nir - (self.green + self.red)) / (
             self.nir + (self.green + self.red)
         )






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

         """simple docstring"""
         return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))






   def 	_lowerCAmelCase   (		self       :		Any   )			->						Any:

         """simple docstring"""
         return (self.nir - (self.green + self.red + self.blue)) / (
             self.nir + (self.green + self.red + self.blue)
         )






   def 	_lowerCAmelCase   (		self       :		str   ,     UpperCamelCase       :		Union[str, Any]=0.08   ,     UpperCamelCase       :		Union[str, Any]=1.22   ,     UpperCamelCase       :		Dict=0.03   )			->						int:

         """simple docstring"""
         return a * (
             (self.nir - a * self.red - b)
             / (a * self.nir + self.red - a * b + x * (1 + a**2))
         )






   def 	_lowerCAmelCase   (		self       :		Dict   )			->						Dict:

         """simple docstring"""
         return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)






   def 	_lowerCAmelCase   (		self       :		Any   )			->						Tuple:

         """simple docstring"""
         return (self.nir / self.green) - 1






   def 	_lowerCAmelCase   (		self       :		List[Any]   )			->						int:

         """simple docstring"""
         return (self.nir / self.redEdge) - 1






   def 	_lowerCAmelCase   (		self       :		str   )			->						Tuple:

         """simple docstring"""
         return (self.red - self.blue) / self.red






   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	self.ndvi()
         return ((ndvi + 0.5) / (abs(ndvi + 0.5   ))) * (abs(ndvi + 0.5   ) ** (1 / 2))






   def 	_lowerCAmelCase   (		self       :		int   )			->						Dict:

         """simple docstring"""
         return self.nir - self.green






   def 	_lowerCAmelCase   (		self       :		int   )			->						Any:

         """simple docstring"""
         return 2.5 * (
             (self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
         )






   def 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						Optional[int]:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[Any]   =	(2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
             self.nir + self.red + 0.5
         )
         return n * (1 - 0.25 * n) - (self.red - 0.125) / (1 - self.red)






   def 	_lowerCAmelCase   (		self       :		Dict   ,     UpperCamelCase       :		Tuple=0.16   )			->						Dict:

         """simple docstring"""
         return (self.nir - self.green) / (self.nir + self.green + y)






   def 	_lowerCAmelCase   (		self       :		Any   ,     UpperCamelCase       :		List[str]=0.5   )			->						str:

         """simple docstring"""
         return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)






   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						Any:

         """simple docstring"""
         return np.arctan(
             ((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue)   )






   def 	_lowerCAmelCase   (		self       :		Optional[int]   ,     UpperCamelCase       :		str=None   ,     UpperCamelCase       :		Optional[Any]=None   )			->						List[Any]:

         """simple docstring"""
         return (self.nir - b) / (a * self.red)






   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						Union[str, Any]:

         """simple docstring"""
         return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)






   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   )			->						List[Any]:

         """simple docstring"""
         return (self.red + self.green + self.blue) / 30.5






   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   )			->						List[Any]:

         """simple docstring"""
         return self.nir / self.red






   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						Optional[Any]:

         """simple docstring"""
         return (self.rvi() - 1) / (self.rvi() + 1)






   def 	_lowerCAmelCase   (		self       :		int   )			->						Union[str, Any]:

         """simple docstring"""
         return (
             (2 * self.nir + 1)
             - ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
         ) / 2






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						Union[str, Any]:

         """simple docstring"""
         return self.green / (self.nir + self.red + self.green)






   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						List[str]:

         """simple docstring"""
         return self.nir / (self.nir + self.red + self.green)






   def 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						Optional[int]:

         """simple docstring"""
         return self.red / (self.nir + self.red + self.green)






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						Any:

         """simple docstring"""
         return (self.green - self.red) / (self.green + self.red)






   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						Any:

         """simple docstring"""
         return (self.red - self.green) / (self.red + self.green)






   def 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			Tuple   =	np.max([np.max(self.red   ), np.max(self.green   ), np.max(self.blue   )]   )
         lowerCAmelCase__      :			Optional[Any]   =	np.min([np.min(self.red   ), np.min(self.green   ), np.min(self.blue   )]   )
         return (max_value - min_value) / max_value






   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   )			->						Any:

         """simple docstring"""
         return (2 * self.red - self.green - self.blue) / (self.green - self.blue)






   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						Dict:

         """simple docstring"""
         return self.nir / self.red






   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						List[str]:

         """simple docstring"""
         return (self.ndvi() + 0.5) ** (1 / 2)






   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						Any:

         """simple docstring"""
         return (self.nir - self.redEdge) / (self.nir + self.redEdge)











"""simple docstring"""


import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin


class _lowerCamelCase    (					a_						):
   # to overwrite at feature extractactor specific tests
   _lowerCamelCase    :Optional[int]     				=     None
   _lowerCamelCase    :List[Any]     				=     None






   @property
   def 	_lowerCAmelCase   (		self       :		int   )			->						Tuple:

         """simple docstring"""
         return self.feat_extract_tester.prepare_feat_extract_dict()






   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						List[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	self.feature_extraction_class(**self.feat_extract_dict   )
         self.assertTrue(hasattr(UpperCamelCase   ,     """feature_size"""   )   )
         self.assertTrue(hasattr(UpperCamelCase   ,     """sampling_rate"""   )   )
         self.assertTrue(hasattr(UpperCamelCase   ,     """padding_value"""   )   )






   def 	_lowerCAmelCase   (		self       :		Any   )			->						int:

         """simple docstring"""
         lowerCAmelCase__      :			Dict   =	self.feat_extract_tester.prepare_inputs_for_common()
         lowerCAmelCase__      :			List[Any]   =	self.feature_extraction_class(**self.feat_extract_dict   )
         lowerCAmelCase__      :			Union[str, Any]   =	feat_extract.model_input_names[0]

         lowerCAmelCase__      :			Any   =	BatchFeature({input_name: speech_inputs}   )

         self.assertTrue(all(len(UpperCamelCase   ) == len(UpperCamelCase   ) for x, y in zip(UpperCamelCase   ,     processed_features[input_name]   )   )   )

         lowerCAmelCase__      :			List[str]   =	self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase   )
         lowerCAmelCase__      :			List[Any]   =	BatchFeature({input_name: speech_inputs}   ,     tensor_type="""np"""   )

         lowerCAmelCase__      :			Dict   =	processed_features[input_name]

         if len(batch_features_input.shape   ) < 3:
               lowerCAmelCase__      :			int   =	batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0]   ), self.feat_extract_tester.feature_size)   )






   @require_torch
   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			int   =	self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase   )
         lowerCAmelCase__      :			str   =	self.feature_extraction_class(**self.feat_extract_dict   )
         lowerCAmelCase__      :			Optional[int]   =	feat_extract.model_input_names[0]

         lowerCAmelCase__      :			Union[str, Any]   =	BatchFeature({input_name: speech_inputs}   ,     tensor_type="""pt"""   )

         lowerCAmelCase__      :			str   =	processed_features[input_name]

         if len(batch_features_input.shape   ) < 3:
               lowerCAmelCase__      :			int   =	batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0]   ), self.feat_extract_tester.feature_size)   )






   @require_tf
   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						int:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase   )
         lowerCAmelCase__      :			str   =	self.feature_extraction_class(**self.feat_extract_dict   )
         lowerCAmelCase__      :			Dict   =	feat_extract.model_input_names[0]

         lowerCAmelCase__      :			Tuple   =	BatchFeature({input_name: speech_inputs}   ,     tensor_type="""tf"""   )

         lowerCAmelCase__      :			Union[str, Any]   =	processed_features[input_name]

         if len(batch_features_input.shape   ) < 3:
               lowerCAmelCase__      :			Optional[Any]   =	batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0]   ), self.feat_extract_tester.feature_size)   )






   def 	_lowerCAmelCase   (		self       :		List[Any]   ,     UpperCamelCase       :		Union[str, Any]=False   )			->						int:

         """simple docstring"""
         def _inputs_have_equal_length(UpperCamelCase       :		int   ):
               lowerCAmelCase__      :			Optional[Any]   =	len(input[0]   )
               for input_slice in input[1:]:
                     if len(UpperCamelCase   ) != length:
                           return False
               return True

         def _inputs_are_equal(UpperCamelCase       :		int   ,     UpperCamelCase       :		Dict   ):
               if len(UpperCamelCase   ) != len(UpperCamelCase   ):
                     return False

               for input_slice_a, input_slice_a in zip(UpperCamelCase   ,     UpperCamelCase   ):
                     if not np.allclose(np.asarray(UpperCamelCase   )   ,     np.asarray(UpperCamelCase   )   ,     atol=1E-3   ):
                           return False
               return True

         lowerCAmelCase__      :			str   =	self.feature_extraction_class(**self.feat_extract_dict   )
         lowerCAmelCase__      :			Dict   =	self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCamelCase   )
         lowerCAmelCase__      :			str   =	feat_extract.model_input_names[0]

         lowerCAmelCase__      :			str   =	BatchFeature({input_name: speech_inputs}   )

         lowerCAmelCase__      :			Any   =	self.feat_extract_tester.seq_length_diff
         lowerCAmelCase__      :			List[str]   =	self.feat_extract_tester.max_seq_length + pad_diff
         lowerCAmelCase__      :			Optional[int]   =	self.feat_extract_tester.min_seq_length
         lowerCAmelCase__      :			Optional[int]   =	self.feat_extract_tester.batch_size
         lowerCAmelCase__      :			str   =	self.feat_extract_tester.feature_size

         # test padding for List[int] + numpy
         lowerCAmelCase__      :			List[Any]   =	feat_extract.pad(UpperCamelCase   ,     padding=UpperCamelCase   )
         lowerCAmelCase__      :			int   =	input_a[input_name]

         lowerCAmelCase__      :			Tuple   =	feat_extract.pad(UpperCamelCase   ,     padding="""longest"""   )
         lowerCAmelCase__      :			Any   =	input_a[input_name]

         lowerCAmelCase__      :			List[str]   =	feat_extract.pad(UpperCamelCase   ,     padding="""max_length"""   ,     max_length=len(speech_inputs[-1]   )   )
         lowerCAmelCase__      :			Dict   =	input_a[input_name]

         lowerCAmelCase__      :			str   =	feat_extract.pad(UpperCamelCase   ,     padding="""longest"""   ,     return_tensors="""np"""   )
         lowerCAmelCase__      :			Optional[Any]   =	input_a[input_name]

         # max_length parameter has to be provided when setting `padding="max_length"`
         with self.assertRaises(UpperCamelCase   ):
               feat_extract.pad(UpperCamelCase   ,     padding="""max_length"""   )[input_name]

         lowerCAmelCase__      :			Optional[int]   =	feat_extract.pad(
             UpperCamelCase   ,     padding="""max_length"""   ,     max_length=UpperCamelCase   ,     return_tensors="""np"""   )
         lowerCAmelCase__      :			int   =	input_a[input_name]

         self.assertFalse(_inputs_have_equal_length(UpperCamelCase   )   )
         self.assertTrue(_inputs_have_equal_length(UpperCamelCase   )   )
         self.assertTrue(_inputs_have_equal_length(UpperCamelCase   )   )
         self.assertTrue(_inputs_are_equal(UpperCamelCase   ,     UpperCamelCase   )   )
         self.assertTrue(len(input_a[0]   ) == pad_min_length   )
         self.assertTrue(len(input_a[1]   ) == pad_min_length + pad_diff   )
         self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]   ))   )
         self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)   )

         if feature_size > 1:
               self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size   )

         # test padding for `pad_to_multiple_of` for List[int] + numpy
         lowerCAmelCase__      :			Any   =	feat_extract.pad(UpperCamelCase   ,     pad_to_multiple_of=10   )
         lowerCAmelCase__      :			int   =	input_a[input_name]

         lowerCAmelCase__      :			Dict   =	feat_extract.pad(UpperCamelCase   ,     padding="""longest"""   ,     pad_to_multiple_of=10   )
         lowerCAmelCase__      :			Union[str, Any]   =	input_a[input_name]

         lowerCAmelCase__      :			Optional[int]   =	feat_extract.pad(
             UpperCamelCase   ,     padding="""max_length"""   ,     pad_to_multiple_of=10   ,     max_length=UpperCamelCase   )
         lowerCAmelCase__      :			str   =	input_a[input_name]

         lowerCAmelCase__      :			Union[str, Any]   =	feat_extract.pad(
             UpperCamelCase   ,     padding="""max_length"""   ,     pad_to_multiple_of=10   ,     max_length=UpperCamelCase   ,     return_tensors="""np"""   ,     )
         lowerCAmelCase__      :			List[Any]   =	input_a[input_name]

         self.assertTrue(all(len(UpperCamelCase   ) % 10 == 0 for x in input_a   )   )
         self.assertTrue(_inputs_are_equal(UpperCamelCase   ,     UpperCamelCase   )   )

         lowerCAmelCase__      :			Optional[int]   =	pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
         self.assertTrue(all(len(UpperCamelCase   ) == expected_mult_pad_length for x in input_a   )   )
         self.assertEqual(input_a.shape[:2]   ,     (batch_size, expected_mult_pad_length)   )

         if feature_size > 1:
               self.assertTrue(input_a.shape[2] == feature_size   )

         # Check padding value is correct
         lowerCAmelCase__      :			str   =	(np.ones(self.feat_extract_tester.feature_size   ) * feat_extract.padding_value).sum()
         self.assertTrue(
             abs(np.asarray(input_a[0]   )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)   )
             < 1E-3   )
         self.assertTrue(
             abs(
                 np.asarray(input_a[1]   )[pad_min_length + pad_diff :].sum()
                 - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)   )
             < 1E-3   )
         self.assertTrue(
             abs(
                 np.asarray(input_a[2]   )[pad_min_length + 2 * pad_diff :].sum()
                 - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)   )
             < 1E-3   )
         self.assertTrue(
             abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)   ) < 1E-3   )
         self.assertTrue(
             abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)   )
             < 1E-3   )






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   ,     UpperCamelCase       :		int=False   )			->						List[str]:

         """simple docstring"""
         def _inputs_have_equal_length(UpperCamelCase       :		List[Any]   ):
               lowerCAmelCase__      :			List[str]   =	len(input[0]   )
               for input_slice in input[1:]:
                     if len(UpperCamelCase   ) != length:
                           return False
               return True

         def _inputs_are_equal(UpperCamelCase       :		Union[str, Any]   ,     UpperCamelCase       :		str   ):
               if len(UpperCamelCase   ) != len(UpperCamelCase   ):
                     return False

               for input_slice_a, input_slice_a in zip(UpperCamelCase   ,     UpperCamelCase   ):
                     if not np.allclose(np.asarray(UpperCamelCase   )   ,     np.asarray(UpperCamelCase   )   ,     atol=1E-3   ):
                           return False
               return True

         lowerCAmelCase__      :			Dict   =	self.feature_extraction_class(**self.feat_extract_dict   )
         lowerCAmelCase__      :			Any   =	self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCamelCase   )
         lowerCAmelCase__      :			str   =	feat_extract.model_input_names[0]

         lowerCAmelCase__      :			Optional[Any]   =	BatchFeature({input_name: speech_inputs}   )

         # truncate to smallest
         lowerCAmelCase__      :			str   =	feat_extract.pad(
             UpperCamelCase   ,     padding="""max_length"""   ,     max_length=len(speech_inputs[0]   )   ,     truncation=UpperCamelCase   )
         lowerCAmelCase__      :			Dict   =	input_a[input_name]

         lowerCAmelCase__      :			str   =	feat_extract.pad(UpperCamelCase   ,     padding="""max_length"""   ,     max_length=len(speech_inputs[0]   )   )
         lowerCAmelCase__      :			Optional[int]   =	input_a[input_name]

         self.assertTrue(_inputs_have_equal_length(UpperCamelCase   )   )
         self.assertFalse(_inputs_have_equal_length(UpperCamelCase   )   )

         # truncate to smallest with np
         lowerCAmelCase__      :			Tuple   =	feat_extract.pad(
             UpperCamelCase   ,     padding="""max_length"""   ,     max_length=len(speech_inputs[0]   )   ,     return_tensors="""np"""   ,     truncation=UpperCamelCase   ,     )
         lowerCAmelCase__      :			List[Any]   =	input_a[input_name]

         lowerCAmelCase__      :			List[Any]   =	feat_extract.pad(
             UpperCamelCase   ,     padding="""max_length"""   ,     max_length=len(speech_inputs[0]   )   ,     return_tensors="""np"""   )
         lowerCAmelCase__      :			Union[str, Any]   =	input_a[input_name]

         self.assertTrue(_inputs_have_equal_length(UpperCamelCase   )   )
         self.assertTrue(input_a.shape[1] == len(speech_inputs[0]   )   )

         # since truncation forces padding to be smaller than longest input
         # function can't return `np.ndarray`, but has to return list
         self.assertFalse(_inputs_have_equal_length(UpperCamelCase   )   )

         # truncate to middle
         lowerCAmelCase__      :			List[Any]   =	feat_extract.pad(
             UpperCamelCase   ,     padding="""max_length"""   ,     max_length=len(speech_inputs[1]   )   ,     truncation=UpperCamelCase   ,     return_tensors="""np"""   ,     )
         lowerCAmelCase__      :			List[Any]   =	input_a[input_name]

         lowerCAmelCase__      :			List[Any]   =	feat_extract.pad(
             UpperCamelCase   ,     padding="""max_length"""   ,     max_length=len(speech_inputs[1]   )   ,     truncation=UpperCamelCase   )
         lowerCAmelCase__      :			List[str]   =	input_a[input_name]

         lowerCAmelCase__      :			Any   =	feat_extract.pad(
             UpperCamelCase   ,     padding="""max_length"""   ,     max_length=len(speech_inputs[1]   )   ,     return_tensors="""np"""   )
         lowerCAmelCase__      :			Dict   =	input_a[input_name]

         self.assertTrue(input_a.shape[1] == len(speech_inputs[1]   )   )
         self.assertTrue(_inputs_have_equal_length(UpperCamelCase   )   )
         self.assertTrue(_inputs_have_equal_length(UpperCamelCase   )   )
         self.assertTrue(_inputs_are_equal(UpperCamelCase   ,     UpperCamelCase   )   )

         # since truncation forces padding to be smaller than longest input
         # function can't return `np.ndarray`, but has to return list
         self.assertFalse(_inputs_have_equal_length(UpperCamelCase   )   )
         self.assertTrue(len(input_a[-1]   ) == len(speech_inputs[-1]   )   )

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(UpperCamelCase   ):
               feat_extract.pad(UpperCamelCase   ,     truncation=UpperCamelCase   )[input_name]

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(UpperCamelCase   ):
               feat_extract.pad(UpperCamelCase   ,     padding="""longest"""   ,     truncation=UpperCamelCase   )[input_name]

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(UpperCamelCase   ):
               feat_extract.pad(UpperCamelCase   ,     padding="""longest"""   ,     truncation=UpperCamelCase   )[input_name]

         # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
         with self.assertRaises(UpperCamelCase   ):
               feat_extract.pad(UpperCamelCase   ,     padding="""max_length"""   ,     truncation=UpperCamelCase   )[input_name]

         # test truncation for `pad_to_multiple_of` for List[int] + numpy
         lowerCAmelCase__      :			Any   =	12
         lowerCAmelCase__      :			Optional[int]   =	feat_extract.pad(
             UpperCamelCase   ,     padding="""max_length"""   ,     max_length=len(speech_inputs[0]   )   ,     pad_to_multiple_of=UpperCamelCase   ,     truncation=UpperCamelCase   ,     )
         lowerCAmelCase__      :			List[Any]   =	input_a[input_name]

         lowerCAmelCase__      :			Any   =	feat_extract.pad(
             UpperCamelCase   ,     padding="""max_length"""   ,     max_length=len(speech_inputs[0]   )   ,     pad_to_multiple_of=UpperCamelCase   ,     )
         lowerCAmelCase__      :			Optional[Any]   =	input_a[input_name]

         # retrieve expected_length as multiple of pad_to_multiple_of
         lowerCAmelCase__      :			Union[str, Any]   =	len(speech_inputs[0]   )
         if expected_length % pad_to_multiple_of != 0:
               lowerCAmelCase__      :			Any   =	((len(speech_inputs[0]   ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

         self.assertTrue(len(input_a[0]   ) == expected_length   )
         self.assertTrue(_inputs_have_equal_length(UpperCamelCase   )   )
         self.assertFalse(_inputs_have_equal_length(UpperCamelCase   )   )






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

         """simple docstring"""
         self._check_padding(numpify=UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		List[Any]   )			->						List[Any]:

         """simple docstring"""
         self._check_padding(numpify=UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Dict   )			->						Optional[int]:

         """simple docstring"""
         self._check_truncation(numpify=UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		List[Any]   )			->						Union[str, Any]:

         """simple docstring"""
         self._check_truncation(numpify=UpperCamelCase   )






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

         """simple docstring"""
         lowerCAmelCase__      :			int   =	self.feature_extraction_class(**self.feat_extract_dict   )
         lowerCAmelCase__      :			str   =	self.feat_extract_tester.prepare_inputs_for_common()
         lowerCAmelCase__      :			str   =	feat_extract.model_input_names[0]

         lowerCAmelCase__      :			Optional[int]   =	BatchFeature({input_name: speech_inputs}   )

         lowerCAmelCase__      :			Dict   =	feat_extract.pad(UpperCamelCase   ,     padding="""longest"""   ,     return_tensors="""np"""   )[input_name]
         lowerCAmelCase__      :			str   =	feat_extract.pad(UpperCamelCase   ,     padding="""longest"""   ,     return_tensors="""pt"""   )[input_name]

         self.assertTrue(abs(input_np.astype(np.floataa   ).sum() - input_pt.numpy().astype(np.floataa   ).sum()   ) < 1E-2   )






   @require_tf
   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			Tuple   =	self.feature_extraction_class(**self.feat_extract_dict   )
         lowerCAmelCase__      :			Tuple   =	self.feat_extract_tester.prepare_inputs_for_common()
         lowerCAmelCase__      :			List[Any]   =	feat_extract.model_input_names[0]

         lowerCAmelCase__      :			int   =	BatchFeature({input_name: speech_inputs}   )

         lowerCAmelCase__      :			int   =	feat_extract.pad(UpperCamelCase   ,     padding="""longest"""   ,     return_tensors="""np"""   )[input_name]
         lowerCAmelCase__      :			Any   =	feat_extract.pad(UpperCamelCase   ,     padding="""longest"""   ,     return_tensors="""tf"""   )[input_name]

         self.assertTrue(abs(input_np.astype(np.floataa   ).sum() - input_tf.numpy().astype(np.floataa   ).sum()   ) < 1E-2   )






   def 	_lowerCAmelCase   (		self       :		List[Any]   )			->						int:

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	self.feat_extract_dict
         lowerCAmelCase__      :			int   =	True
         lowerCAmelCase__      :			str   =	self.feature_extraction_class(**UpperCamelCase   )
         lowerCAmelCase__      :			int   =	self.feat_extract_tester.prepare_inputs_for_common()
         lowerCAmelCase__      :			List[Any]   =	[len(UpperCamelCase   ) for x in speech_inputs]
         lowerCAmelCase__      :			Optional[Any]   =	feat_extract.model_input_names[0]

         lowerCAmelCase__      :			List[str]   =	BatchFeature({input_name: speech_inputs}   )

         lowerCAmelCase__      :			Tuple   =	feat_extract.pad(UpperCamelCase   ,     padding="""longest"""   ,     return_tensors="""np"""   )
         self.assertIn("""attention_mask"""   ,     UpperCamelCase   )
         self.assertListEqual(list(processed.attention_mask.shape   )   ,     list(processed[input_name].shape[:2]   )   )
         self.assertListEqual(processed.attention_mask.sum(-1   ).tolist()   ,     UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	self.feat_extract_dict
         lowerCAmelCase__      :			List[Any]   =	True
         lowerCAmelCase__      :			str   =	self.feature_extraction_class(**UpperCamelCase   )
         lowerCAmelCase__      :			int   =	self.feat_extract_tester.prepare_inputs_for_common()
         lowerCAmelCase__      :			Any   =	[len(UpperCamelCase   ) for x in speech_inputs]
         lowerCAmelCase__      :			int   =	feat_extract.model_input_names[0]

         lowerCAmelCase__      :			Optional[Any]   =	BatchFeature({input_name: speech_inputs}   )
         lowerCAmelCase__      :			Any   =	min(UpperCamelCase   )

         lowerCAmelCase__      :			Tuple   =	feat_extract.pad(
             UpperCamelCase   ,     padding="""max_length"""   ,     max_length=UpperCamelCase   ,     truncation=UpperCamelCase   ,     return_tensors="""np"""   )
         self.assertIn("""attention_mask"""   ,     UpperCamelCase   )
         self.assertListEqual(
             list(processed_pad.attention_mask.shape   )   ,     [processed_pad[input_name].shape[0], max_length]   )
         self.assertListEqual(
             processed_pad.attention_mask[:, :max_length].sum(-1   ).tolist()   ,     [max_length for x in speech_inputs]   )





