




"""simple docstring"""






# Imports
import numpy as np






class lowerCAmelCase__	:






			'''simple docstring'''



			def __init__(  self  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ):
							self.set_matricies(red=lowercase  ,      green=lowercase  ,      blue=lowercase  ,      red_edge=lowercase  ,      nir=lowercase  )
			def 					A_    (  self  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ):
							if red is not None:
											_lowerCamelCase     :			Optional[int]    =    red
							if green is not None:
											_lowerCamelCase     :			Optional[Any]    =    green
							if blue is not None:
											_lowerCamelCase     :			Tuple    =    blue
							if red_edge is not None:
											_lowerCamelCase     :			Optional[Any]    =    red_edge
							if nir is not None:
											_lowerCamelCase     :			Union[str, Any]    =    nir
							return True
			def 					A_    (  self  ,      lowercase=""  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ):
							self.set_matricies(red=lowercase  ,      green=lowercase  ,      blue=lowercase  ,      red_edge=lowercase  ,      nir=lowercase  )
							_lowerCamelCase     :			str    =    {
							    '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 					A_    (  self  ):
							return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))
			def 					A_    (  self  ):
							return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
							    (self.nir - self.red) / (self.nir + self.red)
							)
			def 					A_    (  self  ):
							return self.nir * (self.red / (self.green**2))
			def 					A_    (  self  ):
							return (2 * self.green - self.red - self.blue) / (
							    2 * self.green + self.red + self.blue
							)
			def 					A_    (  self  ):
							return (self.nir - self.red) / (self.nir + self.red)
			def 					A_    (  self  ):
							return (self.nir - self.blue) / (self.nir + self.blue)
			def 					A_    (  self  ):
							return (self.redEdge - self.red) / (self.redEdge + self.red)
			def 					A_    (  self  ):
							return (self.nir - self.green) / (self.nir + self.green)
			def 					A_    (  self  ):
							return (self.nir - (self.green + self.blue)) / (
							    self.nir + (self.green + self.blue)
							)
			def 					A_    (  self  ):
							return (self.nir - (self.green + self.red)) / (
							    self.nir + (self.green + self.red)
							)
			def 					A_    (  self  ):
							return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
			def 					A_    (  self  ):
							return (self.nir - (self.green + self.red + self.blue)) / (
							    self.nir + (self.green + self.red + self.blue)
							)
			def 					A_    (  self  ,      lowercase=0.08  ,      lowercase=1.22  ,      lowercase=0.03  ):
							return a * (
							    (self.nir - a * self.red - b)
							    / (a * self.nir + self.red - a * b + x * (1 + a**2))
							)
			def 					A_    (  self  ):
							return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
			def 					A_    (  self  ):
							return (self.nir / self.green) - 1
			def 					A_    (  self  ):
							return (self.nir / self.redEdge) - 1
			def 					A_    (  self  ):
							return (self.red - self.blue) / self.red
			def 					A_    (  self  ):
							_lowerCamelCase     :			Any    =    self.ndvi()
							return ((ndvi + 0.5) / (abs(ndvi + 0.5  ))) * (abs(ndvi + 0.5  ) ** (1 / 2))
			def 					A_    (  self  ):
							return self.nir - self.green
			def 					A_    (  self  ):
							return 2.5 * (
							    (self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
							)
			def 					A_    (  self  ):
							_lowerCamelCase     :			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 - 0.1_25) / (1 - self.red)
			def 					A_    (  self  ,      lowercase=0.16  ):
							return (self.nir - self.green) / (self.nir + self.green + y)
			def 					A_    (  self  ,      lowercase=0.5  ):
							return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
			def 					A_    (  self  ):
							return np.arctan(
							    ((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue)  )
			def 					A_    (  self  ,      lowercase=None  ,      lowercase=None  ):
							return (self.nir - b) / (a * self.red)
			def 					A_    (  self  ):
							return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
			def 					A_    (  self  ):
							return (self.red + self.green + self.blue) / 30.5
			def 					A_    (  self  ):
							return self.nir / self.red
			def 					A_    (  self  ):
							return (self.rvi() - 1) / (self.rvi() + 1)
			def 					A_    (  self  ):
							return (
							    (2 * self.nir + 1)
							    - ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
							) / 2
			def 					A_    (  self  ):
							return self.green / (self.nir + self.red + self.green)
			def 					A_    (  self  ):
							return self.nir / (self.nir + self.red + self.green)
			def 					A_    (  self  ):
							return self.red / (self.nir + self.red + self.green)
			def 					A_    (  self  ):
							return (self.green - self.red) / (self.green + self.red)
			def 					A_    (  self  ):
							return (self.red - self.green) / (self.red + self.green)
			def 					A_    (  self  ):
							_lowerCamelCase     :			Union[str, Any]    =    np.max([np.max(self.red  ), np.max(self.green  ), np.max(self.blue  )]  )
							_lowerCamelCase     :			Dict    =    np.min([np.min(self.red  ), np.min(self.green  ), np.min(self.blue  )]  )
							return (max_value - min_value) / max_value
			def 					A_    (  self  ):
							return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
			def 					A_    (  self  ):
							return self.nir / self.red
			def 					A_    (  self  ):
							return (self.ndvi() + 0.5) ** (1 / 2)






			def 					A_    (  self  ):
							return (self.nir - self.redEdge) / (self.nir + self.redEdge)


'''simple docstring'''






import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
 from transformers import WhisperFeatureExtractor

if is_torch_available():
 import torch

lowerCAmelCase__											=	random.Random()


def 		_A     (    A__     ,				A__=1.0     ,				A__=None     ,				A__=None      ):






 """simple docstring"""



 if rng is None:
  __lowercase							  = global_rng

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

 return values






@require_torch
@require_torchaudio
class 			lowercase_	(unittest.TestCase      ):




  """simple docstring"""




  def __init__(      self   :     List[str]					,lowercase__   :     List[str]					,lowercase__   :     List[Any]=7					,lowercase__   :     Dict=4_0_0					,lowercase__   :     Tuple=2_0_0_0					,lowercase__   :     Optional[int]=1_0					,lowercase__   :     Optional[int]=1_6_0					,lowercase__   :     Dict=8					,lowercase__   :     str=0.0					,lowercase__   :     Union[str, Any]=4_0_0_0					,lowercase__   :     Optional[int]=False					,lowercase__   :     List[str]=True					,):
   __lowercase							  = parent
   __lowercase							  = batch_size
   __lowercase							  = min_seq_length
   __lowercase							  = max_seq_length
   __lowercase							  = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
   __lowercase							  = padding_value
   __lowercase							  = sampling_rate
   __lowercase							  = return_attention_mask
   __lowercase							  = do_normalize
   __lowercase							  = feature_size
   __lowercase							  = chunk_length
   __lowercase							  = hop_length






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   return {
       "feature_size": self.feature_size,
       "hop_length": self.hop_length,
       "chunk_length": self.chunk_length,
       "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   :     int					,lowercase__   :     List[str]=False					,lowercase__   :     Dict=False			):
   def _flatten(lowercase__   :     Optional[Any]			):
    return list(itertools.chain(*lowercase__			)			)

   if equal_length:
    __lowercase							  = [floats_list((self.max_seq_length, self.feature_size)			) for _ in range(self.batch_size			)]
   else:
    # make sure that inputs increase in size
    __lowercase							  = [
        floats_list((x, self.feature_size)			)
        for x in range(self.min_seq_length					,self.max_seq_length					,self.seq_length_diff			)
    ]
   if numpify:
    __lowercase							  = [np.asarray(lowercase__			) for x in speech_inputs]
   return speech_inputs






@require_torch
@require_torchaudio
class 			lowercase_	(lowerCamelCase__				,       unittest.TestCase      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Optional[int]							=			WhisperFeatureExtractor if is_speech_available() else None






  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   __lowercase							  = WhisperFeatureExtractionTester(self			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = self.feature_extraction_class(**self.feat_extract_dict			)

   with tempfile.TemporaryDirectory() as tmpdirname:
    __lowercase							  = feat_extract_first.save_pretrained(lowercase__			)[0]
    check_json_file_has_correct_format(lowercase__			)
    __lowercase							  = self.feature_extraction_class.from_pretrained(lowercase__			)

   __lowercase							  = feat_extract_first.to_dict()
   __lowercase							  = feat_extract_second.to_dict()
   __lowercase							  = feat_extract_first.mel_filters
   __lowercase							  = feat_extract_second.mel_filters
   self.assertTrue(np.allclose(lowercase__					,lowercase__			)			)
   self.assertEqual(lowercase__					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   __lowercase							  = self.feature_extraction_class(**self.feat_extract_dict			)

   with tempfile.TemporaryDirectory() as tmpdirname:
    __lowercase							  = os.path.join(lowercase__					,'''feat_extract.json'''			)
    feat_extract_first.to_json_file(lowercase__			)
    __lowercase							  = self.feature_extraction_class.from_json_file(lowercase__			)

   __lowercase							  = feat_extract_first.to_dict()
   __lowercase							  = feat_extract_second.to_dict()
   __lowercase							  = feat_extract_first.mel_filters
   __lowercase							  = feat_extract_second.mel_filters
   self.assertTrue(np.allclose(lowercase__					,lowercase__			)			)
   self.assertEqual(lowercase__					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   # Tests that all call wrap to encode_plus and batch_encode_plus
   __lowercase							  = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
   # create three inputs of length 800, 1000, and 1200
   __lowercase							  = [floats_list((1, x)			)[0] for x in range(8_0_0					,1_4_0_0					,2_0_0			)]
   __lowercase							  = [np.asarray(lowercase__			) for speech_input in speech_inputs]

   # Test feature size
   __lowercase							  = feature_extractor(lowercase__					,padding='''max_length'''					,return_tensors='''np'''			).input_features
   self.assertTrue(input_features.ndim == 3			)
   self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames			)
   self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size			)

   # Test not batched input
   __lowercase							  = feature_extractor(speech_inputs[0]					,return_tensors='''np'''			).input_features
   __lowercase							  = feature_extractor(np_speech_inputs[0]					,return_tensors='''np'''			).input_features
   self.assertTrue(np.allclose(lowercase__					,lowercase__					,atol=1e-3			)			)

   # Test batched
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   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.
   __lowercase							  = [floats_list((1, x)			)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
   __lowercase							  = np.asarray(lowercase__			)
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   for enc_seq_a, enc_seq_a in zip(lowercase__					,lowercase__			):
    self.assertTrue(np.allclose(lowercase__					,lowercase__					,atol=1e-3			)			)

   # Test truncation required
   __lowercase							  = [floats_list((1, x)			)[0] for x in range(2_0_0					,(feature_extractor.n_samples + 5_0_0)					,2_0_0			)]
   __lowercase							  = [np.asarray(lowercase__			) for speech_input in speech_inputs]

   __lowercase							  = [x[: feature_extractor.n_samples] for x in speech_inputs]
   __lowercase							  = [np.asarray(lowercase__			) for speech_input in speech_inputs_truncated]

   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   for enc_seq_a, enc_seq_a in zip(lowercase__					,lowercase__			):
    self.assertTrue(np.allclose(lowercase__					,lowercase__					,atol=1e-3			)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   import torch

   __lowercase							  = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
   __lowercase							  = np.random.rand(1_0_0					,3_2			).astype(np.floataa			)
   __lowercase							  = np_speech_inputs.tolist()

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






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     int			):
   __lowercase							  = load_dataset('''hf-internal-testing/librispeech_asr_dummy'''					,'''clean'''					,split='''validation'''			)
   # automatic decoding with librispeech
   __lowercase							  = ds.sort('''id'''			).select(range(lowercase__			)			)[:num_samples]['''audio''']

   return [x["array"] for x in speech_samples]






  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   # fmt: off
   __lowercase							  = torch.tensor(
       [
           0.1_1_9_3, -0.0_9_4_6, -0.1_0_9_8, -0.0_1_9_6, 0.0_2_2_5, -0.0_6_9_0, -0.1_7_3_6, 0.0_9_5_1,
           0.0_9_7_1, -0.0_8_1_7, -0.0_7_0_2, 0.0_1_6_2, 0.0_2_6_0, 0.0_0_1_7, -0.0_1_9_2, -0.1_6_7_8,
           0.0_7_0_9, -0.1_8_6_7, -0.0_6_5_5, -0.0_2_7_4, -0.0_2_3_4, -0.1_8_8_4, -0.0_5_1_6, -0.0_5_5_4,
           -0.0_2_7_4, -0.1_4_2_5, -0.1_4_2_3, 0.0_8_3_7, 0.0_3_7_7, -0.0_8_5_4
       ]			)
   # fmt: on

   __lowercase							  = self._load_datasamples(1			)
   __lowercase							  = WhisperFeatureExtractor()
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''pt'''			).input_features
   self.assertEqual(input_features.shape					,(1, 8_0, 3_0_0_0)			)
   self.assertTrue(torch.allclose(input_features[0, 0, :3_0]					,lowercase__					,atol=1e-4			)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
   __lowercase							  = self._load_datasamples(1			)[0]
   __lowercase							  = ((audio - audio.min()) / (audio.max() - audio.min())) * 6_5_5_3_5  # Rescale to [0, 65535] to show issue
   __lowercase							  = feat_extract.zero_mean_unit_var_norm([audio]					,attention_mask=lowercase__			)[0]

   self.assertTrue(np.all(np.mean(lowercase__			) < 1e-3			)			)
   self.assertTrue(np.all(np.abs(np.var(lowercase__			) - 1			) < 1e-3			)			)

