






"""simple docstring"""





import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


_SCREAMING_SNAKE_CASE					:				Dict					    =						random.Random()

if is_torch_available():
  import torch







def      lowerCamelCase__       (							_lowerCamelCase       :		Optional[Any]		,				_lowerCamelCase       :		Any=1.0		,				_lowerCamelCase       :		int=None		,				_lowerCamelCase       :		Optional[Any]=None )      ->				Union[str, Any]:
  if rng is None:
    lowerCamelCase_         =						global_rng

  lowerCamelCase_         =						[]
  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		):






    def __init__(       self     :							Any       ,						__SCREAMING_SNAKE_CASE     :							int       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]=7       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=400       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]=2000       ,						__SCREAMING_SNAKE_CASE     :							List[str]=1       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=0.0       ,						__SCREAMING_SNAKE_CASE     :							Tuple=16000       ,						__SCREAMING_SNAKE_CASE     :							List[str]=True       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]=True       ,						)     ->		Optional[int]:
      lowerCamelCase_         =						parent
      lowerCamelCase_         =						batch_size
      lowerCamelCase_         =						min_seq_length
      lowerCamelCase_         =						max_seq_length
      lowerCamelCase_         =						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
      lowerCamelCase_         =						feature_size
      lowerCamelCase_         =						padding_value
      lowerCamelCase_         =						sampling_rate
      lowerCamelCase_         =						return_attention_mask
      lowerCamelCase_         =						do_normalize






    def    UpperCamelCase       (       self     :							Dict					)     ->		Union[str, 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    UpperCamelCase       (       self     :							Any       ,						__SCREAMING_SNAKE_CASE     :							Any=False       ,						__SCREAMING_SNAKE_CASE     :							List[Any]=False					)     ->		Tuple:
      def _flatten(__SCREAMING_SNAKE_CASE     :							str					):
        return list(itertools.chain(*__SCREAMING_SNAKE_CASE					)					)

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

      return speech_inputs






@require_torch
@require_torchaudio
class      a						(					SCREAMING_SNAKE_CASE__  ,      unittest.TestCase		):
    SCREAMING_SNAKE_CASE						:     Union[str, Any]						= ASTFeatureExtractor






    def    UpperCamelCase       (       self     :							List[Any]					)     ->		Union[str, Any]:
      lowerCamelCase_         =						ASTFeatureExtractionTester(self					)






    def    UpperCamelCase       (       self     :							str					)     ->		str:
      # Tests that all call wrap to encode_plus and batch_encode_plus
      lowerCamelCase_         =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
      # create three inputs of length 800, 1000, and 1200
      lowerCamelCase_         =						[floats_list((1, x)					)[0] for x in range(800       ,						1400       ,						200					)]
      lowerCamelCase_         =						[np.asarray(__SCREAMING_SNAKE_CASE					) for speech_input in speech_inputs]

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

      # Test batched
      lowerCamelCase_         =						feat_extract(__SCREAMING_SNAKE_CASE       ,						padding=__SCREAMING_SNAKE_CASE       ,						return_tensors='np'					).input_values
      lowerCamelCase_         =						feat_extract(__SCREAMING_SNAKE_CASE       ,						padding=__SCREAMING_SNAKE_CASE       ,						return_tensors='np'					).input_values
      for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					):
        self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						atol=1e-3					)					)

      # Test 2-D numpy arrays are batched.
      lowerCamelCase_         =						[floats_list((1, x)					)[0] for x in (800, 800, 800)]
      lowerCamelCase_         =						np.asarray(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						feat_extract(__SCREAMING_SNAKE_CASE       ,						return_tensors='np'					).input_values
      lowerCamelCase_         =						feat_extract(__SCREAMING_SNAKE_CASE       ,						return_tensors='np'					).input_values
      for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					):
        self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						atol=1e-3					)					)






    @require_torch
    def    UpperCamelCase       (       self     :							Dict					)     ->		Union[str, Any]:
      import torch

      lowerCamelCase_         =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
      lowerCamelCase_         =						np.random.rand(100					).astype(np.floataa					)
      lowerCamelCase_         =						np_speech_inputs.tolist()

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






    def    UpperCamelCase       (       self     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]					)     ->		Optional[int]:
      from datasets import load_dataset

      lowerCamelCase_         =						load_dataset('hf-internal-testing/librispeech_asr_dummy'       ,						'clean'       ,						split='validation'					)
      # automatic decoding with librispeech
      lowerCamelCase_         =						ds.sort('id'					).select(range(__SCREAMING_SNAKE_CASE					)					)[:num_samples]['audio']

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






    @require_torch
    def    UpperCamelCase       (       self     :							Optional[Any]					)     ->		Union[str, Any]:
      # fmt: off
      lowerCamelCase_         =						torch.tensor(
          [-0.9_894, -1.2_776, -0.9_066, -1.2_776, -0.9_349, -1.2_609, -1.0_386, -1.2_776,
           -1.1_561, -1.2_776, -1.2_052, -1.2_723, -1.2_190, -1.2_132, -1.2_776, -1.1_133,
           -1.1_953, -1.1_343, -1.1_584, -1.2_203, -1.1_770, -1.2_474, -1.2_381, -1.1_936,
           -0.9_270, -0.8_317, -0.8_049, -0.7_706, -0.7_565, -0.7_869]					)
      # fmt: on

      lowerCamelCase_         =						self._load_datasamples(1					)
      lowerCamelCase_         =						ASTFeatureExtractor()
      lowerCamelCase_         =						feature_extractor(__SCREAMING_SNAKE_CASE       ,						return_tensors='pt'					).input_values
      self.assertEquals(input_values.shape       ,						(1, 1024, 128)					)
      self.assertTrue(torch.allclose(input_values[0, 0, :30]       ,						__SCREAMING_SNAKE_CASE       ,						atol=1e-4					)					)





from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     k_size // 2
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     mgrid[0 - center : k_size - center, 0 - center : k_size - center]
      SCREAMING_SNAKE_CASE_      =     1 / (2 * pi * sigma) * exp(-(square(__UpperCamelCase ) + square(__UpperCamelCase )) / (2 * square(__UpperCamelCase )) )
      return g


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     image.shape[0], image.shape[1]
      # dst image height and width
      SCREAMING_SNAKE_CASE_      =     height - k_size + 1
      SCREAMING_SNAKE_CASE_      =     width - k_size + 1

      # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
      SCREAMING_SNAKE_CASE_      =     zeros((dst_height * dst_width, k_size * k_size) )
      SCREAMING_SNAKE_CASE_      =     0
      for i, j in product(range(__UpperCamelCase )   ,					range(__UpperCamelCase ) ):
            SCREAMING_SNAKE_CASE_      =     ravel(image[i : i + k_size, j : j + k_size] )
            SCREAMING_SNAKE_CASE_      =     window
            row += 1

      #  turn the kernel into shape(k*k, 1)
      SCREAMING_SNAKE_CASE_      =     gen_gaussian_kernel(__UpperCamelCase   ,					__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     ravel(__UpperCamelCase )

      # reshape and get the dst image
      SCREAMING_SNAKE_CASE_      =     dot(__UpperCamelCase   ,					__UpperCamelCase ).reshape(__UpperCamelCase   ,					__UpperCamelCase ).astype(__UpperCamelCase )

      return dst


if __name__ == "__main__":
      # read original image
      A   :     Tuple       =				imread(r"../image_data/lena.jpg")
      # turn image in gray scale value
      A   :     Optional[int]       =				cvtColor(img, COLOR_BGR2GRAY)

      # get values with two different mask size
      A   :     Tuple       =				gaussian_filter(gray, 3, sigma=1)
      A   :     Optional[int]       =				gaussian_filter(gray, 5, sigma=0.8)

      # show result images
      imshow("gaussian filter with 3x3 mask", gaussianaxa)
      imshow("gaussian filter with 5x5 mask", gaussianaxa)
      waitKey()


