



"""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    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    # to overwrite at feature extractactor specific tests
    lowercase__      =   None
    lowercase__      =   None

    @property
    def 					_UpperCAmelCase	(				self			:				int):







      """simple docstring"""







      return self.feat_extract_tester.prepare_feat_extract_dict()

    def 					_UpperCAmelCase	(				self			:				Any):







      """simple docstring"""







      lowercase_								= self.feature_extraction_class(**self.feat_extract_dict)
      self.assertTrue(hasattr(lowerCAmelCase_ ,  """feature_size"""))
      self.assertTrue(hasattr(lowerCAmelCase_ ,  """sampling_rate"""))
      self.assertTrue(hasattr(lowerCAmelCase_ ,  """padding_value"""))

    def 					_UpperCAmelCase	(				self			:				str):







      """simple docstring"""







      lowercase_								= self.feat_extract_tester.prepare_inputs_for_common()
      lowercase_								= self.feature_extraction_class(**self.feat_extract_dict)
      lowercase_								= feat_extract.model_input_names[0]

      lowercase_								= BatchFeature({input_name: speech_inputs})

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

      lowercase_								= self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCAmelCase_)
      lowercase_								= BatchFeature({input_name: speech_inputs} ,  tensor_type="""np""")

      lowercase_								= processed_features[input_name]

      if len(batch_features_input.shape) < 3:
        lowercase_								= 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 					_UpperCAmelCase	(				self			:				Dict):







      """simple docstring"""







      lowercase_								= self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCAmelCase_)
      lowercase_								= self.feature_extraction_class(**self.feat_extract_dict)
      lowercase_								= feat_extract.model_input_names[0]

      lowercase_								= BatchFeature({input_name: speech_inputs} ,  tensor_type="""pt""")

      lowercase_								= processed_features[input_name]

      if len(batch_features_input.shape) < 3:
        lowercase_								= 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 					_UpperCAmelCase	(				self			:				Optional[Any]):







      """simple docstring"""







      lowercase_								= self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCAmelCase_)
      lowercase_								= self.feature_extraction_class(**self.feat_extract_dict)
      lowercase_								= feat_extract.model_input_names[0]

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

      lowercase_								= processed_features[input_name]

      if len(batch_features_input.shape) < 3:
        lowercase_								= 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 					_UpperCAmelCase	(				self			:				Optional[Any] ,  lowerCAmelCase_			:				Tuple=False):







      """simple docstring"""







      def _inputs_have_equal_length(lowerCAmelCase_			:				Dict):
        lowercase_								= len(input[0])
        for input_slice in input[1:]:
          if len(lowerCAmelCase_) != length:
            return False
        return True

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

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

      lowercase_								= self.feature_extraction_class(**self.feat_extract_dict)
      lowercase_								= self.feat_extract_tester.prepare_inputs_for_common(numpify=lowerCAmelCase_)
      lowercase_								= feat_extract.model_input_names[0]

      lowercase_								= BatchFeature({input_name: speech_inputs})

      lowercase_								= self.feat_extract_tester.seq_length_diff
      lowercase_								= self.feat_extract_tester.max_seq_length + pad_diff
      lowercase_								= self.feat_extract_tester.min_seq_length
      lowercase_								= self.feat_extract_tester.batch_size
      lowercase_								= self.feat_extract_tester.feature_size

      # test padding for List[int] + numpy
      lowercase_								= feat_extract.pad(lowerCAmelCase_ ,  padding=lowerCAmelCase_)
      lowercase_								= input_a[input_name]

      lowercase_								= feat_extract.pad(lowerCAmelCase_ ,  padding="""longest""")
      lowercase_								= input_a[input_name]

      lowercase_								= feat_extract.pad(lowerCAmelCase_ ,  padding="""max_length""" ,  max_length=len(speech_inputs[-1]))
      lowercase_								= input_a[input_name]

      lowercase_								= feat_extract.pad(lowerCAmelCase_ ,  padding="""longest""" ,  return_tensors="""np""")
      lowercase_								= input_a[input_name]

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

      lowercase_								= feat_extract.pad(
          lowerCAmelCase_ ,  padding="""max_length""" ,  max_length=lowerCAmelCase_ ,  return_tensors="""np""")
      lowercase_								= input_a[input_name]

      self.assertFalse(_inputs_have_equal_length(lowerCAmelCase_))
      self.assertTrue(_inputs_have_equal_length(lowerCAmelCase_))
      self.assertTrue(_inputs_have_equal_length(lowerCAmelCase_))
      self.assertTrue(_inputs_are_equal(lowerCAmelCase_ ,  lowerCAmelCase_))
      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
      lowercase_								= feat_extract.pad(lowerCAmelCase_ ,  pad_to_multiple_of=1_0)
      lowercase_								= input_a[input_name]

      lowercase_								= feat_extract.pad(lowerCAmelCase_ ,  padding="""longest""" ,  pad_to_multiple_of=1_0)
      lowercase_								= input_a[input_name]

      lowercase_								= feat_extract.pad(
          lowerCAmelCase_ ,  padding="""max_length""" ,  pad_to_multiple_of=1_0 ,  max_length=lowerCAmelCase_)
      lowercase_								= input_a[input_name]

      lowercase_								= feat_extract.pad(
          lowerCAmelCase_ ,  padding="""max_length""" ,  pad_to_multiple_of=1_0 ,  max_length=lowerCAmelCase_ ,  return_tensors="""np""" ,  )
      lowercase_								= input_a[input_name]

      self.assertTrue(all(len(lowerCAmelCase_) % 1_0 == 0 for x in input_a))
      self.assertTrue(_inputs_are_equal(lowerCAmelCase_ ,  lowerCAmelCase_))

      lowercase_								= pad_max_length if pad_max_length % 1_0 == 0 else (pad_max_length // 1_0 + 1) * 1_0
      self.assertTrue(all(len(lowerCAmelCase_) == 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
      lowercase_								= (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 					_UpperCAmelCase	(				self			:				Any ,  lowerCAmelCase_			:				List[str]=False):







      """simple docstring"""







      def _inputs_have_equal_length(lowerCAmelCase_			:				Union[str, Any]):
        lowercase_								= len(input[0])
        for input_slice in input[1:]:
          if len(lowerCAmelCase_) != length:
            return False
        return True

      def _inputs_are_equal(lowerCAmelCase_			:				Any ,  lowerCAmelCase_			:				int):
        if len(lowerCAmelCase_) != len(lowerCAmelCase_):
          return False

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

      lowercase_								= self.feature_extraction_class(**self.feat_extract_dict)
      lowercase_								= self.feat_extract_tester.prepare_inputs_for_common(numpify=lowerCAmelCase_)
      lowercase_								= feat_extract.model_input_names[0]

      lowercase_								= BatchFeature({input_name: speech_inputs})

      # truncate to smallest
      lowercase_								= feat_extract.pad(
          lowerCAmelCase_ ,  padding="""max_length""" ,  max_length=len(speech_inputs[0]) ,  truncation=lowerCAmelCase_)
      lowercase_								= input_a[input_name]

      lowercase_								= feat_extract.pad(lowerCAmelCase_ ,  padding="""max_length""" ,  max_length=len(speech_inputs[0]))
      lowercase_								= input_a[input_name]

      self.assertTrue(_inputs_have_equal_length(lowerCAmelCase_))
      self.assertFalse(_inputs_have_equal_length(lowerCAmelCase_))

      # truncate to smallest with np
      lowercase_								= feat_extract.pad(
          lowerCAmelCase_ ,  padding="""max_length""" ,  max_length=len(speech_inputs[0]) ,  return_tensors="""np""" ,  truncation=lowerCAmelCase_ ,  )
      lowercase_								= input_a[input_name]

      lowercase_								= feat_extract.pad(
          lowerCAmelCase_ ,  padding="""max_length""" ,  max_length=len(speech_inputs[0]) ,  return_tensors="""np""")
      lowercase_								= input_a[input_name]

      self.assertTrue(_inputs_have_equal_length(lowerCAmelCase_))
      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(lowerCAmelCase_))

      # truncate to middle
      lowercase_								= feat_extract.pad(
          lowerCAmelCase_ ,  padding="""max_length""" ,  max_length=len(speech_inputs[1]) ,  truncation=lowerCAmelCase_ ,  return_tensors="""np""" ,  )
      lowercase_								= input_a[input_name]

      lowercase_								= feat_extract.pad(
          lowerCAmelCase_ ,  padding="""max_length""" ,  max_length=len(speech_inputs[1]) ,  truncation=lowerCAmelCase_)
      lowercase_								= input_a[input_name]

      lowercase_								= feat_extract.pad(
          lowerCAmelCase_ ,  padding="""max_length""" ,  max_length=len(speech_inputs[1]) ,  return_tensors="""np""")
      lowercase_								= input_a[input_name]

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

      # 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(lowerCAmelCase_))
      self.assertTrue(len(input_a[-1]) == len(speech_inputs[-1]))

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

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

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

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

      # test truncation for `pad_to_multiple_of` for List[int] + numpy
      lowercase_								= 1_2
      lowercase_								= feat_extract.pad(
          lowerCAmelCase_ ,  padding="""max_length""" ,  max_length=len(speech_inputs[0]) ,  pad_to_multiple_of=lowerCAmelCase_ ,  truncation=lowerCAmelCase_ ,  )
      lowercase_								= input_a[input_name]

      lowercase_								= feat_extract.pad(
          lowerCAmelCase_ ,  padding="""max_length""" ,  max_length=len(speech_inputs[0]) ,  pad_to_multiple_of=lowerCAmelCase_ ,  )
      lowercase_								= input_a[input_name]

      # retrieve expected_length as multiple of pad_to_multiple_of
      lowercase_								= len(speech_inputs[0])
      if expected_length % pad_to_multiple_of != 0:
        lowercase_								= ((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(lowerCAmelCase_))
      self.assertFalse(_inputs_have_equal_length(lowerCAmelCase_))

    def 					_UpperCAmelCase	(				self			:				Any):







      """simple docstring"""







      self._check_padding(numpify=lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				int):







      """simple docstring"""







      self._check_padding(numpify=lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Union[str, Any]):







      """simple docstring"""







      self._check_truncation(numpify=lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Dict):







      """simple docstring"""







      self._check_truncation(numpify=lowerCAmelCase_)

    @require_torch
    def 					_UpperCAmelCase	(				self			:				str):







      """simple docstring"""







      lowercase_								= self.feature_extraction_class(**self.feat_extract_dict)
      lowercase_								= self.feat_extract_tester.prepare_inputs_for_common()
      lowercase_								= feat_extract.model_input_names[0]

      lowercase_								= BatchFeature({input_name: speech_inputs})

      lowercase_								= feat_extract.pad(lowerCAmelCase_ ,  padding="""longest""" ,  return_tensors="""np""")[input_name]
      lowercase_								= feat_extract.pad(lowerCAmelCase_ ,  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 					_UpperCAmelCase	(				self			:				List[Any]):







      """simple docstring"""







      lowercase_								= self.feature_extraction_class(**self.feat_extract_dict)
      lowercase_								= self.feat_extract_tester.prepare_inputs_for_common()
      lowercase_								= feat_extract.model_input_names[0]

      lowercase_								= BatchFeature({input_name: speech_inputs})

      lowercase_								= feat_extract.pad(lowerCAmelCase_ ,  padding="""longest""" ,  return_tensors="""np""")[input_name]
      lowercase_								= feat_extract.pad(lowerCAmelCase_ ,  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 					_UpperCAmelCase	(				self			:				Dict):







      """simple docstring"""







      lowercase_								= self.feat_extract_dict
      lowercase_								= True
      lowercase_								= self.feature_extraction_class(**lowerCAmelCase_)
      lowercase_								= self.feat_extract_tester.prepare_inputs_for_common()
      lowercase_								= [len(lowerCAmelCase_) for x in speech_inputs]
      lowercase_								= feat_extract.model_input_names[0]

      lowercase_								= BatchFeature({input_name: speech_inputs})

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




    def 					_UpperCAmelCase	(				self			:				str):







      """simple docstring"""







      lowercase_								= self.feat_extract_dict
      lowercase_								= True
      lowercase_								= self.feature_extraction_class(**lowerCAmelCase_)
      lowercase_								= self.feat_extract_tester.prepare_inputs_for_common()
      lowercase_								= [len(lowerCAmelCase_) for x in speech_inputs]
      lowercase_								= feat_extract.model_input_names[0]

      lowercase_								= BatchFeature({input_name: speech_inputs})
      lowercase_								= min(lowerCAmelCase_)

      lowercase_								= feat_extract.pad(
          lowerCAmelCase_ ,  padding="""max_length""" ,  max_length=lowerCAmelCase_ ,  truncation=lowerCAmelCase_ ,  return_tensors="""np""")
      self.assertIn("""attention_mask""" ,  lowerCAmelCase_)
      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])






"""simple docstring"""
from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						None:







  '''simple docstring'''


  lowercase_					,    lowercase_								= analyze_text(__lowerCAmelCase							)
  lowercase_								= list(""" """ + ascii_lowercase							)
  # what is our total sum of probabilities.
  lowercase_								= sum(single_char_strings.values()							)

  # one length string
  lowercase_								= 0
  # for each alpha we go in our dict and if it is in it we calculate entropy
  for ch in my_alphas:
    if ch in single_char_strings:
      lowercase_								= single_char_strings[ch]
      lowercase_								= my_str / all_sum
      my_fir_sum += prob * math.loga(__lowerCAmelCase							)  # entropy formula.

    # print entropy
  print(F'''{round(-1 * my_fir_sum							):.1f}'''							)

  # two len string
  lowercase_								= sum(two_char_strings.values()							)
  lowercase_								= 0
  # for each alpha (two in size) calculate entropy.
  for cha in my_alphas:
    for cha in my_alphas:
      lowercase_								= cha + cha
      if sequence in two_char_strings:
        lowercase_								= two_char_strings[sequence]
        lowercase_								= int(__lowerCAmelCase							) / all_sum
        my_sec_sum += prob * math.loga(__lowerCAmelCase							)

    # print second entropy
  print(F'''{round(-1 * my_sec_sum							):.1f}'''							)

  # print the difference between them
  print(F'''{round((-1 * my_sec_sum) - (-1 * my_fir_sum)							):.1f}'''							)


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						tuple[dict, dict]:







  '''simple docstring'''


  lowercase_								= Counter()  # type: ignore
  lowercase_								= Counter()  # type: ignore
  single_char_strings[text[-1]] += 1

  # first case when we have space at start.
  two_char_strings[" " + text[0]] += 1
  for i in range(0 ,  len(__lowerCAmelCase							) - 1							):
    single_char_strings[text[i]] += 1
    two_char_strings[text[i : i + 2]] += 1
  return single_char_strings, two_char_strings


def 			_SCREAMING_SNAKE_CASE						() ->						str:







  '''simple docstring'''


  import doctest

  doctest.testmod()
  # text = (
  #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
  #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
  #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
  #     "beloved evident through no service elderly is. Blind there if every no so "
  #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
  #     "of message cottage windows do besides against uncivil.  Delightful "
  #     "unreserved impossible few estimating men favourable see entreaties. She "
  #     "propriety immediate was improving. He or entrance humoured likewise "
  #     "moderate. Much nor game son say feel. Fat make met can must form into "
  #     "gate. Me we offending prevailed discovery. "
  # )

  # calculate_prob(text)


if __name__ == "__main__":
       main()

