






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  _lowercase   (   _A						):
      # to overwrite at feature extractactor specific tests
      _a							:						List[str]										=							None
      _a							:						int										=							None
      @property
      def 						lowercase__					(     self   ):
       return self.feat_extract_tester.prepare_feat_extract_dict()
      def 						lowercase__					(     self   ):
       snake_case__  :    str							       =self.feature_extraction_class(**self.feat_extract_dict   )
       self.assertTrue(hasattr(a				,			"""feature_size"""   )   )
       self.assertTrue(hasattr(a				,			"""sampling_rate"""   )   )
       self.assertTrue(hasattr(a				,			"""padding_value"""   )   )
      def 						lowercase__					(     self   ):
       snake_case__  :    List[Any]							       =self.feat_extract_tester.prepare_inputs_for_common()
       snake_case__  :    List[str]							       =self.feature_extraction_class(**self.feat_extract_dict   )
       snake_case__  :    Dict							       =feat_extract.model_input_names[0]

       snake_case__  :    Union[str, Any]							       =BatchFeature({input_name: speech_inputs}   )

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

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

       snake_case__  :    List[Any]							       =processed_features[input_name]

       if len(batch_features_input.shape   ) < 3:
        snake_case__  :    str							       =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 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       =self.feat_extract_tester.prepare_inputs_for_common(equal_length=a   )
       snake_case__  :    Dict							       =self.feature_extraction_class(**self.feat_extract_dict   )
       snake_case__  :    str							       =feat_extract.model_input_names[0]

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

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

       if len(batch_features_input.shape   ) < 3:
        snake_case__  :    List[str]							       =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 						lowercase__					(     self   ):
       snake_case__  :    str							       =self.feat_extract_tester.prepare_inputs_for_common(equal_length=a   )
       snake_case__  :    int							       =self.feature_extraction_class(**self.feat_extract_dict   )
       snake_case__  :    Tuple							       =feat_extract.model_input_names[0]

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

       snake_case__  :    Any							       =processed_features[input_name]

       if len(batch_features_input.shape   ) < 3:
        snake_case__  :    Optional[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)   )
      def 						lowercase__					(     self				,			a=False   ):
       def _inputs_have_equal_length(a   ):
        snake_case__  :    List[Any]							       =len(input[0]   )
        for input_slice in input[1:]:
         if len(a   ) != length:
          return False
        return True

       def _inputs_are_equal(a				,			a   ):
        if len(a   ) != len(a   ):
         return False

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

       snake_case__  :    Tuple							       =self.feature_extraction_class(**self.feat_extract_dict   )
       snake_case__  :    List[str]							       =self.feat_extract_tester.prepare_inputs_for_common(numpify=a   )
       snake_case__  :    Any							       =feat_extract.model_input_names[0]

       snake_case__  :    Tuple							       =BatchFeature({input_name: speech_inputs}   )

       snake_case__  :    Any							       =self.feat_extract_tester.seq_length_diff
       snake_case__  :    List[str]							       =self.feat_extract_tester.max_seq_length + pad_diff
       snake_case__  :    Any							       =self.feat_extract_tester.min_seq_length
       snake_case__  :    Union[str, Any]							       =self.feat_extract_tester.batch_size
       snake_case__  :    List[str]							       =self.feat_extract_tester.feature_size

       # test padding for List[int] + numpy
       snake_case__  :    List[str]							       =feat_extract.pad(a				,			padding=a   )
       snake_case__  :    Optional[Any]							       =input_a[input_name]

       snake_case__  :    Optional[int]							       =feat_extract.pad(a				,			padding="""longest"""   )
       snake_case__  :    Tuple							       =input_a[input_name]

       snake_case__  :    Any							       =feat_extract.pad(a				,			padding="""max_length"""				,			max_length=len(speech_inputs[-1]   )   )
       snake_case__  :    int							       =input_a[input_name]

       snake_case__  :    Tuple							       =feat_extract.pad(a				,			padding="""longest"""				,			return_tensors="""np"""   )
       snake_case__  :    int							       =input_a[input_name]

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

       snake_case__  :    Dict							       =feat_extract.pad(
           a				,			padding="""max_length"""				,			max_length=a				,			return_tensors="""np"""   )
       snake_case__  :    Any							       =input_a[input_name]

       self.assertFalse(_inputs_have_equal_length(a   )   )
       self.assertTrue(_inputs_have_equal_length(a   )   )
       self.assertTrue(_inputs_have_equal_length(a   )   )
       self.assertTrue(_inputs_are_equal(a				,			a   )   )
       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
       snake_case__  :    Tuple							       =feat_extract.pad(a				,			pad_to_multiple_of=1_0   )
       snake_case__  :    Union[str, Any]							       =input_a[input_name]

       snake_case__  :    Optional[Any]							       =feat_extract.pad(a				,			padding="""longest"""				,			pad_to_multiple_of=1_0   )
       snake_case__  :    List[Any]							       =input_a[input_name]

       snake_case__  :    int							       =feat_extract.pad(
           a				,			padding="""max_length"""				,			pad_to_multiple_of=1_0				,			max_length=a   )
       snake_case__  :    int							       =input_a[input_name]

       snake_case__  :    Dict							       =feat_extract.pad(
           a				,			padding="""max_length"""				,			pad_to_multiple_of=1_0				,			max_length=a				,			return_tensors="""np"""				,			)
       snake_case__  :    List[str]							       =input_a[input_name]

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

       snake_case__  :    Tuple							       =pad_max_length if pad_max_length % 1_0 == 0 else (pad_max_length // 1_0 + 1) * 1_0
       self.assertTrue(all(len(a   ) == 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
       snake_case__  :    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 						lowercase__					(     self				,			a=False   ):
       def _inputs_have_equal_length(a   ):
        snake_case__  :    Any							       =len(input[0]   )
        for input_slice in input[1:]:
         if len(a   ) != length:
          return False
        return True

       def _inputs_are_equal(a				,			a   ):
        if len(a   ) != len(a   ):
         return False

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

       snake_case__  :    str							       =self.feature_extraction_class(**self.feat_extract_dict   )
       snake_case__  :    Optional[int]							       =self.feat_extract_tester.prepare_inputs_for_common(numpify=a   )
       snake_case__  :    Tuple							       =feat_extract.model_input_names[0]

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

       # truncate to smallest
       snake_case__  :    List[Any]							       =feat_extract.pad(
           a				,			padding="""max_length"""				,			max_length=len(speech_inputs[0]   )				,			truncation=a   )
       snake_case__  :    Union[str, Any]							       =input_a[input_name]

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

       self.assertTrue(_inputs_have_equal_length(a   )   )
       self.assertFalse(_inputs_have_equal_length(a   )   )

       # truncate to smallest with np
       snake_case__  :    Any							       =feat_extract.pad(
           a				,			padding="""max_length"""				,			max_length=len(speech_inputs[0]   )				,			return_tensors="""np"""				,			truncation=a				,			)
       snake_case__  :    Optional[Any]							       =input_a[input_name]

       snake_case__  :    Optional[int]							       =feat_extract.pad(
           a				,			padding="""max_length"""				,			max_length=len(speech_inputs[0]   )				,			return_tensors="""np"""   )
       snake_case__  :    Dict							       =input_a[input_name]

       self.assertTrue(_inputs_have_equal_length(a   )   )
       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(a   )   )

       # truncate to middle
       snake_case__  :    Tuple							       =feat_extract.pad(
           a				,			padding="""max_length"""				,			max_length=len(speech_inputs[1]   )				,			truncation=a				,			return_tensors="""np"""				,			)
       snake_case__  :    Optional[int]							       =input_a[input_name]

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

       snake_case__  :    List[Any]							       =feat_extract.pad(
           a				,			padding="""max_length"""				,			max_length=len(speech_inputs[1]   )				,			return_tensors="""np"""   )
       snake_case__  :    List[Any]							       =input_a[input_name]

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

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

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

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

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

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

       # test truncation for `pad_to_multiple_of` for List[int] + numpy
       snake_case__  :    str							       =1_2
       snake_case__  :    Dict							       =feat_extract.pad(
           a				,			padding="""max_length"""				,			max_length=len(speech_inputs[0]   )				,			pad_to_multiple_of=a				,			truncation=a				,			)
       snake_case__  :    Tuple							       =input_a[input_name]

       snake_case__  :    Union[str, Any]							       =feat_extract.pad(
           a				,			padding="""max_length"""				,			max_length=len(speech_inputs[0]   )				,			pad_to_multiple_of=a				,			)
       snake_case__  :    int							       =input_a[input_name]

       # retrieve expected_length as multiple of pad_to_multiple_of
       snake_case__  :    Optional[Any]							       =len(speech_inputs[0]   )
       if expected_length % pad_to_multiple_of != 0:
        snake_case__  :    Dict							       =((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(a   )   )
       self.assertFalse(_inputs_have_equal_length(a   )   )
      def 						lowercase__					(     self   ):
       self._check_padding(numpify=a   )
      def 						lowercase__					(     self   ):
       self._check_padding(numpify=a   )
      def 						lowercase__					(     self   ):
       self._check_truncation(numpify=a   )
      def 						lowercase__					(     self   ):
       self._check_truncation(numpify=a   )
      @require_torch
      def 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       =self.feature_extraction_class(**self.feat_extract_dict   )
       snake_case__  :    Optional[Any]							       =self.feat_extract_tester.prepare_inputs_for_common()
       snake_case__  :    List[Any]							       =feat_extract.model_input_names[0]

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

       snake_case__  :    Optional[Any]							       =feat_extract.pad(a				,			padding="""longest"""				,			return_tensors="""np"""   )[input_name]
       snake_case__  :    Optional[Any]							       =feat_extract.pad(a				,			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 						lowercase__					(     self   ):
       snake_case__  :    List[str]							       =self.feature_extraction_class(**self.feat_extract_dict   )
       snake_case__  :    Dict							       =self.feat_extract_tester.prepare_inputs_for_common()
       snake_case__  :    Union[str, Any]							       =feat_extract.model_input_names[0]

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

       snake_case__  :    Optional[Any]							       =feat_extract.pad(a				,			padding="""longest"""				,			return_tensors="""np"""   )[input_name]
       snake_case__  :    Dict							       =feat_extract.pad(a				,			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 						lowercase__					(     self   ):
       snake_case__  :    Dict							       =self.feat_extract_dict
       snake_case__  :    List[str]							       =True
       snake_case__  :    Any							       =self.feature_extraction_class(**a   )
       snake_case__  :    Dict							       =self.feat_extract_tester.prepare_inputs_for_common()
       snake_case__  :    Optional[int]							       =[len(a   ) for x in speech_inputs]
       snake_case__  :    Dict							       =feat_extract.model_input_names[0]

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

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



      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[Any]							       =self.feat_extract_dict
       snake_case__  :    Union[str, Any]							       =True
       snake_case__  :    Optional[Any]							       =self.feature_extraction_class(**a   )
       snake_case__  :    int							       =self.feat_extract_tester.prepare_inputs_for_common()
       snake_case__  :    List[Any]							       =[len(a   ) for x in speech_inputs]
       snake_case__  :    int							       =feat_extract.model_input_names[0]

       snake_case__  :    Any							       =BatchFeature({input_name: speech_inputs}   )
       snake_case__  :    Any							       =min(a   )

       snake_case__  :    Optional[Any]							       =feat_extract.pad(
           a				,			padding="""max_length"""				,			max_length=a				,			truncation=a				,			return_tensors="""np"""   )
       self.assertIn("""attention_mask"""				,			a   )
       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]   )













def 			A__							(      _a  :					list	):





 '''simple docstring'''







 if len(_a	) <= 1:
  return [tuple(_a	)]

 snake_case__  :    Optional[int]							       =[]

 def generate(_a  :					int		,  _a  :					list	):
  if k == 1:
   res.append(tuple(arr[:]	)	)
   return

  generate(k - 1		,  _a	)

  for i in range(k - 1	):
   if k % 2 == 0:  # k is even
    snake_case__       ,   snake_case__  :    Dict							       =arr[k - 1], arr[i]
   else:  # k is odd
    snake_case__       ,   snake_case__  :    int							       =arr[k - 1], arr[0]
   generate(k - 1		,  _a	)

 generate(len(_a	)		,  _a	)
 return res


if __name__ == "__main__":
       __lowerCamelCase	:		Optional[int]										=   input("""Enter numbers separated by a comma:\n""").strip()
       __lowerCamelCase	:		Any										=   [int(item) for item in user_input.split(""",""")]
       print(heaps(arr))





