
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
      _A						:		List[str]    	=     None
      _A						:		Dict    	=     None
      @property
      def A_			(			self ):
          return self.feat_extract_tester.prepare_feat_extract_dict()
      def A_			(			self ):
          snake_case__						=				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 A_			(			self ):
          snake_case__						=				self.feat_extract_tester.prepare_inputs_for_common()
          snake_case__						=				self.feature_extraction_class(**self.feat_extract_dict )
          snake_case__						=				feat_extract.model_input_names[0]

          snake_case__						=				BatchFeature({input_name: speech_inputs} )

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

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

          snake_case__						=				processed_features[input_name]

          if len(batch_features_input.shape ) < 3:
              snake_case__						=				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 A_			(			self ):
          snake_case__						=				self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase )
          snake_case__						=				self.feature_extraction_class(**self.feat_extract_dict )
          snake_case__						=				feat_extract.model_input_names[0]

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

          snake_case__						=				processed_features[input_name]

          if len(batch_features_input.shape ) < 3:
              snake_case__						=				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 A_			(			self ):
          snake_case__						=				self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase )
          snake_case__						=				self.feature_extraction_class(**self.feat_extract_dict )
          snake_case__						=				feat_extract.model_input_names[0]

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

          snake_case__						=				processed_features[input_name]

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

          def _inputs_are_equal(lowerCamelCase		,	lowerCamelCase ):
              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

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

          snake_case__						=				BatchFeature({input_name: speech_inputs} )

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

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

          snake_case__						=				feat_extract.pad(lowerCamelCase		,	padding="longest" )
          snake_case__						=				input_a[input_name]

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

          snake_case__						=				feat_extract.pad(lowerCamelCase		,	padding="longest"		,	return_tensors="np" )
          snake_case__						=				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]

          snake_case__						=				feat_extract.pad(
              lowerCamelCase		,	padding="max_length"		,	max_length=lowerCamelCase		,	return_tensors="np" )
          snake_case__						=				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
          snake_case__						=				feat_extract.pad(lowerCamelCase		,	pad_to_multiple_of=10 )
          snake_case__						=				input_a[input_name]

          snake_case__						=				feat_extract.pad(lowerCamelCase		,	padding="longest"		,	pad_to_multiple_of=10 )
          snake_case__						=				input_a[input_name]

          snake_case__						=				feat_extract.pad(
              lowerCamelCase		,	padding="max_length"		,	pad_to_multiple_of=10		,	max_length=lowerCamelCase )
          snake_case__						=				input_a[input_name]

          snake_case__						=				feat_extract.pad(
              lowerCamelCase		,	padding="max_length"		,	pad_to_multiple_of=10		,	max_length=lowerCamelCase		,	return_tensors="np"		,	)
          snake_case__						=				input_a[input_name]

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

          snake_case__						=				pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
          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
          snake_case__						=				(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 A_			(			self		,	lowerCamelCase=False ):
          def _inputs_have_equal_length(lowerCamelCase ):
              snake_case__						=				len(input[0] )
              for input_slice in input[1:]:
                  if len(lowerCamelCase ) != length:
                      return False
              return True

          def _inputs_are_equal(lowerCamelCase		,	lowerCamelCase ):
              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

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

          snake_case__						=				BatchFeature({input_name: speech_inputs} )

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

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

          self.assertTrue(_inputs_have_equal_length(lowerCamelCase ) )
          self.assertFalse(_inputs_have_equal_length(lowerCamelCase ) )

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

          snake_case__						=				feat_extract.pad(
              lowerCamelCase		,	padding="max_length"		,	max_length=len(speech_inputs[0] )		,	return_tensors="np" )
          snake_case__						=				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
          snake_case__						=				feat_extract.pad(
              lowerCamelCase		,	padding="max_length"		,	max_length=len(speech_inputs[1] )		,	truncation=lowerCamelCase		,	return_tensors="np"		,	)
          snake_case__						=				input_a[input_name]

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

          snake_case__						=				feat_extract.pad(
              lowerCamelCase		,	padding="max_length"		,	max_length=len(speech_inputs[1] )		,	return_tensors="np" )
          snake_case__						=				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
          snake_case__						=				12
          snake_case__						=				feat_extract.pad(
              lowerCamelCase		,	padding="max_length"		,	max_length=len(speech_inputs[0] )		,	pad_to_multiple_of=lowerCamelCase		,	truncation=lowerCamelCase		,	)
          snake_case__						=				input_a[input_name]

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

          # retrieve expected_length as multiple of pad_to_multiple_of
          snake_case__						=				len(speech_inputs[0] )
          if expected_length % pad_to_multiple_of != 0:
              snake_case__						=				((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 A_			(			self ):
          self._check_padding(numpify=lowerCamelCase )
      def A_			(			self ):
          self._check_padding(numpify=lowerCamelCase )
      def A_			(			self ):
          self._check_truncation(numpify=lowerCamelCase )
      def A_			(			self ):
          self._check_truncation(numpify=lowerCamelCase )
      @require_torch
      def A_			(			self ):
          snake_case__						=				self.feature_extraction_class(**self.feat_extract_dict )
          snake_case__						=				self.feat_extract_tester.prepare_inputs_for_common()
          snake_case__						=				feat_extract.model_input_names[0]

          snake_case__						=				BatchFeature({input_name: speech_inputs} )

          snake_case__						=				feat_extract.pad(lowerCamelCase		,	padding="longest"		,	return_tensors="np" )[input_name]
          snake_case__						=				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 A_			(			self ):
          snake_case__						=				self.feature_extraction_class(**self.feat_extract_dict )
          snake_case__						=				self.feat_extract_tester.prepare_inputs_for_common()
          snake_case__						=				feat_extract.model_input_names[0]

          snake_case__						=				BatchFeature({input_name: speech_inputs} )

          snake_case__						=				feat_extract.pad(lowerCamelCase		,	padding="longest"		,	return_tensors="np" )[input_name]
          snake_case__						=				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 A_			(			self ):
          snake_case__						=				self.feat_extract_dict
          snake_case__						=				True
          snake_case__						=				self.feature_extraction_class(**lowerCamelCase )
          snake_case__						=				self.feat_extract_tester.prepare_inputs_for_common()
          snake_case__						=				[len(lowerCamelCase ) for x in speech_inputs]
          snake_case__						=				feat_extract.model_input_names[0]

          snake_case__						=				BatchFeature({input_name: speech_inputs} )

          snake_case__						=				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 A_			(			self ):
          snake_case__						=				self.feat_extract_dict
          snake_case__						=				True
          snake_case__						=				self.feature_extraction_class(**lowerCamelCase )
          snake_case__						=				self.feat_extract_tester.prepare_inputs_for_common()
          snake_case__						=				[len(lowerCamelCase ) for x in speech_inputs]
          snake_case__						=				feat_extract.model_input_names[0]

          snake_case__						=				BatchFeature({input_name: speech_inputs} )
          snake_case__						=				min(lowerCamelCase )

          snake_case__						=				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] )





from __future__ import annotations





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    if len(__lowerCAmelCase							) == 0:
        return False
    snake_case__						=				len(__lowerCAmelCase							) // 2
    if a_list[midpoint] == item:
        return True
    if item < a_list[midpoint]:
        return binary_search(a_list[:midpoint]    ,		__lowerCAmelCase							)
    else:
        return binary_search(a_list[midpoint + 1 :]    ,		__lowerCAmelCase							)


if __name__ == "__main__":
     __magic_name__            =  input('''Enter numbers separated by comma:\n''').strip()
     __magic_name__            =  [int(item.strip()) for item in user_input.split(''',''')]
     __magic_name__            =  int(input('''Enter the number to be found in the list:\n''').strip())
     __magic_name__            =  '''''' if binary_search(sequence, target) else '''not '''
     print(F'''{target} was {not_str}found in {sequence}''')



