


"""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   _a      (      _UpperCAmelCase ):
    a_    :				List[Any]             =    None
    a_    :				Optional[Any]             =    None
    @property
    def      _UpperCamelCase       ( self							:					Optional[Any]				):
          return self.feat_extract_tester.prepare_feat_extract_dict()
    def      _UpperCamelCase       ( self							:					List[Any]				):
          lowerCamelCase__           =		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      _UpperCamelCase       ( self							:					str				):
          lowerCamelCase__           =		self.feat_extract_tester.prepare_inputs_for_common()
          lowerCamelCase__           =		self.feature_extraction_class(**self.feat_extract_dict				)
          lowerCamelCase__           =		feat_extract.model_input_names[0]

          lowerCamelCase__           =		BatchFeature({input_name: speech_inputs}				)

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

          lowerCamelCase__           =		self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_				)
          lowerCamelCase__           =		BatchFeature({input_name: speech_inputs}							,					tensor_type='np'				)

          lowerCamelCase__           =		processed_features[input_name]

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

          lowerCamelCase__           =		BatchFeature({input_name: speech_inputs}							,					tensor_type='pt'				)

          lowerCamelCase__           =		processed_features[input_name]

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

          lowerCamelCase__           =		BatchFeature({input_name: speech_inputs}							,					tensor_type='tf'				)

          lowerCamelCase__           =		processed_features[input_name]

          if len(batch_features_input.shape				) < 3:
                lowerCamelCase__           =		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							:					int							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=False				):
          def _inputs_have_equal_length(SCREAMING_SNAKE_CASE__							:					int				):
                lowerCamelCase__           =		len(input[0]				)
                for input_slice in input[1:]:
                      if len(A_				) != length:
                            return False
                return True

          def _inputs_are_equal(SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					Optional[int]				):
                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

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

          lowerCamelCase__           =		BatchFeature({input_name: speech_inputs}				)

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

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

          lowerCamelCase__           =		feat_extract.pad(A_							,					padding='longest'				)
          lowerCamelCase__           =		input_a[input_name]

          lowerCamelCase__           =		feat_extract.pad(A_							,					padding='max_length'							,					max_length=len(speech_inputs[-1]				)				)
          lowerCamelCase__           =		input_a[input_name]

          lowerCamelCase__           =		feat_extract.pad(A_							,					padding='longest'							,					return_tensors='np'				)
          lowerCamelCase__           =		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]

          lowerCamelCase__           =		feat_extract.pad(
              A_							,					padding='max_length'							,					max_length=A_							,					return_tensors='np'				)
          lowerCamelCase__           =		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
          lowerCamelCase__           =		feat_extract.pad(A_							,					pad_to_multiple_of=10				)
          lowerCamelCase__           =		input_a[input_name]

          lowerCamelCase__           =		feat_extract.pad(A_							,					padding='longest'							,					pad_to_multiple_of=10				)
          lowerCamelCase__           =		input_a[input_name]

          lowerCamelCase__           =		feat_extract.pad(
              A_							,					padding='max_length'							,					pad_to_multiple_of=10							,					max_length=A_				)
          lowerCamelCase__           =		input_a[input_name]

          lowerCamelCase__           =		feat_extract.pad(
              A_							,					padding='max_length'							,					pad_to_multiple_of=10							,					max_length=A_							,					return_tensors='np'							,					)
          lowerCamelCase__           =		input_a[input_name]

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

          lowerCamelCase__           =		pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
          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
          lowerCamelCase__           =		(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							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=False				):
          def _inputs_have_equal_length(SCREAMING_SNAKE_CASE__							:					Any				):
                lowerCamelCase__           =		len(input[0]				)
                for input_slice in input[1:]:
                      if len(A_				) != length:
                            return False
                return True

          def _inputs_are_equal(SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					str				):
                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

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

          lowerCamelCase__           =		BatchFeature({input_name: speech_inputs}				)

          # truncate to smallest
          lowerCamelCase__           =		feat_extract.pad(
              A_							,					padding='max_length'							,					max_length=len(speech_inputs[0]				)							,					truncation=A_				)
          lowerCamelCase__           =		input_a[input_name]

          lowerCamelCase__           =		feat_extract.pad(A_							,					padding='max_length'							,					max_length=len(speech_inputs[0]				)				)
          lowerCamelCase__           =		input_a[input_name]

          self.assertTrue(_inputs_have_equal_length(A_				)				)
          self.assertFalse(_inputs_have_equal_length(A_				)				)

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

          lowerCamelCase__           =		feat_extract.pad(
              A_							,					padding='max_length'							,					max_length=len(speech_inputs[0]				)							,					return_tensors='np'				)
          lowerCamelCase__           =		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
          lowerCamelCase__           =		feat_extract.pad(
              A_							,					padding='max_length'							,					max_length=len(speech_inputs[1]				)							,					truncation=A_							,					return_tensors='np'							,					)
          lowerCamelCase__           =		input_a[input_name]

          lowerCamelCase__           =		feat_extract.pad(
              A_							,					padding='max_length'							,					max_length=len(speech_inputs[1]				)							,					truncation=A_				)
          lowerCamelCase__           =		input_a[input_name]

          lowerCamelCase__           =		feat_extract.pad(
              A_							,					padding='max_length'							,					max_length=len(speech_inputs[1]				)							,					return_tensors='np'				)
          lowerCamelCase__           =		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
          lowerCamelCase__           =		12
          lowerCamelCase__           =		feat_extract.pad(
              A_							,					padding='max_length'							,					max_length=len(speech_inputs[0]				)							,					pad_to_multiple_of=A_							,					truncation=A_							,					)
          lowerCamelCase__           =		input_a[input_name]

          lowerCamelCase__           =		feat_extract.pad(
              A_							,					padding='max_length'							,					max_length=len(speech_inputs[0]				)							,					pad_to_multiple_of=A_							,					)
          lowerCamelCase__           =		input_a[input_name]

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

          lowerCamelCase__           =		BatchFeature({input_name: speech_inputs}				)

          lowerCamelCase__           =		feat_extract.pad(A_							,					padding='longest'							,					return_tensors='np'				)[input_name]
          lowerCamelCase__           =		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      _UpperCamelCase       ( self							:					int				):
          lowerCamelCase__           =		self.feature_extraction_class(**self.feat_extract_dict				)
          lowerCamelCase__           =		self.feat_extract_tester.prepare_inputs_for_common()
          lowerCamelCase__           =		feat_extract.model_input_names[0]

          lowerCamelCase__           =		BatchFeature({input_name: speech_inputs}				)

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

          lowerCamelCase__           =		BatchFeature({input_name: speech_inputs}				)

          lowerCamelCase__           =		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      _UpperCamelCase       ( self							:					Union[str, Any]				):
          lowerCamelCase__           =		self.feat_extract_dict
          lowerCamelCase__           =		True
          lowerCamelCase__           =		self.feature_extraction_class(**A_				)
          lowerCamelCase__           =		self.feat_extract_tester.prepare_inputs_for_common()
          lowerCamelCase__           =		[len(A_				) for x in speech_inputs]
          lowerCamelCase__           =		feat_extract.model_input_names[0]

          lowerCamelCase__           =		BatchFeature({input_name: speech_inputs}				)
          lowerCamelCase__           =		min(A_				)

          lowerCamelCase__           =		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]				)






"""simple docstring"""


from __future__ import annotations

_snake_case          =							[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]



def   snake_case						(				_a:     list[list[int]]		,   _a:     list[int]		,   _a:     list[int]		,   _a:     int		,   _a:     list[list[int]]		,   )->		tuple[list[list[int]], list[list[int]]]:
      '''simple docstring'''


      lowerCamelCase__           =		[
          [0 for col in range(len(grid[0]							)							)] for row in range(len(_a							)							)
      ]  # the reference grid
      lowerCamelCase__           =		1
      lowerCamelCase__           =		[
          [0 for col in range(len(grid[0]							)							)] for row in range(len(_a							)							)
      ]  # the action grid

      lowerCamelCase__           =		init[0]
      lowerCamelCase__           =		init[1]
      lowerCamelCase__           =		0
      lowerCamelCase__           =		g + heuristic[x][y]  # cost from starting cell to destination cell
      lowerCamelCase__           =		[[f, g, x, y]]

      lowerCamelCase__           =		False  # flag that is set when search is complete
      lowerCamelCase__           =		False  # flag set if we can't find expand

      while not found and not resign:
            if len(_a							) == 0:
                  raise ValueError('Algorithm is unable to find solution'							)
            else:  # to choose the least costliest action so as to move closer to the goal
                  cell.sort()
                  cell.reverse()
                  lowerCamelCase__           =		cell.pop()
                  lowerCamelCase__           =		next_cell[2]
                  lowerCamelCase__           =		next_cell[3]
                  lowerCamelCase__           =		next_cell[1]

                  if x == goal[0] and y == goal[1]:
                        lowerCamelCase__           =		True
                  else:
                        for i in range(len(_a							)							):  # to try out different valid actions
                              lowerCamelCase__           =		x + DIRECTIONS[i][0]
                              lowerCamelCase__           =		y + DIRECTIONS[i][1]
                              if xa >= 0 and xa < len(_a							) and ya >= 0 and ya < len(grid[0]							):
                                    if closed[xa][ya] == 0 and grid[xa][ya] == 0:
                                          lowerCamelCase__           =		g + cost
                                          lowerCamelCase__           =		ga + heuristic[xa][ya]
                                          cell.append([fa, ga, xa, ya]							)
                                          lowerCamelCase__           =		1
                                          lowerCamelCase__           =		i
      lowerCamelCase__           =		[]
      lowerCamelCase__           =		goal[0]
      lowerCamelCase__           =		goal[1]
      invpath.append([x, y]							)  # we get the reverse path from here
      while x != init[0] or y != init[1]:
            lowerCamelCase__           =		x - DIRECTIONS[action[x][y]][0]
            lowerCamelCase__           =		y - DIRECTIONS[action[x][y]][1]
            lowerCamelCase__           =		xa
            lowerCamelCase__           =		ya
            invpath.append([x, y]							)

      lowerCamelCase__           =		[]
      for i in range(len(_a							)							):
            path.append(invpath[len(_a							) - 1 - i]							)
      return path, action


if __name__ == "__main__":
   _snake_case          =							[
       [0, 1, 0, 0, 0, 0],
       [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
       [0, 1, 0, 0, 0, 0],
       [0, 1, 0, 0, 1, 0],
       [0, 0, 0, 0, 1, 0],
   ]

   _snake_case          =							[0, 0]
   # all coordinates are given in format [y,x]
   _snake_case          =							[len(grid) - 1, len(grid[0]) - 1]
   _snake_case          =							1

   # the cost map which pushes the path closer to the goal
   _snake_case          =							[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
   for i in range(len(grid)):
      for j in range(len(grid[0])):
         _snake_case          =							abs(i - goal[0]) + abs(j - goal[1])
         if grid[i][j] == 1:
            # added extra penalty in the heuristic map
            _snake_case          =							99

   _snake_case      ,					_snake_case          =							search(grid, init, goal, cost, heuristic)

   print("ACTION MAP")
   for i in range(len(action)):
      print(action[i])

   for i in range(len(path)):
      print(path[i])



