


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__	(						lowerCAmelCase_    ):
       # to overwrite at feature extractactor specific tests
       lowerCamelCase__:     int							    =     None
       lowerCamelCase__:     Optional[int]							    =     None

       @property
       def 						UpperCAmelCase(   self      :     int    ):
            return self.feat_extract_tester.prepare_feat_extract_dict()

       def 						UpperCAmelCase(   self      :     List[Any]    ):
            a_    :     List[str]										=    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      :     int    ):
            a_    :     List[Any]										=    self.feat_extract_tester.prepare_inputs_for_common()
            a_    :     Optional[int]										=    self.feature_extraction_class(**self.feat_extract_dict    )
            a_    :     Any										=    feat_extract.model_input_names[0]

            a_    :     Dict										=    BatchFeature({input_name: speech_inputs}    )

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

            a_    :     Optional[Any]										=    self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase_    )
            a_    :     int										=    BatchFeature({input_name: speech_inputs}      ,							tensor_type="""np"""    )

            a_    :     str										=    processed_features[input_name]

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

            a_    :     Optional[Any]										=    BatchFeature({input_name: speech_inputs}      ,							tensor_type="""pt"""    )

            a_    :     List[Any]										=    processed_features[input_name]

            if len(batch_features_input.shape    ) < 3:
                 a_    :     Any										=    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      :     Dict    ):
            a_    :     Dict										=    self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase_    )
            a_    :     List[Any]										=    self.feature_extraction_class(**self.feat_extract_dict    )
            a_    :     Optional[int]										=    feat_extract.model_input_names[0]

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

            a_    :     Dict										=    processed_features[input_name]

            if len(batch_features_input.shape    ) < 3:
                 a_    :     Dict										=    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_      :     str=False    ):
            def _inputs_have_equal_length(lowerCamelCase_      :     Tuple    ):
                 a_    :     Tuple										=    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_      :     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

            a_    :     str										=    self.feature_extraction_class(**self.feat_extract_dict    )
            a_    :     Optional[Any]										=    self.feat_extract_tester.prepare_inputs_for_common(numpify=lowerCamelCase_    )
            a_    :     Dict										=    feat_extract.model_input_names[0]

            a_    :     List[Any]										=    BatchFeature({input_name: speech_inputs}    )

            a_    :     str										=    self.feat_extract_tester.seq_length_diff
            a_    :     List[Any]										=    self.feat_extract_tester.max_seq_length + pad_diff
            a_    :     List[str]										=    self.feat_extract_tester.min_seq_length
            a_    :     Tuple										=    self.feat_extract_tester.batch_size
            a_    :     Optional[Any]										=    self.feat_extract_tester.feature_size

            # test padding for List[int] + numpy
            a_    :     List[Any]										=    feat_extract.pad(lowerCamelCase_      ,							padding=lowerCamelCase_    )
            a_    :     Union[str, Any]										=    input_a[input_name]

            a_    :     Optional[Any]										=    feat_extract.pad(lowerCamelCase_      ,							padding="""longest"""    )
            a_    :     Dict										=    input_a[input_name]

            a_    :     Tuple										=    feat_extract.pad(lowerCamelCase_      ,							padding="""max_length"""      ,							max_length=len(speech_inputs[-1]    )    )
            a_    :     Tuple										=    input_a[input_name]

            a_    :     List[Any]										=    feat_extract.pad(lowerCamelCase_      ,							padding="""longest"""      ,							return_tensors="""np"""    )
            a_    :     Dict										=    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]

            a_    :     int										=    feat_extract.pad(
                lowerCamelCase_      ,							padding="""max_length"""      ,							max_length=lowerCamelCase_      ,							return_tensors="""np"""    )
            a_    :     Tuple										=    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
            a_    :     List[Any]										=    feat_extract.pad(lowerCamelCase_      ,							pad_to_multiple_of=1_0    )
            a_    :     Tuple										=    input_a[input_name]

            a_    :     Tuple										=    feat_extract.pad(lowerCamelCase_      ,							padding="""longest"""      ,							pad_to_multiple_of=1_0    )
            a_    :     Dict										=    input_a[input_name]

            a_    :     Union[str, Any]										=    feat_extract.pad(
                lowerCamelCase_      ,							padding="""max_length"""      ,							pad_to_multiple_of=1_0      ,							max_length=lowerCamelCase_    )
            a_    :     Optional[Any]										=    input_a[input_name]

            a_    :     Optional[int]										=    feat_extract.pad(
                lowerCamelCase_      ,							padding="""max_length"""      ,							pad_to_multiple_of=1_0      ,							max_length=lowerCamelCase_      ,							return_tensors="""np"""      ,							)
            a_    :     Union[str, Any]										=    input_a[input_name]

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

            a_    :     Union[str, Any]										=    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
            a_    :     Dict										=    (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      :     Optional[int]      ,							lowerCamelCase_      :     Any=False    ):
            def _inputs_have_equal_length(lowerCamelCase_      :     Tuple    ):
                 a_    :     List[str]										=    len(input[0]    )
                 for input_slice in input[1:]:
                      if len(lowerCamelCase_    ) != length:
                           return False
                 return True

            def _inputs_are_equal(lowerCamelCase_      :     Any      ,							lowerCamelCase_      :     Dict    ):
                 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

            a_    :     Any										=    self.feature_extraction_class(**self.feat_extract_dict    )
            a_    :     Optional[int]										=    self.feat_extract_tester.prepare_inputs_for_common(numpify=lowerCamelCase_    )
            a_    :     Dict										=    feat_extract.model_input_names[0]

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

            # truncate to smallest
            a_    :     Optional[Any]										=    feat_extract.pad(
                lowerCamelCase_      ,							padding="""max_length"""      ,							max_length=len(speech_inputs[0]    )      ,							truncation=lowerCamelCase_    )
            a_    :     Any										=    input_a[input_name]

            a_    :     List[str]										=    feat_extract.pad(lowerCamelCase_      ,							padding="""max_length"""      ,							max_length=len(speech_inputs[0]    )    )
            a_    :     int										=    input_a[input_name]

            self.assertTrue(_inputs_have_equal_length(lowerCamelCase_    )    )
            self.assertFalse(_inputs_have_equal_length(lowerCamelCase_    )    )

            # truncate to smallest with np
            a_    :     int										=    feat_extract.pad(
                lowerCamelCase_      ,							padding="""max_length"""      ,							max_length=len(speech_inputs[0]    )      ,							return_tensors="""np"""      ,							truncation=lowerCamelCase_      ,							)
            a_    :     Union[str, Any]										=    input_a[input_name]

            a_    :     List[str]										=    feat_extract.pad(
                lowerCamelCase_      ,							padding="""max_length"""      ,							max_length=len(speech_inputs[0]    )      ,							return_tensors="""np"""    )
            a_    :     str										=    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
            a_    :     str										=    feat_extract.pad(
                lowerCamelCase_      ,							padding="""max_length"""      ,							max_length=len(speech_inputs[1]    )      ,							truncation=lowerCamelCase_      ,							return_tensors="""np"""      ,							)
            a_    :     List[str]										=    input_a[input_name]

            a_    :     Tuple										=    feat_extract.pad(
                lowerCamelCase_      ,							padding="""max_length"""      ,							max_length=len(speech_inputs[1]    )      ,							truncation=lowerCamelCase_    )
            a_    :     Union[str, Any]										=    input_a[input_name]

            a_    :     str										=    feat_extract.pad(
                lowerCamelCase_      ,							padding="""max_length"""      ,							max_length=len(speech_inputs[1]    )      ,							return_tensors="""np"""    )
            a_    :     Any										=    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
            a_    :     Tuple										=    1_2
            a_    :     List[Any]										=    feat_extract.pad(
                lowerCamelCase_      ,							padding="""max_length"""      ,							max_length=len(speech_inputs[0]    )      ,							pad_to_multiple_of=lowerCamelCase_      ,							truncation=lowerCamelCase_      ,							)
            a_    :     Union[str, Any]										=    input_a[input_name]

            a_    :     Optional[Any]										=    feat_extract.pad(
                lowerCamelCase_      ,							padding="""max_length"""      ,							max_length=len(speech_inputs[0]    )      ,							pad_to_multiple_of=lowerCamelCase_      ,							)
            a_    :     Dict										=    input_a[input_name]

            # retrieve expected_length as multiple of pad_to_multiple_of
            a_    :     Any										=    len(speech_inputs[0]    )
            if expected_length % pad_to_multiple_of != 0:
                 a_    :     str										=    ((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      :     Optional[Any]    ):
            self._check_padding(numpify=lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     str    ):
            self._check_padding(numpify=lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     List[Any]    ):
            self._check_truncation(numpify=lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Dict    ):
            self._check_truncation(numpify=lowerCamelCase_    )

       @require_torch
       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     List[str]										=    self.feature_extraction_class(**self.feat_extract_dict    )
            a_    :     Optional[Any]										=    self.feat_extract_tester.prepare_inputs_for_common()
            a_    :     Any										=    feat_extract.model_input_names[0]

            a_    :     Dict										=    BatchFeature({input_name: speech_inputs}    )

            a_    :     List[Any]										=    feat_extract.pad(lowerCamelCase_      ,							padding="""longest"""      ,							return_tensors="""np"""    )[input_name]
            a_    :     List[str]										=    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      :     int    ):
            a_    :     List[str]										=    self.feature_extraction_class(**self.feat_extract_dict    )
            a_    :     List[str]										=    self.feat_extract_tester.prepare_inputs_for_common()
            a_    :     Any										=    feat_extract.model_input_names[0]

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

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

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

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

            a_    :     Optional[Any]										=    BatchFeature({input_name: speech_inputs}    )
            a_    :     int										=    min(lowerCamelCase_    )

            a_    :     int										=    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 heapq import heappop, heappush

import numpy as np



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  ):
     a_			,			a_    :     Any										=    grid.shape
     a_    :     Dict										=    [-1, 1, 0, 0]
     a_    :     List[Any]										=    [0, 0, -1, 1]
     if allow_diagonal:
          dx += [-1, -1, 1, 1]
          dy += [-1, 1, -1, 1]

     a_			,			a_    :     int										=    [(0, source)], set()
     a_    :     Optional[Any]										=    np.full((rows, cols)  ,  np.inf )
     a_    :     Tuple										=    0
     a_    :     Optional[Any]										=    np.empty((rows, cols)  ,  dtype=__UpperCamelCase )
     a_    :     Optional[Any]										=    None

     while queue:
          ((a_)			,			(a_))    :     Any										=    heappop(__UpperCamelCase )
          if (x, y) in visited:
               continue
          visited.add((x, y) )

          if (x, y) == destination:
               a_    :     Tuple										=    []
               while (x, y) != source:
                    path.append((x, y) )
                    a_			,			a_    :     Any										=    predecessors[x, y]
               path.append(__UpperCamelCase )  # add the source manually
               path.reverse()
               return matrix[destination], path

          for i in range(len(__UpperCamelCase ) ):
               a_			,			a_    :     Dict										=    x + dx[i], y + dy[i]
               if 0 <= nx < rows and 0 <= ny < cols:
                    a_    :     List[Any]										=    grid[nx][ny]
                    if next_node == 1 and matrix[nx, ny] > dist + 1:
                         heappush(__UpperCamelCase  ,  (dist + 1, (nx, ny)) )
                         a_    :     List[str]										=    dist + 1
                         a_    :     Union[str, Any]										=    (x, y)

     return np.inf, []


if __name__ == "__main__":
       import doctest

       doctest.testmod()



