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get_all_dtypes ,
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get_complex_dtypes ,
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get_float_dtypes ,
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- is_cuda_device ,
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)
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@@ -443,11 +442,6 @@ def setup_method(self):
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@pytest .mark .parametrize ("norm" , [None , "backward" , "forward" , "ortho" ])
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@pytest .mark .parametrize ("order" , ["C" , "F" ])
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def test_fftn (self , dtype , axes , norm , order ):
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- if is_cuda_device ():
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- if order == "C" and axes == (0 , 1 , 2 ):
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- pass
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- else :
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- pytest .skip ("SAT-7587" )
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a_np = generate_random_numpy_array ((2 , 3 , 4 , 5 ), dtype , order )
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a = dpnp .array (a_np )
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@@ -482,9 +476,6 @@ def test_fftn_repeated_axes(self, axes):
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@pytest .mark .parametrize ("axes" , [(2 , 3 , 3 , 2 ), (0 , 0 , 3 , 3 )])
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@pytest .mark .parametrize ("s" , [(5 , 4 , 3 , 3 ), (7 , 8 , 10 , 9 )])
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def test_fftn_repeated_axes_with_s (self , axes , s ):
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- if is_cuda_device ():
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- if axes == (0 , 0 , 3 , 3 ) and s == (7 , 8 , 10 , 9 ):
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- pytest .skip ("SAT-7587" )
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a_np = generate_random_numpy_array ((2 , 3 , 4 , 5 ), dtype = numpy .complex64 )
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a = dpnp .array (a_np )
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@@ -504,11 +495,6 @@ def test_fftn_repeated_axes_with_s(self, axes, s):
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@pytest .mark .parametrize ("axes" , [(0 , 1 , 2 , 3 ), (1 , 2 , 1 , 2 ), (2 , 2 , 2 , 3 )])
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@pytest .mark .parametrize ("s" , [(2 , 3 , 4 , 5 ), (5 , 4 , 7 , 8 ), (2 , 5 , 1 , 2 )])
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def test_fftn_out (self , axes , s ):
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- if is_cuda_device ():
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- if axes == (0 , 1 , 2 , 3 ):
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- pytest .skip ("SAT-7587" )
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- elif s == (2 , 5 , 1 , 2 ) and axes in [(1 , 2 , 1 , 2 ), (2 , 2 , 2 , 3 )]:
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- pytest .skip ("SAT-7587" )
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a_np = generate_random_numpy_array ((2 , 3 , 4 , 5 ), dtype = numpy .complex64 )
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a = dpnp .array (a_np )
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@@ -1082,9 +1068,6 @@ def test_rfftn_repeated_axes_with_s(self, axes, s):
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@pytest .mark .parametrize ("axes" , [(0 , 1 , 2 , 3 ), (1 , 2 , 1 , 2 ), (2 , 2 , 2 , 3 )])
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@pytest .mark .parametrize ("s" , [(2 , 3 , 4 , 5 ), (5 , 6 , 7 , 9 ), (2 , 5 , 1 , 2 )])
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def test_rfftn_out (self , axes , s ):
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- if is_cuda_device ():
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- if axes == (0 , 1 , 2 , 3 ) and s == (2 , 5 , 1 , 2 ):
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- pytest .skip ("SAT-7587" )
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x = numpy .random .uniform (- 10 , 10 , 120 )
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a_np = numpy .array (x , dtype = numpy .float32 ).reshape (2 , 3 , 4 , 5 )
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a = dpnp .asarray (a_np )
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