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+240
-146
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5 files changed

+240
-146
lines changed

torch_np/_funcs.py

Lines changed: 31 additions & 26 deletions
Original file line numberDiff line numberDiff line change
@@ -56,7 +56,7 @@ def normalize_subok_like(arg, name):
5656
ArrayLike: normalize_array_like,
5757
Optional[ArrayLike]: normalize_optional_array_like,
5858
Sequence[ArrayLike]: normalize_seq_array_like,
59-
UnpackedSeqArrayLike: normalize_seq_array_like, # cf handling in normalize
59+
UnpackedSeqArrayLike: normalize_seq_array_like, # cf handling in normalize
6060
DTypeLike: normalize_dtype,
6161
SubokLike: normalize_subok_like,
6262
}
@@ -99,7 +99,6 @@ def wrapped(*args, **kwds):
9999
print(arg, name, parm.annotation)
100100
lst.append(normalize_this(arg, parm))
101101

102-
103102
# normalize keyword arguments
104103
for name, arg in kwds.items():
105104
if not name in sig.parameters:
@@ -156,24 +155,30 @@ def argwhere(a):
156155

157156

158157
@normalizer
159-
def clip(a : ArrayLike, min : Optional[ArrayLike]=None, max : Optional[ArrayLike]=None, out=None):
158+
def clip(
159+
a: ArrayLike,
160+
min: Optional[ArrayLike] = None,
161+
max: Optional[ArrayLike] = None,
162+
out=None,
163+
):
160164
# np.clip requires both a_min and a_max not None, while ndarray.clip allows
161165
# one of them to be None. Follow the more lax version.
162166
result = _impl.clip(a, min, max)
163167
return _helpers.result_or_out(result, out)
164168

165169

166170
@normalizer
167-
def repeat(a : ArrayLike, repeats: ArrayLike, axis=None):
171+
def repeat(a: ArrayLike, repeats: ArrayLike, axis=None):
168172
# XXX: scalar repeats; ArrayLikeOrScalar ?
169173
result = torch.repeat_interleave(a, repeats, axis)
170174
return _helpers.array_from(result)
171175

172176

173177
# ### diag et al ###
174178

179+
175180
@normalizer
176-
def diagonal(a : ArrayLike, offset=0, axis1=0, axis2=1):
181+
def diagonal(a: ArrayLike, offset=0, axis1=0, axis2=1):
177182
result = _impl.diagonal(a, offset, axis1, axis2)
178183
return _helpers.array_from(result)
179184

@@ -199,13 +204,13 @@ def identity(n, dtype: DTypeLike = None, *, like: SubokLike = None):
199204

200205

201206
@normalizer
202-
def diag(v : ArrayLike, k=0):
207+
def diag(v: ArrayLike, k=0):
203208
result = torch.diag(v, k)
204209
return _helpers.array_from(result)
205210

206211

207212
@normalizer
208-
def diagflat(v : ArrayLike, k=0):
213+
def diagflat(v: ArrayLike, k=0):
209214
result = torch.diagflat(v, k)
210215
return _helpers.array_from(result)
211216

@@ -216,27 +221,25 @@ def diag_indices(n, ndim=2):
216221

217222

218223
@normalizer
219-
def diag_indices_from(arr : ArrayLike):
224+
def diag_indices_from(arr: ArrayLike):
220225
result = _impl.diag_indices_from(arr)
221226
return _helpers.tuple_arrays_from(result)
222227

223228

224229
@normalizer
225-
def fill_diagonal(a : ArrayLike, val : ArrayLike, wrap=False):
230+
def fill_diagonal(a: ArrayLike, val: ArrayLike, wrap=False):
226231
result = _impl.fill_diagonal(a, val, wrap)
227232
return _helpers.array_from(result)
228233

229234

230235
@normalizer
231-
def vdot(a : ArrayLike, b : ArrayLike, /):
232-
# t_a, t_b = _helpers.to_tensors(a, b)
236+
def vdot(a: ArrayLike, b: ArrayLike, /):
233237
result = _impl.vdot(a, b)
234238
return result.item()
235239

236240

237241
@normalizer
238-
def dot(a : ArrayLike, b : ArrayLike, out=None):
239-
# t_a, t_b = _helpers.to_tensors(a, b)
242+
def dot(a: ArrayLike, b: ArrayLike, out=None):
240243
result = _impl.dot(a, b)
241244
return _helpers.result_or_out(result, out)
242245

@@ -245,19 +248,21 @@ def dot(a : ArrayLike, b : ArrayLike, out=None):
245248

246249

247250
@normalizer
248-
def sort(a : ArrayLike, axis=-1, kind=None, order=None):
251+
def sort(a: ArrayLike, axis=-1, kind=None, order=None):
249252
result = _impl.sort(a, axis, kind, order)
250253
return _helpers.array_from(result)
251254

252255

253256
@normalizer
254-
def argsort(a : ArrayLike, axis=-1, kind=None, order=None):
257+
def argsort(a: ArrayLike, axis=-1, kind=None, order=None):
255258
result = _impl.argsort(a, axis, kind, order)
256259
return _helpers.array_from(result)
257260

258261

259262
@normalizer
260-
def searchsorted(a : ArrayLike, v : ArrayLike, side="left", sorter : Optional[ArrayLike]=None):
263+
def searchsorted(
264+
a: ArrayLike, v: ArrayLike, side="left", sorter: Optional[ArrayLike] = None
265+
):
261266
result = torch.searchsorted(a, v, side=side, sorter=sorter)
262267
return _helpers.array_from(result)
263268

@@ -266,19 +271,19 @@ def searchsorted(a : ArrayLike, v : ArrayLike, side="left", sorter : Optional[Ar
266271

267272

268273
@normalizer
269-
def moveaxis(a : ArrayLike, source, destination):
274+
def moveaxis(a: ArrayLike, source, destination):
270275
result = _impl.moveaxis(a, source, destination)
271276
return _helpers.array_from(result)
272277

273278

274279
@normalizer
275-
def swapaxes(a : ArrayLike, axis1, axis2):
280+
def swapaxes(a: ArrayLike, axis1, axis2):
276281
result = _flips.swapaxes(a, axis1, axis2)
277282
return _helpers.array_from(result)
278283

279284

280285
@normalizer
281-
def rollaxis(a : ArrayLike, axis, start=0):
286+
def rollaxis(a: ArrayLike, axis, start=0):
282287
result = _flips.rollaxis(a, axis, start)
283288
return _helpers.array_from(result)
284289

@@ -287,32 +292,32 @@ def rollaxis(a : ArrayLike, axis, start=0):
287292

288293

289294
@normalizer
290-
def squeeze(a : ArrayLike, axis=None):
295+
def squeeze(a: ArrayLike, axis=None):
291296
result = _impl.squeeze(a, axis)
292297
return _helpers.array_from(result, a)
293298

294299

295300
@normalizer
296-
def reshape(a : ArrayLike, newshape, order="C"):
301+
def reshape(a: ArrayLike, newshape, order="C"):
297302
result = _impl.reshape(a, newshape, order=order)
298303
return _helpers.array_from(result, a)
299304

300305

301306
@normalizer
302-
def transpose(a : ArrayLike, axes=None):
307+
def transpose(a: ArrayLike, axes=None):
303308
result = _impl.transpose(a, axes)
304309
return _helpers.array_from(result, a)
305310

306311

307312
@normalizer
308-
def ravel(a : ArrayLike, order="C"):
313+
def ravel(a: ArrayLike, order="C"):
309314
result = _impl.ravel(a)
310315
return _helpers.array_from(result, a)
311316

312317

313318
# leading underscore since arr.flatten exists but np.flatten does not
314319
@normalizer
315-
def _flatten(a : ArrayLike, order="C"):
320+
def _flatten(a: ArrayLike, order="C"):
316321
result = _impl._flatten(a)
317322
return _helpers.array_from(result, a)
318323

@@ -321,7 +326,7 @@ def _flatten(a : ArrayLike, order="C"):
321326

322327

323328
@normalizer
324-
def real(a : ArrayLike):
329+
def real(a: ArrayLike):
325330
result = torch.real(a)
326331
return _helpers.array_from(result)
327332

@@ -333,7 +338,7 @@ def imag(a: ArrayLike):
333338

334339

335340
@normalizer
336-
def round_(a : ArrayLike, decimals=0, out=None):
341+
def round_(a: ArrayLike, decimals=0, out=None):
337342
result = _impl.round(a, decimals)
338343
return _helpers.result_or_out(result, out)
339344

torch_np/_helpers.py

Lines changed: 0 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -117,4 +117,3 @@ def to_tensors(*inputs):
117117
from ._ndarray import asarray, ndarray
118118

119119
return tuple(asarray(value).get() for value in inputs)
120-

torch_np/_ndarray.py

Lines changed: 0 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1,4 +1,3 @@
1-
import functools
21
import operator
32

43
import torch

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