chore: import upstream snapshot with attribution

python/paddle/distribution/chi2.py

@@ -0,0 +1,77 @@
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import paddle
+from paddle.base.data_feeder import check_type, convert_dtype
+from paddle.base.framework import Variable
+from paddle.distribution.gamma import Gamma
+from paddle.framework import in_dynamic_mode
+
+if TYPE_CHECKING:
+    from paddle import Tensor, dtype
+
+__all__ = ["Chi2"]
+
+
+class Chi2(Gamma):
+    r"""
+    Creates a Chi-squared distribution parameterized by shape parameter.
+    This is exactly equivalent to Gamma(concentration=0.5*df, rate=0.5), :ref:`api_paddle_distribution_Gamma`.
+
+    Args:
+        df (float or Tensor): The degree of freedom of the distribution, which should be non-negative. If the input data type is Tensor, it indicates the batch creation of distributions with multiple different parameters, and the `batch_shape` (refer to the :ref:`api_paddle_distribution_Distribution` base class) is the parameter.
+
+    Example:
+        .. code-block:: pycon
+
+            >>> import paddle
+            >>> m = paddle.distribution.Chi2(paddle.to_tensor([1.0]))
+            >>> sample = m.sample()
+            >>> sample.shape
+            paddle.Size([1])
+
+    """
+
+    df: Tensor
+    rate: Tensor
+    dtype: dtype
+
+    def __init__(self, df: float | Tensor) -> None:
+        if not in_dynamic_mode():
+            check_type(
+                df,
+                'df',
+                (float, Variable, paddle.pir.Value),
+                'Chi2',
+            )
+
+        # Get/convert concentration to tensor.
+        if self._validate_args(df):
+            self.df = df
+            self.dtype = convert_dtype(df.dtype)
+        else:
+            [self.df] = self._to_tensor(df)
+            self.dtype = paddle.get_default_dtype()
+
+        self.rate = paddle.full_like(self.df, 0.5)
+
+        if in_dynamic_mode():
+            if not paddle.all(self.df > 0):
+                raise ValueError("The arg of `df` must be positive.")
+
+        super().__init__(self.df * 0.5, self.rate)