prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  `reduction` for `ContrastiveLoss`
  **Describe the bug**
  the error messages and docstring should be consistent
  https://github.com/Project-MONAI/MONAI/blob/a7bc4a3cbaeaa3c505a25ca2ddf6922bda8ea7dc/monai/losses/contrastive.py#L89-L91
  
  https://github.com/Project-MONAI/MONAI/blob/a7bc4a3cbaeaa3c505a25ca2ddf6922bda8ea7dc/monai/losses/contrastive.py#L58
  
  **Expected behavior**
  implementing the option `reduction="none"`?
  
  
  </issue>
  <code>
  [start of monai/losses/contrastive.py]
  1 # Copyright 2020 - 2021 MONAI Consortium
  2 # Licensed under the Apache License, Version 2.0 (the "License");
  3 # you may not use this file except in compliance with the License.
  4 # You may obtain a copy of the License at
  5 #     http://www.apache.org/licenses/LICENSE-2.0
  6 # Unless required by applicable law or agreed to in writing, software
  7 # distributed under the License is distributed on an "AS IS" BASIS,
  8 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  9 # See the License for the specific language governing permissions and
  10 # limitations under the License.
  11 
  12 from typing import Union
  13 
  14 import torch
  15 from torch.nn import functional as F
  16 from torch.nn.modules.loss import _Loss
  17 
  18 from monai.utils import LossReduction
  19 
  20 
  21 class ContrastiveLoss(_Loss):
  22 
  23     """
  24     Compute the Contrastive loss defined in:
  25 
  26         Chen, Ting, et al. "A simple framework for contrastive learning of visual representations." International
  27         conference on machine learning. PMLR, 2020. (http://proceedings.mlr.press/v119/chen20j.html)
  28 
  29     Adapted from:
  30         https://github.com/Sara-Ahmed/SiT/blob/1aacd6adcd39b71efc903d16b4e9095b97dda76f/losses.py#L5
  31 
  32     """
  33 
  34     def __init__(
  35         self, temperature: float = 0.5, batch_size: int = 1, reduction: Union[LossReduction, str] = LossReduction.SUM
  36     ) -> None:
  37         """
  38         Args:
  39             temperature: Can be scaled between 0 and 1 for learning from negative samples, ideally set to 0.5.
  40 
  41         Raises:
  42             AssertionError: When an input of dimension length > 2 is passed
  43             AssertionError: When input and target are of different shapes
  44 
  45         """
  46         super().__init__(reduction=LossReduction(reduction).value)
  47 
  48         self.batch_size = batch_size
  49         self.temperature = temperature
  50 
  51     def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
  52         """
  53         Args:
  54             input: the shape should be B[F].
  55             target: the shape should be B[F].
  56 
  57         Raises:
  58             ValueError: When ``self.reduction`` is not one of ["sum", "none"].
  59         """
  60         if len(target.shape) > 2 or len(input.shape) > 2:
  61             raise AssertionError(
  62                 f"Either target or input has dimensions greater than 2 where target "
  63                 f"shape is ({target.shape}) and input shape is ({input.shape})"
  64             )
  65 
  66         if target.shape != input.shape:
  67             raise AssertionError(f"ground truth has differing shape ({target.shape}) from input ({input.shape})")
  68 
  69         temperature_tensor = torch.tensor(self.temperature).to(input.device)
  70 
  71         norm_i = F.normalize(input, dim=1)
  72         norm_j = F.normalize(target, dim=1)
  73 
  74         negatives_mask = ~torch.eye(self.batch_size * 2, self.batch_size * 2, dtype=torch.bool)
  75         negatives_mask = torch.tensor(negatives_mask, dtype=torch.float)
  76         negatives_mask = torch.clone(torch.as_tensor(negatives_mask)).to(input.device)
  77 
  78         repr = torch.cat([norm_i, norm_j], dim=0)
  79         sim_matrix = F.cosine_similarity(repr.unsqueeze(1), repr.unsqueeze(0), dim=2)
  80         sim_ij = torch.diag(sim_matrix, self.batch_size)
  81         sim_ji = torch.diag(sim_matrix, -self.batch_size)
  82 
  83         positives = torch.cat([sim_ij, sim_ji], dim=0)
  84         nominator = torch.exp(positives / temperature_tensor)
  85         denominator = negatives_mask * torch.exp(sim_matrix / temperature_tensor)
  86 
  87         loss_partial = -torch.log(nominator / torch.sum(denominator, dim=1))
  88 
  89         if self.reduction == LossReduction.SUM.value:
  90             return torch.sum(loss_partial) / (2 * self.batch_size)
  91         raise ValueError(f"Unsupported reduction: {self.reduction}, " f'available options are ["mean", "sum", "none"].')
  92
  [end of monai/losses/contrastive.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: diff --git a/monai/losses/contrastive.py b/monai/losses/contrastive.py
  --- a/monai/losses/contrastive.py
  +++ b/monai/losses/contrastive.py
  @@ -9,13 +9,11 @@
   # See the License for the specific language governing permissions and
   # limitations under the License.
   
  -from typing import Union
  -
   import torch
   from torch.nn import functional as F
   from torch.nn.modules.loss import _Loss
   
  -from monai.utils import LossReduction
  +from monai.utils import deprecated_arg
   
   
   class ContrastiveLoss(_Loss):
  @@ -31,19 +29,23 @@
   
       """
   
  -    def __init__(
  -        self, temperature: float = 0.5, batch_size: int = 1, reduction: Union[LossReduction, str] = LossReduction.SUM
  -    ) -> None:
  +    @deprecated_arg(name="reduction", since="0.8", msg_suffix="`reduction` is no longer supported.")
  +    def __init__(self, temperature: float = 0.5, batch_size: int = 1, reduction="sum") -> None:
           """
           Args:
               temperature: Can be scaled between 0 and 1 for learning from negative samples, ideally set to 0.5.
  +            batch_size: The number of samples.
   
           Raises:
  -            AssertionError: When an input of dimension length > 2 is passed
  -            AssertionError: When input and target are of different shapes
  +            ValueError: When an input of dimension length > 2 is passed
  +            ValueError: When input and target are of different shapes
  +
  +        .. deprecated:: 0.8.0
  +
  +            `reduction` is no longer supported.
   
           """
  -        super().__init__(reduction=LossReduction(reduction).value)
  +        super().__init__()
   
           self.batch_size = batch_size
           self.temperature = temperature
  @@ -53,18 +55,15 @@
           Args:
               input: the shape should be B[F].
               target: the shape should be B[F].
  -
  -        Raises:
  -            ValueError: When ``self.reduction`` is not one of ["sum", "none"].
           """
           if len(target.shape) > 2 or len(input.shape) > 2:
  -            raise AssertionError(
  +            raise ValueError(
                   f"Either target or input has dimensions greater than 2 where target "
                   f"shape is ({target.shape}) and input shape is ({input.shape})"
               )
   
           if target.shape != input.shape:
  -            raise AssertionError(f"ground truth has differing shape ({target.shape}) from input ({input.shape})")
  +            raise ValueError(f"ground truth has differing shape ({target.shape}) from input ({input.shape})")
   
           temperature_tensor = torch.tensor(self.temperature).to(input.device)
   
  @@ -86,6 +85,4 @@
   
           loss_partial = -torch.log(nominator / torch.sum(denominator, dim=1))
   
  -        if self.reduction == LossReduction.SUM.value:
  -            return torch.sum(loss_partial) / (2 * self.batch_size)
  -        raise ValueError(f"Unsupported reduction: {self.reduction}, " f'available options are ["mean", "sum", "none"].')
  +        return torch.sum(loss_partial) / (2 * self.batch_size)
  
verification_info: {"golden_diff": "diff --git a/monai/losses/contrastive.py b/monai/losses/contrastive.py\n--- a/monai/losses/contrastive.py\n+++ b/monai/losses/contrastive.py\n@@ -9,13 +9,11 @@\n # See the License for the specific language governing permissions and\n # limitations under the License.\n \n-from typing import Union\n-\n import torch\n from torch.nn import functional as F\n from torch.nn.modules.loss import _Loss\n \n-from monai.utils import LossReduction\n+from monai.utils import deprecated_arg\n \n \n class ContrastiveLoss(_Loss):\n@@ -31,19 +29,23 @@\n \n     \"\"\"\n \n-    def __init__(\n-        self, temperature: float = 0.5, batch_size: int = 1, reduction: Union[LossReduction, str] = LossReduction.SUM\n-    ) -> None:\n+    @deprecated_arg(name=\"reduction\", since=\"0.8\", msg_suffix=\"`reduction` is no longer supported.\")\n+    def __init__(self, temperature: float = 0.5, batch_size: int = 1, reduction=\"sum\") -> None:\n         \"\"\"\n         Args:\n             temperature: Can be scaled between 0 and 1 for learning from negative samples, ideally set to 0.5.\n+            batch_size: The number of samples.\n \n         Raises:\n-            AssertionError: When an input of dimension length > 2 is passed\n-            AssertionError: When input and target are of different shapes\n+            ValueError: When an input of dimension length > 2 is passed\n+            ValueError: When input and target are of different shapes\n+\n+        .. deprecated:: 0.8.0\n+\n+            `reduction` is no longer supported.\n \n         \"\"\"\n-        super().__init__(reduction=LossReduction(reduction).value)\n+        super().__init__()\n \n         self.batch_size = batch_size\n         self.temperature = temperature\n@@ -53,18 +55,15 @@\n         Args:\n             input: the shape should be B[F].\n             target: the shape should be B[F].\n-\n-        Raises:\n-            ValueError: When ``self.reduction`` is not one of [\"sum\", \"none\"].\n         \"\"\"\n         if len(target.shape) > 2 or len(input.shape) > 2:\n-            raise AssertionError(\n+            raise ValueError(\n                 f\"Either target or input has dimensions greater than 2 where target \"\n                 f\"shape is ({target.shape}) and input shape is ({input.shape})\"\n             )\n \n         if target.shape != input.shape:\n-            raise AssertionError(f\"ground truth has differing shape ({target.shape}) from input ({input.shape})\")\n+            raise ValueError(f\"ground truth has differing shape ({target.shape}) from input ({input.shape})\")\n \n         temperature_tensor = torch.tensor(self.temperature).to(input.device)\n \n@@ -86,6 +85,4 @@\n \n         loss_partial = -torch.log(nominator / torch.sum(denominator, dim=1))\n \n-        if self.reduction == LossReduction.SUM.value:\n-            return torch.sum(loss_partial) / (2 * self.batch_size)\n-        raise ValueError(f\"Unsupported reduction: {self.reduction}, \" f'available options are [\"mean\", \"sum\", \"none\"].')\n+        return torch.sum(loss_partial) / (2 * self.batch_size)\n", "issue": "`reduction` for `ContrastiveLoss`\n**Describe the bug**\r\nthe error messages and docstring should be consistent\r\nhttps://github.com/Project-MONAI/MONAI/blob/a7bc4a3cbaeaa3c505a25ca2ddf6922bda8ea7dc/monai/losses/contrastive.py#L89-L91\r\n\r\nhttps://github.com/Project-MONAI/MONAI/blob/a7bc4a3cbaeaa3c505a25ca2ddf6922bda8ea7dc/monai/losses/contrastive.py#L58\r\n\r\n**Expected behavior**\r\nimplementing the option `reduction=\"none\"`?\r\n\n", "before_files": [{"content": "# Copyright 2020 - 2021 MONAI Consortium\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#     http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Union\n\nimport torch\nfrom torch.nn import functional as F\nfrom torch.nn.modules.loss import _Loss\n\nfrom monai.utils import LossReduction\n\n\nclass ContrastiveLoss(_Loss):\n\n    \"\"\"\n    Compute the Contrastive loss defined in:\n\n        Chen, Ting, et al. \"A simple framework for contrastive learning of visual representations.\" International\n        conference on machine learning. PMLR, 2020. (http://proceedings.mlr.press/v119/chen20j.html)\n\n    Adapted from:\n        https://github.com/Sara-Ahmed/SiT/blob/1aacd6adcd39b71efc903d16b4e9095b97dda76f/losses.py#L5\n\n    \"\"\"\n\n    def __init__(\n        self, temperature: float = 0.5, batch_size: int = 1, reduction: Union[LossReduction, str] = LossReduction.SUM\n    ) -> None:\n        \"\"\"\n        Args:\n            temperature: Can be scaled between 0 and 1 for learning from negative samples, ideally set to 0.5.\n\n        Raises:\n            AssertionError: When an input of dimension length > 2 is passed\n            AssertionError: When input and target are of different shapes\n\n        \"\"\"\n        super().__init__(reduction=LossReduction(reduction).value)\n\n        self.batch_size = batch_size\n        self.temperature = temperature\n\n    def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Args:\n            input: the shape should be B[F].\n            target: the shape should be B[F].\n\n        Raises:\n            ValueError: When ``self.reduction`` is not one of [\"sum\", \"none\"].\n        \"\"\"\n        if len(target.shape) > 2 or len(input.shape) > 2:\n            raise AssertionError(\n                f\"Either target or input has dimensions greater than 2 where target \"\n                f\"shape is ({target.shape}) and input shape is ({input.shape})\"\n            )\n\n        if target.shape != input.shape:\n            raise AssertionError(f\"ground truth has differing shape ({target.shape}) from input ({input.shape})\")\n\n        temperature_tensor = torch.tensor(self.temperature).to(input.device)\n\n        norm_i = F.normalize(input, dim=1)\n        norm_j = F.normalize(target, dim=1)\n\n        negatives_mask = ~torch.eye(self.batch_size * 2, self.batch_size * 2, dtype=torch.bool)\n        negatives_mask = torch.tensor(negatives_mask, dtype=torch.float)\n        negatives_mask = torch.clone(torch.as_tensor(negatives_mask)).to(input.device)\n\n        repr = torch.cat([norm_i, norm_j], dim=0)\n        sim_matrix = F.cosine_similarity(repr.unsqueeze(1), repr.unsqueeze(0), dim=2)\n        sim_ij = torch.diag(sim_matrix, self.batch_size)\n        sim_ji = torch.diag(sim_matrix, -self.batch_size)\n\n        positives = torch.cat([sim_ij, sim_ji], dim=0)\n        nominator = torch.exp(positives / temperature_tensor)\n        denominator = negatives_mask * torch.exp(sim_matrix / temperature_tensor)\n\n        loss_partial = -torch.log(nominator / torch.sum(denominator, dim=1))\n\n        if self.reduction == LossReduction.SUM.value:\n            return torch.sum(loss_partial) / (2 * self.batch_size)\n        raise ValueError(f\"Unsupported reduction: {self.reduction}, \" f'available options are [\"mean\", \"sum\", \"none\"].')\n", "path": "monai/losses/contrastive.py"}]}