Fix some typos. (onnx#4228)

Signed-off-by: Yulv-git <yulvchi@qq.com>

Co-authored-by: Chun-Wei Chen <jacky82226@gmail.com>

.github/workflows/manylinux/entrypoint.sh

@@ -59,5 +59,5 @@ fi
 # Remove useless *-linux*.whl; only keep manylinux*.whl
 rm -f dist/*-linux*.whl
 
-echo "Succesfully build wheels:"
+echo "Successfully build wheels:"
 find . -type f -iname "*manylinux*.whl"

docs/Changelog-ml.md

@@ -408,7 +408,7 @@ This version of the operator has been available since version 1 of the 'ai.onnx.
 
 <dl>
 <dt><tt>T1</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32)</dt>
-<dd>The input must be a tensor of a numeric type, and of of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]</dd>
+<dd>The input must be a tensor of a numeric type, and of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]</dd>
 <dt><tt>T2</tt> : tensor(string), tensor(int64)</dt>
 <dd>The output will be a tensor of strings or integers.</dd>
 </dl>
@@ -609,7 +609,7 @@ This version of the operator has been available since version 1 of the 'ai.onnx.
 <dt><tt>T1</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32)</dt>
 <dd>The input must be a tensor of a numeric type, either [C] or [N,C].</dd>
 <dt><tt>T2</tt> : tensor(string), tensor(int64)</dt>
-<dd>The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used. Its size will match the bactch size of the input.</dd>
+<dd>The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used. Its size will match the bactch size of the input.</dd>
 </dl>
 
 ### <a name="ai.onnx.ml.SVMRegressor-1"></a>**ai.onnx.ml.SVMRegressor-1**</a>
@@ -777,7 +777,7 @@ This version of the operator has been available since version 1 of the 'ai.onnx.
 <dt><tt>T1</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32)</dt>
 <dd>The input type must be a tensor of a numeric type.</dd>
 <dt><tt>T2</tt> : tensor(string), tensor(int64)</dt>
-<dd>The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used.</dd>
+<dd>The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used.</dd>
 </dl>
 
 ### <a name="ai.onnx.ml.TreeEnsembleRegressor-1"></a>**ai.onnx.ml.TreeEnsembleRegressor-1**</a>
@@ -1057,7 +1057,7 @@ This version of the operator has been available since version 3 of the 'ai.onnx.
 <dt><tt>T1</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32)</dt>
 <dd>The input type must be a tensor of a numeric type.</dd>
 <dt><tt>T2</tt> : tensor(string), tensor(int64)</dt>
-<dd>The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used.</dd>
+<dd>The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used.</dd>
 </dl>
 
 ### <a name="ai.onnx.ml.TreeEnsembleRegressor-3"></a>**ai.onnx.ml.TreeEnsembleRegressor-3**</a>

docs/Changelog.md

@@ -2112,7 +2112,7 @@ This version of the operator has been available since version 1 of the default O
           user_defined_vals[i] = b + b;
           /* End user-defined code */
         }
-        // my_local = 123; // Can't do this. my_local was defined in the the body
+        // my_local = 123; // Can't do this. my_local was defined in the body
 
         // These below values are live-out from the loop and therefore accessible
         b_out; user_defined_vals; keepgoing_out;
@@ -8546,7 +8546,7 @@ This version of the operator has been available since version 9 of the default O
 ### <a name="MaxUnpool-9"></a>**MaxUnpool-9**</a>
 
   MaxUnpool essentially computes the partial inverse of the MaxPool op.
-   The input information to this op is typically the the output information from a MaxPool op. The first
+   The input information to this op is typically the output information from a MaxPool op. The first
    input tensor X is the tensor that needs to be unpooled, which is typically the pooled tensor (first output)
    from MaxPool. The second input tensor, I, contains the indices to the (locally maximal) elements corrsponding
    to the elements in the first input tensor X. Input tensor I is typically the second output of the MaxPool op.
@@ -11513,7 +11513,7 @@ This version of the operator has been available since version 11 of the default
 
 <dl>
 <dt><tt>outputs</tt> (variadic, heterogeneous) : V</dt>
-<dd>Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.</dd>
+<dd>Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.</dd>
 </dl>
 
 #### Type Constraints
@@ -11881,7 +11881,7 @@ This version of the operator has been available since version 11 of the default
 ### <a name="MaxUnpool-11"></a>**MaxUnpool-11**</a>
 
   MaxUnpool essentially computes the partial inverse of the MaxPool op.
-   The input information to this op is typically the the output information from a MaxPool op. The first
+   The input information to this op is typically the output information from a MaxPool op. The first
    input tensor X is the tensor that needs to be unpooled, which is typically the pooled tensor (first output)
    from MaxPool. The second input tensor, I, contains the indices to the (locally maximal) elements corrsponding
    to the elements in the first input tensor X. Input tensor I is typically the second output of the MaxPool op.
@@ -16114,7 +16114,7 @@ This version of the operator has been available since version 13 of the default
 
 <dl>
 <dt><tt>outputs</tt> (variadic, heterogeneous) : V</dt>
-<dd>Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.</dd>
+<dd>Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.</dd>
 </dl>
 
 #### Type Constraints
@@ -20021,7 +20021,7 @@ This version of the operator has been available since version 16 of the default
 
 <dl>
 <dt><tt>outputs</tt> (variadic, heterogeneous) : V</dt>
-<dd>Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.</dd>
+<dd>Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.</dd>
 </dl>
 
 #### Type Constraints

docs/MetadataProps.md

@@ -14,7 +14,7 @@ The motivation of such a mechanism is to allow model authors to convey to model
 In the case of images there are many option for providing valid image data.  However a model which consumes images was trained with a particular set of these options which must
 be used during inferencing.
 
-The goal is this proposal is to provide enough metadata that the model consumer can perform their own featurization prior to running the model and provide a compatible input or retrive an output and know what its format is.
+The goal is this proposal is to provide enough metadata that the model consumer can perform their own featurization prior to running the model and provide a compatible input or retrieve an output and know what its format is.
 
 ## Image Category Definition
 

docs/Operators-ml.md

@@ -459,7 +459,7 @@ This version of the operator has been available since version 1 of the 'ai.onnx.
 
 <dl>
 <dt><tt>T1</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32)</dt>
-<dd>The input must be a tensor of a numeric type, and of of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]</dd>
+<dd>The input must be a tensor of a numeric type, and of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]</dd>
 <dt><tt>T2</tt> : tensor(string), tensor(int64)</dt>
 <dd>The output will be a tensor of strings or integers.</dd>
 </dl>
@@ -664,7 +664,7 @@ This version of the operator has been available since version 1 of the 'ai.onnx.
 <dt><tt>T1</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32)</dt>
 <dd>The input must be a tensor of a numeric type, either [C] or [N,C].</dd>
 <dt><tt>T2</tt> : tensor(string), tensor(int64)</dt>
-<dd>The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used. Its size will match the bactch size of the input.</dd>
+<dd>The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used. Its size will match the bactch size of the input.</dd>
 </dl>
 
 
@@ -847,7 +847,7 @@ Other versions of this operator: <a href="Changelog-ml.md#ai.onnx.ml.TreeEnsembl
 <dt><tt>T1</tt> : tensor(float), tensor(double), tensor(int64), tensor(int32)</dt>
 <dd>The input type must be a tensor of a numeric type.</dd>
 <dt><tt>T2</tt> : tensor(string), tensor(int64)</dt>
-<dd>The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used.</dd>
+<dd>The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used.</dd>
 </dl>
 
 

docs/Operators.md

@@ -8971,7 +8971,7 @@ Other versions of this operator: <a href="Changelog.md#If-1">1</a>, <a href="Cha
 
 <dl>
 <dt><tt>outputs</tt> (variadic, heterogeneous) : V</dt>
-<dd>Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.</dd>
+<dd>Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.</dd>
 </dl>
 
 #### Type Constraints
@@ -12095,7 +12095,7 @@ This version of the operator has been available since version 1 of the default O
 ### <a name="MaxUnpool"></a><a name="maxunpool">**MaxUnpool**</a>
 
   MaxUnpool essentially computes the partial inverse of the MaxPool op.
-   The input information to this op is typically the the output information from a MaxPool op. The first
+   The input information to this op is typically the output information from a MaxPool op. The first
    input tensor X is the tensor that needs to be unpooled, which is typically the pooled tensor (first output)
    from MaxPool. The second input tensor, I, contains the indices to the (locally maximal) elements corrsponding
    to the elements in the first input tensor X. Input tensor I is typically the second output of the MaxPool op.

docs/TypeDenotation.md

@@ -6,7 +6,7 @@ Type Denotation is used to describe semantic information around what the inputs
 
 ## Motivation
 
-The motivation of such a mechanism can be illustrated via a simple example. In the the neural network SqueezeNet, it takes in an NCHW image input float[1,3,244,244] and produces a output float[1,1000,1,1]:
+The motivation of such a mechanism can be illustrated via a simple example. In the neural network SqueezeNet, it takes in an NCHW image input float[1,3,244,244] and produces a output float[1,1000,1,1]:
 
 ```
 input_in_NCHW -> data_0 -> SqueezeNet() -> output_softmaxout_1

onnx/compose.py

@@ -287,7 +287,7 @@ def merge_models(
     if m1.ir_version != m2.ir_version:
         raise ValueError(
             f"IR version mismatch {m1.ir_version} != {m2.ir_version}."
-            " Both models should have have the same IR version")
+            " Both models should have the same IR version")
     ir_version = m1.ir_version
 
     opset_import_map: MutableMapping[str, int] = {}

onnx/defs/controlflow/defs.cc

@@ -381,7 +381,7 @@ ONNX_OPERATOR_SET_SCHEMA(
             "static shapes S1 and S2, then the shape of the corresponding output "
             "variable of the if-node (if present) must be compatible with both S1 "
             "and S2 as it represents the union of both possible shapes."
-            "For example, if in a model file, the the first "
+            "For example, if in a model file, the first "
             "output of `then_branch` is typed float tensor with shape [2] and the "
             "first output of `else_branch` is another float tensor with shape [3], "
             "If's first output should have (a) no shape set, or (b) "

onnx/defs/controlflow/old.cc

@@ -679,7 +679,7 @@ C-style code:
         user_defined_vals[i] = b + b;
         /* End user-defined code */
       }
-      // my_local = 123; // Can't do this. my_local was defined in the the body
+      // my_local = 123; // Can't do this. my_local was defined in the body
 
       // These below values are live-out from the loop and therefore accessible
       b_out; user_defined_vals; keepgoing_out;
@@ -1409,7 +1409,7 @@ ONNX_OPERATOR_SET_SCHEMA(
             "static shapes S1 and S2, then the shape of the corresponding output "
             "variable of the if-node (if present) must be compatible with both S1 "
             "and S2 as it represents the union of both possible shapes."
-            "For example, if in a model file, the the first "
+            "For example, if in a model file, the first "
             "output of `then_branch` is typed float tensor with shape [2] and the "
             "first output of `else_branch` is another float tensor with shape [3], "
             "If's first output should have (a) no shape set, or (b) "
@@ -1501,7 +1501,7 @@ ONNX_OPERATOR_SET_SCHEMA(
             "static shapes S1 and S2, then the shape of the corresponding output "
             "variable of the if-node (if present) must be compatible with both S1 "
             "and S2 as it represents the union of both possible shapes."
-            "For example, if in a model file, the the first "
+            "For example, if in a model file, the first "
             "output of `then_branch` is typed float tensor with shape [2] and the "
             "first output of `else_branch` is another float tensor with shape [3], "
             "If's first output should have (a) no shape set, or (b) "